In silico prediction of B-cell and T-cell epitopes of the Sudan Ebola virus glycoprotein for peptide-based vaccine design

In silico prediction of B-cell and T-cell epitopes of the Sudan Ebola virus glycoprotein for peptide-based vaccine design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article In silico prediction of B-cell and T-cell epitopes of the Sudan Ebola virus glycoprotein for peptide-based vaccine design Anguzu Simon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6600860/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Ebola virus disease (EVD) remains a deadly global health threat, especially due to the lack of approved vaccines or specific antiviral treatments specifically for Sudan ebolavirus (SUDV). The high mortality rate and recurring outbreaks of SUDV in sub-Saharan Africa call for urgent strategies to develop more effective and broadly protective vaccines for Ebola viruses. Methods This study used in silico immunoinformatics approaches to identify B-cell and T-cell epitopes from the Sudan ebolavirus glycoprotein for the development of a peptide-based subunit vaccine. Conserved sequences and antigenic motifs were predicted using MEME and IEDB tools in order to identify B-cell epitopes. T-cell epitopes were selected based on their immunogenicity, population coverage, and allergenicity using NetCTL 1.2, IEDB population coverage tools, and AllerCatPro 2.0, respectively. Molecular docking simulations were performed for the T-cell epitopes using HPEPDOCK 2.0, with validation through LigPlot + v2.2 and X-Score. Results This analysis led to the identification of two highly conserved B-cell epitopes that can be further tested in vitro—“IDQLVCKDHLASTDQLKSVGLNLEGSGVSTDIPSATKRWGFRSGVPPKVV” and “YEAGEWAENCYNLEIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCP.” These also showed strong immunogenic potential from the analysis. Four T-cell epitopes—“YTENTSSYY,” “KCNPNLHYW,” “RLASTVIYR,” and “EVTEIDQLV”—showed strong immunogenic potential and high binding affinity (binding energy > 9.0 kcal/mol) to their respective HLA molecules. These T-cell epitopes also demonstrated extensive population coverage across different African regions. Conclusion The predicted B-cell and T-cell epitope sequences show strong potential for the development of peptide-based subunit vaccines against Sudan ebolavirus. Therefore, these findings may contribute to global vaccine development efforts of SUDV. Sudan Ebola virus epitope glycoprotein peptide-based vaccine design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 INTRODUCTION Ebola virus disease (EVD), also known as Ebola hemorrhagic fever (EHF), is one of the deadliest viral diseases(CDC, 2022 ). In 1976, two concurrent outbreaks of this fatal hemorrhagic fever occurred in different parts of Central Africa that same year(CDC, 2022 ). The Ebola virus was named from the Ebola River in the Democratic Republic of the Congo (D.R.C.), where it was first identified (CDC, 2022 ). It includes two species: Zaire ebolavirus (ZEBOV) and Sudan ebolavirus (SEBOV) (CDC, 2022 ). Ebola viruses are enveloped negative-sense RNA viruses belonging to the family Filoviridae within the order Mononegavirales (Yadav & Mohite, 2020 ). The genera include; Cuevavirus, Marburgvirus, and Ebolavirus (Feldmann H & Klenk HD., 1996). There are five distinct varieties of Ebola viruses: Zaire, Sudan, Bundibugyo, Taï Forest, and Reston virus (which do not infect humans) (Yu et al., 2017 ). They are pleomorphic enveloped viruses that can cause viral hemorrhagic fever in humans and nonhuman primates (NHPs)(Feldmann H & Klenk HD., 1996). The strand RNA genome encodes 7 genes, including GP, RNA-dependent RNA polymerase (L), NP, and 4 VPs (Rivera & Messaoudi, 2016 ). It also has leader and trailer sequences at the genome end that contain encapsidation signals (Rivera & Messaoudi, 2016 ). It has promoters for replication and transcription and produces soluble forms of GP through RNA editing (Rivera & Messaoudi, 2016 ). The natural hosts of Ebola viruses have not yet conclusively identified, but the most likely host appears to be the fruit bats of the Pteropodidae family (Passi et al., 2015 ). EVD outbreaks typically begin with a single probable zoonotic transmission case (Jacob et al., 2020 ). Human-to-human transmission occurs through direct contact or exposure to infected bodily fluids or contaminated objects (Jacob et al., 2020 ). Sudan ebolavirus is one of the five known species of ebolavirus (Yu et al., 2017 ). It belongs to the Ebolavirus genus (Feldmann H & Klenk HD., 1996). It is the pathogen responsible for causing Sudan virus disease (SVD), a subcategory of Ebola virus disease (WHO, 2022 ). In 1976, nearly simultaneous outbreaks of Ebola viruses, specifically Sudan and Zaire strains, occurred in the Democratic Republic of the Congo (DRC) (Adnan I, 2016 ). Sudan's strain of the Ebola virus was initially identified in 1977 and was later recognized as Ebola hemorrhagic fever by the WHO in 1978 (Passi et al., 2015 ). After the 2002 outbreak, it was referred to as the Sudan Ebola virus (Passi et al., 2015 ). Its last outbreak in Uganda occurred in October 2022, and it caused significant adverse effects on both health and economic status in the nation (WHO, 2022 ). Since October 26, 2022, a total of 130 confirmed cases and 43 confirmed deaths from EHF have been reported from seven districts in Uganda (CDC, 2022 ). An additional 21 new confirmed cases and 12 new confirmed deaths (with a CFR of 57%) were reported, and 45 recoveries were registered. Healthcare workers account for 13.8% (18) of the cases and 13.9% (6) of all deaths (African CDC, 2022 ). There is no specific treatment or vaccine for SUDV, but it exists for the Zaire strain (WHO, 2022 ). During the EHF outbreak in southern Sudan between June and November 1976, there were a total of 284 cases, with 53% overall mortality and prolonged recovery periods for survivors (Report of a WHO/International Study Team, 1978 ). The symptoms of EHF include influenza-like syndrome, diarrhea, vomiting, chest pain, throat pain and dryness, rash, and hemorrhagic manifestations (Report of a WHO/International Study Team, 1978 ). There was no outbreak of EHF between 1980 and 1993, but it later reemerged in Africa more frequently, and new species were discovered, including Côte d’Ivoire ebolavirus (CIEBOV) in 1994 in the Ivory Coast and Bundibugyo ebolavirus (BEBOV) in 2007 in Uganda (Muyembe-Tamfum et al., 2012 ). CIEBOV has been associated with only one human case (Le Guenno & B., 1995). An outbreak of EVD caused by the Sudan ebolavirus was declared in the Mubende district in Central Uganda in September 2022 (CDC, 2022 ). By November, there had been a total of 132 confirmed cases, with 39% of those infected resulting in death and only 61 patients recovering and being discharged (CDC, 2022 ). Since the outbreak began, seven districts in Uganda, including Mubende, Kassanda, Kyegegwa, Bunyangabu, Kagadi, Wakiso, and the capital city of Kampala, have reported cases of EBOV infection (CDC, 2022 ). EBOV GPs are vital for attachment to host cells, catalyzing membrane fusion, and are therefore targets of neutralizing antibodies and attachment and fusion inhibitors, as well as crucial components of vaccines (Lennemann et al., 2021 ). EBOV enters host cells such as a human hepatoma cell line (Huh 7) and primary human macrophages (Mpg) (Albariño et al., 2018 ). It enters through receptor-mediated endocytosis via various receptors, including the asialoglycoprotein receptor, folate receptor a, C-type lectins, and human macrophage lectin (Rivera & Messaoudi, 2016 ). The entry of EBOV into host cells begins with the interaction of the viral GP with receptors on the cell surface, which are then internalized through the macropinocytosis pathway (Yu et al., 2017 ). During uncoating and fusion, GP1 binds to the endosome via the RBD, and GP2 facilitates fusion through the fusion loop (Yu et al., 2017 ). Currently, there are no approved antiviral drugs or vaccines specifically designed to combat Sudan ebolavirus (Muyembe-Tamfum et al., 2012 ). The current management strategy for SVD involves the use of hyperimmune serum from patients who have recovered from the disease and some antiviral medications, such as remdesivir (Muyembe-Tamfum et al., 2012 ). Compared with traditional methods, computer-aided discovery of immunogenic proteins and epitopes in combination with various vaccine discovery techniques has helped to shorten the process of vaccine production (Flower, 2013 ). The potential of predictive computational vaccine identification is clear since immunogenic proteins identified as antigens from pathogen genomes are potential subunit vaccines. These immunogenic epitopes are vital components of epitope ensemble vaccines (Flower, 2013 ). Peptide-based subunit vaccines are safe for immunocompromised patients, cannot revert to virulence, and cannot cause the disease they are meant to protect against. They can also withstand changes in temperature, light exposure, and humidity (Malonis et al., 2020 ). While the amino acid sequences of the Sudan and Zaire strains of the Ebola virus are distinct, when their gene product sequences are compared, they both show a slightly closer relationship to the Reston species than to each other (Sanchez & Rollin, 2005 ). Notably, the GP demonstrates the least conservation among these gene products, suggesting that a GP vaccine designed for the Zaire strain would not be effective against the Sudan strain (Sanchez & Rollin, 2005 ). Within the context of vaccines for infectious diseases, it is essential to recognize that protective immunity against a particular virus species or strain may not confer cross-reactive immunity to closely related viruses.(Sebastian et al., 2020 ) As a consequence, individuals might remain vulnerable to infection even after encountering related virus species. (Sebastian et al., 2020 ). This study used various in silico tools to virtually screen for the available GP sequences of Sudan ebolavirus and that of the current Mubende isolate. This will be done to identify potential B-cell and T-cell SUEBOV GP epitopes that will be exploited for peptide-based subunit vaccine designs that have not been previously screened. Problem Statement And Significance EHF is a global threat that continues to reoccur in central African countries and their neighbors, as well as some countries outside Africa (CDC, 2022 ). Currently, EHF is managed with the use of nonspecific antiviral therapies such as remdesivir and hyperimmune serum from recovered patients. There are also no approved antiviral drugs specifically for infection, and the available options are less efficient (CDC, 2022 ). There are no approved vaccines for the prevention and management of Sudan ebolavirus infections in humans, yet this approach could be more effective than chemotherapy (Muyembe-Tamfum et al., 2012 ). The outbreak of Sudan ebolavirus (SUDV) highlights our ongoing vulnerability to re-emerging high-consequence infectious diseases. (Ibrahim et al., 2022 ) Therefore, this study aims to identify potential B-cell and T-cell SUEBOV GP epitopes that can be exploited for the design of safe, effective, and affordable peptide-based subunit vaccines to effectively manage and prevent future outbreaks of Sudan ebolavirus disease. Justification These findings provide information about B-cell and T-cell Sudan ebolavirus glycoprotein epitopes that can be exploited for peptide-based subunit vaccine design. Thus, the results of this research will contribute to the ongoing efforts of scientific researchers to create a vaccine for the Sudan ebolavirus to prevent future outbreaks of this disease. The best epitope peptides can eventually be used by vaccine production industries to develop actual commercial peptide-based subunit vaccines, which can then be used by the Ministry of Health to inform policy about vaccination activities in the country. MATERIALS AND METHODS Study design The in silico study involved retrieving the Mubende Sudan ebolavirus genome sequence, predicting antigenicity, identifying B-cell and T-cell epitopes, assessing population coverage and allergenicity, and conducting molecular structure and docking analyses. See Fig. 7 below for a summary of the steps. Data retrieval of Ebola viral sequences. Retrieval of the Mubende Sudan ebolavirus genome. The genome sequence of the Mubende Sudan ebolavirus isolate was accessed via the link https://github.com/evk3/UVRI_Sudan_EBOV_Uganda_2022 . The consensus sequence was selected and downloaded from the page displayed in FASTA file format. Retrieval of the Sudan ebolavirus GP protein sequence The NCBI website was accessed via the link, http://www.ncbi.nlm.nih.gov/ . On the home page displayed, “All Databases” near the search bar were selected, which was then changed to the “Protein” Database. In the search bar, the search terms “Structural glycoprotein” AND “Sudan ebolavirus” [orgn] were used. The “PDB database” option on the left of the page was then clicked, and then “Search” was clicked, which returned results linked to “ Sudan ebolavirus structural glycoprotein.” From the results, two Sudan ebolavirus glycoprotein sequences and one genome sequence were downloaded. The files were all independently saved on the computer by clicking on “send to” , followed by “File”, “FASTA” , and finally “create file.” These files were then used in the next step. Multiple sequence alignment of Sudanic genomes and glycoprotein sequences The files with the above downloaded Sudan ebolavirus isolate genomes and glycoprotein sequences were combined and saved as a single FASTA file. With Clastal Omega, https://www.ebi.ac.uk/Tools/msa/clustalo/ , MSA was selected. It uses seeded guide trees and HMM profile techniques to generate alignments between three or more sequences (Sievers et al., 2011 ). The file with the combined sequences in FASTA format was chosen for alignment. The format of the results to be output was selected as the FASTA format, but other parameters were set to the defaults. When the MSA had finished running, the results were returned and accessed on a new page. The alignment was saved in FASTA format by clicking on the “ Download Alignment File ”. The alignment results were viewed with Jalview. The alignment file was then uploaded to visualize the areas of conservation. The color settings were changed to nucleotides to visualize the nucleotides (Waterhouse et al., 2009 ). To obtain the region of the CDS for the GP in the Mubende isolate, the GP sequence in the alignment was used to show the region of the genome in the Mubende isolate genome to annotate. The region was identified; thus, the GP sequence in the genome was cut via Jalview tools and then saved as a fasta file. Using Jalview, the three GP sequences were analysed by comparison with the Mubende isolate. The differences between the 4 sequences were identified, and the points of differences were noted. The GP sequence in the Mubende isolate was selected and saved as a FASTA file. Translation of the GP CDS to the protein sequence The GP CDS was then translated into an amino acid sequence via the ExPASy Translate Tool (Gasteiger et al., 2003 ), https://web.expasy.org/translate/ , whereby the DNA sequence was entered into the tool and run to obtain the protein sequence. The best frame was then selected and saved as a FASTA file. Comparative Study and Phylogenetic Classification of Ebolavirus GPs In a comparative study aiming to classify Ebolavirus GPs phylogenetically, a Blastp analysis was conducted in UniProt. On the basis of the BLAST results, the GP and sGP sequences of Sudan, Reston, Zaire, and Tai Forest Ebolavirus were selected and downloaded. along with the Mubende sGP sequence obtained from ExPASy, in a combined FASTA file. A combined file including the Mubende GP sequence obtained from ExPASy was made and saved as a single FASTA file. To further analyse the sequences, MSA was performed via MUSCLE (Edgar, 2004 ), https://www.ebi.ac.uk/Tools/msa/muscle/ . The combined file formed was chosen for alignment, and the output format was changed to FASTA. The results were saved as an alignment file and then visualized via Jalview (Waterhouse et al., 2009 ). Using Jalview, the MSA results were used to generate a phylogenetic tree. On the basis of Blastp and phylogenetic tree analyses, the full GP sequence to be used was identified by the UnitProt ID, which was then searched in UniProt, https://www.uniprot.org/ , downloaded, and saved as a FASTA file. B-cell epitope prediction Motif sequence identification The combined FASTA file of Ebolavirus GP sequences from the previous step was used in this step after removing the non-Sudan strains' GP sequences. The Mubende strain partial GP sequence and the obtained UniProt Sudanic GP sequences in FASTA format were then analysed via the Multiple Em for Motif Elicitation ( MEME ) tool Version 5.5.3 (Bailey et al., 2015 ) with the default settings ( http://meme-suite.org/tools/meme ). This motif-based sequence analysis tool was designed to pick at most 21 motifs that appeared in at least 2 amino acid sequences. A motif heatmap was generated via a mast text file from MEME and an in-house Python script (Srinath, 2017 ). The motifs were analysed for their conservation among different Sudan ebolavirus species. The highly conserved motifs were then selected for further analysis. Allergenicity determination The motif peptide sequences obtained via MEME were saved in a single FASTA file. AllerCatPro (version 2.0) (Maurer-Stroh et al., 2019 ), which is a protein allergenicity potential prediction, was then accessed via the link https://allercatpro.bii.a-star.edu.sg/ . The tool provides a text area where you can paste your protein sequences or an option to upload a FASTA file containing the many protein sequences. The motifs were copied and pasted into the text area since the number was small. Once sequences were input, the "Submit" button was clicked to start the allergenicity prediction process. The tool then processed the input sequences and performed allergenicity predictions on the basis of the sequences' properties. Since the number of sequences was small, it took a short time to generate results. After the analysis was complete, AllerProt provided the allergenicity potential predictions for each input protein sequence. The results are presented in a table that is downloadable as an Excel file. The output was reviewed to identify the protein sequences that had high allergenicity potential. Motif antigenicity determination via IEDB Tools Sudan ebolavirus GP peptide motifs were subjected to antigenicity analysis via the IEDB tool (Fleri et al., 2017 ), which employs semiempirical methods on the basis of the physicochemical properties of amino acid residues and their frequency of occurrence (Chou & Fasman, 1978 ; Kolaskar & Tongaonkar, 1990 ; Parker et al., 1986 ). This approach allowed for the determination of peptide motifs with the best potential immunogenicity. The antigenicity analyses of the peptide motifs were conducted via the Immune Epitope Database Analysis Resource (IEDB) available at http://tools.iedb.org/main/ . Seven different IEDB methods were employed to assess the antigenicity of these peptide motifs: BepiPred-1.0 : This employs hidden Markov models (HMMs) and a propensity scale to predict linear B-cell epitopes. In this approach, residues with a score above the default threshold of 0.350 were deemed to have a high likelihood of being part of an epitope (Jespersen et al., 2017). BepiPred-2.0 : This analysis uses a sequential B-cell epitope predictor that utilizes a random forest algorithm trained on epitopes and nonepitope amino acids identified from protein crystal structures. Residues with a score exceeding the default threshold of 0.500 were identified as having a high likelihood of being part of an epitope (Jespersen et al., 2017). Chou and Fasman beta -turn prediction methods : This epitope analysis uses turn prediction to identify potential epitopes. Residues with a score higher than the threshold of 1.007 were categorized as having a high likelihood of being part of an epitope (Chou & Fasman, 1974). Emini surface accessibility scale: This analysis calculates the peptide's surface accessibility. A score exceeding 1.000 indicates a strong probability of the peptide being located on the protein surface (Emini et al., 1985). Karplus and Schulz methods : This method was utilized to assess the flexibility of protein segments and relies on the B-factors of 31 protein structures. Residues with a score exceeding the threshold of 1.008 were identified as having a high probability of being part of an epitope (Karplus & Schulz, 1985). Kolaskar and Tongaonkar antigenicity scale: This employs a semiempirical approach on the basis of the physicochemical probabilities of amino acid residues within the protein of interest, along with their frequencies of occurrence in experimentally known epitopes from other proteins. Residues with a score surpassing the threshold of 1.016 were considered to have a high likelihood of being part of an epitope (Kolaskar & Tongaonkar, 1990). Parker-hydrophobicity prediction method : This method is based on a hydrophilic scale derived from the analysis of peptide retention times via high-performance liquid chromatography (HPLC) using a reversed-phase column. This scale assigns hydrophobicity scores to individual amino acid residues on the basis of their behavior in the chromatography process. Residues with scores above the threshold value of 1.608 are identified as having a high probability of being part of an epitope. This finding indicates that such residues are more likely to be involved in antigenic regions capable of inducing an immune response (Parker et al., 1986) For each of the seven methods mentioned above, the Sudan ebolavirus GP sequence was input into the corresponding tool, and the results were obtained in the form of graphs and Excel files, which were downloaded from the IEDB tools. Excel files were then used to generate CSV files containing information on the residue number and residue score for each amino acid. The scoring is based on the algorithm of each method. The generated CSV files were then used by the R program to generate graphs (John K., 2015). An R -script was employed, which incorporated the location of the motifs within the GP sequence using the residue positions. By running the script, CSV graphs were generated for each tool. Additionally, the script facilitated the extraction of motif average values for each method, which were subsequently recorded in a table for further analysis and epitope selection. On the basis of the information obtained from the graphs and table results, the most promising motifs to be used as vaccines were identified and identified. Motif Structure Prediction Structural prediction of the best antigenic motifs on the basis of the IEDB tool analysis was performed via the PEP-FOLD server (Lamiable et al., 2016). It is essential to ensure that the motifs meet the requirements for this tool to predict the structures of proteins that are 9 to 36 residues in length. The protein sequences for each motif were copied and pasted in the submission area, and “Submit” was selected for the prediction to begin. A clustering report with scores was provided, as were the top 5 clusters. The PDB structures of the top 5 models represent the best structural predictions on the basis of the selected sorting key. Model 1 was downloaded and further analysed via the UCSF Chimera visualization tool to examine the predicted structure and conduct additional analyses, such as identifying the secondary structure (Pettersen et al., 2004). T-cell epitope prediction The prediction of T-cell epitopes involves the use of the NetCTL server , which is available via the link https://services.healthtech.dtu.dk/services/NetCTL-1.2/, which identifies potential T-cell epitopes in a given sequence and provides diverse results, including a combined score (Larsen et al., 2007). The peptide length is set at 9 residues. The Sudan Ebolavirus GP sequence was uploaded in FASTA format. Default settings were used for parameters such as weight on C-terminal cleavage, weight on TAP transport efficiency, and thresholds for epitope identification, including sensitivity and accuracy. This process was performed for all 12 MHC-I supertypes, ranging from A1, A2, A3 to B68, resulting in individual tables of results for each supertype. These tables were then merged to create a single comprehensive table of results as an Excel file. MHC-1 binding of epitopes The table of epitopes was sorted on the basis of the combined score to identify the top epitopes for further analysis. After sorting, the top 13 epitopes with COMB scores above 1.720 were saved in a fasta file. The MHC-I binding of these 13 epitopes was subsequently evaluated via tools available on the IEDB platform (https://www.iedb.org/). These tools utilize the stabilized matrix-based method to calculate the half maximal inhibitory concentration (IC50) value, indicating that the epitope binds to human leukocyte antigen (HLA) molecules (Fleri et al., 2017). Each of the 13 epitopes was matched to its respective HLA structure, yielding information on the binding affinity score. The results were then sorted on the basis of the binding score, and those with a score exceeding 9.0 were selected for further analysis in the subsequent steps. Prediction of population coverage The population coverage analysis was conducted via the IEDB population coverage tool (https://www.iedb.org/). This tool was employed to assess the potential coverage of all 13 epitopes by selecting the corresponding HLA alleles for the epitopes obtained in the previous step. The analysis focused on four subregions of Africa, namely, East, Central, West, and North (Fleri et al., 2017). For each epitope, the tool provides results for each subregion, indicating the extent of coverage on the basis of the presence of HLA alleles matching the epitope in the subregion. The coverage in each region was then given by a percentage value (Fleri et al., 2017). To identify the most promising epitopes with broad population coverage, the average subregion percentage was calculated across all four subregions. Epitopes with average population coverage above 8.0% were singled out for further analysis. These selected epitopes are of particular interest, as they have the potential to be effective in a significant proportion of the African population, making them valuable candidates for further epidemiological investigations and vaccine development considerations. Allergenicity Assessment For the identification of allergenicity, peptide T-cell epitope sequences with high population coverage were obtained and placed into a single FASTA file. Subsequently, AllerCatPro (version 2.0), a protein allergenicity potential prediction tool, was accessed through the provided link https://allercatpro.bii.a-star.edu.sg/ (Maurer-Stroh et al., 2019). The tool offers a text area where the sequences can be pasted or an option to upload a FASTA file containing multiple protein sequences. As the number of T-cell epitopes was large, the FASTA file was uploaded. Upon uploading, the sequences were submitted for the allergenicity prediction process by clicking on the " Submit " button. The AllerCatPro tool then processed the input sequences and performed allergenicity predictions on the basis of the properties of the peptides. Owing to the small number of sequences, the analysis was completed swiftly, generating the results in a timely manner. After the analysis was finished, AllerProt provided the allergenicity potential predictions for each input peptide sequence. The results were presented in a downloadable table format as an Excel file. To assess potential allergenicity, the output was thoroughly reviewed to identify peptide sequences with high allergenicity potential, facilitating the selection of candidate nonallergenic peptides for further investigation and analysis. HLA Homology Modelling This was done for the top 9 HLA structures ( HLA-A*01, HLA-A*02, HLA-A*03, HLA-A*15, HLA-A*30, HLA-A*31, HLA-B*57, HLA-B*58, and HLA-A*68 ) that showed high population coverage. Thus, the HLA and corresponding T-epitopes were subjected to further analysis. UniProt was accessed via the link https://www.uniprot.org/. The query term used for each HLA allele structure was “ mhc 1 HLA `Allele subtype` .” According to the UniProtKB results, the result required for each allele type was clicked, which provided a protein ID that was copied and pasted into Alphafold via the link https://alphafold.ebi.ac.uk/. This provided the structure and the PDB download option for the structure, which was used to download the HLA structure. Visualization was then performed with UCSF Chimera for analysis of the structure. Molecular Docking This was accomplished via two docking applications, namely, HPEPDOCK 2.0 and CABS-Dock. To utilize HPEPDOCK 2.0 for flexible protein‒peptide docking, the server was accessed via the platform link http://huanglab.phys.hust.edu.cn/hpepdock/. This provided the receptor and peptide input options, such as uploading PDB files, specifying PDB IDs and chain IDs, or pasting FASTA sequences. For the receptors, the HLA structure PDB files were uploaded, whereas for the T-cell epitopes, the peptide sequences were pasted in the input section. The job name and email for the results were then entered, and the job was submitted. Upon submission, the platform generated docking results, including a complex image and the top 10 models of the peptide and their docking scores with the HLA, although the receptor and peptide components were downloaded separately and combined via UCSF Chimera for further use. For CABS-Dock, the server was accessed via the link https://biocomp.chem.uw.edu.pl/CABSdock/, which provided a section for uploading the HLA protein and for inputting the peptide. The job was then given a name and submitted. Upon submission, the results included a generated docking complex, which was then downloadable in PDB format. As a result, both applications provided the necessary structural information for further analysis. Docking Validation For the docking validation process, the X-Score tool was installed on a computer. The procedure required both the HLA in PDB format and the peptide in mol2 file format. For each complex, the peptide was initially selected by launching the complex via UCSF Chimera. The peptide was subsequently selected and saved as a PDB file, with the "only all atoms selected" option chosen. This saved PDB was then converted into a mol2 file required for the X score. The X-score terminal was opened within each complex's directory, encompassing the HLA and peptide components. The formal command used to yield the results was " xscore -score HLA.pdb Peptide.mol2", which enabled the extraction of the binding energy of the protein and peptide for all 24 docking complexes previously obtained. Interacting Residue Determination The LigPlot application was acquired and installed on the computer for analysis of docking residue interactions for the best 4 complexes according to HPEPDOCK. Upon launching the application, each complex was submitted individually for processing by Ligplot to generate LigPlot diagrams, which visually present the interaction outcomes. The generated results allowed the saving of PS files, which were then utilized to create PNG images for clarity. RESULTS Data retrieval of Ebola virus sequences The Sudan ebolavirus genomes and Sudan ebolavirus GP protein sequences were obtained, resulting in a total of 4 sequences: "2022002270_consensus_Mubende_Uganda_Isolate_2022”, "JN638998.1 Sudan ebolavirus - Nakisamata, complete genome", "NC_006432.1 Sudan ebolavirus isolate Sudan virus/H.sapiens-tc/UGA/2000/Gulu-808892, complete genome" and "AY316199.1 Sudan ebolavirus strain Gulu structural glycoprotein and secreted glycoprotein genes, complete cds Sudan ebolavirus." Identification of Conserved Regions and Variations The alignment of the retrieved sequences in Jalview revealed alignment patterns, conserved regions, and pinpoint variations across the sequences. The analysis revealed distinct differences between the GP sequences of the Mubende isolate and the other isolates. Specifically, at position 7 in the genome, Mubende has an adenine (A), whereas the other isolates have guanine (G). Additionally, at positions 1222 and 1583, Mubende displays adenine (A) instead of guanine (G), as observed in the other sequences. These variations in nucleotides were consistently observed across all four GP sequences. Notably, there were also variations among the other isolates themselves, contributing to the overall diversity of the Ebola virus genomes. The genome sequences were cut to show only the region of the GP sequence, as shown in Fig. 2 below. Translation and Analysis of the GP Coding Sequence The GP coding sequence for the Mubende isolate was translated into an amino acid sequence via the ExPASy Translate Tool and subsequently assessed. This allowed analysis of the GPs at the protein level. The best frame identified had a stop codon in the GP sequence. Thus, only the sGP protein sequence could be obtained, as shown in Fig. 3. The sGP protein sequence is shown below. > VIRT-24397:5'3' Frame 1, start_pos=0 MGSLSLLQLPRDKFRKSSFFVWVIILFQKAFSMPLGVVTNSTLEVTEIDQLVCKDH LASTDQLKSVGLNLEGSGVSTDIPSATKRWGFRSGVPPKVVSYEAGEWAENCYNL EIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCPGDYAFHKDGAFFLYDRLAS TVIYRGVNFAEGVIAFLILAKPKETFLQSPPIREAVNYTENTSSYYATSYLEYEIENF GAQHSTTLFKIDNNTFVRLDRPHTPQFLFQLNDTIHLHQQLSNTTGRLIWTLDANI NADIGEWAFWENKKISPNNYVEKSCLSKLYRSTRQKTMMRHRRELQREESPTGPP GSIRTWFQRIPLGWFHCTYQKGKQHCRLRIRQKVEE Stop codons indicate the termination of protein synthesis and can hinder the production of the full GP protein. To overcome this limitation, RNA editing is necessary to modify the GP coding sequence and remove or modify the stop codons. This additional step is important to ensure the generation of a complete and functional GP protein. However, this process is more complex and time-consuming. Thus, a similar GP to that of the Mubende isolate was selected . Comparative Study and Phylogenetic Classification of Ebolavirus GP Results The analysis involved performing MSA and generating a phylogenetic tree to gain insights into the relationships between different GP sequences of different strains. The generated phylogenetic tree visually represents the evolutionary relationships between the GP sequences. By analysing the tree, we were able to identify the GP sequence that is most closely related to the Mubende strain. On the basis of the results of the phylogenetic tree shown in Fig. 4 , we selected and retrieved the final full structural GP sequence ( Q7T9D9.1 ) for further analysis since it is very similar to the Mubende isolate. The sequence is shown below. >Q7T9D9.1 RecName: Full=Envelope glycoprotein; AltName: Full=GP1,2; Short=GP; Contains: RecName: Full=GP1; Contains: RecName: Full=GP2; Contains: RecName: Full=Shed GP; AltName: Full=GP1,2-delta; Flags: Precursor [Sudan ebolavirus - Uganda (2000)] MGGLSLLQLPRDKFRKSSFFVWVIILFQKAFSMPLGVVTNSTLEVTEIDQLVCKDH LASTDQLKSVGLNLEGSGVSTDIPSATKRWGFRSGVPPKVVSYEAGEWAENCYNL EIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCPGDYAFHKDGAFFLYDRLAS TVIYRGVNFAEGVIAFLILAKPKETFLQSPPIREAVNYTENTSSYYATSYLEYEIENF GAQHSTTLFKIDNNTFVRLDRPHTPQFLFQLNDTIHLHQQLSNTTGRLIWTLDANI NADIGEWAFWENKKNLSEQLRGEELSFEALSLNETEDDDAASSRITKGRISDRATR KYSDLVPKNSPGMVPLHIPEGETTLPSQNSTEGRRVGVNTQETITETAATIIGTNGN HMQISTIGIRPSSSQIPSSSPTTAPSPEAQTPTTHTSGPSVMATEEPTTPPGSSPGPTTE APTLTTPENITTAVKTVLPQESTSNGLITSTVTGILGSLGLRKRSRRQTNTKATGKCN PNLHYWTAQEQHNAAGIAWIPYFGPGAEGIYTEGLMHNQNALVCGLRQLANETT QALQLFLRATTELRTYTILNRKAIDFLLRRWGGTCRILGPDCCIEPHDWTKNITDKI NQIIHDFIDNPLPNQDNDDNWWTGWRQWIPAGIGITGIIIAIIALLCVCKLLC B -Cell epitope prediction Motif sequence identification From the completed MEME analysis, a motif heatmap was generated using the output from MEME in the form of a master text file. This file was then used to create the heatmap in Fig. 5 via a Python script. The heatmap provides a visual representation of the 21 motifs, indicating their presence and distribution across the 24 analysed sequences. Peptide motif sequences The 7 highly conserved motifs were selected for further analysis. These included M1, M2, M3, M4, M5, M6, and M17, suggesting that these motifs may play crucial roles in the biological function or interaction of the studied proteins. MEME provides the results sorted by the E value, as shown in Table 1 below, as well as the logo site and width. Peptide-motif Allerginicity Assessment The results show the analysis of protein sequences M1 to M17. For each protein, the sequence length, the number of Q-repeats in gluten allergens, and the number of 3x6-mer overlaps were determined. The best known allergen hit name, species, various hit details and allergen information were examined. For all proteins, no significant hits were identified within the E value threshold of 0.001, indicating that there was no evidence of known allergen matches or related hits. The % identity to the closest human hit and the % identity to the closest low-allergen hit were also assessed, both of which revealed no evidence of hits or similarities. Therefore, these results indicate no significant evidence of allergenicity or related hits for the analysed protein sequences, as shown in the summary in Table 2 below. Table 2 Peptide-Motif Allergenicity Assessment: Analysis of protein sequences M1 to M17 reveals no matches to known allergens or related hits within the E value threshold of 0.001. Protein Sequence Length Best known allergen hit name Result Comment M1 41 No significant hit (E-value threshold 0.001) no evidence no hits M2 50 No significant hit (E-value threshold 0.001) no evidence no hits M3 50 No significant hit (E-value threshold 0.001) no evidence no hits M4 50 No significant hit (E-value threshold 0.001) no evidence no hits M5 41 No significant hit (E-value threshold 0.001) no evidence no hits M6 50 No significant hit (E-value threshold 0.001) no evidence no hits M17 6 No significant hit (E-value threshold 0.001) no evidence no hits Motif antigenicity determination via the IEDB Motif M7 was not continued since it was too short. The IEDB methods were used to evaluate whether the motifs were located in potential B cell epitopes. These results indicate that the motifs M2, M3 and M5 might be potential antigenic peptide epitopes. Motifs M2, M3 and M5 had mean BepiPred 2.0 linear epitope prediction scores of 0.547, 0.538, and 0.602, respectively, which were above the 0.500 threshold value. Motifs M2, M3 and M5 also had mean Parker hydrophilicity scores of 2.014, 2.602, and 2.044, respectively, which were above the 1.608 threshold score. The motifs also presented mean Chou and Fasman beta-turn predictions and Karplus and Schulz flexibility predictions that were slightly above or below their respective thresholds. Additional supporting information on B cell peptide motif antigenicity has been provided as graphs (Figs. 6, 7, 8 and 9 ), and more information is provided in the Table 3 summary. Table 3 Peptide Motif Antigenicity Assessment: Analysis indicates potential antigenic peptide epitopes for Motifs M2, M3, and M5. Motifs Bepipred 1.0 (0.350) Bepipred 2.0 (0.500) Surface Accessibility (1.000) Karplus & C. Flexibility (1.008) Kolaskar & T. Antigenicity (1.016) Chou & Fasman (1.007) Paker Hydrophilicity (1.608) M1 -0.824 (-) 0.402 (-) 0.820 (-) 0.976 (-) 1.066 (+) 0.903 (-) -0.955 (-) M2 0.395 (+) 0.547 (+) 0.739 (-) 1.017 (+) 1.044 (+) 1.041 (+) 2.014 (+) M3 0.920 (+) 0.538 (+) 0.901 (-) 1.015 (+) 1.028 (+) 1.122 (+) 2.602 (+) M4 -0.363 (-) 0.471 (-) 0.741 (-) 0.984 (-) 1.052 (+) 0.904 (-) 0.045 (-) M5 0.155 (-) 0.602 (+) 1.155 (+) 1.000 (-) 1.000 (-) 1.000 (-) 2.044 (+) M6 -0.134 (-) 0.617 (+) 1.002 (+) 0.992 (-) 0.997 (-) 0.980 (-) 0.793 (-) Peptide-Motif 3D Structure Design The 3D structures of motifs 2 and 3 were designed via the PEP-FOLD server, which generated the top 10 models for each motif. The best models (Model 1) for both motifs were selected and visualized, as depicted in Figs. 10 and 11 below. T-cell epitope prediction The NetCTL 1.2 server provided a total of 184 epitopes for all the MHC supertypes, which were sorted by the COMB score. The top 10 terms are shown in Table 4 below, and the full table is found in Additional file 1. Table 4 T-cell epitope prediction: Top 10 predicted epitopes for all MHC supertypes sorted by COMB score . No. ID PEPTIDES AFF AFF RES CLE TAP COMB MHC Supertype 205 Q7T9D9.1 YTENTSSYY 0.8151 3.4607 0.9708 2.88 3.7503 A1 209 Q7T9D9.1 TSSYYATSY 0.7094 3.0121 0.927 3.081 3.3052 A1 331 Q7T9D9.1 ISDRATRKY 0.5357 2.2744 0.9114 2.944 2.5583 A1 153 Q7T9D9.1 FHKDGAFFL 0.6314 2.0214 0.9121 1.019 2.2091 B39 212 Q7T9D9.1 YYATSYLEY 0.4301 1.8263 0.9704 3.24 2.1339 A1 205 Q7T9D9.1 YTENTSSYY 0.6738 1.8082 0.9708 2.88 2.0978 A26 303 Q7T9D9.1 GEELSFEAL 0.7586 1.8799 0.9419 0.628 2.0526 B44 627 Q7T9D9.1 IHDFIDNPL 0.5539 1.7732 0.9436 0.818 1.9557 B39 564 Q7T9D9.1 ETTQALQLF 0.626 1.68 0.6796 2.414 1.9027 A26 539 Q7T9D9.1 AEGIYTEGL 0.6645 1.6467 0.914 0.77 1.8223 B44 MHC 1 binding of epitopes Table 5 displays the top 17 allele combinations of the 13 epitopes, sorted by score. It presents the epitopes and their respective MHC class I allele combinations. The full table can be found in Additional file 2 . The epitope " EVTEIDQLV " is associated with HLA-A*68:02, " FLYDRLAST " with HLA-A*02:01 and HLA-A*02:03, " FQLNDTIHL " with HLA-A*02:06, " GEELSFEAL " with HLA-B*40:01, " ISDRATRKY " with HLA-A*01:01, " KCNPNLHYW " with HLA-B*57:01 and HLA-B*58:01, " LEVTEIDQL " with HLA-B*40:01, " RISDRATRK " with HLA-A*03:01, " RLASTVIYR " with HLA-A*03:01 and HLA-A*31:01, " RSRRQTNTK " with HLA-A*30:01, " RTYTILNRK " with HLA-A*03:01 and HLA-A*11:01, " SEQLRGEEL " with HLA-B*40:01, and " YTENTSSYY " with HLA-A*01:01. A cut-off score of 9.0 was used to determine these top 17 allele combinations. Table 5 MHC-I binding of epitopes: The top 17 allele combinations of 13 epitopes, sorted by score. A cut-off score of 9.0 was used for these top combinations. Allele seq_num Start End length Peptide core Icore score rank HLA-A*01:01 1 1 9 9 YTENTSSYY YTENTSSYY YTENTSSYY 0.996 0.01 HLA-B*57:01 15 1 9 9 KCNPNLHYW KCNPNLHYW KCNPNLHYW 0.986 0.01 HLA-B*58:01 15 1 9 9 KCNPNLHYW KCNPNLHYW KCNPNLHYW 0.979 0.01 HLA-A*03:01 17 1 9 9 RTYTILNRK RTYTILNRK RTYTILNRK 0.976 0.01 HLA-A*02:03 51 1 9 9 FLYDRLAST FLYDRLAST FLYDRLAST 0.973 0.01 HLA-B*40:01 7 1 9 9 GEELSFEAL GEELSFEAL GEELSFEAL 0.963 0.02 HLA-A*11:01 17 1 9 9 RTYTILNRK RTYTILNRK RTYTILNRK 0.963 0.01 HLA-A*01:01 3 1 9 9 ISDRATRKY ISDRATRKY ISDRATRKY 0.961 0.01 HLA-A*68:02 67 1 9 9 EVTEIDQLV EVTEIDQLV EVTEIDQLV 0.948 0.01 HLA-A*03:01 23 1 9 9 RISDRATRK RISDRATRK RISDRATRK 0.935 0.02 HLA-A*02:06 45 1 9 9 FQLNDTIHL FQLNDTIHL FQLNDTIHL 0.932 0.03 HLA-A*02:01 51 1 9 9 FLYDRLAST FLYDRLAST FLYDRLAST 0.93 0.03 HLA-A*31:01 24 1 9 9 RLASTVIYR RLASTVIYR RLASTVIYR 0.928 0.01 HLA-B*40:01 34 1 9 9 SEQLRGEEL SEQLRGEEL SEQLRGEEL 0.919 0.06 HLA-A*30:01 41 1 9 9 RSRRQTNTK RSRRQTNTK RSRRQTNTK 0.913 0.01 HLA-A*03:01 24 1 9 9 RLASTVIYR RLASTVIYR RLASTVIYR 0.912 0.03 HLA-B*40:01 35 1 9 9 LEVTEIDQL LEVTEIDQL LEVTEIDQL 0.9 0.06 From Table 5 above, the top 13 peptides were labelled with the seq numbers as follows: PEP-1 (YTENTSSYY), PEP-3 (ISDRATRKY), PEP-7 (GEELSFEAL), PEP-15 (KCNPNLHYW), PEP-17 (RTYTILNRK), PEP-23 (RISDRATRK), PEP-24 (RLASTVIYR), PEP-34 (SEQLRGEEL), PEP-35 (LEVTEIDQL), PEP-41 (RSRRQTNTK), PEP-45 (FQLNDTIHL), PEP-51 (FLYDRLAST) and PEP-67 (EVTEIDQLV). Population Coverage The IEDB's population coverage tool was used to determine the percentage of individuals within specific regions of Africa who are likely to possess HLA alleles capable of presenting each epitope. The population coverage percentages for each epitope in East Africa, West Africa, Central Africa, and North Africa are shown in Table 6. On the basis of the results obtained from the population coverage analysis, the epitope “ FLYDRLAST ” shows the highest coverage across all regions of Africa, with percentages ranging from 13.40–25.99%. This finding indicates that a significant proportion of individuals within the studied African populations may possess HLA alleles capable of presenting this epitope to T cells. Thus, it has potential as a vaccine candidate because of its wide coverage across different regions. Its high population coverage suggests that it could be recognized by a relatively large proportion of individuals within the population, potentially enhancing the effectiveness of immune responses and immune-based interventions. In this analysis of peptide epitopes and MHC-I class alleles from different regions in Africa, only peptides with an average binding affinity score above 9% were considered for further investigation. The average binding percentages for each peptide in East Africa, West Africa, Central Africa, and North Africa are also provided. Peptides with an average score above 9%: PEP-51 (FLYDRLAST), PEP-17 (RTYTILNRK), PEP-24 (RLASTVIYR), PEP-67 (EVTEIDQLV), PEP-23 (RISDRATRK), PEP-15 (KCNPNLHYW), PEP-41 (RSRRQTNTK), PEP-3 (ISDRATRKY), and PEP-1 (YTENTSSYY), where considered for further analysis, as shown in Table 5 . Table 6 The percentage of individuals in different regions of Africa with HLA alleles capable of presenting specific epitopes, with "FLYDRLAST" having the highest coverage. Peptide_ID Epitopes MHC-I Class Alleles East Africa West Africa Central Africa North Africa Average PEP-51 FLYDRLAST HLA-A*02:01 HLA-A*02:03 20.24% 19.99% 13.40% 25.99% 19.91% PEP-17 RTYTILNRK HLA-A*03:01 HLA-A*11:01 9.20% 12.21% 13.89% 13.23% 12.13% PEP-24 RLASTVIYR HLA-A*03:01 HLA-A*31:01 9.51% 11.10% 13.36% 12.20% 11.54% PEP-67 EVTEIDQLV HLA-A*68:02 15.00% 5.63% 8.50% 9.16% 9.57% PEP-23 RISDRATRK HLA-A*03:01 7.49% 9.48% 12.29% 8.96% 9.56% PEP-15 KCNPNLHYW HLA-B*57:01 HLA-B*58:01 12.81% 10.02% 6.51% 8.63% 9.49% PEP-41 RSRRQTNTK HLA-A*30:01 11.78% 7.40% 6.09% 12.50% 9.44% PEP-3 ISDRATRKY HLA-A*01:01 11.24% 7.31% 2.84% 14.18% 8.89% PEP-1 YTENTSSYY HLA-A*01:01 11.24% 7.31% 2.84% 14.18% 8.89% PEP-7 GEELSFEAL HLA-B*40:01 0.76% 1.16% 0.28% 1.19% 0.85% PEP-35 LEVTEIDQL HLA-B*40:01 0.76% 1.16% 0.28% 1.19% 0.85% PEP-34 SEQLRGEEL HLA-B*40:01 0.76% 1.16% 0.28% 1.19% 0.85% PEP-45 FQLNDTIHL HLA-A*02:06 0.26% 0.85% 0 0.00% 0.28% Allergenicity assessment Table 7 displays the results of the allergenicity assessment for peptide sequences (Peptide_ID) with a length of 9 amino acids. The "Best-known allergen hit name" column indicates that no significant hits were found on the basis of the E value threshold of 0.001. The "Number of known allergen hits" column indicates zero hits for all peptides. Consequently, the "Result" column shows "no evidence" of allergenicity, and the "Comment" column confirms "no hits" for allergens in the dataset. Table 7 Allergenicity assessment of 9-amino acid peptide sequences: No significant hits to known allergens were found, indicating no evidence of allergenicity in the dataset. Peptide_ID Sequence Length Best-known allergen-hit name Number of known allergen hits Result Comment PEP-1 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-3 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-15 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-17 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-23 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-24 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-41 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-51 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits PEP-67 9 No significant hit (E-value threshold 0.001) 0 no evidence no hits HLA Homology Modelling The result of this procedure was the attainment of the structures of the nine HLAs, which are essential for subsequent docking analyses. Below is one of the structures obtained. Molecular Docking The outcome of this process was the acquisition of HLA-PEPTIDE binding structures, facilitating a detailed docking analysis to determine whether the peptide was successfully accommodated within the HLA or not. The results revealed that, in the case of CABS, the peptides PEP-1, PEP-24, and PEP-17 bound outside the HLA structure, whereas for HPEPDOCK, only the peptide PEP-17 bound outside the HLA. An example structure of one of the HLA-PEPTIDE complexes obtained is shown in Fig. 13 . Docking Validation This step led to the development of the docking validation results shown in Table 8 below, which enabled the comparison of the docking scores obtained from the two different docking applications, CABS-Dock and HPEPDOCK, for the various peptide‒MHC-1 complexes. Notably, the docking results generated by HPEPDOCK demonstrated superior scoring compared with CABS-Dock. HPEPDOCK scoring was based on binding energy, and the top four complexes with binding energies greater than 9.0 were selected as the most favourable candidates for T-epitope vaccine development. These selected peptide‒MHC-1 complexes, which exhibit strong binding affinity, are essential in vaccine design. Table 8 Docking Scores for Peptide–MHC-1 Complexes: Comparison between CABS-Dock and HPEPDOCK, with the Top Four Complexes Selected for T-Epitope Vaccine Design PEPTIDE - MHC-1 Binding energy (Kcal/mol) CABS-dock HPEPDOCK PEP-1-HLA-A1 -5.42 -9.53 PEP-24-HLA-A31 -6.21 -9.18 PEP-15-HLA-B57 -6.32 -9.02 PEP-67-HLA-A68 -8.97 -9.01 PEP-15-HLA-B58 -7.33 -8.85 PEP-17-HLA-A11 -3.23 -8.83 PEP-3-HLA-A1 -4.70 -8.30 PEP-24-HLA-A3 -3.31 -8.18 PEP-51-HLA-A2 -7.54 -8.04 PEP-17-HLA-A3 -6.33 -7.72 PEP-23-HLA-A3 -5.01 -6.74 PEP-41-HLA-A30 -6.78 -6.69 Interacting residues The residue interaction analysis of the top four complexes revealed the presence of hydrogen bonds (Fig. 1 4 ) between residues crucial for molecular interactions, as shown in Table 9. Table 9 Interaction of peptide residues and HLA structures Complex Bonds Hydrogen bond pairs between Ligand (A) and receptor (B) PEP-1-HLA-A1 5 Tyr31(A) - Tyr1(B), Tyr183(A) - Thr2(B), Arg187(A) - Glu3(B), Gln179(A) - Tyr8(B), Tyr147(A) - Tyr9(B) PEP-15-HLA-B57 3 Asn101(A) - His7(B), Asp138(A) - His7(B), Tyr98(A) - His7(B) PEP-24-HLA-A31 5 Asp140(A) - Tyr8(B), Met121(A) - Tyr8(B), Glu87(A) - Thr5(B), Glu87(A) - Ser4(B), Tyr123(A) - Val6(B) PEP-67-HLA-A68 8 Asn65(A) - Gln7(B), Asp76(A) - Glu1(B), Lys145(A) - Glu1(B), Thr142(A) - Glu1(B), Trp146(A) - Glu1(B), Trp155(A) - Thr3(B), Asn62(A) - Val9(B), Tyr158(A) - Val9(B) DISCUSSIONS With a global population exceeding 8 billion, the world has encountered significant health challenges posed by viral infections(Iversen, 2018 ). The incidence of viral infections is notably greater in developing nations, particularly in Africa, than in more developed regions (Uwishema et al., 2021 ). Therefore, there is an urgent need to combat this devastating threat of these viruses by developing vaccines for them. Vaccine development has undergone significant advancements in recent years, with bioinformatics tools playing an increasingly crucial role in the identification and design of potential vaccine candidates (Nandy & Basak, 2019 ). Medical biotechnology has also assumed a pivotal role in combatting viral threats by crafting potent vaccines (Aileni et al., 2022 ). However, the traditional method of epitope identification via experimentation is both time intensive and costly (Yin et al., 2022 ). Advances in immunoinformatics methodologies have revolutionized this process, substantially reducing both time and expenditure while concurrently increasing the precision of epitope-based vaccine design (Parihar et al., 2022 ). These cutting-edge techniques facilitate the identification of antigenic regions on surface-exposed proteins that are primed to trigger robust immune responses. In the past decade, there has been a significant emphasis on targeting glycoproteins (GPs) by vaccine developers. This approach was also used by most SARS-CoV-2 vaccines during the 2019 pandemic (Martínez-Flores et al., 2021 ). EBOV GP, a surface-exposed and highly immunogenic protein described in previous studies, plays an important role in the attachment and entry of viral particles into the host (Davidson et al., 2015 ; Fan et al., 2020 ). Owing to surface exposure and reported antigenicity, GP was chosen as the target protein to develop the epitope-based vaccine in the present study. Numerous GP peptides have been reported and preliminarily checked in primates, and they provide positive feedback; however, there is no approved or licenced vaccine available to date (Duehr et al., 2017 ; Jones et al., 2005 ). A study by the International AIDS Vaccine Initiative (IAVI) initiated an inaugural human trial of an investigational Sudan Ebola virus vaccine, which employs a vesicular stomatitis virus (VSV) vector (Suder et al., 2018 ). This vaccine represents a significant step forward in combatting Sudan's Ebola virus infections. Despite all the reported studies, there is still no licenced vaccine for the Sudan Ebola virus present in the market until the present day (Malik et al., 2023 ). Therefore, the present study was designed to identify potential B and T-cell epitopes, which are worthy of exploration and could be used as vaccine candidates to prevent further spread and outbreak of EBOV, particularly the Sudan strain. Predominantly, recent vaccine development has centered around B-cell immunity. Nevertheless, there is a current shift in strategy, placing greater importance on T-cell epitopes owing to their potential to confer more durable and effective immunity, especially against viral infections (Rosendahl Huber et al., 2014 ). For the prediction of B cell epitopes, several analyses were carried out on the primary, secondary, and tertiary structures of the protein. Most B cell epitopes are discontinuous, and nearly 10% of epitopes are linear (Patronov & Doytchinova, 2013 ). B-cell epitopes were predicted with the help of Bepipred and other B-cell epitope-finding tools of the IEDB server. All the predicted B-cell epitopes analysed were long and highly conserved among all five species: ZEBOV, TAFV, SUDV, BDBV, and RESTV. Among these peptides, motif 2, “MGGLSLLQLPRDKFRKSSFFVWVIILFQKAFSMPLGVVTNS”, and motif 3, “YEAGEWAENCYNLEIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCP”, presented the highest immunogenicity scores from the IEBD server tools, and therefore could act as a robust B-cell epitope and vaccine candidate. Notably, all 7 identified motifs were conserved in nearly all Ebolavirus species, indicating that stable and long-acting epitopes can be obtained under high-frequency amino acid mutations, which deserve further analysis as potential B-cell epitopes (Lon et al., 2020 ). For T-cell epitope prediction, analyses were also performed. However, such vaccines based on T-cell epitopes can generate a strong immune response via CD8 + T cells against infected cells, making them better than B-cell-mediated immunity (Shrestha & Diamond, 2004 ). This method is highly efficient in combating viral infections because it harnesses cell-mediated immunity, which is predominantly directed by T cells and is known for its effectiveness against such infections (Riley & Montaner, 2017 ). To date, the modern strategy of T-cell epitope-based vaccinology has yielded successful results against diseases such as malaria and cancer and has exhibited potent immunogenicity regarding the activation of T-cell responses (Oyarzún & Kobe, 2016 ). In this study, several T-cell epitopes that can bind to MHC class I were predicted. The T-cell epitopes were predicted via the NetCTL server 2.0, which uses a combination of prediction algorithms to identify potential epitopes within the antigen sequence (Larsen et al., 2007 ). It predicted 184 epitopes from the GP that were further analysed to select the best candidate epitopes. Allergicity testing clearly revealed that the peptides predicted in the current study are stable and safe to use. Moreover, we performed interaction analysis of the predicted MHC class-I binding peptides with HLA alleles to assess the binding and immune response evoking ability of the predicted peptides. Flexible docking results, scores, and interacting residues revealed that all the predicted peptides strongly interact with MHC class-I alleles and can play a positive role in the initiation of the immune response and prevention of risks caused by EBOV. The epitopes that showed exception prediction results for all the analyses included “YTENTSSYY”, “KCNPNLHYW”, “RLASTVIYR” and “EVTEIDQLV.” These T-cell epitopes, composed of 9 amino acids each, effectively bind to the MHC I surface. Docking scores, as displayed in Table 8, along with the visualization of interacting residues in Fig. 19, collectively affirm the suitability of these peptides as promising vaccine candidates. The identification of interacting residues was accomplished through LIGPLOT analysis. Nonetheless, a significant knowledge gap persists concerning the extent of T-cell epitope population coverage for ebolaviruses, which is dependent upon the prevalence of HLA alleles within various populations (Hensen et al., 2022 ). Therefore, this research endeavoured to address this particular challenge. In this context, computational epitope screening stands out as an efficient and cost-effective approach, particularly when considering HLA class I molecules (Sundar et al., 2007 ). Considering their immunogenicity scores, degree of conservation, population coverage, and interaction analysis, it is reasonable to suggest that the peptides proposed in this study are likely to be more effective in triggering immune responses in Africa and are preserved across various Sudan ebolavirus strains, than previously reported peptides (Jain & Baranwal, 2021 ). CONCLUSION In our study, we focused on vaccine development against Sudan ebolavirus , with a particular emphasis on glycoproteins (GPs), which have been used the most in recent vaccine trends. The predominant approach in recent vaccine development has also focused on B-cell immunity. Nevertheless, the current strategy places greater emphasis on T-cell epitopes because of their potential to confer enduring better immunity, especially for viral infections. However, this study encompasses both B-cell and T-cell epitopes as a comprehensive strategy to elicit very strong immunity against SUEBOV. The resulting peptides have demonstrated selectivity for both B cells and T cells, exhibiting high immunogenicity, nonallergenicity, broader population coverage, and strong interactions with MHC-1 alleles characterized by favourable binding affinities and interactions. Importantly, these predicted epitopes are expected to provide robust, long-lasting protection against the Sudan ebolavirus . RECOMMENDATIONS We recommend conducting experimental validation to confirm the immunogenicity of the identified epitopes through in vitro assays to validate the results from this in silico study. We also recommend exploring B and T-cell responses and immune activation by eiptopes through animal models or in vitro assays for a deeper understanding of vaccine candidate efficacy. Finally, we recommend expanding the analysis to encompass more diverse datasets from various geographic regions other than Africa, which could increase the population coverage of the vaccine worldwide. Declarations ACKNOWLEDGEMENTS I want to express my appreciation to my undergraduate project supervisor, Dr. Magambo Phillip Kimuda, for his guidance, especially in the proposal and report development mentorship. Funding There was no funding for this research. Conflict of interest/Competing interests The author declares no conflicts of interest. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Data availability Available on request Material availability Not applicable Code availability Not applicable Author contributions Authors participated in the analysis, writing, and proofreading of the manuscript and approved the final manuscript for publication. Manuscript was fully curie AI reviewed and Chat GPT was used as the main AI-assisting in paraphrasing statements better SUPPLEMENTARY INFORMATION Accompanying supplementary files containing more information on the results include Additional files 1 and file 2. References Adnan I, Q. (2016). Ebola Virus. In Ebola Virus Disease . Animal Models for the Study of Human Disease, 2013, https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/sudan-ebola-virus. African CDC. (2022). Sudan Ebola Virus Disease in Uganda outbreak briefing. https://africacdc.org/disease-outbreak/final-outbreak-brief-16-sudan-virus-disease-in-uganda/ Aileni, M., Rohela, G. K., Jogam, P., Soujanya, S., & Zhang, B. (2022). Biotechnological Perspectives to Combat the COVID-19 Pandemic: Precise Diagnostics and Inevitable Vaccine Paradigms. Cells , 11 (7). https://doi.org/10.3390/cells11071182 Albariño, C. G., Guerrero, L. W., Chakrabarti, A. K., & Nichol, S. T. (2018). Transcriptional analysis of viral mRNAs reveals common transcription patterns in cells infected by five different filoviruses. PLoS ONE , 13 (8). https://doi.org/10.1371/journal.pone.0201827 Bailey, T. L., Johnson, J., Grant, C. E., & Noble, W. S. (2015). The MEME Suite. Nucleic Acids Research , 43 (W1), W39–W49. https://doi.org/10.1093/nar/gkv416 CDC. (2022). Update on Ebola Virus Disease (Sudan ebolavirus) Outbreak in Central Uganda. Health Alert Network , November 7, 2022, 9:15 AM ET CDCHAN-00480, https://emergency.cdc.gov/han/2022/han00480.asp#print , 1–5. Chou, P. Y., & Fasman, G. D. (1974). Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins. Biochemistry , 13 (2), 211–222. https://doi.org/10.1021/bi00699a001 Chou, P. Y., & Fasman, G. D. (1978). Prediction of the secondary structure of proteins from their amino acid sequence. Advances in Enzymology and Related Areas of Molecular Biology , 47 , 45–148. https://doi.org/10.1002/9780470122921.ch2 Davidson, E., Bryan, C., Fong, R. H., Barnes, T., Pfaff, J. M., Mabila, M., Rucker, J. B., & Doranz, B. J. (2015). Mechanism of Binding to Ebola Virus Glycoprotein by the ZMapp, ZMAb, and MB-003 Cocktail Antibodies. Journal of Virology , 89 (21), 10982–10992. https://doi.org/10.1128/JVI.01490-15 Duehr, J., Wohlbold, T. J., Oestereich, L., Chromikova, V., Amanat, F., Rajendran, M., Gomez-Medina, S., Mena, I., tenOever, B. R., García-Sastre, A., Basler, C. F., Munoz-Fontela, C., & Krammer, F. (2017). Novel Cross-Reactive Monoclonal Antibodies against Ebolavirus Glycoproteins Show Protection in a Murine Challenge Model. Journal of Virology , 91 (16). https://doi.org/10.1128/JVI.00652-17 Edgar, R. C. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research , 32 (5), 1792–1797. https://doi.org/10.1093/nar/gkh340 Emini, E. A., Hughes, J. V, Perlow, D. S., & Boger, J. (1985). Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of Virology , 55 (3), 836–839. https://doi.org/10.1128/JVI.55.3.836-839.1985 Fan, P., Chi, X., Liu, G., Zhang, G., Chen, Z., Liu, Y., Fang, T., Li, J., Banadyga, L., He, S., Yu, C., Qiu, X., & Chen, W. (2020). Potent neutralizing monoclonal antibodies against Ebola virus isolated from vaccinated donors. MAbs , 12 (1). https://doi.org/10.1080/19420862.2020.1742457 Feldmann H, & Klenk HD. (1996). Filoviruses. Medical Microbiology. 4th Edition , In: Baron S, editor. . Galveston (TX): University of Texas Medical Branch at Galveston; . Chapter 72. Available from: https://www.ncbi.nlm.nih.gov/books/NBK8129/ . Fleri, W., Paul, S., Dhanda, S. K., Mahajan, S., Xu, X., Peters, B., & Sette, A. (2017). The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. In Frontiers in Immunology (Vol. 8, Issue MAR). Frontiers Research Foundation. https://doi.org/10.3389/fimmu.2017.00278 Flower, D. (2013). Computer-Aided Vaccine Design. Human Vaccines & Immunotherapeutics , 10 (2), 241–243. https://doi.org/10.4161/hv.26687 Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R. D., & Bairoch, A. (2003). ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Research , 31 (13), 3784–3788. https://doi.org/10.1093/nar/gkg563 Hensen, L., Illing, P. T., Rowntree, L. C., Davies, J., Miller, A., Tong, S. Y. C., Habel, J. R., van de Sandt, C. E., Flanagan, K. L., Purcell, A. W., Kedzierska, K., & Clemens, E. B. (2022). T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles. Frontiers in Immunology , 13 , 812393. https://doi.org/10.3389/fimmu.2022.812393 Ibrahim, S. K., Ndwandwe, D. E., Thomas, K., Sigfrid, L., & Norton, A. (2022). Sudan virus disease outbreak in Uganda: urgent research gaps. BMJ Global Health , 7 (12). https://doi.org/10.1136/bmjgh-2022-010982 Iversen, P. L. (2018). The Threat from Viruses. In Molecular Basis of Resilience (pp. 45–76). Springer International Publishing. https://doi.org/10.1007/978-3-319-98164-2_3 Jacob, S. T., Crozier, I., Fischer, W. A., Hewlett, A., Kraft, C. S., Vega, M.-A. de La, Soka, M. J., Wahl, V., Griffiths, A., Bollinger, L., & Kuhn, J. H. (2020). Ebola virus disease. Nature Reviews. Disease Primers , 6 (1), 13. https://doi.org/10.1038/s41572-020-0147-3 Jain, S., & Baranwal, M. (2021). Conserved immunogenic peptides of Ebola glycoprotein elicit immune response in human peripheral blood mononuclear cells. Microbiology and Immunology , 65 (11), 505–511. https://doi.org/10.1111/1348-0421.12935 Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017). BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Research , 45 (W1), W24–W29. https://doi.org/10.1093/nar/gkx346 John K., K. (2015). The R Programming Language, in Doing Bayesian Data Analysis (Second Edition). ScienceDirect , https://www.sciencedirect.com/topics/mathematics/r-programming-language . Jones, S. M., Feldmann, H., Ströher, U., Geisbert, J. B., Fernando, L., Grolla, A., Klenk, H.-D., Sullivan, N. J., Volchkov, V. E., Fritz, E. A., Daddario, K. M., Hensley, L. E., Jahrling, P. B., & Geisbert, T. W. (2005). Live attenuated recombinant vaccine protects nonhuman primates against Ebola and Marburg viruses. Nature Medicine , 11 (7), 786–790. https://doi.org/10.1038/nm1258 Karplus, P. A., & Schulz, G. E. (1985). Prediction of chain flexibility in proteins. . Naturwissenschaften 72. , https://doi.org/10.1007/BF01195768 , 212–213. Kolaskar, A. S., & Tongaonkar, P. C. (1990). A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Letters , 276 (1–2), 172–174. https://doi.org/10.1016/0014-5793(90)80535-q Lamiable, A., Thévenet, P., Rey, J., Vavrusa, M., Derreumaux, P., & Tufféry, P. (2016). PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Research , 44 (W1), W449-54. https://doi.org/10.1093/nar/gkw329 Larsen, M. V, Lundegaard, C., Lamberth, K., Buus, S., Lund, O., & Nielsen, M. (2007). Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics , 8 , 424. https://doi.org/10.1186/1471-2105-8-424 Le Guenno, & B. (1995). Emerging viruses. Scientific American , 273(4) , 56–64. Lennemann, N. J., Dillard, J., Ruggio, N., Cooney, A. L., Schaack, G. A., Davey, R. A., & Maury, W. (2021). A Naturally Occurring Polymorphism in the Base of Sudan Virus Glycoprotein Decreases Glycoprotein Stability in a Species-Dependent Manner. Journal of Virology , 95 (18). https://doi.org/10.1128/jvi.01073-21 Lon, J. R., Bai, Y., Zhong, B., Cai, F., & Du, H. (2020). Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2. Virology Journal , 17 (1), 165. https://doi.org/10.1186/s12985-020-01437-4 Malik, S., Kishore, S., Nag, S., Dhasmana, A., Preetam, S., Mitra, O., León-Figueroa, D. A., Mohanty, A., Chattu, V. K., Assefi, M., Padhi, B. K., & Sah, R. (2023). Ebola Virus Disease Vaccines: Development, Current Perspectives & Challenges. Vaccines , 11 (2), 268. https://doi.org/10.3390/vaccines11020268 Malonis, R. J., Lai, J. R., & Vergnolle, O. (2020). Peptide-Based Vaccines: Current Progress and Future Challenges. In Chemical Reviews (Vol. 120, Issue 6, pp. 3210–3229). American Chemical Society. https://doi.org/10.1021/acs.chemrev.9b00472 Martínez-Flores, D., Zepeda-Cervantes, J., Cruz-Reséndiz, A., Aguirre-Sampieri, S., Sampieri, A., & Vaca, L. (2021). SARS-CoV-2 Vaccines Based on the Spike Glycoprotein and Implications of New Viral Variants. Frontiers in Immunology , 12 , 701501. https://doi.org/10.3389/fimmu.2021.701501 Maurer-Stroh, S., Krutz, N. L., Kern, P. S., Gunalan, V., Nguyen, M. N., Limviphuvadh, V., Eisenhaber, F., & Gerberick, G. F. (2019). AllerCatPro-prediction of protein allergenicity potential from the protein sequence. Bioinformatics (Oxford, England) , 35 (17), 3020–3027. https://doi.org/10.1093/bioinformatics/btz029 Muyembe-Tamfum, J. J., Mulangu, S., Masumu, J., Kayembe, J. M., Kemp, A., & Paweska, J. T. (2012). Ebola virus outbreaks in Africa: Past and present. Onderstepoort Journal of Veterinary Research , 79 (2). https://doi.org/10.4102/ojvr.v79i2.451 Nandy, A., & Basak, S. C. (2019). Bioinformatics in Design of Antiviral Vaccines. In Encyclopedia of Biomedical Engineering (pp. 280–290). Elsevier. https://doi.org/10.1016/B978-0-12-801238-3.10878-5 Oyarzún, P., & Kobe, B. (2016). Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production. Human Vaccines & Immunotherapeutics , 12 (3), 763–767. https://doi.org/10.1080/21645515.2015.1094595 Parihar, A., Malviya, S., & Khan, R. (2022). Immunoinformatics and reverse vaccinomic approaches for effective design. In Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection (pp. 357–378). Elsevier. https://doi.org/10.1016/B978-0-323-91172-6.00004-2 Parker, J. M., Guo, D., & Hodges, R. S. (1986). New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry , 25 (19), 5425–5432. https://doi.org/10.1021/bi00367a013 Passi, D., Sharma, S., Dutta, S. R., Dudeja, P., & Sharma, V. (2015). Ebola Virus Disease (The Killer Virus): Another Threat to Humans and Bioterrorism: Brief Review and Recent Updates. Journal of Clinical and Diagnostic Research : JCDR , 9 (6), LE01-8. https://doi.org/10.7860/JCDR/2015/13062.6100 Patronov, A., & Doytchinova, I. (2013). T-cell epitope vaccine design by immunoinformatics. Open Biology , 3 (1), 120139. https://doi.org/10.1098/rsob.120139 Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. Journal of Computational Chemistry , 25 (13), 1605–1612. https://doi.org/10.1002/jcc.20084 Report of a WHO/International Study Team. (1978). Ebola haemorrhagic fever in Sudan, 1976. Report of a WHO/International Study Team. Bulletin of the World Health Organization , 56 (2), 247–270. Riley, J. L., & Montaner, L. J. (2017). Cell-Mediated Immunity to Target the Persistent Human Immunodeficiency Virus Reservoir. The Journal of Infectious Diseases , 215 (suppl_3), S160–S171. https://doi.org/10.1093/infdis/jix002 Rivera, A., & Messaoudi, I. (2016). Molecular mechanisms of Ebola pathogenesis. Journal of Leukocyte Biology , 100 (5), 889–904. https://doi.org/10.1189/jlb.4ri0316-099rr Rosendahl Huber, S., van Beek, J., de Jonge, J., Luytjes, W., & van Baarle, D. (2014). T cell responses to viral infections - opportunities for Peptide vaccination. Frontiers in Immunology , 5 , 171. https://doi.org/10.3389/fimmu.2014.00171 Sanchez, A., & Rollin, P. E. (2005). Complete genome sequence of an Ebola virus (Sudan species) responsible for a 2000 outbreak of human disease in Uganda. Virus Research , 113 (1), 16–25. https://doi.org/10.1016/j.virusres.2005.03.028 Sebastian, S., Flaxman, A., Cha, K. M., Ulaszewska, M., Gilbride, C., Sharpe, H., Wright, E., Spencer, A. J., Dowall, S., Hewson, R., Gilbert, S., & Lambe, T. (2020). A Multi-Filovirus Vaccine Candidate: Co-Expression of Ebola, Sudan, and Marburg Antigens in a Single Vector. Vaccines , 8 (2). https://doi.org/10.3390/vaccines8020241 Shrestha, B., & Diamond, M. S. (2004). Role of CD8 + T Cells in Control of West Nile Virus Infection. Journal of Virology , 78 (15), 8312–8321. https://doi.org/10.1128/JVI.78.15.8312-8321.2004 Sievers, F., Wilm, A., Dineen, D., Gibson, T. J., Karplus, K., Li, W., Lopez, R., Mcwilliam, H., Remmert, M., Söding, J., Thompson, J. D., & Higgins, D. G. (2011). Fast, scalable generation of high quality protein multiple sequence alignments using Clustal Omega Transaction Report . http://www.nature.com/msb/authors Srinath, K. R. (2017). Python-The Fastest Growing Programming Language. International Research Journal of Engineering and Technology . www.irjet.net Suder, E., Furuyama, W., Feldmann, H., Marzi, A., & de Wit, E. (2018). The vesicular stomatitis virus-based Ebola virus vaccine: From concept to clinical trials. Human Vaccines & Immunotherapeutics , 14 (9), 2107–2113. https://doi.org/10.1080/21645515.2018.1473698 Sundar, K., Boesen, A., & Coico, R. (2007). Computational prediction and identification of HLA-A2.1-specific Ebola virus CTL epitopes. Virology , 360 (2), 257–263. https://doi.org/10.1016/j.virol.2006.09.042 Uwishema, O., Adanur, I., Babatunde, A. O., Hasan, M. M., Elmahi, O. K. O., Olajumoke, K. B., Aborode, A. T., Emmanuella, N., Costa, A. C. D. S., Ahmad, S., & Essar, M. Y. (2021). Viral infections amidst COVID-19 in Africa: Implications and recommendations. Journal of Medical Virology , 93 (12), 6798–6802. https://doi.org/10.1002/jmv.27211 Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M., & Barton, G. J. (2009). Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics (Oxford, England) , 25 (9), 1189–1191. https://doi.org/10.1093/bioinformatics/btp033 WHO. (2022). SUDAN VIRUS DISEASE (SVD) OUTBREAK, UGANDA Member State Briefing – 13 October 2022. WHO Member State Briefing , https://apps.who.int/gb/COVID-19/pdf_files/2022/13_10/Item1.pdf . Yadav, A. R., & Mohite, S. K. (2020). An overview on ebola virus disease. RESEARCH JOURNAL OF PHARMACEUTICAL DOSAGE FORMS AND TECHNOLOGY , 12 (4), 267–270. https://doi.org/10.5958/0975-4377.2020.00044.0 Yin, R., Zhu, X., Zeng, M., Wu, P., Li, M., & Kwoh, C. K. (2022). A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods. Briefings in Bioinformatics , 23 (5). https://doi.org/10.1093/bib/bbac281 Yu, D.-S., Weng, T.-H., Wu, X.-X., Wang, F. X. C., Lu, X.-Y., Wu, H.-B., Wu, N.-P., Li, L.-J., & Yao, H.-P. (2017). Oncotarget 55750 www.impactjournals.com/oncotarget The lifecycle of the Ebola virus in host cells. In Oncotarget (Vol. 8, Issue 33). www.impactjournals.com/oncotarget/ Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfile2.xlsx APPENDICES.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6600860","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452524917,"identity":"82fec120-af7e-4420-a37f-a6cec0fdbcf6","order_by":0,"name":"Anguzu Simon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBADHhBxIKECSDIzNxCthfHBhzMgLYzEaQEBZsOZbSCagBb+2e3XJH+21cnotvcek+adVxvN3w7U8qNiG04tEnfOlElIth3mMTtzLk2ad9vx3BmHGRsYe87cxm3NjZw0CcO2AzxmN3LMgFqO5TYAtTAztuHWIg/SkthWB9Uy51jufEJaDG6kH5M42MYM0mJsOLOhJncDIS2GN3KYLRvOgfxyxvDBh2MHcjcCtRzE5xe5G+kPb/4oq7M3O95jcCChpi533vnDBx/8qMDjfQYeA2TeYTB5AI96IGB/gMyrw694FIyCUTAKRiQAACTXXuK/5uGUAAAAAElFTkSuQmCC","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"Anguzu","middleName":"","lastName":"Simon","suffix":""}],"badges":[],"createdAt":"2025-05-06 08:38:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6600860/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6600860/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82318625,"identity":"17fa15e5-7468-40c2-945b-3518621416d8","added_by":"auto","created_at":"2025-05-09 04:10:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e study workflow from sequence retrieval, predicting antigenicity, identifying B-cell and T-cell epitopes, assessing population coverage, allergenicity, and molecular docking.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/186261a8b68b3af9f390a8cd.png"},{"id":82317904,"identity":"1b69d615-71d1-49a6-a0cb-d183a981ed84","added_by":"auto","created_at":"2025-05-09 04:02:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":272971,"visible":true,"origin":"","legend":"\u003cp\u003eGP sequence alignment in Jalview reveals variations between the Mubende isolate and others at positions 7, 1222, and 1583. Other isolates also exhibit variations, contributing to genome diversity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/aec071eb64d4732a6e8d6b9a.png"},{"id":82317949,"identity":"59afe5ce-af8b-4df1-b93a-4190733943de","added_by":"auto","created_at":"2025-05-09 04:02:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204672,"visible":true,"origin":"","legend":"\u003cp\u003eTranslation and analysis of the GP coding sequence from the Mubende isolate viathe ExPASy Translate Tool in the 5'3' frame. The resulting sGP protein sequence is displayed above.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/9fda2833f4ab9af2da99db3a.png"},{"id":82317899,"identity":"4ca2d389-ad42-4ff0-8160-56384e01e6d8","added_by":"auto","created_at":"2025-05-09 04:02:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153971,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree showing the evolutionary relationships between different Ebolavirus GP sequences. The tree was constructed on the basis of MSA data, providing insights into the relatedness of different strains.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/2b100f6b6a30739e4b2db85e.png"},{"id":82318626,"identity":"473e2c98-25f9-4187-a58c-a3dca2e343ec","added_by":"auto","created_at":"2025-05-09 04:10:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55775,"visible":true,"origin":"","legend":"\u003cp\u003eThe motif heatmap representing the 21 identified motifs, showing their distribution across the 24 analysed sequences, including the sGP and GP of the SUEBV, ZEBV, REBV, and TFEBV ebolavirus strains.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/a6af5f60b0a1f1553c826b77.png"},{"id":82317900,"identity":"2ff0bc26-a6b5-423d-bd45-34e4d344cc5f","added_by":"auto","created_at":"2025-05-09 04:02:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":73853,"visible":true,"origin":"","legend":"\u003cp\u003eBepiPred 2.0 linear epitope prediction method. All the motifs showed thepotential to be part of the epitope; however, motifs M1 and M4 were the only motifs with most residues below the threshold (0.5).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/2419e5b5e2286b3f39a7c119.png"},{"id":82317907,"identity":"f9123d64-db90-408f-b2e9-78b4a89afab4","added_by":"auto","created_at":"2025-05-09 04:02:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":83444,"visible":true,"origin":"","legend":"\u003cp\u003eKolaskar \u0026amp; Tongaomkar Antigenicity Prediction. The motifs for potential antigenic epitopes were analysed. Among all the motifs examined, M5 and M6 were the only ones whose residues were mostly below the threshold value (1.016).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/1c50b1a7a56e6ca2d06b2d6d.png"},{"id":82317902,"identity":"09484b6e-d1f8-457f-a7be-10cdf5ff7f5e","added_by":"auto","created_at":"2025-05-09 04:02:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":96608,"visible":true,"origin":"","legend":"\u003cp\u003eThe Parker hydrophilicity prediction method. Assessed motifs for potential hydrophilic regions. Among the motifs, M2 and M5 presentedresidues below the threshold value (1.608), suggesting their likelihood of being hydrophilic regions.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/cc27824cd8f419525bdd77b8.png"},{"id":82317913,"identity":"c846721b-b75e-4107-9f3a-1ce3ab046782","added_by":"auto","created_at":"2025-05-09 04:02:13","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":78762,"visible":true,"origin":"","legend":"\u003cp\u003eEmini surface accessibility prediction: This method analyses the motifs for potential surface accessibility. All the motifs presented values above the threshold (1.05), indicating their likelihood of being accessible on the protein surface. M2 had low values.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/bf947cf040ec0491f17c6cc5.png"},{"id":82317905,"identity":"dbe9bf10-13d7-4c8a-ad04-7a145d9b4b93","added_by":"auto","created_at":"2025-05-09 04:02:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":32377,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure design of peptide motif 2.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/9f7578ed52e53e720b13749e.png"},{"id":82317959,"identity":"ff0e7b43-98c9-4fb5-b1e7-2306d63d7700","added_by":"auto","created_at":"2025-05-09 04:02:15","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":36397,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure design of peptide-motif 3.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/b7b4eeff8933b0d98e2717dc.png"},{"id":82317916,"identity":"b837b9a0-6c0a-4179-afa3-ba2cba301bea","added_by":"auto","created_at":"2025-05-09 04:02:13","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":33341,"visible":true,"origin":"","legend":"\u003cp\u003eStructural representation of HLA-B*57: An outcome of the HLA homology modelling process.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/9bfd4d951fed7d860dc6f397.png"},{"id":82317941,"identity":"3deeaca7-0e5a-4777-83e9-7919816fd33a","added_by":"auto","created_at":"2025-05-09 04:02:15","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":226811,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of Molecular Docking Investigation\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/f50946366521913f5743fde0.png"},{"id":82318634,"identity":"0e79f70e-317c-4d0f-aa03-6c29e7090f00","added_by":"auto","created_at":"2025-05-09 04:10:14","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":182362,"visible":true,"origin":"","legend":"\u003cp\u003eLigPlot images showing interactions between HLA-PEPTIDE complexes (PEP-1-HLA-A1, PEP-24-HLA-A31, PEP-15-HLA-B57, and PEP-67-HLA-A68).\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/4387955e6de22b640020cd53.png"},{"id":83627087,"identity":"bf98180a-b159-41ca-9aef-177db4222ece","added_by":"auto","created_at":"2025-05-29 17:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4261994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/7acfa50f-675d-489b-b367-df35eff29e38.pdf"},{"id":82317898,"identity":"854b965f-4817-4ab1-938f-b529d89ed093","added_by":"auto","created_at":"2025-05-09 04:02:12","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":59445,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/d1ca70dfefa2a049dc97131c.xlsx"},{"id":82318639,"identity":"e2cd2287-450b-44da-bd83-c6b23e6de472","added_by":"auto","created_at":"2025-05-09 04:10:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":198853,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/3430a386f5d2defc6b5f1257.xlsx"},{"id":82318628,"identity":"88a452d4-c9b0-4bef-ac7e-d26a9cb63b09","added_by":"auto","created_at":"2025-05-09 04:10:13","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1750360,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDICES.docx","url":"https://assets-eu.researchsquare.com/files/rs-6600860/v1/d66e67849ff5a0f7ba0dd26e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico prediction of B-cell and T-cell epitopes of the Sudan Ebola virus glycoprotein for peptide-based vaccine design","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEbola virus disease (EVD), also known as Ebola hemorrhagic fever (EHF), is one of the deadliest viral diseases(CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In 1976, two concurrent outbreaks of this fatal hemorrhagic fever occurred in different parts of Central Africa that same year(CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Ebola virus was named from the Ebola River in the Democratic Republic of the Congo (D.R.C.), where it was first identified (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It includes two species: Zaire ebolavirus (ZEBOV) and Sudan ebolavirus (SEBOV) (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEbola viruses are enveloped negative-sense RNA viruses belonging to the family Filoviridae within the order Mononegavirales (Yadav \u0026amp; Mohite, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The genera include; Cuevavirus, Marburgvirus, and Ebolavirus (Feldmann H \u0026amp; Klenk HD., 1996). There are five distinct varieties of Ebola viruses: Zaire, Sudan, Bundibugyo, Ta\u0026iuml; Forest, and Reston virus (which do not infect humans) (Yu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). They are pleomorphic enveloped viruses that can cause viral hemorrhagic fever in humans and nonhuman primates (NHPs)(Feldmann H \u0026amp; Klenk HD., 1996). The strand RNA genome encodes 7 genes, including GP, RNA-dependent RNA polymerase (L), NP, and 4 VPs (Rivera \u0026amp; Messaoudi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It also has leader and trailer sequences at the genome end that contain encapsidation signals (Rivera \u0026amp; Messaoudi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It has promoters for replication and transcription and produces soluble forms of GP through RNA editing (Rivera \u0026amp; Messaoudi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The natural hosts of Ebola viruses have not yet conclusively identified, but the most likely host appears to be the fruit bats of the Pteropodidae family (Passi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). EVD outbreaks typically begin with a single probable zoonotic transmission case (Jacob et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Human-to-human transmission occurs through direct contact or exposure to infected bodily fluids or contaminated objects (Jacob et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSudan ebolavirus is one of the five known species of ebolavirus (Yu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It belongs to the Ebolavirus genus (Feldmann H \u0026amp; Klenk HD., 1996). It is the pathogen responsible for causing Sudan virus disease (SVD), a subcategory of Ebola virus disease (WHO, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In 1976, nearly simultaneous outbreaks of Ebola viruses, specifically Sudan and Zaire strains, occurred in the Democratic Republic of the Congo (DRC) (Adnan I, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sudan's strain of the Ebola virus was initially identified in 1977 and was later recognized as Ebola hemorrhagic fever by the WHO in 1978 (Passi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). After the 2002 outbreak, it was referred to as the Sudan Ebola virus (Passi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Its last outbreak in Uganda occurred in October 2022, and it caused significant adverse effects on both health and economic status in the nation (WHO, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since October 26, 2022, a total of 130 confirmed cases and 43 confirmed deaths from EHF have been reported from seven districts in Uganda (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). An additional 21 new confirmed cases and 12 new confirmed deaths (with a CFR of 57%) were reported, and 45 recoveries were registered. Healthcare workers account for 13.8% (18) of the cases and 13.9% (6) of all deaths (African CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is no specific treatment or vaccine for SUDV, but it exists for the Zaire strain (WHO, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the EHF outbreak in southern Sudan between June and November 1976, there were a total of 284 cases, with 53% overall mortality and prolonged recovery periods for survivors (Report of a WHO/International Study Team, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). The symptoms of EHF include influenza-like syndrome, diarrhea, vomiting, chest pain, throat pain and dryness, rash, and hemorrhagic manifestations (Report of a WHO/International Study Team, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). There was no outbreak of EHF between 1980 and 1993, but it later reemerged in Africa more frequently, and new species were discovered, including C\u0026ocirc;te d\u0026rsquo;Ivoire ebolavirus (CIEBOV) in 1994 in the Ivory Coast and Bundibugyo ebolavirus (BEBOV) in 2007 in Uganda (Muyembe-Tamfum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCIEBOV has been associated with only one human case (Le Guenno \u0026amp; B., 1995). An outbreak of EVD caused by the Sudan ebolavirus was declared in the Mubende district in Central Uganda in September 2022 (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By November, there had been a total of 132 confirmed cases, with 39% of those infected resulting in death and only 61 patients recovering and being discharged (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since the outbreak began, seven districts in Uganda, including Mubende, Kassanda, Kyegegwa, Bunyangabu, Kagadi, Wakiso, and the capital city of Kampala, have reported cases of EBOV infection (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). EBOV GPs are vital for attachment to host cells, catalyzing membrane fusion, and are therefore targets of neutralizing antibodies and attachment and fusion inhibitors, as well as crucial components of vaccines (Lennemann et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). EBOV enters host cells such as a human hepatoma cell line (Huh 7) and primary human macrophages (Mpg) (Albari\u0026ntilde;o et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It enters through receptor-mediated endocytosis via various receptors, including the asialoglycoprotein receptor, folate receptor a, C-type lectins, and human macrophage lectin (Rivera \u0026amp; Messaoudi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The entry of EBOV into host cells begins with the interaction of the viral GP with receptors on the cell surface, which are then internalized through the macropinocytosis pathway (Yu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). During uncoating and fusion, GP1 binds to the endosome via the RBD, and GP2 facilitates fusion through the fusion loop (Yu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Currently, there are no approved antiviral drugs or vaccines specifically designed to combat Sudan ebolavirus (Muyembe-Tamfum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current management strategy for SVD involves the use of hyperimmune serum from patients who have recovered from the disease and some antiviral medications, such as remdesivir (Muyembe-Tamfum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Compared with traditional methods, computer-aided discovery of immunogenic proteins and epitopes in combination with various vaccine discovery techniques has helped to shorten the process of vaccine production (Flower, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The potential of predictive computational vaccine identification is clear since immunogenic proteins identified as antigens from pathogen genomes are potential subunit vaccines. These immunogenic epitopes are vital components of epitope ensemble vaccines (Flower, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Peptide-based subunit vaccines are safe for immunocompromised patients, cannot revert to virulence, and cannot cause the disease they are meant to protect against. They can also withstand changes in temperature, light exposure, and humidity (Malonis et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While the amino acid sequences of the Sudan and Zaire strains of the Ebola virus are distinct, when their gene product sequences are compared, they both show a slightly closer relationship to the Reston species than to each other (Sanchez \u0026amp; Rollin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Notably, the GP demonstrates the least conservation among these gene products, suggesting that a GP vaccine designed for the Zaire strain would not be effective against the Sudan strain (Sanchez \u0026amp; Rollin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Within the context of vaccines for infectious diseases, it is essential to recognize that protective immunity against a particular virus species or strain may not confer cross-reactive immunity to closely related viruses.(Sebastian et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) As a consequence, individuals might remain vulnerable to infection even after encountering related virus species. (Sebastian et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study used various \u003cem\u003ein silico\u003c/em\u003e tools to virtually screen for the available GP sequences of Sudan ebolavirus and that of the current Mubende isolate. This will be done to identify potential B-cell and T-cell SUEBOV GP epitopes that will be exploited for peptide-based subunit vaccine designs that have not been previously screened.\u003c/p\u003e\n\u003ch3\u003eProblem Statement And Significance\u003c/h3\u003e\n\u003cp\u003eEHF is a global threat that continues to reoccur in central African countries and their neighbors, as well as some countries outside Africa (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Currently, EHF is managed with the use of nonspecific antiviral therapies such as remdesivir and hyperimmune serum from recovered patients. There are also no approved antiviral drugs specifically for infection, and the available options are less efficient (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are no approved vaccines for the prevention and management of Sudan ebolavirus infections in humans, yet this approach could be more effective than chemotherapy (Muyembe-Tamfum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The outbreak of Sudan ebolavirus (SUDV) highlights our ongoing vulnerability to re-emerging high-consequence infectious diseases. (Ibrahim et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Therefore, this study aims to identify potential B-cell and T-cell SUEBOV GP epitopes that can be exploited for the design of safe, effective, and affordable peptide-based subunit vaccines to effectively manage and prevent future outbreaks of \u003cem\u003eSudan ebolavirus\u003c/em\u003e disease.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eJustification\u003c/h2\u003e \u003cp\u003eThese findings provide information about B-cell and T-cell Sudan ebolavirus glycoprotein epitopes that can be exploited for peptide-based subunit vaccine design. Thus, the results of this research will contribute to the ongoing efforts of scientific researchers to create a vaccine for the Sudan ebolavirus to prevent future outbreaks of this disease. The best epitope peptides can eventually be used by vaccine production industries to develop actual commercial peptide-based subunit vaccines, which can then be used by the Ministry of Health to inform policy about vaccination activities in the country.\u003c/p\u003e \u003c/div\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe in silico\u003c/em\u003e study involved retrieving the Mubende Sudan ebolavirus genome sequence, predicting antigenicity, identifying B-cell and T-cell epitopes, assessing population coverage and allergenicity, and conducting molecular structure and docking analyses. See \u003cb\u003eFig.\u0026nbsp;7\u003c/b\u003e below for a summary of the steps.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData retrieval of Ebola viral sequences.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRetrieval of the Mubende Sudan ebolavirus genome.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe genome sequence of the Mubende \u003cem\u003eSudan ebolavirus\u003c/em\u003e isolate was accessed via the link \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/evk3/UVRI_Sudan_EBOV_Uganda_2022\u003c/span\u003e\u003cspan address=\"https://github.com/evk3/UVRI_Sudan_EBOV_Uganda_2022\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The consensus sequence was selected and downloaded from the page displayed in FASTA file format.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRetrieval of the\u003c/b\u003e \u003cb\u003eSudan ebolavirus\u003c/b\u003e \u003cb\u003eGP protein sequence\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe\u003c/b\u003e NCBI website was accessed via the link, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. On the home page displayed, \u003cb\u003e\u0026ldquo;All Databases\u0026rdquo;\u003c/b\u003e near the search bar were selected, which was then changed to the \u003cb\u003e\u0026ldquo;Protein\u0026rdquo;\u003c/b\u003e Database. In the search bar, the search terms \u003cb\u003e\u0026ldquo;Structural glycoprotein\u0026rdquo; AND \u0026ldquo;Sudan ebolavirus\u0026rdquo; [orgn]\u003c/b\u003e were used. The \u003cb\u003e\u0026ldquo;PDB database\u0026rdquo;\u003c/b\u003e option on the left of the page was then clicked, and then \u003cb\u003e\u0026ldquo;Search\u0026rdquo; was\u003c/b\u003e clicked, which returned results linked to \u0026ldquo;\u003cb\u003eSudan ebolavirus structural glycoprotein.\u0026rdquo;\u003c/b\u003e From the results, two \u003cem\u003eSudan ebolavirus\u003c/em\u003e glycoprotein sequences and one genome sequence were downloaded. The files were all independently saved on the computer by clicking on \u003cb\u003e\u0026ldquo;send to\u0026rdquo;\u003c/b\u003e, followed by \u003cb\u003e\u0026ldquo;File\u0026rdquo;, \u0026ldquo;FASTA\u0026rdquo;\u003c/b\u003e, and finally \u003cb\u003e\u0026ldquo;create file.\u0026rdquo;\u003c/b\u003e These files were then used in the next step.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultiple sequence alignment of Sudanic genomes and glycoprotein sequences\u003c/h3\u003e\n\u003cp\u003eThe files with the above downloaded \u003cem\u003eSudan ebolavirus\u003c/em\u003e isolate genomes and glycoprotein sequences were combined and saved as a single FASTA file. With Clastal Omega, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/Tools/msa/clustalo/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/Tools/msa/clustalo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, MSA was selected. It uses seeded guide trees and HMM profile techniques to generate alignments between three or more sequences (Sievers et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The file with the combined sequences in FASTA format was chosen for alignment. The format of the results to be output was selected as the FASTA format, but other parameters were set to the defaults. When the MSA had finished running, the results were returned and accessed on a new page. The alignment was saved in FASTA format by clicking on the \u0026ldquo;\u003cb\u003eDownload Alignment File\u003c/b\u003e\u0026rdquo;. The alignment results were viewed with Jalview. The alignment file was then uploaded to visualize the areas of conservation. The color settings were changed to nucleotides to visualize the nucleotides (Waterhouse et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To obtain the region of the CDS for the GP in the Mubende isolate, the GP sequence in the alignment was used to show the region of the genome in the Mubende isolate genome to annotate. The region was identified; thus, the GP sequence in the genome was cut via Jalview tools and then saved as a fasta file. Using Jalview, the three GP sequences were analysed by comparison with the Mubende isolate. The differences between the 4 sequences were identified, and the points of differences were noted. The GP sequence in the Mubende isolate was selected and saved as a FASTA file.\u003c/p\u003e\n\u003ch3\u003eTranslation of the GP CDS to the protein sequence\u003c/h3\u003e\n\u003cp\u003eThe GP CDS was then translated into an amino acid sequence via the \u003cb\u003eExPASy Translate Tool\u003c/b\u003e (Gasteiger et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/translate/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/translate/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, whereby the DNA sequence was entered into the tool and run to obtain the protein sequence. The best frame was then selected and saved as a FASTA file.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparative Study and Phylogenetic Classification of Ebolavirus GPs\u003c/h2\u003e \u003cp\u003eIn a comparative study aiming to classify Ebolavirus GPs phylogenetically, a Blastp analysis was conducted in UniProt. On the basis of the BLAST results, the GP and sGP sequences of Sudan, Reston, Zaire, and Tai Forest Ebolavirus were selected and downloaded. along with the Mubende sGP sequence obtained from ExPASy, in a combined FASTA file. A combined file including the Mubende GP sequence obtained from ExPASy was made and saved as a single FASTA file.\u003c/p\u003e \u003cp\u003eTo further analyse the sequences, MSA was performed via \u003cb\u003eMUSCLE\u003c/b\u003e (Edgar, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/Tools/msa/muscle/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/Tools/msa/muscle/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The combined file formed was chosen for alignment, and the output format was changed to FASTA. The results were saved as an alignment file and then visualized via \u003cb\u003eJalview\u003c/b\u003e (Waterhouse et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Using Jalview, the MSA results were used to generate a phylogenetic tree. On the basis of Blastp and phylogenetic tree analyses, the full GP sequence to be used was identified by the UnitProt ID, which was then searched in UniProt, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, downloaded, and saved as a FASTA file.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eB-cell epitope prediction\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMotif sequence identification\u003c/h2\u003e \u003cp\u003eThe combined FASTA file of Ebolavirus GP sequences from the previous step was used in this step after removing the non-Sudan strains' GP sequences. The Mubende strain partial GP sequence and the obtained UniProt Sudanic GP sequences in FASTA format were then analysed via the Multiple Em for Motif Elicitation (\u003cb\u003eMEME\u003c/b\u003e) tool Version 5.5.3 (Bailey et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) with the default settings (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://meme-suite.org/tools/meme\u003c/span\u003e\u003cspan address=\"http://meme-suite.org/tools/meme\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e This motif-based sequence analysis tool was designed to pick at most 21 motifs that appeared in at least 2 amino acid sequences. A motif heatmap was generated via a mast text file from MEME and an in-house \u003cb\u003ePython\u003c/b\u003e script (Srinath, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The motifs were analysed for their conservation among different \u003cem\u003eSudan ebolavirus\u003c/em\u003e species. The highly conserved motifs were then selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAllergenicity determination\u003c/h2\u003e \u003cp\u003eThe motif peptide sequences obtained via MEME were saved in a single FASTA file. \u003cb\u003eAllerCatPro\u003c/b\u003e (version 2.0) (Maurer-Stroh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is a protein allergenicity potential prediction, was then accessed via the link \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://allercatpro.bii.a-star.edu.sg/\u003c/span\u003e\u003cspan address=\"https://allercatpro.bii.a-star.edu.sg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The tool provides a text area where you can paste your protein sequences or an option to upload a FASTA file containing the many protein sequences. The motifs were copied and pasted into the text area since the number was small. Once sequences were input, the \"Submit\" button was clicked to start the allergenicity prediction process. The tool then processed the input sequences and performed allergenicity predictions on the basis of the sequences' properties. Since the number of sequences was small, it took a short time to generate results. After the analysis was complete, AllerProt provided the allergenicity potential predictions for each input protein sequence. The results are presented in a table that is downloadable as an Excel file. The output was reviewed to identify the protein sequences that had high allergenicity potential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMotif antigenicity determination via IEDB Tools\u003c/h2\u003e \u003cp\u003eSudan ebolavirus GP peptide motifs were subjected to antigenicity analysis via the IEDB tool (Fleri et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which employs semiempirical methods on the basis of the physicochemical properties of amino acid residues and their frequency of occurrence (Chou \u0026amp; Fasman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Kolaskar \u0026amp; Tongaonkar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). This approach allowed for the determination of peptide motifs with the best potential immunogenicity. The antigenicity analyses of the peptide motifs were conducted via the Immune Epitope Database Analysis Resource (IEDB) available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/main/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/main/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Seven different IEDB methods were employed to assess the antigenicity of these peptide motifs:\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBepiPred-1.0\u003c/strong\u003e: This employs hidden Markov models (HMMs) and a propensity scale to predict linear B-cell epitopes. In this approach, residues with a score above the default threshold of 0.350 were deemed to have a high likelihood of being part of an epitope (Jespersen et al., 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBepiPred-2.0\u003c/strong\u003e: This analysis uses a sequential B-cell epitope predictor that utilizes a random forest algorithm trained on epitopes and nonepitope amino acids identified from protein crystal structures. Residues with a score exceeding the default threshold of 0.500 were identified as having a high likelihood of being part of an epitope (Jespersen et al., 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChou and Fasman\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebeta\u003c/strong\u003e\u003cstrong\u003e-turn prediction\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emethods\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e This epitope analysis uses turn prediction to identify potential epitopes. Residues with a score higher than the threshold of 1.007 were categorized as having a high likelihood of being part of an epitope (Chou \u0026amp; Fasman, 1974).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmini surface accessibility scale:\u003c/strong\u003e This analysis calculates the peptide\u0026apos;s surface accessibility. A score exceeding 1.000 indicates a strong probability of the peptide being located on the protein surface (Emini et al., 1985).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKarplus and Schulz\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emethods\u003c/strong\u003e: This method was utilized to assess the flexibility of protein segments and relies on the B-factors of 31 protein structures. Residues with a score exceeding the threshold of 1.008 were identified as having a high probability of being part of an epitope (Karplus \u0026amp; Schulz, 1985).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKolaskar and Tongaonkar antigenicity scale:\u003c/strong\u003e This employs a semiempirical approach on the basis of the physicochemical probabilities of amino acid residues within the protein of interest, along with their frequencies of occurrence in experimentally known epitopes from other proteins. Residues with a score surpassing the threshold of 1.016 were considered to have a high likelihood of being part of an epitope (Kolaskar \u0026amp; Tongaonkar, 1990).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParker-hydrophobicity prediction method\u003c/strong\u003e: This method is based on a hydrophilic scale derived from the analysis of peptide retention times via high-performance liquid chromatography (HPLC) using a reversed-phase column. This scale assigns hydrophobicity scores to individual amino acid residues on the basis of their behavior in the chromatography process. Residues with scores above the threshold value of 1.608 are identified as having a high probability of being part of an epitope. This finding indicates that such residues are more likely to be involved in antigenic regions capable of inducing an immune response (Parker et al., 1986)\u003c/p\u003e\n\u003cp\u003eFor each of the seven methods mentioned above, the Sudan ebolavirus GP sequence was input into the corresponding tool, and the results were obtained in the form of graphs and Excel files, which were downloaded from the IEDB tools. Excel files were then used to generate CSV files containing information on the residue number and residue score for each amino acid.\u003c/p\u003e\n\u003cp\u003eThe scoring is based on the algorithm of each method. The generated CSV files were then used by the \u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eprogram\u003c/strong\u003e to generate graphs (John K., 2015). An \u003cstrong\u003eR\u003c/strong\u003e-script was employed, which incorporated the location of the motifs within the GP sequence using the residue positions. By running the script, CSV graphs were generated for each tool. Additionally, the script facilitated the extraction of motif average values for each method, which were subsequently recorded in a table for further analysis and epitope selection.\u0026nbsp;On the basis of\u0026nbsp;the information obtained from the graphs and table results, the most promising motifs to be used as\u0026nbsp;vaccines\u0026nbsp;were identified and\u0026nbsp;identified.\u003c/p\u003e\n\u003cp id=\"_Toc143581913\"\u003e\u003cstrong\u003eMotif Structure Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructural prediction of the best antigenic motifs on the basis of the IEDB tool analysis was performed via the \u003cstrong\u003ePEP-FOLD server\u003c/strong\u003e (Lamiable et al., 2016). It is essential to ensure that the motifs meet the requirements for this tool to predict the structures of proteins that are 9 to 36 residues in length. The protein sequences for each motif were copied and pasted in the submission area, and \u0026ldquo;Submit\u0026rdquo; was selected for the prediction to begin. A clustering report with scores was provided, as were the top 5 clusters. The PDB structures of the top 5 models represent the best structural predictions on the basis of the selected sorting key. Model 1 was downloaded and further analysed via the \u003cstrong\u003eUCSF\u003c/strong\u003e \u003cstrong\u003eChimera\u003c/strong\u003e visualization tool to examine the predicted structure and conduct additional analyses, such as identifying the secondary structure (Pettersen et al., 2004).\u003c/p\u003e\n\u003cp id=\"_Toc143581914\"\u003e\u003cstrong\u003eT-cell epitope\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eprediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction of T-cell epitopes involves the use of the \u003cstrong\u003eNetCTL server\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e which is available via the link https://services.healthtech.dtu.dk/services/NetCTL-1.2/, which identifies potential T-cell epitopes in a given sequence and provides diverse results, including a combined score (Larsen et al., 2007). The peptide length is set at 9 residues. The Sudan Ebolavirus GP sequence was uploaded in FASTA format. Default settings were used for parameters such as weight on C-terminal cleavage, weight on TAP transport efficiency, and thresholds for epitope identification, including sensitivity and accuracy. This process was performed for all 12 MHC-I supertypes, ranging from A1, A2, A3 to B68, resulting in individual tables of results for each supertype. These tables were then merged to create a single comprehensive table of results as an Excel file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan id=\"_Toc143581915\"\u003eMHC-1 binding of epitopes\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe table of epitopes was sorted on the basis of the combined score to identify the top epitopes for further analysis. After sorting, the top 13 epitopes with COMB scores above 1.720 were saved in a fasta file. The MHC-I binding of these 13 epitopes was subsequently evaluated via tools available on the \u003cstrong\u003eIEDB\u003c/strong\u003e platform (https://www.iedb.org/).\u003c/p\u003e\n\u003cp\u003eThese tools utilize the stabilized matrix-based method to calculate the half maximal inhibitory concentration (IC50) value, indicating that the epitope binds to human leukocyte antigen (HLA) molecules (Fleri et al., 2017). Each of the 13 epitopes was matched to its respective HLA structure, yielding information on the binding affinity score. The results were then sorted on the basis of the binding score, and those with a score exceeding 9.0 were selected for further analysis in the subsequent steps.\u003c/p\u003e\n\u003cp id=\"_Toc143581916\"\u003e\u003cstrong\u003ePrediction of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epopulation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;coverage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population coverage analysis was conducted via the \u003cstrong\u003eIEDB\u003c/strong\u003e population coverage tool (https://www.iedb.org/). This tool was employed to assess the potential coverage of all 13 epitopes by selecting the corresponding HLA alleles for the epitopes obtained in the previous step. The analysis focused on four subregions of Africa, namely, East, Central, West, and North (Fleri et al., 2017). For each epitope, the tool provides results for each subregion, indicating the extent of coverage on the basis of the presence of HLA alleles matching the epitope in the subregion. The coverage in each region was then given by a percentage value (Fleri et al., 2017).\u003c/p\u003e\n\u003cp\u003eTo identify the most promising epitopes with broad population coverage, the average subregion percentage was calculated across all four subregions. Epitopes with average population coverage above 8.0% were singled out for further analysis. These selected epitopes are of particular interest, as they have the potential to be effective in a significant proportion of the African population, making them valuable candidates for further epidemiological investigations and vaccine development considerations.\u003c/p\u003e\n\u003cp id=\"_Toc143581917\"\u003e\u003cstrong\u003eAllergenicity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the identification of allergenicity, peptide T-cell epitope sequences with high population coverage were obtained and placed into a single FASTA file. Subsequently, \u003cstrong\u003eAllerCatPro\u003c/strong\u003e (version 2.0), a protein allergenicity potential prediction tool, was accessed through the provided link https://allercatpro.bii.a-star.edu.sg/ (Maurer-Stroh et al., 2019). The tool offers a text area where the sequences can be pasted or an option to upload a FASTA file containing multiple protein sequences. As the number of T-cell epitopes was large, the FASTA file was uploaded. Upon uploading, the sequences were submitted for the allergenicity prediction process by clicking on the \u0026quot;\u003cstrong\u003eSubmit\u003c/strong\u003e\u0026quot; button.\u003c/p\u003e\n\u003cp\u003eThe AllerCatPro tool then processed the input sequences and performed allergenicity predictions on the basis of the properties of the peptides. Owing to the small number of sequences, the analysis was completed swiftly, generating the results in a timely manner. After the analysis was finished, AllerProt provided the allergenicity potential predictions for each input peptide sequence. The results were presented in a downloadable table format as an Excel file. To assess potential allergenicity, the output was thoroughly reviewed to identify peptide sequences with high allergenicity potential, facilitating the selection of candidate nonallergenic peptides for further investigation and analysis.\u003c/p\u003e\n\u003cp id=\"_Toc143581918\"\u003e\u003cstrong\u003eHLA Homology\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eModelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was done for the top 9 HLA structures (\u003cstrong\u003eHLA-A*01, HLA-A*02, HLA-A*03, HLA-A*15, HLA-A*30, HLA-A*31, HLA-B*57, HLA-B*58,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHLA-A*68\u003c/strong\u003e) that showed high population coverage. Thus, the HLA and corresponding T-epitopes were subjected to further analysis. UniProt was accessed via the link https://www.uniprot.org/. The query term used for each HLA allele structure was \u0026ldquo;\u003cstrong\u003emhc 1 HLA `Allele subtype`\u003c/strong\u003e.\u0026rdquo; According to the UniProtKB results, the result required for each allele type was clicked, which provided a protein ID that was copied and pasted into Alphafold via the link https://alphafold.ebi.ac.uk/. This provided the structure and the PDB download option for the structure, which was used to download the HLA structure. Visualization was then performed with \u003cstrong\u003eUCSF Chimera\u003c/strong\u003e for analysis of the structure.\u003c/p\u003e\n\u003cp id=\"_Toc143581919\"\u003e\u003cstrong\u003eMolecular Docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was accomplished via two docking applications, namely, HPEPDOCK 2.0 and CABS-Dock.\u003c/p\u003e\n\u003cp\u003eTo utilize HPEPDOCK 2.0 for flexible protein‒peptide docking, the server was accessed via the platform link http://huanglab.phys.hust.edu.cn/hpepdock/. This provided the receptor and peptide input options, such as uploading PDB files, specifying PDB IDs and chain IDs, or pasting FASTA sequences. For the receptors, the HLA structure PDB files were uploaded, whereas for the T-cell epitopes, the peptide sequences were pasted in the input section. The job name and email for the results were then entered, and the job was submitted. Upon submission, the platform generated docking results, including a complex image and the top 10 models of the peptide and their docking scores with the HLA, although the receptor and peptide components were downloaded separately and combined via UCSF Chimera for further use. For CABS-Dock, the server was accessed via the link https://biocomp.chem.uw.edu.pl/CABSdock/, which provided a section for uploading the HLA protein and for inputting the peptide. The job was then given a name and submitted. Upon submission, the results included a generated docking complex, which was then downloadable in PDB format. As a result, both applications provided the necessary structural information for further analysis.\u003c/p\u003e\n\u003cp id=\"_Toc143581920\"\u003e\u003cstrong\u003eDocking Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the docking validation process, the X-Score tool was installed on a computer. The procedure required both the HLA in PDB format and the peptide in mol2 file format. For each complex, the peptide was initially selected by launching the complex via UCSF Chimera. The peptide was subsequently selected and saved as a PDB file, with the \u0026quot;only all atoms selected\u0026quot; option chosen. This saved PDB was then converted into a mol2 file required for the X score. The X-score terminal was opened within each complex\u0026apos;s directory, encompassing the HLA and peptide components. The formal command used to yield the results was \u0026quot;\u003cstrong\u003exscore -score HLA.pdb Peptide.mol2\u0026quot;,\u003c/strong\u003e which enabled the extraction of the binding energy of the protein and peptide for all 24 docking complexes previously obtained.\u003c/p\u003e\n\u003cp id=\"_Toc143581921\"\u003e\u003cstrong\u003eInteracting Residue Determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LigPlot application was acquired and installed on the computer for analysis of docking residue interactions for the best 4 complexes according to HPEPDOCK. Upon launching the application, each complex was submitted individually for processing by Ligplot to generate LigPlot diagrams, which visually present the interaction outcomes. The generated results allowed the saving of PS files, which were then utilized to create PNG images for clarity.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eData retrieval of Ebola virus sequences\u003c/h2\u003e \u003cp\u003eThe Sudan ebolavirus genomes and Sudan ebolavirus GP protein sequences were obtained, resulting in a total of 4 sequences: \"2022002270_consensus_Mubende_Uganda_Isolate_2022\u0026rdquo;, \"JN638998.1 Sudan ebolavirus - Nakisamata, complete genome\", \"NC_006432.1 Sudan ebolavirus isolate Sudan virus/H.sapiens-tc/UGA/2000/Gulu-808892, complete genome\" and \"AY316199.1 Sudan ebolavirus strain Gulu structural glycoprotein and secreted glycoprotein genes, complete cds Sudan ebolavirus.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Conserved Regions and Variations\u003c/h2\u003e \u003cp\u003eThe alignment of the retrieved sequences in Jalview revealed alignment patterns, conserved regions, and pinpoint variations across the sequences. The analysis revealed distinct differences between the GP sequences of the Mubende isolate and the other isolates. Specifically, at position 7 in the genome, Mubende has an adenine (A), whereas the other isolates have guanine (G). Additionally, at positions 1222 and 1583, Mubende displays adenine (A) instead of guanine (G), as observed in the other sequences. These variations in nucleotides were consistently observed across all four GP sequences. Notably, there were also variations among the other isolates themselves, contributing to the overall diversity of the Ebola virus genomes. The genome sequences were cut to show only the region of the GP sequence, as shown in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e below.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eTranslation and Analysis of the GP Coding Sequence\u003c/h2\u003e \u003cp\u003eThe GP coding sequence for the Mubende isolate was translated into an amino acid sequence via the ExPASy Translate Tool and subsequently assessed. This allowed analysis of the GPs at the protein level. The best frame identified had a stop codon in the GP sequence. Thus, only the sGP protein sequence could be obtained, as shown in \u003cb\u003eFig.\u0026nbsp;3.\u003c/b\u003e The sGP protein sequence is shown below.\u003c/p\u003e \n\u003cp\u003e\u0026gt; VIRT-24397:5\u0026apos;3\u0026apos; Frame 1, start_pos=0\u003c/p\u003e\n\u003cp\u003eMGSLSLLQLPRDKFRKSSFFVWVIILFQKAFSMPLGVVTNSTLEVTEIDQLVCKDH\u003cbr\u003eLASTDQLKSVGLNLEGSGVSTDIPSATKRWGFRSGVPPKVVSYEAGEWAENCYNL\u003cbr\u003eEIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCPGDYAFHKDGAFFLYDRLAS\u003cbr\u003eTVIYRGVNFAEGVIAFLILAKPKETFLQSPPIREAVNYTENTSSYYATSYLEYEIENF\u003cbr\u003eGAQHSTTLFKIDNNTFVRLDRPHTPQFLFQLNDTIHLHQQLSNTTGRLIWTLDANI\u003cbr\u003eNADIGEWAFWENKKISPNNYVEKSCLSKLYRSTRQKTMMRHRRELQREESPTGPP\u003cbr\u003eGSIRTWFQRIPLGWFHCTYQKGKQHCRLRIRQKVEE\u003c/p\u003e\n\u003cp\u003eStop codons indicate the termination of protein synthesis and can hinder the production of the full GP protein. To overcome this limitation, RNA editing is necessary to modify the GP coding sequence and remove or modify the stop codons. This additional step is important to ensure the generation of a complete and functional GP protein. However, this process is more complex and time-consuming. \u003cb\u003eThus, a similar GP to that of the Mubende isolate was selected\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eComparative Study and Phylogenetic Classification of Ebolavirus GP Results\u003c/h2\u003e \u003cp\u003eThe analysis involved performing MSA and generating a phylogenetic tree to gain insights into the relationships between different GP sequences of different strains. The generated phylogenetic tree visually represents the evolutionary relationships between the GP sequences. By analysing the tree, we were able to identify the GP sequence that is most closely related to the Mubende strain. On the basis of the results of the phylogenetic tree shown in \u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e, we selected and retrieved the final full structural GP sequence (\u003cb\u003eQ7T9D9.1\u003c/b\u003e) for further analysis since it is very similar to the Mubende isolate. The sequence is shown below.\u003c/p\u003e \n\u003cp\u003e\u0026gt;Q7T9D9.1 RecName: Full=Envelope glycoprotein; AltName: Full=GP1,2; Short=GP; Contains: RecName: Full=GP1; Contains: RecName: Full=GP2; Contains: RecName: Full=Shed GP; AltName: Full=GP1,2-delta; Flags: Precursor [Sudan ebolavirus - Uganda (2000)]\u003c/p\u003e\n\u003cp\u003eMGGLSLLQLPRDKFRKSSFFVWVIILFQKAFSMPLGVVTNSTLEVTEIDQLVCKDH\u003cbr\u003eLASTDQLKSVGLNLEGSGVSTDIPSATKRWGFRSGVPPKVVSYEAGEWAENCYNL\u003cbr\u003eEIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCPGDYAFHKDGAFFLYDRLAS\u003cbr\u003eTVIYRGVNFAEGVIAFLILAKPKETFLQSPPIREAVNYTENTSSYYATSYLEYEIENF\u003cbr\u003eGAQHSTTLFKIDNNTFVRLDRPHTPQFLFQLNDTIHLHQQLSNTTGRLIWTLDANI\u003cbr\u003eNADIGEWAFWENKKNLSEQLRGEELSFEALSLNETEDDDAASSRITKGRISDRATR\u003cbr\u003eKYSDLVPKNSPGMVPLHIPEGETTLPSQNSTEGRRVGVNTQETITETAATIIGTNGN\u003cbr\u003eHMQISTIGIRPSSSQIPSSSPTTAPSPEAQTPTTHTSGPSVMATEEPTTPPGSSPGPTTE\u003cbr\u003eAPTLTTPENITTAVKTVLPQESTSNGLITSTVTGILGSLGLRKRSRRQTNTKATGKCN\u003cbr\u003ePNLHYWTAQEQHNAAGIAWIPYFGPGAEGIYTEGLMHNQNALVCGLRQLANETT\u003cbr\u003eQALQLFLRATTELRTYTILNRKAIDFLLRRWGGTCRILGPDCCIEPHDWTKNITDKI\u003cbr\u003eNQIIHDFIDNPLPNQDNDDNWWTGWRQWIPAGIGITGIIIAIIALLCVCKLLC\u003c/p\u003e\n\u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eB -Cell epitope prediction\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003eMotif sequence identification\u003c/h2\u003e \u003cp\u003eFrom the completed MEME analysis, a motif heatmap was generated using the output from MEME in the form of a master text file. This file was then used to create the heatmap in \u003cb\u003eFig.\u0026nbsp;5\u003c/b\u003e via a Python script. The heatmap provides a visual representation of the 21 motifs, indicating their presence and distribution across the 24 analysed sequences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003ePeptide motif sequences\u003c/h2\u003e \u003cp\u003eThe 7 highly conserved motifs were selected for further analysis. These included M1, M2, M3, M4, M5, M6, and M17, suggesting that these motifs may play crucial roles in the biological function or interaction of the studied proteins. MEME provides the results sorted by the E value, as shown in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e below, as well as the logo site and width.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"561\" width=\"584\"\u003e\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003ePeptide-motif Allerginicity Assessment\u003c/h2\u003e \u003cp\u003eThe results show the analysis of protein sequences M1 to M17. For each protein, the sequence length, the number of Q-repeats in gluten allergens, and the number of 3x6-mer overlaps were determined. The best known allergen hit name, species, various hit details and allergen information were examined. For all proteins, no significant hits were identified within the E value threshold of 0.001, indicating that there was no evidence of known allergen matches or related hits. The % identity to the closest human hit and the % identity to the closest low-allergen hit were also assessed, both of which revealed no evidence of hits or similarities. Therefore, these results indicate no significant evidence of allergenicity or related hits for the analysed protein sequences, as shown in the summary in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePeptide-Motif Allergenicity Assessment: Analysis of protein sequences M1 to M17 reveals no matches to known allergens or related hits within the E value threshold of 0.001.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence Length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBest known allergen hit name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eMotif antigenicity determination via the IEDB\u003c/h2\u003e \u003cp\u003eMotif M7 was not continued since it was too short. The IEDB methods were used to evaluate whether the motifs were located in potential B cell epitopes. These results indicate that the motifs M2, M3 and M5 might be potential antigenic peptide epitopes. Motifs M2, M3 and M5 had mean BepiPred 2.0 linear epitope prediction scores of 0.547, 0.538, and 0.602, respectively, which were above the 0.500 threshold value.\u003c/p\u003e \u003cp\u003eMotifs M2, M3 and M5 also had mean Parker hydrophilicity scores of 2.014, 2.602, and 2.044, respectively, which were above the 1.608 threshold score. The motifs also presented mean Chou and Fasman beta-turn predictions and Karplus and Schulz flexibility predictions that were slightly above or below their respective thresholds. Additional supporting information on B cell peptide motif antigenicity has been provided as graphs (Figs.\u0026nbsp;6, \u003cb\u003e7, 8 and 9\u003c/b\u003e), and more information is provided in the \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e summary.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePeptide Motif Antigenicity Assessment: Analysis indicates potential antigenic peptide epitopes for Motifs M2, M3, and M5.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotifs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBepipred 1.0\u003c/p\u003e \u003cp\u003e(0.350)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBepipred 2.0\u003c/p\u003e \u003cp\u003e(0.500)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface Accessibility\u003c/p\u003e \u003cp\u003e(1.000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKarplus \u0026amp; C. Flexibility\u003c/p\u003e \u003cp\u003e(1.008)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKolaskar \u0026amp; T. Antigenicity\u003c/p\u003e \u003cp\u003e(1.016)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChou \u0026amp; Fasman\u003c/p\u003e \u003cp\u003e(1.007)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePaker Hydrophilicity\u003c/p\u003e \u003cp\u003e(1.608)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.824\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.955\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.014\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.602\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.363\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.044\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003ePeptide-Motif 3D Structure Design\u003c/h2\u003e \u003cp\u003eThe 3D structures of motifs 2 and 3 were designed via the PEP-FOLD server, which generated the top 10 models for each motif. The best models (Model 1) for both motifs were selected and visualized, as depicted in \u003cb\u003eFigs.\u0026nbsp;10 and 11\u003c/b\u003e below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eT-cell epitope prediction\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eThe\u003c/b\u003e NetCTL 1.2 server provided a total of 184 epitopes for all the MHC supertypes, which were sorted by the COMB score. The top 10 terms are shown in \u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e below, and the full table is found in \u003cb\u003eAdditional file 1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eT-cell epitope prediction: Top 10 predicted epitopes for all MHC supertypes sorted by COMB score\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePEPTIDES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFF RES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOMB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMHC\u003c/p\u003e \u003cp\u003eSupertype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYTENTSSYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.7503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSSYYATSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.3052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISDRATRKY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.5583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFHKDGAFFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.2091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYYATSYLEY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.1339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYTENTSSYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGEELSFEAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIHDFIDNPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.9557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETTQALQLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.9027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7T9D9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAEGIYTEGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.8223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMHC 1 binding of epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e displays the top 17 allele combinations of the 13 epitopes, sorted by score. It presents the epitopes and their respective MHC class I allele combinations. The full table can be found in \u003cstrong\u003eAdditional file 2\u003c/strong\u003e. The epitope \u0026quot;\u003cstrong\u003eEVTEIDQLV\u003c/strong\u003e\u0026quot; is associated with HLA-A*68:02, \u0026quot;\u003cstrong\u003eFLYDRLAST\u003c/strong\u003e\u0026quot; with HLA-A*02:01 and HLA-A*02:03, \u0026quot;\u003cstrong\u003eFQLNDTIHL\u003c/strong\u003e\u0026quot; with HLA-A*02:06, \u0026quot;\u003cstrong\u003eGEELSFEAL\u003c/strong\u003e\u0026quot; with HLA-B*40:01, \u0026quot;\u003cstrong\u003eISDRATRKY\u003c/strong\u003e\u0026quot; with HLA-A*01:01, \u0026quot;\u003cstrong\u003eKCNPNLHYW\u003c/strong\u003e\u0026quot; with HLA-B*57:01 and HLA-B*58:01, \u0026quot;\u003cstrong\u003eLEVTEIDQL\u003c/strong\u003e\u0026quot; with HLA-B*40:01, \u0026quot;\u003cstrong\u003eRISDRATRK\u003c/strong\u003e\u0026quot; with HLA-A*03:01, \u0026quot;\u003cstrong\u003eRLASTVIYR\u003c/strong\u003e\u0026quot; with HLA-A*03:01 and HLA-A*31:01, \u0026quot;\u003cstrong\u003eRSRRQTNTK\u003c/strong\u003e\u0026quot; with HLA-A*30:01, \u0026quot;\u003cstrong\u003eRTYTILNRK\u003c/strong\u003e\u0026quot; with HLA-A*03:01 and HLA-A*11:01, \u0026quot;\u003cstrong\u003eSEQLRGEEL\u003c/strong\u003e\u0026quot; with HLA-B*40:01, and \u0026quot;\u003cstrong\u003eYTENTSSYY\u003c/strong\u003e\u0026quot; with HLA-A*01:01. A cut-off score of 9.0 was used to determine these top 17 allele combinations.\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMHC-I binding of epitopes: The top 17 allele combinations of 13 epitopes, sorted by score. A cut-off score of 9.0 was used for these top combinations.\u003c/p\u003e\u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eseq_num\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIcore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003escore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003erank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*01:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYTENTSSYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYTENTSSYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYTENTSSYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B*57:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B*58:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*02:03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGEELSFEAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGEELSFEAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGEELSFEAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*11:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*01:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eISDRATRKY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eISDRATRKY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eISDRATRKY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*68:02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEVTEIDQLV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEVTEIDQLV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEVTEIDQLV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRISDRATRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRISDRATRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRISDRATRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*02:06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFQLNDTIHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFQLNDTIHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFQLNDTIHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*02:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*31:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEQLRGEEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSEQLRGEEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSEQLRGEEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*30:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRSRRQTNTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRSRRQTNTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRSRRQTNTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLEVTEIDQL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLEVTEIDQL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLEVTEIDQL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom \u003cb\u003eTable\u0026nbsp;5\u003c/b\u003e above, the top 13 peptides were labelled with the seq numbers as follows: PEP-1 (YTENTSSYY), PEP-3 (ISDRATRKY), PEP-7 (GEELSFEAL), PEP-15 (KCNPNLHYW), PEP-17 (RTYTILNRK), PEP-23 (RISDRATRK), PEP-24 (RLASTVIYR), PEP-34 (SEQLRGEEL), PEP-35 (LEVTEIDQL), PEP-41 (RSRRQTNTK), PEP-45 (FQLNDTIHL), PEP-51 (FLYDRLAST) and PEP-67 (EVTEIDQLV).\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Coverage\u003c/h2\u003e \u003cp\u003eThe IEDB's population coverage tool was used to determine the percentage of individuals within specific regions of Africa who are likely to possess HLA alleles capable of presenting each epitope. The population coverage percentages for each epitope in East Africa, West Africa, Central Africa, and North Africa are shown in \u003cb\u003eTable\u0026nbsp;6.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOn the basis of the results obtained from the population coverage analysis, the epitope \u0026ldquo;\u003cb\u003eFLYDRLAST\u003c/b\u003e\u0026rdquo; shows the highest coverage across all regions of Africa, with percentages ranging from 13.40\u0026ndash;25.99%. This finding indicates that a significant proportion of individuals within the studied African populations may possess HLA alleles capable of presenting this epitope to T cells.\u003c/p\u003e \u003cp\u003eThus, it has potential as a vaccine candidate because of its wide coverage across different regions. Its high population coverage suggests that it could be recognized by a relatively large proportion of individuals within the population, potentially enhancing the effectiveness of immune responses and immune-based interventions.\u003c/p\u003e \u003cp\u003eIn this analysis of peptide epitopes and MHC-I class alleles from different regions in Africa, only peptides with an average binding affinity score above 9% were considered for further investigation. The average binding percentages for each peptide in East Africa, West Africa, Central Africa, and North Africa are also provided. Peptides with an average score above 9%: PEP-51 (FLYDRLAST), PEP-17 (RTYTILNRK), PEP-24 (RLASTVIYR), PEP-67 (EVTEIDQLV), PEP-23 (RISDRATRK), PEP-15 (KCNPNLHYW), PEP-41 (RSRRQTNTK), PEP-3 (ISDRATRKY), and PEP-1 (YTENTSSYY), where considered for further analysis, as shown in \u003cb\u003eTable\u0026nbsp;5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe percentage of individuals in different regions of Africa with HLA alleles capable of presenting specific epitopes, with \"FLYDRLAST\" having the highest coverage.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptide_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpitopes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMHC-I Class Alleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEast Africa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWest Africa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCentral Africa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNorth Africa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFLYDRLAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*02:01\u003c/p\u003e \u003cp\u003eHLA-A*02:03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRTYTILNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003cp\u003eHLA-A*11:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRLASTVIYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003cp\u003eHLA-A*31:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVTEIDQLV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*68:02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRISDRATRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*03:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCNPNLHYW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-B*57:01\u003c/p\u003e \u003cp\u003eHLA-B*58:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRSRRQTNTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*30:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISDRATRKY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*01:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYTENTSSYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*01:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEELSFEAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLEVTEIDQL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEQLRGEEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-B*40:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFQLNDTIHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-A*02:06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eAllergenicity assessment\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;7\u003c/b\u003e displays the results of the allergenicity assessment for peptide sequences (Peptide_ID) with a length of 9 amino acids. The \"Best-known allergen hit name\" column indicates that no significant hits were found on the basis of the E value threshold of 0.001. The \"Number of known allergen hits\" column indicates zero hits for all peptides. Consequently, the \"Result\" column shows \"no evidence\" of allergenicity, and the \"Comment\" column confirms \"no hits\" for allergens in the dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAllergenicity assessment of 9-amino acid peptide sequences: No significant hits to known allergens were found, indicating no evidence of allergenicity in the dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptide_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence Length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBest-known allergen-hit name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of known allergen hits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo significant hit (E-value threshold 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno hits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eHLA Homology Modelling\u003c/h2\u003e \u003cp\u003eThe result of this procedure was the attainment of the structures of the nine HLAs, which are essential for subsequent docking analyses. Below is one of the structures obtained.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003eMolecular Docking\u003c/h2\u003e \u003cp\u003eThe outcome of this process was the acquisition of HLA-PEPTIDE binding structures, facilitating a detailed docking analysis to determine whether the peptide was successfully accommodated within the HLA or not.\u003c/p\u003e \u003cp\u003eThe results revealed that, in the case of CABS, the peptides PEP-1, PEP-24, and PEP-17 bound outside the HLA structure, whereas for HPEPDOCK, only the peptide PEP-17 bound outside the HLA. An example structure of one of the HLA-PEPTIDE complexes obtained is shown in \u003cb\u003eFig.\u0026nbsp;13\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDocking Validation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis step led to the development of the docking validation results shown in \u003cb\u003eTable\u0026nbsp;8\u003c/b\u003e below, which enabled the comparison of the docking scores obtained from the two different docking applications, CABS-Dock and HPEPDOCK, for the various peptide‒MHC-1 complexes.\u003c/p\u003e \u003cp\u003eNotably, the docking results generated by HPEPDOCK demonstrated superior scoring compared with CABS-Dock. HPEPDOCK scoring was based on binding energy, and the top four complexes with binding energies greater than 9.0 were selected as the most favourable candidates for T-epitope vaccine development. These selected peptide‒MHC-1 complexes, which exhibit strong binding affinity, are essential in vaccine design.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDocking Scores for Peptide\u0026ndash;MHC-1 Complexes: Comparison between CABS-Dock and HPEPDOCK, with the Top Four Complexes Selected for T-Epitope Vaccine Design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePEPTIDE - MHC-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBinding energy (Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCABS-dock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHPEPDOCK\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-1-HLA-A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-24-HLA-A31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-15-HLA-B57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-67-HLA-A68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-15-HLA-B58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-17-HLA-A11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-3-HLA-A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-24-HLA-A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-51-HLA-A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-17-HLA-A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-23-HLA-A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-41-HLA-A30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInteracting residues\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe residue interaction analysis of the top four complexes revealed the presence of hydrogen bonds (Fig.\u0026nbsp;1\u003cb\u003e4\u003c/b\u003e) between residues crucial for molecular interactions, as shown in \u003cb\u003eTable\u0026nbsp;9.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction of peptide residues and HLA structures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBonds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydrogen bond pairs between Ligand (A) and receptor (B)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-1-HLA-A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyr31(A) - Tyr1(B), Tyr183(A) - Thr2(B), Arg187(A) - Glu3(B), Gln179(A) - Tyr8(B), Tyr147(A) - Tyr9(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-15-HLA-B57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsn101(A) - His7(B), Asp138(A) - His7(B), Tyr98(A) - His7(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-24-HLA-A31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsp140(A) - Tyr8(B), Met121(A) - Tyr8(B), Glu87(A) - Thr5(B), Glu87(A) - Ser4(B), Tyr123(A) - Val6(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEP-67-HLA-A68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsn65(A) - Gln7(B), Asp76(A) - Glu1(B), Lys145(A) - Glu1(B), Thr142(A) - Glu1(B), Trp146(A) - Glu1(B), Trp155(A) - Thr3(B), Asn62(A) - Val9(B), Tyr158(A) - Val9(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSIONS","content":"\u003cp\u003eWith a global population exceeding 8\u0026nbsp;billion, the world has encountered significant health challenges posed by viral infections(Iversen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The incidence of viral infections is notably greater in developing nations, particularly in Africa, than in more developed regions (Uwishema et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, there is an urgent need to combat this devastating threat of these viruses by developing vaccines for them.\u003c/p\u003e \u003cp\u003eVaccine development has undergone significant advancements in recent years, with bioinformatics tools playing an increasingly crucial role in the identification and design of potential vaccine candidates (Nandy \u0026amp; Basak, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Medical biotechnology has also assumed a pivotal role in combatting viral threats by crafting potent vaccines (Aileni et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the traditional method of epitope identification via experimentation is both time intensive and costly (Yin et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Advances in immunoinformatics methodologies have revolutionized this process, substantially reducing both time and expenditure while concurrently increasing the precision of epitope-based vaccine design (Parihar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These cutting-edge techniques facilitate the identification of antigenic regions on surface-exposed proteins that are primed to trigger robust immune responses.\u003c/p\u003e \u003cp\u003eIn the past decade, there has been a significant emphasis on targeting \u003cb\u003eglycoproteins (GPs)\u003c/b\u003e by vaccine developers. This approach was also used by most \u003cb\u003eSARS-CoV-2\u003c/b\u003e vaccines during the 2019 pandemic (Mart\u0026iacute;nez-Flores et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). EBOV GP, a surface-exposed and highly immunogenic protein described in previous studies, plays an important role in the attachment and entry of viral particles into the host (Davidson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Owing to surface exposure and reported antigenicity, GP was chosen as the target protein to develop the epitope-based vaccine in the present study.\u003c/p\u003e \u003cp\u003eNumerous GP peptides have been reported and preliminarily checked in primates, and they provide positive feedback; however, there is no approved or licenced vaccine available to date (Duehr et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A study by the International AIDS Vaccine Initiative (IAVI) initiated an inaugural human trial of an investigational Sudan Ebola virus vaccine, which employs a vesicular stomatitis virus (VSV) vector (Suder et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This vaccine represents a significant step forward in combatting Sudan's Ebola virus infections.\u003c/p\u003e \u003cp\u003eDespite all the reported studies, there is still no licenced vaccine for the Sudan Ebola virus present in the market until the present day (Malik et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the present study was designed to identify potential B and T-cell epitopes, which are worthy of exploration and could be used as vaccine candidates to prevent further spread and outbreak of EBOV, particularly the Sudan strain.\u003c/p\u003e \u003cp\u003ePredominantly, recent vaccine development has centered around B-cell immunity. Nevertheless, there is a current shift in strategy, placing greater importance on T-cell epitopes owing to their potential to confer more durable and effective immunity, especially against viral infections (Rosendahl Huber et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the prediction of B cell epitopes, several analyses were carried out on the primary, secondary, and tertiary structures of the protein. Most B cell epitopes are discontinuous, and nearly 10% of epitopes are linear (Patronov \u0026amp; Doytchinova, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). B-cell epitopes were predicted with the help of Bepipred and other B-cell epitope-finding tools of the IEDB server.\u003c/p\u003e \u003cp\u003eAll the predicted B-cell epitopes analysed were long and highly conserved among all five species: ZEBOV, TAFV, SUDV, BDBV, and RESTV. Among these peptides, motif 2, \u0026ldquo;MGGLSLLQLPRDKFRKSSFFVWVIILFQKAFSMPLGVVTNS\u0026rdquo;, and motif 3, \u0026ldquo;YEAGEWAENCYNLEIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCP\u0026rdquo;, presented the highest immunogenicity scores from the IEBD server tools, and therefore could act as a robust B-cell epitope and vaccine candidate. Notably, all 7 identified motifs were conserved in nearly all Ebolavirus species, indicating that stable and long-acting epitopes can be obtained under high-frequency amino acid mutations, which deserve further analysis as potential B-cell epitopes (Lon et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor T-cell epitope prediction, analyses were also performed. However, such vaccines based on T-cell epitopes can generate a strong immune response via CD8\u0026thinsp;+\u0026thinsp;T cells against infected cells, making them better than B-cell-mediated immunity (Shrestha \u0026amp; Diamond, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This method is highly efficient in combating viral infections because it harnesses cell-mediated immunity, which is predominantly directed by T cells and is known for its effectiveness against such infections (Riley \u0026amp; Montaner, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To date, the modern strategy of T-cell epitope-based vaccinology has yielded successful results against diseases such as malaria and cancer and has exhibited potent immunogenicity regarding the activation of T-cell responses (Oyarz\u0026uacute;n \u0026amp; Kobe, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, several T-cell epitopes that can bind to MHC class I were predicted. The T-cell epitopes were predicted via the NetCTL server 2.0, which uses a combination of prediction algorithms to identify potential epitopes within the antigen sequence (Larsen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). It predicted 184 epitopes from the GP that were further analysed to select the best candidate epitopes. Allergicity testing clearly revealed that the peptides predicted in the current study are stable and safe to use. Moreover, we performed interaction analysis of the predicted MHC class-I binding peptides with HLA alleles to assess the binding and immune response evoking ability of the predicted peptides. Flexible docking results, scores, and interacting residues revealed that all the predicted peptides strongly interact with MHC class-I alleles and can play a positive role in the initiation of the immune response and prevention of risks caused by EBOV.\u003c/p\u003e \u003cp\u003eThe epitopes that showed exception prediction results for all the analyses included \u0026ldquo;YTENTSSYY\u0026rdquo;, \u0026ldquo;KCNPNLHYW\u0026rdquo;, \u0026ldquo;RLASTVIYR\u0026rdquo; and \u0026ldquo;EVTEIDQLV.\u0026rdquo; These T-cell epitopes, composed of 9 amino acids each, effectively bind to the MHC I surface. Docking scores, as displayed in Table\u0026nbsp;8, along with the visualization of interacting residues in Fig.\u0026nbsp;19, collectively affirm the suitability of these peptides as promising vaccine candidates. The identification of interacting residues was accomplished through LIGPLOT analysis.\u003c/p\u003e \u003cp\u003eNonetheless, a significant knowledge gap persists concerning the extent of T-cell epitope population coverage for ebolaviruses, which is dependent upon the prevalence of HLA alleles within various populations (Hensen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, this research endeavoured to address this particular challenge. In this context, computational epitope screening stands out as an efficient and cost-effective approach, particularly when considering HLA class I molecules (Sundar et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering their immunogenicity scores, degree of conservation, population coverage, and interaction analysis, it is reasonable to suggest that the peptides proposed in this study are likely to be more effective in triggering immune responses in Africa and are preserved across various Sudan ebolavirus strains, than previously reported peptides (Jain \u0026amp; Baranwal, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn our study, we focused on vaccine development against \u003cem\u003eSudan ebolavirus\u003c/em\u003e, with a particular emphasis on glycoproteins (GPs), which have been used the most in recent vaccine trends. The predominant approach in recent vaccine development has also focused on B-cell immunity. Nevertheless, the current strategy places greater emphasis on T-cell epitopes because of their potential to confer enduring better immunity, especially for viral infections. However, this study encompasses both B-cell and T-cell epitopes as a comprehensive strategy to elicit very strong immunity against SUEBOV. The resulting peptides have demonstrated selectivity for both B cells and T cells, exhibiting high immunogenicity, nonallergenicity, broader population coverage, and strong interactions with MHC-1 alleles characterized by favourable binding affinities and interactions. Importantly, these predicted epitopes are expected to provide robust, long-lasting protection against the \u003cem\u003eSudan ebolavirus\u003c/em\u003e.\u003c/p\u003e"},{"header":"RECOMMENDATIONS","content":"\u003cp\u003eWe recommend conducting experimental validation to confirm the immunogenicity of the identified epitopes through in vitro assays to validate the results from this \u003cem\u003ein silico\u003c/em\u003e study. We also recommend exploring B and T-cell responses and immune activation by eiptopes through animal models or in vitro assays for a deeper understanding of vaccine candidate efficacy. Finally, we recommend expanding the analysis to encompass more diverse datasets from various geographic regions other than Africa, which could increase the population coverage of the vaccine worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI want to express my appreciation to my undergraduate project supervisor, Dr. Magambo Phillip Kimuda, for his guidance, especially in the proposal and report development mentorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailable on request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors participated in the analysis, writing, and proofreading of the manuscript and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003eManuscript was fully curie AI reviewed and Chat GPT was used as the main AI-assisting in paraphrasing statements better\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccompanying supplementary files containing more information on the results include Additional files 1 and file 2.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdnan I, Q. (2016). Ebola Virus. In \u003cem\u003eEbola Virus Disease\u003c/em\u003e. Animal Models for the Study of Human Disease, 2013, https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/sudan-ebola-virus.\u003c/li\u003e\n\u003cli\u003eAfrican CDC. (2022). \u003cem\u003eSudan Ebola Virus Disease in Uganda outbreak briefing.\u003c/em\u003e https://africacdc.org/disease-outbreak/final-outbreak-brief-16-sudan-virus-disease-in-uganda/\u003c/li\u003e\n\u003cli\u003eAileni, M., Rohela, G. K., Jogam, P., Soujanya, S., \u0026amp; Zhang, B. (2022). Biotechnological Perspectives to Combat the COVID-19 Pandemic: Precise Diagnostics and Inevitable Vaccine Paradigms. \u003cem\u003eCells\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(7). https://doi.org/10.3390/cells11071182\u003c/li\u003e\n\u003cli\u003eAlbari\u0026ntilde;o, C. G., Guerrero, L. W., Chakrabarti, A. K., \u0026amp; Nichol, S. T. (2018). Transcriptional analysis of viral mRNAs reveals common transcription patterns in cells infected by five different filoviruses. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(8). https://doi.org/10.1371/journal.pone.0201827\u003c/li\u003e\n\u003cli\u003eBailey, T. L., Johnson, J., Grant, C. E., \u0026amp; Noble, W. S. (2015). The MEME Suite. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(W1), W39\u0026ndash;W49. https://doi.org/10.1093/nar/gkv416\u003c/li\u003e\n\u003cli\u003eCDC. (2022). Update on Ebola Virus Disease (Sudan ebolavirus) Outbreak in Central Uganda. \u003cem\u003eHealth Alert Network\u003c/em\u003e, \u003cem\u003eNovember 7, 2022, 9:15 AM ET CDCHAN-00480, https://emergency.cdc.gov/han/2022/han00480.asp#print\u003c/em\u003e, 1\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eChou, P. Y., \u0026amp; Fasman, G. D. (1974). Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins. \u003cem\u003eBiochemistry\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 211\u0026ndash;222. https://doi.org/10.1021/bi00699a001\u003c/li\u003e\n\u003cli\u003eChou, P. Y., \u0026amp; Fasman, G. D. (1978). Prediction of the secondary structure of proteins from their amino acid sequence. \u003cem\u003eAdvances in Enzymology and Related Areas of Molecular Biology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, 45\u0026ndash;148. https://doi.org/10.1002/9780470122921.ch2\u003c/li\u003e\n\u003cli\u003eDavidson, E., Bryan, C., Fong, R. H., Barnes, T., Pfaff, J. M., Mabila, M., Rucker, J. B., \u0026amp; Doranz, B. J. (2015). Mechanism of Binding to Ebola Virus Glycoprotein by the ZMapp, ZMAb, and MB-003 Cocktail Antibodies. \u003cem\u003eJournal of Virology\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(21), 10982\u0026ndash;10992. https://doi.org/10.1128/JVI.01490-15\u003c/li\u003e\n\u003cli\u003eDuehr, J., Wohlbold, T. J., Oestereich, L., Chromikova, V., Amanat, F., Rajendran, M., Gomez-Medina, S., Mena, I., tenOever, B. R., Garc\u0026iacute;a-Sastre, A., Basler, C. F., Munoz-Fontela, C., \u0026amp; Krammer, F. (2017). Novel Cross-Reactive Monoclonal Antibodies against Ebolavirus Glycoproteins Show Protection in a Murine Challenge Model. \u003cem\u003eJournal of Virology\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(16). https://doi.org/10.1128/JVI.00652-17\u003c/li\u003e\n\u003cli\u003eEdgar, R. C. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(5), 1792\u0026ndash;1797. https://doi.org/10.1093/nar/gkh340\u003c/li\u003e\n\u003cli\u003eEmini, E. A., Hughes, J. V, Perlow, D. S., \u0026amp; Boger, J. (1985). Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. \u003cem\u003eJournal of Virology\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(3), 836\u0026ndash;839. https://doi.org/10.1128/JVI.55.3.836-839.1985\u003c/li\u003e\n\u003cli\u003eFan, P., Chi, X., Liu, G., Zhang, G., Chen, Z., Liu, Y., Fang, T., Li, J., Banadyga, L., He, S., Yu, C., Qiu, X., \u0026amp; Chen, W. (2020). Potent neutralizing monoclonal antibodies against Ebola virus isolated from vaccinated donors. \u003cem\u003eMAbs\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1). https://doi.org/10.1080/19420862.2020.1742457\u003c/li\u003e\n\u003cli\u003eFeldmann H, \u0026amp; Klenk HD. (1996). Filoviruses. \u003cem\u003eMedical Microbiology. 4th Edition\u003c/em\u003e, \u003cem\u003eIn: Baron S, editor. . Galveston (TX): University of Texas Medical Branch at Galveston; . Chapter 72. Available from: https://www.ncbi.nlm.nih.gov/books/NBK8129/\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eFleri, W., Paul, S., Dhanda, S. K., Mahajan, S., Xu, X., Peters, B., \u0026amp; Sette, A. (2017). The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. In \u003cem\u003eFrontiers in Immunology\u003c/em\u003e (Vol. 8, Issue MAR). Frontiers Research Foundation. https://doi.org/10.3389/fimmu.2017.00278\u003c/li\u003e\n\u003cli\u003eFlower, D. (2013). Computer-Aided Vaccine Design. \u003cem\u003eHuman Vaccines \u0026amp; Immunotherapeutics\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 241\u0026ndash;243. https://doi.org/10.4161/hv.26687\u003c/li\u003e\n\u003cli\u003eGasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R. D., \u0026amp; Bairoch, A. (2003). ExPASy: The proteomics server for in-depth protein knowledge and analysis. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(13), 3784\u0026ndash;3788. https://doi.org/10.1093/nar/gkg563\u003c/li\u003e\n\u003cli\u003eHensen, L., Illing, P. T., Rowntree, L. C., Davies, J., Miller, A., Tong, S. Y. C., Habel, J. R., van de Sandt, C. E., Flanagan, K. L., Purcell, A. W., Kedzierska, K., \u0026amp; Clemens, E. B. (2022). T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 812393. https://doi.org/10.3389/fimmu.2022.812393\u003c/li\u003e\n\u003cli\u003eIbrahim, S. K., Ndwandwe, D. E., Thomas, K., Sigfrid, L., \u0026amp; Norton, A. (2022). Sudan virus disease outbreak in Uganda: urgent research gaps. \u003cem\u003eBMJ Global Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(12). https://doi.org/10.1136/bmjgh-2022-010982\u003c/li\u003e\n\u003cli\u003eIversen, P. L. (2018). The Threat from Viruses. In \u003cem\u003eMolecular Basis of Resilience\u003c/em\u003e (pp. 45\u0026ndash;76). Springer International Publishing. https://doi.org/10.1007/978-3-319-98164-2_3\u003c/li\u003e\n\u003cli\u003eJacob, S. T., Crozier, I., Fischer, W. A., Hewlett, A., Kraft, C. S., Vega, M.-A. de La, Soka, M. J., Wahl, V., Griffiths, A., Bollinger, L., \u0026amp; Kuhn, J. H. (2020). Ebola virus disease. \u003cem\u003eNature Reviews. Disease Primers\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 13. https://doi.org/10.1038/s41572-020-0147-3\u003c/li\u003e\n\u003cli\u003eJain, S., \u0026amp; Baranwal, M. (2021). Conserved immunogenic peptides of Ebola glycoprotein elicit immune response in human peripheral blood mononuclear cells. \u003cem\u003eMicrobiology and Immunology\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(11), 505\u0026ndash;511. https://doi.org/10.1111/1348-0421.12935\u003c/li\u003e\n\u003cli\u003eJespersen, M. C., Peters, B., Nielsen, M., \u0026amp; Marcatili, P. (2017). BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(W1), W24\u0026ndash;W29. https://doi.org/10.1093/nar/gkx346\u003c/li\u003e\n\u003cli\u003eJohn K., K. (2015). The R Programming Language, in Doing Bayesian Data Analysis (Second Edition). \u003cem\u003eScienceDirect\u003c/em\u003e, \u003cem\u003ehttps://www.sciencedirect.com/topics/mathematics/r-programming-language\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eJones, S. M., Feldmann, H., Str\u0026ouml;her, U., Geisbert, J. B., Fernando, L., Grolla, A., Klenk, H.-D., Sullivan, N. J., Volchkov, V. E., Fritz, E. A., Daddario, K. M., Hensley, L. E., Jahrling, P. B., \u0026amp; Geisbert, T. W. (2005). Live attenuated recombinant vaccine protects nonhuman primates against Ebola and Marburg viruses. \u003cem\u003eNature Medicine\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(7), 786\u0026ndash;790. https://doi.org/10.1038/nm1258\u003c/li\u003e\n\u003cli\u003eKarplus, P. A., \u0026amp; Schulz, G. E. (1985). Prediction of chain flexibility in proteins. . \u003cem\u003eNaturwissenschaften 72.\u003c/em\u003e, \u003cem\u003ehttps://doi.org/10.1007/BF01195768\u003c/em\u003e, 212\u0026ndash;213.\u003c/li\u003e\n\u003cli\u003eKolaskar, A. S., \u0026amp; Tongaonkar, P. C. (1990). A semi-empirical method for prediction of antigenic determinants on protein antigens. \u003cem\u003eFEBS Letters\u003c/em\u003e, \u003cem\u003e276\u003c/em\u003e(1\u0026ndash;2), 172\u0026ndash;174. https://doi.org/10.1016/0014-5793(90)80535-q\u003c/li\u003e\n\u003cli\u003eLamiable, A., Th\u0026eacute;venet, P., Rey, J., Vavrusa, M., Derreumaux, P., \u0026amp; Tuff\u0026eacute;ry, P. (2016). PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(W1), W449-54. https://doi.org/10.1093/nar/gkw329\u003c/li\u003e\n\u003cli\u003eLarsen, M. V, Lundegaard, C., Lamberth, K., Buus, S., Lund, O., \u0026amp; Nielsen, M. (2007). Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 424. https://doi.org/10.1186/1471-2105-8-424\u003c/li\u003e\n\u003cli\u003eLe Guenno, \u0026amp; B. (1995). Emerging viruses. \u003cem\u003eScientific American\u003c/em\u003e, \u003cem\u003e273(4)\u003c/em\u003e, 56\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eLennemann, N. J., Dillard, J., Ruggio, N., Cooney, A. L., Schaack, G. A., Davey, R. A., \u0026amp; Maury, W. (2021). A Naturally Occurring Polymorphism in the Base of Sudan Virus Glycoprotein Decreases Glycoprotein Stability in a Species-Dependent Manner. \u003cem\u003eJournal of Virology\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e(18). https://doi.org/10.1128/jvi.01073-21\u003c/li\u003e\n\u003cli\u003eLon, J. R., Bai, Y., Zhong, B., Cai, F., \u0026amp; Du, H. (2020). Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2. \u003cem\u003eVirology Journal\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 165. https://doi.org/10.1186/s12985-020-01437-4\u003c/li\u003e\n\u003cli\u003eMalik, S., Kishore, S., Nag, S., Dhasmana, A., Preetam, S., Mitra, O., Le\u0026oacute;n-Figueroa, D. A., Mohanty, A., Chattu, V. K., Assefi, M., Padhi, B. K., \u0026amp; Sah, R. (2023). Ebola Virus Disease Vaccines: Development, Current Perspectives \u0026amp;amp; Challenges. \u003cem\u003eVaccines\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 268. https://doi.org/10.3390/vaccines11020268\u003c/li\u003e\n\u003cli\u003eMalonis, R. J., Lai, J. R., \u0026amp; Vergnolle, O. (2020). Peptide-Based Vaccines: Current Progress and Future Challenges. In \u003cem\u003eChemical Reviews\u003c/em\u003e (Vol. 120, Issue 6, pp. 3210\u0026ndash;3229). American Chemical Society. https://doi.org/10.1021/acs.chemrev.9b00472\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Flores, D., Zepeda-Cervantes, J., Cruz-Res\u0026eacute;ndiz, A., Aguirre-Sampieri, S., Sampieri, A., \u0026amp; Vaca, L. (2021). SARS-CoV-2 Vaccines Based on the Spike Glycoprotein and Implications of New Viral Variants. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 701501. https://doi.org/10.3389/fimmu.2021.701501\u003c/li\u003e\n\u003cli\u003eMaurer-Stroh, S., Krutz, N. L., Kern, P. S., Gunalan, V., Nguyen, M. N., Limviphuvadh, V., Eisenhaber, F., \u0026amp; Gerberick, G. F. (2019). AllerCatPro-prediction of protein allergenicity potential from the protein sequence. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(17), 3020\u0026ndash;3027. https://doi.org/10.1093/bioinformatics/btz029\u003c/li\u003e\n\u003cli\u003eMuyembe-Tamfum, J. J., Mulangu, S., Masumu, J., Kayembe, J. M., Kemp, A., \u0026amp; Paweska, J. T. (2012). Ebola virus outbreaks in Africa: Past and present. \u003cem\u003eOnderstepoort Journal of Veterinary Research\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(2). https://doi.org/10.4102/ojvr.v79i2.451\u003c/li\u003e\n\u003cli\u003eNandy, A., \u0026amp; Basak, S. C. (2019). Bioinformatics in Design of Antiviral Vaccines. In \u003cem\u003eEncyclopedia of Biomedical Engineering\u003c/em\u003e (pp. 280\u0026ndash;290). Elsevier. https://doi.org/10.1016/B978-0-12-801238-3.10878-5\u003c/li\u003e\n\u003cli\u003eOyarz\u0026uacute;n, P., \u0026amp; Kobe, B. (2016). Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production. \u003cem\u003eHuman Vaccines \u0026amp; Immunotherapeutics\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 763\u0026ndash;767. https://doi.org/10.1080/21645515.2015.1094595\u003c/li\u003e\n\u003cli\u003eParihar, A., Malviya, S., \u0026amp; Khan, R. (2022). Immunoinformatics and reverse vaccinomic approaches for effective design. In \u003cem\u003eComputational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection\u003c/em\u003e (pp. 357\u0026ndash;378). Elsevier. https://doi.org/10.1016/B978-0-323-91172-6.00004-2\u003c/li\u003e\n\u003cli\u003eParker, J. M., Guo, D., \u0026amp; Hodges, R. S. (1986). New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. \u003cem\u003eBiochemistry\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(19), 5425\u0026ndash;5432. https://doi.org/10.1021/bi00367a013\u003c/li\u003e\n\u003cli\u003ePassi, D., Sharma, S., Dutta, S. R., Dudeja, P., \u0026amp; Sharma, V. (2015). Ebola Virus Disease (The Killer Virus): Another Threat to Humans and Bioterrorism: Brief Review and Recent Updates. \u003cem\u003eJournal of Clinical and Diagnostic Research : JCDR\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(6), LE01-8. https://doi.org/10.7860/JCDR/2015/13062.6100\u003c/li\u003e\n\u003cli\u003ePatronov, A., \u0026amp; Doytchinova, I. (2013). T-cell epitope vaccine design by immunoinformatics. \u003cem\u003eOpen Biology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 120139. https://doi.org/10.1098/rsob.120139\u003c/li\u003e\n\u003cli\u003ePettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., \u0026amp; Ferrin, T. E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. \u003cem\u003eJournal of Computational Chemistry\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(13), 1605\u0026ndash;1612. https://doi.org/10.1002/jcc.20084\u003c/li\u003e\n\u003cli\u003eReport of a WHO/International Study Team. (1978). Ebola haemorrhagic fever in Sudan, 1976. Report of a WHO/International Study Team. \u003cem\u003eBulletin of the World Health Organization\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(2), 247\u0026ndash;270.\u003c/li\u003e\n\u003cli\u003eRiley, J. L., \u0026amp; Montaner, L. J. (2017). Cell-Mediated Immunity to Target the Persistent Human Immunodeficiency Virus Reservoir. \u003cem\u003eThe Journal of Infectious Diseases\u003c/em\u003e, \u003cem\u003e215\u003c/em\u003e(suppl_3), S160\u0026ndash;S171. https://doi.org/10.1093/infdis/jix002\u003c/li\u003e\n\u003cli\u003eRivera, A., \u0026amp; Messaoudi, I. (2016). Molecular mechanisms of Ebola pathogenesis. \u003cem\u003eJournal of Leukocyte Biology\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(5), 889\u0026ndash;904. https://doi.org/10.1189/jlb.4ri0316-099rr\u003c/li\u003e\n\u003cli\u003eRosendahl Huber, S., van Beek, J., de Jonge, J., Luytjes, W., \u0026amp; van Baarle, D. (2014). T cell responses to viral infections - opportunities for Peptide vaccination. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 171. https://doi.org/10.3389/fimmu.2014.00171\u003c/li\u003e\n\u003cli\u003eSanchez, A., \u0026amp; Rollin, P. E. (2005). Complete genome sequence of an Ebola virus (Sudan species) responsible for a 2000 outbreak of human disease in Uganda. \u003cem\u003eVirus Research\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e(1), 16\u0026ndash;25. https://doi.org/10.1016/j.virusres.2005.03.028\u003c/li\u003e\n\u003cli\u003eSebastian, S., Flaxman, A., Cha, K. M., Ulaszewska, M., Gilbride, C., Sharpe, H., Wright, E., Spencer, A. J., Dowall, S., Hewson, R., Gilbert, S., \u0026amp; Lambe, T. (2020). A Multi-Filovirus Vaccine Candidate: Co-Expression of Ebola, Sudan, and Marburg Antigens in a Single Vector. \u003cem\u003eVaccines\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(2). https://doi.org/10.3390/vaccines8020241\u003c/li\u003e\n\u003cli\u003eShrestha, B., \u0026amp; Diamond, M. S. (2004). Role of CD8 + T Cells in Control of West Nile Virus Infection. \u003cem\u003eJournal of Virology\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(15), 8312\u0026ndash;8321. https://doi.org/10.1128/JVI.78.15.8312-8321.2004\u003c/li\u003e\n\u003cli\u003eSievers, F., Wilm, A., Dineen, D., Gibson, T. J., Karplus, K., Li, W., Lopez, R., Mcwilliam, H., Remmert, M., S\u0026ouml;ding, J., Thompson, J. D., \u0026amp; Higgins, D. G. (2011). \u003cem\u003eFast, scalable generation of high quality protein multiple sequence alignments using Clustal Omega Transaction Report\u003c/em\u003e. http://www.nature.com/msb/authors\u003c/li\u003e\n\u003cli\u003eSrinath, K. R. (2017). Python-The Fastest Growing Programming Language. \u003cem\u003eInternational Research Journal of Engineering and Technology\u003c/em\u003e. www.irjet.net\u003c/li\u003e\n\u003cli\u003eSuder, E., Furuyama, W., Feldmann, H., Marzi, A., \u0026amp; de Wit, E. (2018). The vesicular stomatitis virus-based Ebola virus vaccine: From concept to clinical trials. \u003cem\u003eHuman Vaccines \u0026amp; Immunotherapeutics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(9), 2107\u0026ndash;2113. https://doi.org/10.1080/21645515.2018.1473698\u003c/li\u003e\n\u003cli\u003eSundar, K., Boesen, A., \u0026amp; Coico, R. (2007). Computational prediction and identification of HLA-A2.1-specific Ebola virus CTL epitopes. \u003cem\u003eVirology\u003c/em\u003e, \u003cem\u003e360\u003c/em\u003e(2), 257\u0026ndash;263. https://doi.org/10.1016/j.virol.2006.09.042\u003c/li\u003e\n\u003cli\u003eUwishema, O., Adanur, I., Babatunde, A. O., Hasan, M. M., Elmahi, O. K. O., Olajumoke, K. B., Aborode, A. T., Emmanuella, N., Costa, A. C. D. S., Ahmad, S., \u0026amp; Essar, M. Y. (2021). Viral infections amidst COVID-19 in Africa: Implications and recommendations. \u003cem\u003eJournal of Medical Virology\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e(12), 6798\u0026ndash;6802. https://doi.org/10.1002/jmv.27211\u003c/li\u003e\n\u003cli\u003eWaterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M., \u0026amp; Barton, G. J. (2009). Jalview Version 2--a multiple sequence alignment editor and analysis workbench. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(9), 1189\u0026ndash;1191. https://doi.org/10.1093/bioinformatics/btp033\u003c/li\u003e\n\u003cli\u003eWHO. (2022). SUDAN VIRUS DISEASE (SVD) OUTBREAK, UGANDA Member State Briefing \u0026ndash; 13 October 2022. \u003cem\u003eWHO Member State Briefing \u003c/em\u003e, \u003cem\u003ehttps://apps.who.int/gb/COVID-19/pdf_files/2022/13_10/Item1.pdf\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eYadav, A. R., \u0026amp; Mohite, S. K. (2020). An overview on ebola virus disease. \u003cem\u003eRESEARCH JOURNAL OF PHARMACEUTICAL DOSAGE FORMS AND TECHNOLOGY\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 267\u0026ndash;270. https://doi.org/10.5958/0975-4377.2020.00044.0\u003c/li\u003e\n\u003cli\u003eYin, R., Zhu, X., Zeng, M., Wu, P., Li, M., \u0026amp; Kwoh, C. K. (2022). A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods. \u003cem\u003eBriefings in Bioinformatics\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5). https://doi.org/10.1093/bib/bbac281\u003c/li\u003e\n\u003cli\u003eYu, D.-S., Weng, T.-H., Wu, X.-X., Wang, F. X. C., Lu, X.-Y., Wu, H.-B., Wu, N.-P., Li, L.-J., \u0026amp; Yao, H.-P. (2017). Oncotarget 55750 www.impactjournals.com/oncotarget The lifecycle of the Ebola virus in host cells. In \u003cem\u003eOncotarget\u003c/em\u003e (Vol. 8, Issue 33). www.impactjournals.com/oncotarget/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sudan Ebola virus, epitope, glycoprotein, peptide-based, vaccine design","lastPublishedDoi":"10.21203/rs.3.rs-6600860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6600860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEbola virus disease (EVD) remains a deadly global health threat, especially due to the lack of approved vaccines or specific antiviral treatments specifically for Sudan ebolavirus (SUDV). The high mortality rate and recurring outbreaks of SUDV in sub-Saharan Africa call for urgent strategies to develop more effective and broadly protective vaccines for Ebola viruses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used in silico immunoinformatics approaches to identify B-cell and T-cell epitopes from the Sudan ebolavirus glycoprotein for the development of a peptide-based subunit vaccine. Conserved sequences and antigenic motifs were predicted using MEME and IEDB tools in order to identify B-cell epitopes. T-cell epitopes were selected based on their immunogenicity, population coverage, and allergenicity using NetCTL 1.2, IEDB population coverage tools, and AllerCatPro 2.0, respectively. Molecular docking simulations were performed for the T-cell epitopes using HPEPDOCK 2.0, with validation through LigPlot\u0026thinsp;+\u0026thinsp;v2.2 and X-Score.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis analysis led to the identification of two highly conserved B-cell epitopes that can be further tested in vitro\u0026mdash;\u0026ldquo;IDQLVCKDHLASTDQLKSVGLNLEGSGVSTDIPSATKRWGFRSGVPPKVV\u0026rdquo; and \u0026ldquo;YEAGEWAENCYNLEIKKPDGSECLPPPPDGVRGFPRCRYVHKAQGTGPCP.\u0026rdquo; These also showed strong immunogenic potential from the analysis. Four T-cell epitopes\u0026mdash;\u0026ldquo;YTENTSSYY,\u0026rdquo; \u0026ldquo;KCNPNLHYW,\u0026rdquo; \u0026ldquo;RLASTVIYR,\u0026rdquo; and \u0026ldquo;EVTEIDQLV\u0026rdquo;\u0026mdash;showed strong immunogenic potential and high binding affinity (binding energy\u0026thinsp;\u0026gt;\u0026thinsp;9.0 kcal/mol) to their respective HLA molecules. These T-cell epitopes also demonstrated extensive population coverage across different African regions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe predicted B-cell and T-cell epitope sequences show strong potential for the development of peptide-based subunit vaccines against Sudan ebolavirus. Therefore, these findings may contribute to global vaccine development efforts of SUDV.\u003c/p\u003e","manuscriptTitle":"In silico prediction of B-cell and T-cell epitopes of the Sudan Ebola virus glycoprotein for peptide-based vaccine design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 04:02:07","doi":"10.21203/rs.3.rs-6600860/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6966bbc-a3dd-4beb-aad9-f5101955f463","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-29T17:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 04:02:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6600860","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6600860","identity":"rs-6600860","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}