Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity

Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity | 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 Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity Ting-Yu Chen, Yann-Jen Ho, Fang-Yu Ko, Pei-Yin Wu, Chia-Jung Chang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3784481/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Aug, 2024 Read the published version in AMB Express → Version 1 posted 5 You are reading this latest preprint version Abstract T and B cell activation are equally important in triggering and orchestrating adaptive host responses to design multi-epitope African swine fever virus (ASFV) vaccines. However, few design methods have considered the trade-off between T and B cell immunogenicity when identifying promising ASFV epitopes. This work proposed a novel Pareto front-based ASFV screening method PFAS to identify promising epitopes for designing multi-epitope vaccines utilizing five ASFV Georgia 2007/1 sequences. To accurately predict T cell immunogenicity, four scoring methods were used to estimate the T cell activation in the four stages, including proteasomal cleavage probability, transporter associated with antigen processing transport efficiency, class I binding affinity of the major histocompatibility complex, and CD8 + cytotoxic T cell immunogenicity. PFAS ranked promising epitopes using a Pareto front method considering T and B cell immunogenicity. The coefficient of determination between the Pareto ranks of multi-epitope vaccines and survival days of swine vaccinations was R 2 = 0.95. Consequently, PFAS scored complete epitope profiles and identified 72 promising top-ranked epitopes, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. PFAS is the first method of using the Pareto front approach to identify promising epitopes that considers the objectives of maximizing both T and B cell immunogenicity. The top-ranked promising epitopes can be cost-effectively validated in vitro . The Pareto front approach can be adaptively applied to various epitope predictors for bacterial, viral and cancer vaccine developments. The MATLAB code of the Pareto front method was available at https://github.com/NYCU-ICLAB/PFAS. African swine fever virus immunogenicity Pareto front promising epitope T and B cell activation vaccine design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points Proposing a Pareto front-based method for designing swine multi-epitope vaccine. The method maximizes T and B cell immunogenicity while ranking promising epitopes. Higher the epitope Pareto ranks leads to longer vaccination survival (R 2 = 0.95). Introduction African swine fever virus (ASFV) causes a lethal hemorrhagic disease and has become an epidemic swine viral disease in Asia. Simultaneous activation of T and B cells results in a better immune response and more immunological memory against ASFV than activation of one of the cell types alone (Bosch-Camos et al. 2020 ; Teklue et al. 2020 ). The development of subunit vaccines, especially multi-epitope vaccines, is more challenging than that of live-attenuated virus vaccines. Multi-epitope vaccines have essential applications in non-epidemic areas, increasing research and development requirements (Teklue et al. 2020 ). However, there is currently no effective multi-epitope vaccine for the prevention of ASFV (Blome et al. 2020 ). The selection of protein candidates for designing a multi-epitope vaccine should consider several factors, including the conservation, abundance, extracellular localization, and cross-protection against various viral genotypes (Adamczyk-Poplawska et al. 2011 ; Alejo et al. 2018 ; Kessler et al. 2018 ). CD2v is a hemagglutinin and the main antigen protein involved in regulating immune responses and cell adhesion (Burmakina et al. 2019 ; Gaudreault and Richt 2019 ; Jia et al. 2017 ). p30 is a structural protein involved in the attachment and internalization of ASFV (Gomez-Puertas et al. 1996 ). p54 is the only membranous structural protein in the inner viral envelope associated with viral attachment, and a multi-epitope vaccine containing p30 and p54 has shown partial protection (Gomez-Puertas et al. 1998 ). p72 is a major structural protein (approximately 31–33% of the entire virus) and an important antigenic protein owing to its high conservation and thermostable nature (Liu et al. 2019 ; Yu et al. 1996 ). pp220 is the largest multi-precursor protein and presents many peptides to CD8 + cytotoxic T cells (CTLs), triggering a strong antibody response (Lokhandwala et al. 2019 ). Bioinformatic methods using a machine learning approach serve as an effective strategy to identify vaccine candidates for human (Guo et al. 2022 ; Hajialibeigi et al. 2021 ; Kibria et al. 2022 ) and swine pathogens, including ASFV (Gao et al. 2021 ), influenza A virus (Baratelli et al. 2020 ; Fan et al. 2018 ), and porcine circovirus type 2 (Bandrick et al. 2020 ). Machine learning methods used to identify epitopes in the design of multi-epitope vaccines consider the biological presentation and activation of ASFV epitopes. Figure 1 shows the presentation and activation of ASFV epitopes, and the correspondence between biological and computational processes. In viral infections, CTLs (mainly involved in viral T cell immunity) support cell-mediated immunity against intracellular viruses, while B cells trigger humoral immunity and produce memory cells for future infection (Clem 2011 ). The objective of vaccine design is to induce immune responses in both T and B cells. Existing computational methods for identifying ASFV epitopes screen potential epitopes by separately considering T and B cell immunogenicity (Bosch-Camos et al. 2021 ; Lopera-Madrid et al. 2017 ; Ros-Lucas et al. 2020 ). The Pareto front is a popular approach used for obtaining a set of non-dominated solutions to a bi-objective problem. Pareto-optimal methods have been used to dock proteins and peptides (Masoudi-Sobhanzadeh et al. 2021 ) and improve amino acid and protein production in Yarrowia lipolytica (Jach et al. 2020 ). A study related to epitope-based vaccine design against human immunodeficiency virus used the Pareto front to simultaneously optimize cleavage and immunogenicity (Dorigatti and Schubert 2020 ). However, few studies have used the Pareto front method to simultaneously accommodate T and B cell immunogenicity. This work proposes a novel Pareto front-based screening method PFAS to identify promising epitopes with high T and B cell immunogenicity for designing ASFV recombinant multi-epitope vaccines. First, PFAS used experimental T and B cell epitopes from the Immune Epitope Database (IEDB) to verify the state-of-the-art computational methods and their parameter settings. Next, PFAS used the Pareto front technique to deal with T and B cell prediction scores as bi-objective ranks and identify the top-ranked epitopes. PFAS scored whole epitope profiles and identified 72 promising epitopes. Based on the three combinations of epitopes in pp220, p30, p72, and p54 for a vaccination study against ASFV, the determination coefficient of determination between the Pareto ranks of recombinant multi-epitope vaccines and swine survival was R 2 = 0.95. The identified epitopes can be cost-effectively validated in vitro to design epitope-based ASFV vaccines. Materials and methods Collection of ASFV protein sequences The most lethal ASFV type, Georgia 2007/1 (GenBank: FR682468), was used as the target virus for screening. The protein sequences of Georgia 2007/1 were obtained from the NCBI, including CD2v (EP402R), p30 (CP204L), p54 (E183L), p72 (B646L), and pp220 (CP2475L). All sequences were cut into fragments using a sliding window. Finally, two datasets of ASFV proteins consisting of 9mer (Figure S1 A) and 15mer (Figure S1 B) fragments served as candidates for CTL and B cell epitopes, respectively. Collection of validation datasets To obtain the best parameter settings for PFAS, this work established two datasets from the IEDB, consisting of experimentally validated CTL and B cell epitopes of swine. The CTL epitopes (n = 243) were annotated as Sus scrofa, infectious diseases, and Swine Leukocyte Antigen (SLA) class II. After simultaneously removing duplicate and uncertain sequences belonging to both positive and negative groups, the dataset contained 125 swine 9mer CTL fragments, including 37 epitopes and 88 non-epitopes. Similarly, 1,700 validated swine B cell epitopes (BCEs) which were annotated as Sus scrofa and infectious disease were retrieved. Because IgG production is part of the secondary humoral immune response to an antigen, we extracted 1,389 IgG epitopes. Among them, the 15mer epitope was the largest in the dataset, followed by the 12mer epitope. Therefore, we established two datasets: 1) 650 B cell 15mer epitopes, including 116 positive and 534 negative epitopes, and 2) 293 swine B cell 12mer epitopes, including 35 positive and 258 negative epitopes. Proposed method PFAS Figure 2 shows a flowchart of the proposed method PFAS. Five protein sequences from Georgia 2007/1 were obtained and cut into 9mer and 15mer fragments. The CTL epitope predictor estimates T cell activation of 9mer fragments in the four stages and averages the four scores to obtain a T cell immunogenicity score. Similarly, the BCE predictor estimates B cell activation of 15mer fragments to obtain a B cell immunogenicity score. After normalizing these two scores into the range of [0, 1], the two fragments were superimposed by the central amino acid. Consequently, the fragments were extended, and thus conserved sequences were obtained. The Pareto front method produced ranks of the conserved fragments. The top-ranked fragments were considered as promising epitopes. Calculation of T and B cell scores Good CTL epitopes are involved in viral processing and antigen presentation, with major histocompatibility complex (MHC) I molecules playing a major role. First, pathogen debris is degraded by proteasomal degradation in the cytosol of productively infected cells. NetCTL is based on the NetChop method and predicts the probability of proteasomal cleavage (Larsen et al. 2007 ). Second, peptides are transported to the endoplasmic reticulum (ER) by a transporter associated with antigen processing (TAP). To predict TAP transport efficiency, NetCTL and MHC I Processing in the IEDB use the stabilized matrix method, and TAPPred is based on a support vector machine (SVM) with 33 physical features of amino acids (Bhasin and Raghava 2004 ). Third, an antigen is loaded onto MHC I and appears on the cell surface through vesicles. NetMHCpan (Reynisson et al. 2020 ), MHC I Processing, and NetCTL are the most widely used ANN-based methods to predict MHC I binding affinity using the BLOSUM50 matrix. Finally, the epitope stimulates CTL activation and differentiation. MHC I immunogenicity in the IEDB (Calis et al. 2013 ) is based on an immunogenicity score model to predict immunogenicity. In general, these four predictive roles are equally important. To identify promising T cell epitopes (TCEs), five web predictors were used, including NetCTL ( https://services.healthtech.dtu.dk/service.php?NetCTL-1.2 ), IEDB MHC I Processing ( http://tools.iedb.org/processing/ ), TAPPred ( https://webs.iiitd.edu.in/raghava/tappred/index.html ), NetMHCpan ( https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.0 ), and IEDB MHC I Immunogenicity. NetCTL was used to predict proteasome processing, TAP transport efficiency, and MHC I binding affinity. To examine conserved epitope candidates that cover multiple MHC loci, including A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58 and B62, we used sequences as inputs and applied ensemble learning with 12 supertype models. After averaging all predictive values in the 12 models, we obtained three estimated values: binding affinity, proteasome cleavage, and the TAP score. The IEDB MHC I Processing tool was used to estimate TAP transport efficiency and MHC I binding affinity. We used all 45 SLA I alleles (including 12 SLA1, 16 SLA2, 12 SLA3, and 5 SLA6) and set nine as the peptide length for each allele to obtain a file with the average predictive values, including the TAP and MHC scores in all sequence fragments. PFAS used TAPPred to predict the peptide-TAP transporter binding affinity based on SVM with validated sequences and obtained the prediction score. NetMHCpan was used to predict the binding affinity of peptide-MHC I. To obtain effective epitopes, we considered all 75 SLA alleles (including 23 SLA1, 26 SLA2, 21 SLA3, and five SLA6) and set nine as the peptide length for each allele. The binding affinity scores were estimated with mean scores for all fragments. This work used IEDB MHC I Immunogenicity to predict CTL immunogenicity considering all CTL active factors and obtained scores of all sequence fragments. BCEs can induce the differentiation of naïve and memory B cells into plasma cells, including antigen processing, peptide-MHC II presentation, and cytokine promotion. In studies on BCE presentation, LBtope (Singh et al. 2013 ), iBCE-EL (Manavalan et al. 2018 ), IgPred (Gupta et al. 2013 ), and ABCpred (Saha and Raghava 2006 ) are sequence-based predictors. LBtope uses the sparse matrix and amino acid property profile features and is an SVM-based Weka Classifier using 38,197 IEDB experimental epitopes. iBCE-EL is based on ensemble learning using amino acid composition characteristics and proportions of 5,550 experimentally validated BCEs. IgPred uses 14,725 BCEs in different types of specific epitopes using physicochemical properties (PCPs) features and is based on Weka Classifiers. ABCpred is based on PCP features and the neural network method with a balanced BCE database. Among the aforementioned predictors, LBtope uses the largest dataset with ensemble learning. To estimate the B cell immunogenicity score of 15mer and 12mer fragments, five online predictors (LBtope_Variable, LBtope_Confirm, iBCE-EL, IgPred, and ABCpred) were utilized and validated. Epitope probabilities and IgG scores were determined using the iBCE-EL and IgPred prediction tools, respectively. LBtope is based on multiple peptides from prediction models using two variable-length epitope models. The LBtope_Variable model was trained using 38,197 peptides. The LBtope_Confirm model was reported in at least two studies and contained 2,837 peptides. By submitting multiple fragments, the probability of epitopes was obtained along with the physical property score. As ABCpred exclusively accepts an even number of epitope lengths and continuous amino acid sequences as submissions, PFAS used only one 12mer dataset with parameters containing a threshold of zero and an overlapping filter to obtain the predicted scores. Immunogenicity prediction of T and B cell fragments The CTL activation prediction has four important stages: proteasomal cleavage probability, TAP transport efficiency, MHC I binding affinity, and CTL immunogenicity. These predictions help identify potential TCE candidates. PFAS combined all the prediction values obtained from the online prediction tools in the four stages. The probability of proteasomal cleavage was estimated using NetCTL1.2. The TAP transport efficiency score is the mean score of NetCTL1.2, IEDB MHC I Processing, and TAPPred values. The peptide-MHCI binding affinity score is the mean score of NetCTL1.2, IEDB MHC I Processing, and NetMHCpan predictive values. The CTL immunogenicity score is obtained using IEDB MHC I Immunogenicity. After combining and normalizing the scores of each category using a combination of weights, PFAS compiled four stage score for the TCE prediction. For the BCE prediction, the best predictor was evaluated and used to obtain B cell immunogenicity scores. After compiling the results of the prediction values from the web tools, the output values were normalized into the range of [0, 1] and B cell immunogenicity scores were compiled. Pareto rank of fragments The Pareto front is the set of all efficient solutions to bi-objective problems. In this study, a fragment Frag belonging to the Pareto front means that no other fragment has both larger T and B cell scores than Frag . The T and B cell scores of all fragments which were represented by their central amino acids were used as inputs of the Pareto front method to determine the Pareto rank of fragments. The Pareto front method iteratively removes the Pareto fronts, and Pareto rank of the fragments was the serial number of the removed front. For instance, the segments belonging to the initial Pareto front have a rank one. After removing the Pareto front, the fragments belonging to the new Pareto front have a rank two, and so on. Promising epitopes of the multi-epitope vaccine This work extended the fragments to a length of 16–20 amino acids and obtained the epitope profiles with the average Pareto rank of the extended fragments. The average rank was defined as the sum of the Pareto ranks divided by the total number of fragments included in the extended fragments. Moreover, to select conserved epitopes, PFAS estimated protein variability using the Protein Variability Server (PVS) (Garcia-Boronat et al. 2008 ). PVS contains three methods, the Shannon entropy, the Simpson diversity index, and the Wu–Kabat variability coefficient method, which can be used as indicators of variability. In this study, the Shannon entropy greater than two was considered as the variability point. Accordingly, PFAS removed the variable fragments that contained highly variable sequences. Finally, in the 16mer to 20mer epitope profiles, PFAS ranked conserved fragments according to the average Pareto rank, and the top-ranked promising epitopes were provided to the biological decision makers for in vitro validation. Results Estimation of T and B cell immunogenicity scores After T cell online prediction, the T cell score was the weighted sum of four scores in the four stages: proteasomal cleavage probability, TAP transport efficiency, MHC I binding affinity, and CTL immunogenicity scores. For each stage, the scores were normalized and averaged. To validate the weights of four scores, the experimentally validated swine 9mer CTL epitopes from the IEDB were used. Figure S2 A shows a good performance with an area under the receiver operating characteristic curve (AUC) of 0.71, and the largest AUC was reached when using the top 30% epitopes (Figure S3). The set of four equal weights 1/4, 1/4, 1/4, and 1/4 in determining the T cell score is the most stable one. This result is consistent to the previous hypothesis that the importance of the four stages in the swine immunogenicity prediction is equal. In B cell immunogenicity prediction, previous studies have revealed that the use of sequence-based predictors, such as LBtope (Singh et al. 2013 ), iBCE-EL (Manavalan et al. 2018 ), IgPred (Gupta et al. 2013 ), and ABCpred (Saha and Raghava 2006 ) is an efficient approach to identifying BCEs. Owing to different aims of the training dataset and machine learning approaches, the prediction results were different from these methods. Therefore, PFAS used LBtope, the largest experimental dataset with ensemble learning, as a prediction model to identify BCEs. Experimentally validated swine 15mer and 12mer BCEs were used to validate this hypothesis, the validation pipeline consistent with that used for TCE prediction. Figure S2 B shows performance of the four methods: iBCE-EL, IgPred, LBtope with a variable dataset, and LBtope with a confirmed dataset. LBtope with the variable dataset achieved an AUC of 0.86. When using the top-ranked 30% epitopes, LBtope with a confirmed dataset was better than the other methods (Figure S4–5). These results were in good agreement with the hypothesis, showing that LBtope is an appropriate method for predicting ASFV BCEs. Epitopes identification using the Pareto front method Given that both T and B cell activation are equally important for mobilizing adaptive immunity, we applied the Pareto front method to identify potential epitopes. To rank and identify fragments simultaneously, the Pareto front method iteratively determined 116 Pareto ranks (Figure S6). T and B cell scores in the bi-objective problem were converted into Pareto ranks to identify epitope candidates. Figure 3 shows 15mer epitope profiles for the five ASFV proteins. A higher average Pareto rank indicates a more promising epitope. Table S1 shows the results of the selected fragments in the five proteins, including 346 fragments of CD2v, 187 fragments of p30, 170 fragments of p54, 632 fragments of p72, and 2462 fragments of pp220. In short, Table 1 shows the Pareto ranks of the top three fronts. The best protein is CD2v, which has the most selected and continuous fragments in the rank one Pareto front. Table 1 Fragments of the top three fronts using PFAS. Scores are normalized into the range of [0, 1]. ID, identification of fragments. Front Protein ID Fragment T cell score B cell score Rank Rank 1 CD2v 236 KHVEEIESPPPESNE 0.16 0.97 1 CD2v 237 HVEEIESPPPESNEE 0.13 1.00 1 CD2v 238 VEEIESPPPESNEEE 0.27 0.93 1 CD2v 240 EIESPPPESNEEEQC 0.43 0.90 1 CD2v 276 YSRYQYNTPIYYMRP 1.00 0.73 1 p72 360 KLASQKDLVNEFPGL 0.85 0.88 1 p72 454 KLMSALKWPIEYMFI 1.00 0.33 1 pp220 623 WKATVSAIELEYDVK 0.90 0.84 1 pp220 626 TVSAIELEYDVKRRF 0.41 0.92 1 pp220 2195 FRTQLEDTRREVNNL 0.56 0.90 1 Rank 2 CD2v 94 TYQVVWNQIINYTIK 0.93 0.72 2 CD2v 147 FVKYTNESILEYNWN 0.96 0.69 2 CD2v 233 KRKKHVEEIESPPPE 0.38 0.90 2 CD2v 235 KKHVEEIESPPPESN 0.31 0.90 2 CD2v 265 PSPREPLLPKPYSRY 0.71 0.85 2 p30 140 LAQKTVQHIEQYGKA 0.99 0.52 2 p72 168 GTKNAYRNLVYYCEY 0.99 0.64 2 p72 363 SQKDLVNEFPGLFVR 0.75 0.84 2 p72 364 QKDLVNEFPGLFVRQ 0.83 0.73 2 pp220 785 SPLQIYKTLLEYLQH 0.79 0.79 2 pp220 1453 QSSERFEQYGRVFSR 0.64 0.86 2 Rank 3 CD2v 230 SLRKRKKHVEEIESP 0.71 0.81 3 CD2v 234 RKKHVEEIESPPPES 0.34 0.89 3 CD2v 261 SIHEPSPREPLLPKP 0.64 0.84 3 CD2v 264 EPSPREPLLPKPYSR 0.24 0.90 3 CD2v 277 SRYQYNTPIYYMRPS 0.77 0.77 3 p30 63 VKSARIYAGQGYTEH 0.87 0.68 3 p30 79 AQEEWNMILHVLFEE 0.71 0.83 3 p30 80 QEEWNMILHVLFEEE 0.79 0.76 3 p30 160 VIRAHNFIQTIYGTP 0.95 0.59 3 p54 3 SEFFQPVYPRHYGEC 0.83 0.68 3 p72 86 LGNKLTFGIPQYGDF 0.96 0.59 3 p72 555 SKFCSSYIPFHYGGN 0.93 0.66 3 pp220 1450 NNPQSSERFEQYGRV 0.79 0.74 3 pp220 1452 PQSSERFEQYGRVFS 0.36 0.85 3 Evaluation of screening efficiency To evaluate the screening efficiency of PFAS, two validation datasets were used consisting of 30 experimentally validated and 34 predicted epitopes of T or B cells annotated in previous studies and the IEDB database (Bosch-Camos et al. 2021 ; Ivanov et al. 2011 ; Ros-Lucas et al. 2020 ). PFAS selected the top 30% fragments as promising epitopes. Table 2 lists the public epitopes with the Pareto ranks. PFAS identified 17 epitopes from 30 experimental ones and 24 epitopes from 34 predicted ones. Figure 4 revealed scatter points of experimental, predicted and PFAS selected epitopes. Since animal studies can prove the actual antigenicity and immunogenicity of epitopes, the top-ranked epitopes may be superior to the published epitopes. Three recombinant multi-epitope vaccines with synthesized epitope groups were used to determine the determination coefficient between the predicted ranks of PFAS and swine immunization (Ivanov et al. 2011 ). The combinations 1, 2, and 3 consisting of four pp220 epitopes, six p30 and p72 epitopes, and two p54 epitopes, respectively. The Pareto ranks of peptides in each combination were determined using the Pareto front method. Figure 5 indicates a significant coefficient of determination with R 2 = 0.95 between the mean Pareto ranks of the recombinant vaccine and swine survival days in a vaccination study. The higher the combination Pareto rank, the longer the survival days of the pig (Table S2 ). These results reveal that PFAS is an efficient approach to epitope identification. Table 2 The Pareto rank of the epitopes in the top 30% fragments for the experimentally validated and predicted epitopes. The rank of the evaluated epitope was the highest Pareto rank in the selected epitopes. There were 17 and 24 selected epitopes in the 30 experimentally validated and 34 predicted epitopes of T or B cells. Protein Experimental epitope Rank Protein Predicted epitope Rank CD2v KPCPPPKPCPPPKPC 21 CD2v CTYLTLSSNYFYTFFKLYYIPL - CD2v PPKPCPPPKPCPPPK - p30 SQVVFHAGSLY 6 CD2v YSPPKPLPSIPLLPN 14 p30 AQEEWNMIL 3 CD2v SPPKPLPSIPLLPNI 14 p54 YTHKDLENSL - CD2v PPKPLPSIPLLPNIPPLSTQNISLI - p72 AAIEEEDIQFINPYQD - p30 EVIFKTD 21 p72 KPYVPVGFEY 7 p30 TSSFETLFEQ 8 p72 GFEYNKVRPHTGTPTLGNKLT 20 p30 TVQHIEQYGKA 2 p72 QMGAHGQLQTFPRNGYDWDNQTPLE 4 p30 QHIEQYGKAPDFNKV 27 p72 NVRFDVNGNSL 18 P30 LKEEEKEVVRLMVIKLLKKNKL - p72 YCEYPGERLYENVRFDVNGNSLDEYSSDVTTL 15 p54 MDSEFFQPVYPRHYGECLS 3 p72 HKPHQSKPILTDENDTQRTC - p54 FQPVYPRHYGECLSP - p72 FPENSHNIQTAGKQD - p54 QPVYPRHYGECLSPV 20 p72 HTNPKFLSQHFPENSHNIQTAGKQDITPITD 33 p54 PVYPRHYGECLSPVT - p72 RPSRRNIRF 4 p54 VYPRHYGECLSPVTT - p72 TWNISDQNPHQHRDWHK 22 p54 YPRHYGECLSPVTTP - p72 VTHTNNNHHDEKLMS - p54 PRHYGECLSPVTTPSFF - p72 SFQDRDTALPDACSSISDI 16 p54 YGECLSPVTTPSFFS - p72 LLQNGSAVLRYST - p54 GECLSPVTTPSFFST 23 pp220 NKALQKVGL - p54 SRKKKAAAAIEEEDI - pp220 SQVDLNQAINTFMYYYYVAQIY 26 p54 NKPVTDNPVTDRL - pp220 HNKQEFQSY 15 p72 YCEYPGERLYENVRFDVNGNSLDEYSSDVTTL 15 pp220 ITKTFVNNI 14 p72 LCNIHDLHKPHQSKPILTDENDTQRTCS 20 pp220 DNAPAGHYY 30 p72 QKDLVNEFPGLFIRQSRFIPGRPSRRNIRFKP 2 pp220 TPEEAAQRVY 27 p72 ACSSISDISPVTYPITLPIIKNISVTAHGINLIDK 4 pp220 VNDALSTRW 31 p72 LKPREEYQPS 6 pp220 MAAKIFIVL 9 pp220 YDSCSRLLQIIDFYTDIVQKKYGGGEDCECTRV 19 pp220 EFYQKLFSF 21 pp220 PKGQTRTLGSNRERERI - pp220 ARTMNDFGM - pp220 GYMSRIFRGDNALNM - pp220 NRSNPGSFY 25 pp220 YMSRIFRGDNALNMG 29 pp220 IQNNRSMMMVFNQLIASYITRFY 30 pp220 IPIYLKENY 24 pp220 YMSRYNKEPLMPF 11 pp220 RERERIFNL 18 pp220 YINQALHEL - Identification of promising epitopes for multi-epitope vaccines Clustering epitopes into hotspots (high-ranked epitopes) would be an effective method to obtain vaccine candidates in the multi-epitope vaccine design. The results shown in Table 1 are consistent to previous studies in which highly-ranked sequences were continuous. Accordingly, PFAS extended the fragments to sequences of 16–20 amino acids and produced epitope profiles with the average Pareto ranks of extended fragments (Figure S7–11). The higher the front rank, the greater potential of the epitope. Enrichment of potential epitopes is considered as an epitope hotspot. Additionally, epitope variability is important for biologists in identifying vaccine candidates. To obtain conserved epitopes, PFAS calculated the variability of the five proteins using PVS (Table S3). A total of 45 sites were identified, including two CD2v sites and 43 p54 sites, which can be regarded as sites with high variability because their Shannon entropy was greater than two (Figure S12). Similarly, if a fragment contained highly variable sites, it was regarded as a highly variable region. After removing 69 highly variable fragments (Table S4), 3728 fragments were obtained, including 341 CD2v fragments, 187 p30 fragments, 106 p54 fragments, 632 p72 fragments, and 2462 pp220 fragments. After conservation verification, the mean Pareto rank of the extended fragments for each protein was determined. CD2v had the highest average rank among the five proteins, and p30, p72, p220, and p54 ranked second, third, fourth, and fifth, respectively. Furthermore, we estimated all conserved fragments (Table S5). Biological decision makers can flexibly choose the appropriate epitope length and sample size for experimental validation in vitro (Tables S6–10). For example, Table 3 shows 72 promising epitopes with an average Pareto rank of four, including 26 16mer epitopes, 16 17mer epitopes, 12 18mer epitopes, 10 19mer epitopes, and 8 20mer epitopes. In addition, these epitopes came from four proteins, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. Table 3 The top 72 epitopes and their average ranks with the 16–20 amino acids. Mer type Protein Sequence Average rank Mer type Protein Sequence Average rank 16mer CD2v KHVEEIESPPPESNEE 1.00 17mer CD2v HVEEIESPPPESNEEEQ 4.00 16mer CD2v HVEEIESPPPESNEEE 1.00 17mer p72 KLASQKDLVNEFPGLFV 4.00 16mer CD2v KKHVEEIESPPPESNE 1.50 17mer CD2v SLRKRKKHVEEIESPPP 4.00 16mer CD2v YSRYQYNTPIYYMRPS 2.00 17mer pp220 ATVSAIELEYDVKRRFY 4.00 16mer p72 SQKDLVNEFPGLFVRQ 2.00 17mer pp220 NPQSSERFEQYGRVFSR 4.00 16mer pp220 TVSAIELEYDVKRRFY 2.50 17mer CD2v VEEIESPPPESNEEEQC 4.00 16mer CD2v KRKKHVEEIESPPPES 2.50 18mer CD2v KKHVEEIESPPPESNEEE 1.25 16mer CD2v RKKHVEEIESPPPESN 2.50 18mer CD2v RKKHVEEIESPPPESNEE 1.75 16mer CD2v EPSPREPLLPKPYSRY 2.50 18mer CD2v KRKKHVEEIESPPPESNE 2.00 16mer pp220 PQSSERFEQYGRVFSR 2.50 18mer CD2v RKRKKHVEEIESPPPESN 2.75 16mer CD2v PYSRYQYNTPIYYMRP 2.50 18mer CD2v KHVEEIESPPPESNEEEQ 3.25 16mer p72 KLASQKDLVNEFPGLF 3.00 18mer CD2v HVEEIESPPPESNEEEQC 3.25 16mer pp220 SPLQIYKTLLEYLQHS 3.00 18mer CD2v LRKRKKHVEEIESPPPES 3.50 16mer p30 AQEEWNMILHVLFEEE 3.00 18mer p72 KLASQKDLVNEFPGLFVR 3.50 16mer CD2v RKRKKHVEEIESPPPE 3.00 18mer CD2v SLRKRKKHVEEIESPPPE 3.50 16mer CD2v HEPSPREPLLPKPYSR 3.50 18mer pp220 NNPQSSERFEQYGRVFSR 3.75 16mer pp220 AFRTQLEDTRREVNNL 3.50 18mer p72 LASQKDLVNEFPGLFVRQ 3.75 16mer CD2v EIESPPPESNEEEQCQ 4.00 18mer pp220 WKATVSAIELEYDVKRRF 4.00 16mer pp220 WKATVSAIELEYDVKR 4.00 19mer CD2v RKKHVEEIESPPPESNEEE 1.60 16mer pp220 FRTQLEDTRREVNNLI 4.00 19mer CD2v KRKKHVEEIESPPPESNEE 1.80 16mer CD2v SLRKRKKHVEEIESPP 4.00 19mer CD2v RKRKKHVEEIESPPPESNE 2.40 16mer pp220 PLQIYKTLLEYLQHSA 4.00 19mer CD2v KHVEEIESPPPESNEEEQC 2.80 16mer p30 KVIRAHNFIQTIYGTP 4.00 19mer CD2v KKHVEEIESPPPESNEEEQ 3.00 16mer p72 PGTKNAYRNLVYYCEY 4.00 19mer CD2v LRKRKKHVEEIESPPPESN 3.20 16mer p72 ASQKDLVNEFPGLFVR 4.00 19mer p72 KLASQKDLVNEFPGLFVRQ 3.20 16mer pp220 ATVSAIELEYDVKRRF 4.00 19mer CD2v SLRKRKKHVEEIESPPPES 3.40 17mer CD2v KHVEEIESPPPESNEEE 1.00 19mer CD2v HVEEIESPPPESNEEEQCQ 4.00 17mer CD2v KKHVEEIESPPPESNEE 1.33 19mer pp220 WKATVSAIELEYDVKRRFY 4.00 17mer CD2v RKKHVEEIESPPPESNE 2.00 20mer CD2v KRKKHVEEIESPPPESNEEE 1.67 17mer CD2v KRKKHVEEIESPPPESN 2.33 20mer CD2v RKRKKHVEEIESPPPESNEE 2.17 17mer CD2v PYSRYQYNTPIYYMRPS 2.67 20mer CD2v KKHVEEIESPPPESNEEEQC 2.67 17mer CD2v RKRKKHVEEIESPPPES 3.00 20mer CD2v LRKRKKHVEEIESPPPESNE 2.83 17mer CD2v HEPSPREPLLPKPYSRY 3.00 20mer CD2v RKKHVEEIESPPPESNEEEQ 3.00 17mer pp220 SPLQIYKTLLEYLQHSA 3.33 20mer CD2v SLRKRKKHVEEIESPPPESN 3.17 17mer p72 ASQKDLVNEFPGLFVRQ 3.33 20mer CD2v KHVEEIESPPPESNEEEQCQ 3.50 17mer CD2v LRKRKKHVEEIESPPPE 3.67 20mer p72 IKLASQKDLVNEFPGLFVRQ 4.00 Discussion Since ASFV is a complex and lethal multi-antigen virus, it originated in Africa but has recently caused an emerging epidemic in Asia. With advances in computational biology and machine learning in the field of immunology, computational epitope prediction provides a new opportunity to improve ASFV multi-epitope vaccines. Several studies have demonstrated that ASFV enhances or modulates the host immune response through multiple proteins. Therefore, a recombinant multi-epitope vaccine has a potential to be an excellent ASFV vaccine. In this work, we have analyzed five proteins: CD2v, p30, p54, p72, and pp220. Variation in MHC polymorphisms would induce different immune responses (Opriessnig et al. 2021 ), which plays an important role in identifying potential epitopes. Human epitopes have been widely used to build prediction models. However, very few swine epitopes and prediction models are available. To identify potential epitopes for swine vaccine, PFAS used state-of-the-art predictors with promising parameter setting to calculate T and B cell scores. Even when experimentally validated porcine epitopes were used for parameter validation, cross-species prediction models may still reduce prediction accuracy. Although both the T and B cell immunogenicity are important, there must be a trade-off in identifying promising epitopes for conventional prediction methods. Therefore, the Pareto front method was proposed to cope with the bi-objective problem by converting both the T and B cell scores of a fragment into a single Pareto rank that vaccine designers can easily determine the number of promising epitopes for biological experiments. The validation of ASFV recombinant multi-epitope vaccines reported suggests that the Pareto front method would be a potentially useful approach to identifying promising epitopes in the design of multi-epitope vaccines against ASFV. Because ASFV has multiple antigens and complex immune interactions with the host immune system, ASFV recombinant multi-epitope vaccines require a multi-epitope combination. Therefore, we analyzed the protein features of the top-ranked epitopes in five potential proteins, and some results were consistent to those of the biological studies. Some studies have shown that CD2v exhibits serological specificity, participates in immune evasion, enhances viral replication, and damages lymphocyte functions (Sanna et al. 2017 ). In this work, we observed that CD2v had the highest average rank of extended fragments and the highest proportion (n = 46) of the top 72 epitopes. These results are consistent to the findings of existing studies revealing that CD2v plays an important role in activating adaptive host immune responses (Jia et al. 2017 ). CD2v may be a potential protein candidate in the ASFV vaccine due to its high ranking. p30, a phosphoprotein involved in ASFV entry, is synthesized in the early phase and continues to be synthesized during the late phase of viral infection. Although p30 is an antigenic and conserved structural protein, the immune response triggered by p30 alone is insufficient for antibody-mediated protection. However, combining it with other proteins, such as hemagglutinin, can increase humoral and cellular responses (Argilaguet et al. 2012 ). In this work, p30 had the second highest average rank, and p30 had two of the top 72 epitopes. It appears that p30 can be an important part of multi-epitope vaccine. p54 is important for the recruitment of envelope precursors to assembly factories and induces apoptosis during the early phase of infection (Hernaez et al. 2004 ; Rodriguez et al. 1996 ). p54 is an antigenic structural protein that induces the production of specific antibodies. However, protein variability analysis demonstrated that there is a highly variable region in the C-terminus of the p54 protein. Accordingly, p54 had the lowest average rank among the five proteins in this work, and these show that the p54 epitope selection may depend on the target swine species and predominant MHC type. p72 is conserved and essential for viral icosahedron formation during viral infection (Cobbold and Wileman 1998 ); therefore, p72 has the characteristics of high antigenicity and immunogenicity and is enriched and assembled in the ER during late-stage expression of infection. This study (Neilan et al. 2004 ) showed that p72 may produce high levels of p72-specific IgG antibodies, but there exists partial protection when using p72 epitopes alone. In this work, p72 had the third highest Pareto front and 10 p72 epitopes were selected from the top 72 epitopes, the data show that p72 may increase antibody production in multi-epitope vaccine. The ASFV polyprotein precursor pp220 is highly conserved in the viral genome, and pp220 is cleaved by proteases to produce the mature virion proteins p150, p37, p14, and p34, which account for approximately 30% of the total viral protein mass and play an important role in the assembly process of the viral capsids and viral infection (Andres et al. 2002 ). In this work, pp220 had seven epitopes in the top three fronts, and pp220 had the second highest proportion (n = 14) among the top 72 promising epitopes. These results indicate that pp220 can be an important component of multi-epitope vaccine. However, in the swine computational studies, the prediction models were trained mainly using human datasets and small amounts of animal data. In addition, the percentage of immune cell populations and the function of T cells differ between pigs and humans (Gerner et al. 2015 ; Rubic-Schneider et al. 2016 ), and variations in MHC polymorphisms induce different immune responses (Opriessnig et al. 2021 ). For computational prediction, identifying individual predictors is important to improve swine epitope prediction. Although cross-species epitope prediction increases the uncertainty of the results, this study has demonstrated a relationship between Pareto rank and swine survival days based on the Pareto front approach. These findings support the hypothesis that accurate predictors with the Pareto front method may reduce vaccine development time and costs when applied to human vaccine development. In this study, we use the Pareto front method to consider T and B cell immunogenicity simultaneously and ranked the epitopes with Pareto ranks. This procedure involved state-of-the-art computational methods and confirmed parameters. In addition, the evaluation of the experimental epitope ranks for the vaccination study had a significant coefficient of determination, demonstrating that the Pareto front method has effective screening efficiency. Finally, promising epitopes based on fragment extension and the peptide sequences with Pareto ranks were provided for biological experimental verification and confirmation. Overall, our study has proposed a computational prediction method based on the Pareto front method, provides Pareto rank of all fragments, promising epitopes, and may contribute to the development of recombinant multi-epitope vaccines for ASFV. The method may be used for human or cross-species promising epitope identification. Declarations Compliance with Ethical Standards This article does not contain any studies with animals performed by any of the authors. Data availability statements The data that download from prediction tools are available from the corresponding author upon request. The MATLAB codes of the Pareto front method are available at https://github.com/NYCU-ICLAB/PFAS . The validated epitopes and experimental results are available at supplementary information. The promising epitopes are available at supplementary information. Declaration of competing interest Pei-Yin Wue and Chia-Jung Chang are employed by the Reber Genetics Co. Funding The work was supported by grants from National Science and Technology Council, Taiwan (110-2221-E-A49-099-MY3, 112-2740-B-400-005-), and was financially supported by the “Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B)” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Authors' contributions TC, CC, SH, and PW conceived the study. TC and SH designed the experiments. TC, YH and FK performed the experiments and analyzed the data. CC and PW performed the formal analysis and technical assistance. TC drafted the original manuscript. SH and CC validated, supervised, and proof-read the manuscript. All authors read and approved the final manuscript. Acknowledgements We would like to thank National Core Facility for Biopharmaceuticals (NCFB, 111-2740-B-492-001) and National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs) of Taiwan for providing computational resources and storage resources. References Adamczyk-Poplawska M, Markowicz S, Jagusztyn-Krynicka EK (2011) Proteomics for development of vaccine. J Proteomics 74(12):2596-616 doi:10.1016/j.jprot.2011.01.019 Alejo A, Matamoros T, Guerra M, Andres G (2018) A Proteomic Atlas of the African Swine Fever Virus Particle. J Virol 92(23) doi:10.1128/JVI.01293-18 Andres G, Alejo A, Salas J, Salas ML (2002) African swine fever virus polyproteins pp220 and pp62 assemble into the core shell. J Virol 76(24):12473-82 doi:10.1128/jvi.76.24.12473-12482.2002 Argilaguet JM, Perez-Martin E, Nofrarias M, Gallardo C, Accensi F, Lacasta A, Mora M, Ballester M, Galindo-Cardiel I, Lopez-Soria S, Escribano JM, Reche PA, Rodriguez F (2012) DNA vaccination partially protects against African swine fever virus lethal challenge in the absence of antibodies. PLoS One 7(9):e40942 doi:10.1371/journal.pone.0040942 Bandrick M, Gutierrez AH, Desai P, Rincon G, Martin WD, Terry FE, De Groot AS, Foss DL (2020) T cell epitope content comparison (EpiCC) analysis demonstrates a bivalent PCV2 vaccine has greater T cell epitope overlap with field strains than monovalent PCV2 vaccines. Vet Immunol Immunopathol 223:110034 doi:10.1016/j.vetimm.2020.110034 Baratelli M, Morgan S, Hemmink JD, Reid E, Carr BV, Lefevre E, Montaner-Tarbes S, Charleston B, Fraile L, Tchilian E, Montoya M (2020) Identification of a Newly Conserved SLA-II Epitope in a Structural Protein of Swine Influenza Virus. Front Immunol 11:2083 doi:10.3389/fimmu.2020.02083 Bhasin M, Raghava GP (2004) Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13(3):596-607 doi:10.1110/ps.03373104 Blome S, Franzke K, Beer M (2020) African swine fever - A review of current knowledge. Virus Res 287:198099 doi:10.1016/j.virusres.2020.198099 Bosch-Camos L, Lopez E, Navas MJ, Pina-Pedrero S, Accensi F, Correa-Fiz F, Park C, Carrascal M, Dominguez J, Salas ML, Nikolin V, Collado J, Rodriguez F (2021) Identification of Promiscuous African Swine Fever Virus T-Cell Determinants Using a Multiple Technical Approach. Vaccines (Basel) 9(1) doi:10.3390/vaccines9010029 Bosch-Camos L, Lopez E, Rodriguez F (2020) African swine fever vaccines: a promising work still in progress. Porcine Health Manag 6:17 doi:10.1186/s40813-020-00154-2 Burmakina G, Malogolovkin A, Tulman ER, Xu W, Delhon G, Kolbasov D, Rock DL (2019) Identification of T-cell epitopes in African swine fever virus CD2v and C-type lectin proteins. J Gen Virol 100(2):259-265 doi:10.1099/jgv.0.001195 Calis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, Kesmir C, Peters B (2013) Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 9(10):e1003266 doi:10.1371/journal.pcbi.1003266 Clem AS (2011) Fundamentals of vaccine immunology. J Glob Infect Dis 3(1):73-8 doi:10.4103/0974-777X.77299 Cobbold C, Wileman T (1998) The major structural protein of African swine fever virus, p73, is packaged into large structures, indicative of viral capsid or matrix precursors, on the endoplasmic reticulum. J Virol 72(6):5215-23 doi:10.1128/JVI.72.6.5215-5223.1998 Dorigatti E, Schubert B (2020) Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLoS Comput Biol 16(10):e1008237 doi:10.1371/journal.pcbi.1008237 Fan S, Wang Y, Wang X, Huang L, Zhang Y, Liu X, Zhu W (2018) Analysis of the affinity of influenza A virus protein epitopes for swine MHC I by a modified in vitro refolding method indicated cross-reactivity between swine and human MHC I specificities. Immunogenetics 70(10):671-680 doi:10.1007/s00251-018-1070-6 Gao Z, Shao JJ, Zhang GL, Ge SD, Chang YY, Xiao L, Chang HY (2021) Development of an indirect ELISA to specifically detect antibodies against African swine fever virus: bioinformatics approaches. Virol J 18(1):97 doi:10.1186/s12985-021-01568-2 Garcia-Boronat M, Diez-Rivero CM, Reinherz EL, Reche PA (2008) PVS: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery. Nucleic Acids Res 36(Web Server issue):W35-41 doi:10.1093/nar/gkn211 Gaudreault NN, Richt JA (2019) Subunit Vaccine Approaches for African Swine Fever Virus. Vaccines (Basel) 7(2) doi:10.3390/vaccines7020056 Gerner W, Talker SC, Koinig HC, Sedlak C, Mair KH, Saalmuller A (2015) Phenotypic and functional differentiation of porcine alphabeta T cells: current knowledge and available tools. Mol Immunol 66(1):3-13 doi:10.1016/j.molimm.2014.10.025 Gomez-Puertas P, Rodriguez F, Oviedo JM, Brun A, Alonso C, Escribano JM (1998) The African swine fever virus proteins p54 and p30 are involved in two distinct steps of virus attachment and both contribute to the antibody-mediated protective immune response. Virology 243(2):461-71 doi:10.1006/viro.1998.9068 Gomez-Puertas P, Rodriguez F, Oviedo JM, Ramiro-Ibanez F, Ruiz-Gonzalvo F, Alonso C, Escribano JM (1996) Neutralizing antibodies to different proteins of African swine fever virus inhibit both virus attachment and internalization. J Virol 70(8):5689-94 doi:10.1128/JVI.70.8.5689-5694.1996 Guo F, Tang Y, Zhang W, Yuan H, Xiang J, Teng W, Lei A, Li R, Dai G (2022) DnaJ, a promising vaccine candidate against Ureaplasma urealyticum infection. Appl Microbiol Biotechnol 106(22):7643-7659 doi:10.1007/s00253-022-12230-4 Gupta S, Ansari HR, Gautam A, Open Source Drug Discovery C, Raghava GP (2013) Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol Direct 8:27 doi:10.1186/1745-6150-8-27 Hajialibeigi A, Amani J, Gargari SLM (2021) Identification and evaluation of novel vaccine candidates against Shigella flexneri through reverse vaccinology approach. Appl Microbiol Biotechnol 105(3):1159-1173 doi:10.1007/s00253-020-11054-4 Hernaez B, Diaz-Gil G, Garcia-Gallo M, Ignacio Quetglas J, Rodriguez-Crespo I, Dixon L, Escribano JM, Alonso C (2004) The African swine fever virus dynein-binding protein p54 induces infected cell apoptosis. FEBS Lett 569(1-3):224-8 doi:10.1016/j.febslet.2004.06.001 Ivanov V, Efremov EE, Novikov BV, Balyshev VM, Tsibanov S, Kalinovsky T, Kolbasov DV, Niedzwiecki A, Rath M (2011) Vaccination with viral protein-mimicking peptides postpones mortality in domestic pigs infected by African swine fever virus. Molecular medicine reports 4(3):395-401 doi:10.3892/mmr.2011.454 Jach ME, Baj T, Juda M, Swider R, Mickowska B, Malm A (2020) Statistical evaluation of growth parameters in biofuel waste as a culture medium for improved production of single cell protein and amino acids by Yarrowia lipolytica. AMB Express 10(1):35 doi:10.1186/s13568-020-00968-x Jia N, Ou Y, Pejsak Z, Zhang Y, Zhang J (2017) Roles of African Swine Fever Virus Structural Proteins in Viral Infection. J Vet Res 61(2):135-143 doi:10.1515/jvetres-2017-0017 Kessler C, Forth JH, Keil GM, Mettenleiter TC, Blome S, Karger A (2018) The intracellular proteome of African swine fever virus. Sci Rep 8(1):14714 doi:10.1038/s41598-018-32985-z Kibria KMK, Faruque MO, Islam MSB, Ullah H, Mahmud S, Miah M, Saleh AA (2022) A conserved subunit vaccine designed against SARS-CoV-2 variants showed evidence in neutralizing the virus. Appl Microbiol Biotechnol 106(11):4091-4114 doi:10.1007/s00253-022-11988-x Larsen MV, 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 doi:10.1186/1471-2105-8-424 Liu Q, Ma B, Qian N, Zhang F, Tan X, Lei J, Xiang Y (2019) Structure of the African swine fever virus major capsid protein p72. Cell Res 29(11):953-955 doi:10.1038/s41422-019-0232-x Lokhandwala S, Petrovan V, Popescu L, Sangewar N, Elijah C, Stoian A, Olcha M, Ennen L, Bray J, Bishop RP, Waghela SD, Sheahan M, Rowland RRR, Mwangi W (2019) Adenovirus-vectored African Swine Fever Virus antigen cocktails are immunogenic but not protective against intranasal challenge with Georgia 2007/1 isolate. Vet Microbiol 235:10-20 doi:10.1016/j.vetmic.2019.06.006 Lopera-Madrid J, Osorio JE, He Y, Xiang Z, Adams LG, Laughlin RC, Mwangi W, Subramanya S, Neilan J, Brake D, Burrage TG, Brown WC, Clavijo A, Bounpheng MA (2017) Safety and immunogenicity of mammalian cell derived and Modified Vaccinia Ankara vectored African swine fever subunit antigens in swine. Vet Immunol Immunopathol 185:20-33 doi:10.1016/j.vetimm.2017.01.004 Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G (2018) iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction. Front Immunol 9:1695 doi:10.3389/fimmu.2018.01695 Masoudi-Sobhanzadeh Y, Jafari B, Parvizpour S, Pourseif MM, Omidi Y (2021) A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset. Comput Biol Med 138:104896 doi:10.1016/j.compbiomed.2021.104896 Neilan JG, Zsak L, Lu Z, Burrage TG, Kutish GF, Rock DL (2004) Neutralizing antibodies to African swine fever virus proteins p30, p54, and p72 are not sufficient for antibody-mediated protection. Virology 319(2):337-42 doi:10.1016/j.virol.2003.11.011 Opriessnig T, Mattei AA, Karuppannan AK, Halbur PG (2021) Future perspectives on swine viral vaccines: where are we headed? Porcine Health Manag 7(1):1 doi:10.1186/s40813-020-00179-7 Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M (2020) NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 48(W1):W449-W454 doi:10.1093/nar/gkaa379 Rodriguez F, Ley V, Gomez-Puertas P, Garcia R, Rodriguez JF, Escribano JM (1996) The structural protein p54 is essential for African swine fever virus viability. Virus Res 40(2):161-7 doi:10.1016/0168-1702(95)01268-0 Ros-Lucas A, Correa-Fiz F, Bosch-Camos L, Rodriguez F, Alonso-Padilla J (2020) Computational Analysis of African Swine Fever Virus Protein Space for the Design of an Epitope-Based Vaccine Ensemble. Pathogens 9(12) doi:10.3390/pathogens9121078 Rubic-Schneider T, Christen B, Brees D, Kammuller M (2016) Minipigs in Translational Immunosafety Sciences: A Perspective. Toxicol Pathol 44(3):315-24 doi:10.1177/0192623315621628 Saha S, Raghava GP (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40-8 doi:10.1002/prot.21078 Sanna G, Dei Giudici S, Bacciu D, Angioi PP, Giammarioli M, De Mia GM, Oggiano A (2017) Improved Strategy for Molecular Characterization of African Swine Fever Viruses from Sardinia, Based on Analysis of p30, CD2V and I73R/I329L Variable Regions. Transbound Emerg Dis 64(4):1280-1286 doi:10.1111/tbed.12504 Singh H, Ansari HR, Raghava GP (2013) Improved method for linear B-cell epitope prediction using antigen's primary sequence. PLoS One 8(5):e62216 doi:10.1371/journal.pone.0062216 Teklue T, Sun Y, Abid M, Luo Y, Qiu HJ (2020) Current status and evolving approaches to African swine fever vaccine development. Transbound Emerg Dis 67(2):529-542 doi:10.1111/tbed.13364 Yu M, Morrissy CJ, Westbury HA (1996) Strong sequence conservation of African swine fever virus p72 protein provides the molecular basis for its antigenic stability. Arch Virol 141(9):1795-802 doi:10.1007/BF01718302 Supplementary Files GraphAbstract.tif SupplementaryFigures.pdf SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 31 Aug, 2024 Read the published version in AMB Express → Version 1 posted Editorial decision: Minor Revision 21 Jul, 2024 Reviewers agreed at journal 24 Jan, 2024 Reviewers invited by journal 28 Dec, 2023 Editor assigned by journal 24 Dec, 2023 First submitted to journal 19 Dec, 2023 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-3784481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264226415,"identity":"fade2dfd-cd5b-49e4-8e68-ae1b9ce87329","order_by":0,"name":"Ting-Yu Chen","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Ting-Yu","middleName":"","lastName":"Chen","suffix":""},{"id":264226416,"identity":"5b75dcfe-b4d2-486b-b89f-1f29aacd063a","order_by":1,"name":"Yann-Jen Ho","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Yann-Jen","middleName":"","lastName":"Ho","suffix":""},{"id":264226417,"identity":"0b768be5-acc5-464b-95aa-8ae642f3b2c6","order_by":2,"name":"Fang-Yu Ko","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Fang-Yu","middleName":"","lastName":"Ko","suffix":""},{"id":264226418,"identity":"675a47f3-947d-4feb-b5ec-310731dc7145","order_by":3,"name":"Pei-Yin Wu","email":"","orcid":"","institution":"Reber genetics Co.","correspondingAuthor":false,"prefix":"","firstName":"Pei-Yin","middleName":"","lastName":"Wu","suffix":""},{"id":264226419,"identity":"fac9379d-8626-44ce-a9b7-fccc8b3694da","order_by":4,"name":"Chia-Jung Chang","email":"","orcid":"","institution":"Reber Genetics Co","correspondingAuthor":false,"prefix":"","firstName":"Chia-Jung","middleName":"","lastName":"Chang","suffix":""},{"id":264226420,"identity":"698dbc6e-8f4f-4af3-b41c-d602cb174ac9","order_by":5,"name":"Shinn-Ying Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFACHgaGD1CmBNFaGGcgtBgQp4WZhyQt8jNyDz623XFY3uAA88HbPAx/EhsIaTG4kZdsnHvmsOGGA2zJ1jwMBkRokcgxk85tO5xgcIDHTBqoJZegFvkZQC2WYC3834jTwnADqIURYgsbcVoMzrwxNuxtSzeceZjN2HKOgXE9YYe15xg++NlmLc93vPnhjTcVcsYE3cUgkAAimxkYmMGWEtbAwMB/AETWEaN0FIyCUTAKRioAAA8AN4N47Cw+AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1901-8353","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":true,"prefix":"","firstName":"Shinn-Ying","middleName":"","lastName":"Ho","suffix":""}],"badges":[],"createdAt":"2023-12-21 03:07:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3784481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3784481/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13568-024-01749-6","type":"published","date":"2024-08-31T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49027566,"identity":"c5babe0a-c602-4ba0-91f0-5683879066b3","added_by":"auto","created_at":"2024-01-01 14:13:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1049207,"visible":true,"origin":"","legend":"\u003cp\u003ePresentation and activation of ASFV epitopes, and the correspondence between biological and computational processes. (A) Illustration of the biological processing and presentation of the ASFV epitope. When infected by ASFV, four processing stages activate T cells in APCs and epitope presentation activates B cells in helper T cells. (B) The computational procedure of the Pareto front method. The input is a set of protein sequences. The outputs are prediction scores of T and B cell immunogenicity. The T cell score is the mean score of four stages: 1) proteasomal cleavage probability, 2) peptide-TAP transporter binding affinity, 3) peptide-MHC I binding affinity, and 4) CTL immunogenicity. CTL: Cytotoxic. T lymphocyte. APC: Antigen-presenting cell. TAP: the transporter associated with antigen processing. Images of figure were partly taken from ‘Smart Servier Medical Art’ (https://smart.servier.com/).\u003c/p\u003e","description":"","filename":"graphicsFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/6875a3410159019cdf52f0b2.png"},{"id":49027562,"identity":"be1a2709-fd43-4268-9d87-40c95712ed4f","added_by":"auto","created_at":"2024-01-01 14:13:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":732360,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of PFAS. This flowchart includes three main parts: epitope prediction of T and B cell fragments, Pareto rank of fragments, and promising epitopes of multi-epitope vaccines.\u003c/p\u003e","description":"","filename":"graphicsFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/a9d740e82fc96ce8f697b5d6.png"},{"id":49027560,"identity":"588807ac-94c3-44cb-9c63-79d9a9b5b076","added_by":"auto","created_at":"2024-01-01 14:13:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1782338,"visible":true,"origin":"","legend":"\u003cp\u003eEpitope profiles of the five ASFV proteins. Each 15mer fragment has an average Pareto rank represented by the central amino acid. The higher Pareto rank indicates greater epitope potential. The light pink background indicates the epitope hotspot. (A) CD2v. (B) p30. (C) p54. (D) p72. (E) pp220. ID, identification.\u003c/p\u003e","description":"","filename":"graphicsFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/edb798fad550aae2268a08f5.png"},{"id":49027561,"identity":"519d6842-5e26-4ebc-b028-640d3add8c7d","added_by":"auto","created_at":"2024-01-01 14:13:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1313087,"visible":true,"origin":"","legend":"\u003cp\u003eScatter points of experimental, predicted and selected epitopes in the top 30% of the fragments using PFAS. (A)Experimental epitopes. (B) Predicted epitopes.\u003c/p\u003e","description":"","filename":"graphicsFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/00d4483b867cf5f7e041364c.png"},{"id":49027559,"identity":"8a114116-1370-4ac7-a039-a9badb098da2","added_by":"auto","created_at":"2024-01-01 14:13:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":199019,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between Pareto ranks and swine survival days. Combinations 1, 2, and 3 contain four, six, and two epitopes, respectively. The vaccination experiments of combinations 1, 2, and 3 were performed in triplicate, quadruplicate, and triplicate, respectively. There was an R\u003csup\u003e2\u003c/sup\u003e = 0.95 between mean ranks of the recombinant vaccines and swine survival days. Results are presented using mean ± SD.\u003c/p\u003e","description":"","filename":"graphicsFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/6c59210e3712531252291ffd.png"},{"id":63821059,"identity":"585291e9-d877-4fd0-a741-52f45e67cbe2","added_by":"auto","created_at":"2024-09-02 16:11:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6263650,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/621ef412-be0f-45f7-a8da-d4dc7b82828f.pdf"},{"id":49027661,"identity":"23d7ee0f-4d8d-45ba-abd3-5d4ce8a21688","added_by":"auto","created_at":"2024-01-01 14:21:35","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4346968,"visible":true,"origin":"","legend":"","description":"","filename":"GraphAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/0ef736a5816ad2d2b1c7c724.tif"},{"id":49027662,"identity":"66030af7-07b0-4841-958b-c2135725be9d","added_by":"auto","created_at":"2024-01-01 14:21:35","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":414676,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/7dbd09d9c1524a50da7237c5.pdf"},{"id":49027564,"identity":"38a003d9-5171-43e1-a93f-8772fcca7b55","added_by":"auto","created_at":"2024-01-01 14:13:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1244344,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3784481/v1/29b809e45a84c48098ced066.xlsx"}],"financialInterests":"","formattedTitle":"Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity","fulltext":[{"header":"Key Points","content":"\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eProposing a Pareto front-based method for designing swine multi-epitope vaccine.\u003c/li\u003e\n \u003cli\u003eThe method maximizes T and B cell immunogenicity while ranking promising epitopes.\u003c/li\u003e\n \u003cli\u003eHigher the\u0026nbsp;epitope\u0026nbsp;Pareto ranks leads to\u0026nbsp;longer\u0026nbsp;vaccination survival (R\u003csup\u003e2\u003c/sup\u003e = 0.95).\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eAfrican swine fever virus (ASFV) causes a lethal hemorrhagic disease and has become an epidemic swine viral disease in Asia. Simultaneous activation of T and B cells results in a better immune response and more immunological memory against ASFV than activation of one of the cell types alone (Bosch-Camos et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Teklue et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The development of subunit vaccines, especially multi-epitope vaccines, is more challenging than that of live-attenuated virus vaccines. Multi-epitope vaccines have essential applications in non-epidemic areas, increasing research and development requirements (Teklue et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, there is currently no effective multi-epitope vaccine for the prevention of ASFV (Blome et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe selection of protein candidates for designing a multi-epitope vaccine should consider several factors, including the conservation, abundance, extracellular localization, and cross-protection against various viral genotypes (Adamczyk-Poplawska et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Alejo et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kessler et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). CD2v is a hemagglutinin and the main antigen protein involved in regulating immune responses and cell adhesion (Burmakina et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gaudreault and Richt \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jia et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). p30 is a structural protein involved in the attachment and internalization of ASFV (Gomez-Puertas et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). p54 is the only membranous structural protein in the inner viral envelope associated with viral attachment, and a multi-epitope vaccine containing p30 and p54 has shown partial protection (Gomez-Puertas et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). p72 is a major structural protein (approximately 31\u0026ndash;33% of the entire virus) and an important antigenic protein owing to its high conservation and thermostable nature (Liu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). pp220 is the largest multi-precursor protein and presents many peptides to CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T cells (CTLs), triggering a strong antibody response (Lokhandwala et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBioinformatic methods using a machine learning approach serve as an effective strategy to identify vaccine candidates for human (Guo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hajialibeigi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kibria et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and swine pathogens, including ASFV (Gao et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), influenza A virus (Baratelli et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and porcine circovirus type 2 (Bandrick et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Machine learning methods used to identify epitopes in the design of multi-epitope vaccines consider the biological presentation and activation of ASFV epitopes. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the presentation and activation of ASFV epitopes, and the correspondence between biological and computational processes. In viral infections, CTLs (mainly involved in viral T cell immunity) support cell-mediated immunity against intracellular viruses, while B cells trigger humoral immunity and produce memory cells for future infection (Clem \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe objective of vaccine design is to induce immune responses in both T and B cells. Existing computational methods for identifying ASFV epitopes screen potential epitopes by separately considering T and B cell immunogenicity (Bosch-Camos et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lopera-Madrid et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ros-Lucas et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Pareto front is a popular approach used for obtaining a set of non-dominated solutions to a bi-objective problem. Pareto-optimal methods have been used to dock proteins and peptides (Masoudi-Sobhanzadeh et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and improve amino acid and protein production in \u003cem\u003eYarrowia lipolytica\u003c/em\u003e (Jach et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A study related to epitope-based vaccine design against human immunodeficiency virus used the Pareto front to simultaneously optimize cleavage and immunogenicity (Dorigatti and Schubert \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, few studies have used the Pareto front method to simultaneously accommodate T and B cell immunogenicity.\u003c/p\u003e \u003cp\u003eThis work proposes a novel Pareto front-based screening method PFAS to identify promising epitopes with high T and B cell immunogenicity for designing ASFV recombinant multi-epitope vaccines. First, PFAS used experimental T and B cell epitopes from the Immune Epitope Database (IEDB) to verify the state-of-the-art computational methods and their parameter settings. Next, PFAS used the Pareto front technique to deal with T and B cell prediction scores as bi-objective ranks and identify the top-ranked epitopes. PFAS scored whole epitope profiles and identified 72 promising epitopes. Based on the three combinations of epitopes in pp220, p30, p72, and p54 for a vaccination study against ASFV, the determination coefficient of determination between the Pareto ranks of recombinant multi-epitope vaccines and swine survival was R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.95. The identified epitopes can be cost-effectively validated \u003cem\u003ein vitro\u003c/em\u003e to design epitope-based ASFV vaccines.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of ASFV protein sequences\u003c/h2\u003e \u003cp\u003eThe most lethal ASFV type, Georgia 2007/1 (GenBank: FR682468), was used as the target virus for screening. The protein sequences of Georgia 2007/1 were obtained from the NCBI, including CD2v (EP402R), p30 (CP204L), p54 (E183L), p72 (B646L), and pp220 (CP2475L). All sequences were cut into fragments using a sliding window. Finally, two datasets of ASFV proteins consisting of 9mer (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA) and 15mer (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB) fragments served as candidates for CTL and B cell epitopes, respectively.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCollection of validation datasets\u003c/h3\u003e\n\u003cp\u003eTo obtain the best parameter settings for PFAS, this work established two datasets from the IEDB, consisting of experimentally validated CTL and B cell epitopes of swine. The CTL epitopes (n\u0026thinsp;=\u0026thinsp;243) were annotated as Sus scrofa, infectious diseases, and Swine Leukocyte Antigen (SLA) class II. After simultaneously removing duplicate and uncertain sequences belonging to both positive and negative groups, the dataset contained 125 swine 9mer CTL fragments, including 37 epitopes and 88 non-epitopes.\u003c/p\u003e \u003cp\u003eSimilarly, 1,700 validated swine B cell epitopes (BCEs) which were annotated as Sus scrofa and infectious disease were retrieved. Because IgG production is part of the secondary humoral immune response to an antigen, we extracted 1,389 IgG epitopes. Among them, the 15mer epitope was the largest in the dataset, followed by the 12mer epitope. Therefore, we established two datasets: 1) 650 B cell 15mer epitopes, including 116 positive and 534 negative epitopes, and 2) 293 swine B cell 12mer epitopes, including 35 positive and 258 negative epitopes.\u003c/p\u003e\n\u003ch3\u003eProposed method PFAS\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a flowchart of the proposed method PFAS. Five protein sequences from Georgia 2007/1 were obtained and cut into 9mer and 15mer fragments. The CTL epitope predictor estimates T cell activation of 9mer fragments in the four stages and averages the four scores to obtain a T cell immunogenicity score. Similarly, the BCE predictor estimates B cell activation of 15mer fragments to obtain a B cell immunogenicity score. After normalizing these two scores into the range of [0, 1], the two fragments were superimposed by the central amino acid. Consequently, the fragments were extended, and thus conserved sequences were obtained. The Pareto front method produced ranks of the conserved fragments. The top-ranked fragments were considered as promising epitopes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of T and B cell scores\u003c/h2\u003e \u003cp\u003eGood CTL epitopes are involved in viral processing and antigen presentation, with major histocompatibility complex (MHC) I molecules playing a major role. First, pathogen debris is degraded by proteasomal degradation in the cytosol of productively infected cells. NetCTL is based on the NetChop method and predicts the probability of proteasomal cleavage (Larsen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Second, peptides are transported to the endoplasmic reticulum (ER) by a transporter associated with antigen processing (TAP). To predict TAP transport efficiency, NetCTL and MHC I Processing in the IEDB use the stabilized matrix method, and TAPPred is based on a support vector machine (SVM) with 33 physical features of amino acids (Bhasin and Raghava \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Third, an antigen is loaded onto MHC I and appears on the cell surface through vesicles. NetMHCpan (Reynisson et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), MHC I Processing, and NetCTL are the most widely used ANN-based methods to predict MHC I binding affinity using the BLOSUM50 matrix. Finally, the epitope stimulates CTL activation and differentiation. MHC I immunogenicity in the IEDB (Calis et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) is based on an immunogenicity score model to predict immunogenicity. In general, these four predictive roles are equally important.\u003c/p\u003e \u003cp\u003eTo identify promising T cell epitopes (TCEs), five web predictors were used, including NetCTL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/service.php?NetCTL-1.2\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/service.php?NetCTL-1.2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), IEDB MHC I Processing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/processing/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/processing/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), TAPPred (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webs.iiitd.edu.in/raghava/tappred/index.html\u003c/span\u003e\u003cspan address=\"https://webs.iiitd.edu.in/raghava/tappred/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), NetMHCpan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/service.php?NetMHCpan-4.0\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and IEDB MHC I Immunogenicity. NetCTL was used to predict proteasome processing, TAP transport efficiency, and MHC I binding affinity. To examine conserved epitope candidates that cover multiple MHC loci, including A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58 and B62, we used sequences as inputs and applied ensemble learning with 12 supertype models. After averaging all predictive values in the 12 models, we obtained three estimated values: binding affinity, proteasome cleavage, and the TAP score. The IEDB MHC I Processing tool was used to estimate TAP transport efficiency and MHC I binding affinity. We used all 45 SLA I alleles (including 12 SLA1, 16 SLA2, 12 SLA3, and 5 SLA6) and set nine as the peptide length for each allele to obtain a file with the average predictive values, including the TAP and MHC scores in all sequence fragments. PFAS used TAPPred to predict the peptide-TAP transporter binding affinity based on SVM with validated sequences and obtained the prediction score. NetMHCpan was used to predict the binding affinity of peptide-MHC I. To obtain effective epitopes, we considered all 75 SLA alleles (including 23 SLA1, 26 SLA2, 21 SLA3, and five SLA6) and set nine as the peptide length for each allele. The binding affinity scores were estimated with mean scores for all fragments. This work used IEDB MHC I Immunogenicity to predict CTL immunogenicity considering all CTL active factors and obtained scores of all sequence fragments.\u003c/p\u003e \u003cp\u003eBCEs can induce the differentiation of na\u0026iuml;ve and memory B cells into plasma cells, including antigen processing, peptide-MHC II presentation, and cytokine promotion. In studies on BCE presentation, LBtope (Singh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), iBCE-EL (Manavalan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), IgPred (Gupta et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and ABCpred (Saha and Raghava \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) are sequence-based predictors. LBtope uses the sparse matrix and amino acid property profile features and is an SVM-based Weka Classifier using 38,197 IEDB experimental epitopes. iBCE-EL is based on ensemble learning using amino acid composition characteristics and proportions of 5,550 experimentally validated BCEs. IgPred uses 14,725 BCEs in different types of specific epitopes using physicochemical properties (PCPs) features and is based on Weka Classifiers. ABCpred is based on PCP features and the neural network method with a balanced BCE database. Among the aforementioned predictors, LBtope uses the largest dataset with ensemble learning.\u003c/p\u003e \u003cp\u003eTo estimate the B cell immunogenicity score of 15mer and 12mer fragments, five online predictors (LBtope_Variable, LBtope_Confirm, iBCE-EL, IgPred, and ABCpred) were utilized and validated. Epitope probabilities and IgG scores were determined using the iBCE-EL and IgPred prediction tools, respectively. LBtope is based on multiple peptides from prediction models using two variable-length epitope models. The LBtope_Variable model was trained using 38,197 peptides. The LBtope_Confirm model was reported in at least two studies and contained 2,837 peptides. By submitting multiple fragments, the probability of epitopes was obtained along with the physical property score. As ABCpred exclusively accepts an even number of epitope lengths and continuous amino acid sequences as submissions, PFAS used only one 12mer dataset with parameters containing a threshold of zero and an overlapping filter to obtain the predicted scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmunogenicity prediction of T and B cell fragments\u003c/h2\u003e \u003cp\u003eThe CTL activation prediction has four important stages: proteasomal cleavage probability, TAP transport efficiency, MHC I binding affinity, and CTL immunogenicity. These predictions help identify potential TCE candidates. PFAS combined all the prediction values obtained from the online prediction tools in the four stages. The probability of proteasomal cleavage was estimated using NetCTL1.2. The TAP transport efficiency score is the mean score of NetCTL1.2, IEDB MHC I Processing, and TAPPred values. The peptide-MHCI binding affinity score is the mean score of NetCTL1.2, IEDB MHC I Processing, and NetMHCpan predictive values. The CTL immunogenicity score is obtained using IEDB MHC I Immunogenicity. After combining and normalizing the scores of each category using a combination of weights, PFAS compiled four stage score for the TCE prediction.\u003c/p\u003e \u003cp\u003eFor the BCE prediction, the best predictor was evaluated and used to obtain B cell immunogenicity scores. After compiling the results of the prediction values from the web tools, the output values were normalized into the range of [0, 1] and B cell immunogenicity scores were compiled.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePareto rank of fragments\u003c/h2\u003e \u003cp\u003eThe Pareto front is the set of all efficient solutions to bi-objective problems. In this study, a fragment \u003cem\u003eFrag\u003c/em\u003e belonging to the Pareto front means that no other fragment has both larger T and B cell scores than \u003cem\u003eFrag\u003c/em\u003e. The T and B cell scores of all fragments which were represented by their central amino acids were used as inputs of the Pareto front method to determine the Pareto rank of fragments. The Pareto front method iteratively removes the Pareto fronts, and Pareto rank of the fragments was the serial number of the removed front. For instance, the segments belonging to the initial Pareto front have a rank one. After removing the Pareto front, the fragments belonging to the new Pareto front have a rank two, and so on.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePromising epitopes of the multi-epitope vaccine\u003c/h2\u003e \u003cp\u003eThis work extended the fragments to a length of 16\u0026ndash;20 amino acids and obtained the epitope profiles with the average Pareto rank of the extended fragments. The average rank was defined as the sum of the Pareto ranks divided by the total number of fragments included in the extended fragments. Moreover, to select conserved epitopes, PFAS estimated protein variability using the Protein Variability Server (PVS) (Garcia-Boronat et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). PVS contains three methods, the Shannon entropy, the Simpson diversity index, and the Wu\u0026ndash;Kabat variability coefficient method, which can be used as indicators of variability. In this study, the Shannon entropy greater than two was considered as the variability point. Accordingly, PFAS removed the variable fragments that contained highly variable sequences. Finally, in the 16mer to 20mer epitope profiles, PFAS ranked conserved fragments according to the average Pareto rank, and the top-ranked promising epitopes were provided to the biological decision makers for \u003cem\u003ein vitro\u003c/em\u003e validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of T and B cell immunogenicity scores\u003c/h2\u003e \u003cp\u003eAfter T cell online prediction, the T cell score was the weighted sum of four scores in the four stages: proteasomal cleavage probability, TAP transport efficiency, MHC I binding affinity, and CTL immunogenicity scores. For each stage, the scores were normalized and averaged. To validate the weights of four scores, the experimentally validated swine 9mer CTL epitopes from the IEDB were used. Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA shows a good performance with an area under the receiver operating characteristic curve (AUC) of 0.71, and the largest AUC was reached when using the top 30% epitopes (Figure S3). The set of four equal weights 1/4, 1/4, 1/4, and 1/4 in determining the T cell score is the most stable one. This result is consistent to the previous hypothesis that the importance of the four stages in the swine immunogenicity prediction is equal.\u003c/p\u003e \u003cp\u003eIn B cell immunogenicity prediction, previous studies have revealed that the use of sequence-based predictors, such as LBtope (Singh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), iBCE-EL (Manavalan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), IgPred (Gupta et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and ABCpred (Saha and Raghava \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) is an efficient approach to identifying BCEs. Owing to different aims of the training dataset and machine learning approaches, the prediction results were different from these methods. Therefore, PFAS used LBtope, the largest experimental dataset with ensemble learning, as a prediction model to identify BCEs. Experimentally validated swine 15mer and 12mer BCEs were used to validate this hypothesis, the validation pipeline consistent with that used for TCE prediction. Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB shows performance of the four methods: iBCE-EL, IgPred, LBtope with a variable dataset, and LBtope with a confirmed dataset. LBtope with the variable dataset achieved an AUC of 0.86. When using the top-ranked 30% epitopes, LBtope with a confirmed dataset was better than the other methods (Figure S4\u0026ndash;5). These results were in good agreement with the hypothesis, showing that LBtope is an appropriate method for predicting ASFV BCEs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEpitopes identification using the Pareto front method\u003c/h2\u003e \u003cp\u003eGiven that both T and B cell activation are equally important for mobilizing adaptive immunity, we applied the Pareto front method to identify potential epitopes. To rank and identify fragments simultaneously, the Pareto front method iteratively determined 116 Pareto ranks (Figure S6). T and B cell scores in the bi-objective problem were converted into Pareto ranks to identify epitope candidates.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows 15mer epitope profiles for the five ASFV proteins. A higher average Pareto rank indicates a more promising epitope. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shows the results of the selected fragments in the five proteins, including 346 fragments of CD2v, 187 fragments of p30, 170 fragments of p54, 632 fragments of p72, and 2462 fragments of pp220. In short, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the Pareto ranks of the top three fronts. The best protein is CD2v, which has the most selected and continuous fragments in the rank one Pareto front.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFragments of the top three fronts using PFAS. Scores are normalized into the range of [0, 1]. ID, identification of fragments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFront\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFragment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT cell score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB cell score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eRank 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKHVEEIESPPPESNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHVEEIESPPPESNEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVEEIESPPPESNEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEIESPPPESNEEEQC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYSRYQYNTPIYYMRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKLASQKDLVNEFPGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKLMSALKWPIEYMFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWKATVSAIELEYDVK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTVSAIELEYDVKRRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFRTQLEDTRREVNNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eRank 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTYQVVWNQIINYTIK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFVKYTNESILEYNWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKRKKHVEEIESPPPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKKHVEEIESPPPESN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePSPREPLLPKPYSRY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAQKTVQHIEQYGKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTKNAYRNLVYYCEY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSQKDLVNEFPGLFVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQKDLVNEFPGLFVRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPLQIYKTLLEYLQH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQSSERFEQYGRVFSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eRank 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLRKRKKHVEEIESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRKKHVEEIESPPPES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIHEPSPREPLLPKP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEPSPREPLLPKPYSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRYQYNTPIYYMRPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVKSARIYAGQGYTEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAQEEWNMILHVLFEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQEEWNMILHVLFEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIRAHNFIQTIYGTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSEFFQPVYPRHYGEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGNKLTFGIPQYGDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSKFCSSYIPFHYGGN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNNPQSSERFEQYGRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePQSSERFEQYGRVFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of screening efficiency\u003c/h2\u003e \u003cp\u003eTo evaluate the screening efficiency of PFAS, two validation datasets were used consisting of 30 experimentally validated and 34 predicted epitopes of T or B cells annotated in previous studies and the IEDB database (Bosch-Camos et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ivanov et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ros-Lucas et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PFAS selected the top 30% fragments as promising epitopes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the public epitopes with the Pareto ranks. PFAS identified 17 epitopes from 30 experimental ones and 24 epitopes from 34 predicted ones. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e revealed scatter points of experimental, predicted and PFAS selected epitopes. Since animal studies can prove the actual antigenicity and immunogenicity of epitopes, the top-ranked epitopes may be superior to the published epitopes. Three recombinant multi-epitope vaccines with synthesized epitope groups were used to determine the determination coefficient between the predicted ranks of PFAS and swine immunization (Ivanov et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The combinations 1, 2, and 3 consisting of four pp220 epitopes, six p30 and p72 epitopes, and two p54 epitopes, respectively. The Pareto ranks of peptides in each combination were determined using the Pareto front method. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates a significant coefficient of determination with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.95 between the mean Pareto ranks of the recombinant vaccine and swine survival days in a vaccination study. The higher the combination Pareto rank, the longer the survival days of the pig (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These results reveal that PFAS is an efficient approach to epitope identification.\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\u003eThe Pareto rank of the epitopes in the top 30% fragments for the experimentally validated and predicted epitopes. The rank of the evaluated epitope was the highest Pareto rank in the selected epitopes. There were 17 and 24 selected epitopes in the 30 experimentally validated and 34 predicted epitopes of T or B cells.\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\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExperimental epitope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePredicted epitope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eKPCPPPKPCPPPKPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCTYLTLSSNYFYTFFKLYYIPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePPKPCPPPKPCPPPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSQVVFHAGSLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYSPPKPLPSIPLLPN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAQEEWNMIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSPPKPLPSIPLLPNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYTHKDLENSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePPKPLPSIPLLPNIPPLSTQNISLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAIEEEDIQFINPYQD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEVIFKTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKPYVPVGFEY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTSSFETLFEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGFEYNKVRPHTGTPTLGNKLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTVQHIEQYGKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQMGAHGQLQTFPRNGYDWDNQTPLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eQHIEQYGKAPDFNKV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNVRFDVNGNSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLKEEEKEVVRLMVIKLLKKNKL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYCEYPGERLYENVRFDVNGNSLDEYSSDVTTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMDSEFFQPVYPRHYGECLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHKPHQSKPILTDENDTQRTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFQPVYPRHYGECLSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFPENSHNIQTAGKQD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eQPVYPRHYGECLSPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHTNPKFLSQHFPENSHNIQTAGKQDITPITD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePVYPRHYGECLSPVT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRPSRRNIRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVYPRHYGECLSPVTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTWNISDQNPHQHRDWHK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYPRHYGECLSPVTTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVTHTNNNHHDEKLMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePRHYGECLSPVTTPSFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSFQDRDTALPDACSSISDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYGECLSPVTTPSFFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLLQNGSAVLRYST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGECLSPVTTPSFFST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNKALQKVGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSRKKKAAAAIEEEDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSQVDLNQAINTFMYYYYVAQIY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNKPVTDNPVTDRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHNKQEFQSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYCEYPGERLYENVRFDVNGNSLDEYSSDVTTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eITKTFVNNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLCNIHDLHKPHQSKPILTDENDTQRTCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDNAPAGHYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eQKDLVNEFPGLFIRQSRFIPGRPSRRNIRFKP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTPEEAAQRVY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eACSSISDISPVTYPITLPIIKNISVTAHGINLIDK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVNDALSTRW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLKPREEYQPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAAKIFIVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYDSCSRLLQIIDFYTDIVQKKYGGGEDCECTRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEFYQKLFSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePKGQTRTLGSNRERERI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eARTMNDFGM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGYMSRIFRGDNALNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNRSNPGSFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYMSRIFRGDNALNMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIQNNRSMMMVFNQLIASYITRFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIPIYLKENY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYMSRYNKEPLMPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRERERIFNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYINQALHEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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 \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of promising epitopes for multi-epitope vaccines\u003c/h2\u003e \u003cp\u003eClustering epitopes into hotspots (high-ranked epitopes) would be an effective method to obtain vaccine candidates in the multi-epitope vaccine design. The results shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are consistent to previous studies in which highly-ranked sequences were continuous. Accordingly, PFAS extended the fragments to sequences of 16\u0026ndash;20 amino acids and produced epitope profiles with the average Pareto ranks of extended fragments (Figure S7\u0026ndash;11). The higher the front rank, the greater potential of the epitope. Enrichment of potential epitopes is considered as an epitope hotspot.\u003c/p\u003e \u003cp\u003eAdditionally, epitope variability is important for biologists in identifying vaccine candidates. To obtain conserved epitopes, PFAS calculated the variability of the five proteins using PVS (Table S3). A total of 45 sites were identified, including two CD2v sites and 43 p54 sites, which can be regarded as sites with high variability because their Shannon entropy was greater than two (Figure S12). Similarly, if a fragment contained highly variable sites, it was regarded as a highly variable region. After removing 69 highly variable fragments (Table S4), 3728 fragments were obtained, including 341 CD2v fragments, 187 p30 fragments, 106 p54 fragments, 632 p72 fragments, and 2462 pp220 fragments. After conservation verification, the mean Pareto rank of the extended fragments for each protein was determined. CD2v had the highest average rank among the five proteins, and p30, p72, p220, and p54 ranked second, third, fourth, and fifth, respectively. Furthermore, we estimated all conserved fragments (Table S5). Biological decision makers can flexibly choose the appropriate epitope length and sample size for experimental validation \u003cem\u003ein vitro\u003c/em\u003e (Tables S6\u0026ndash;10). For example, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows 72 promising epitopes with an average Pareto rank of four, including 26 16mer epitopes, 16 17mer epitopes, 12 18mer epitopes, 10 19mer epitopes, and 8 20mer epitopes. In addition, these epitopes came from four proteins, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes.\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\u003eThe top 72 epitopes and their average ranks with the 16\u0026ndash;20 amino acids.\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\u003eMer type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMer type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage rank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKHVEEIESPPPESNEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHVEEIESPPPESNEEEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHVEEIESPPPESNEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKLASQKDLVNEFPGLFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKKHVEEIESPPPESNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLRKRKKHVEEIESPPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYSRYQYNTPIYYMRPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATVSAIELEYDVKRRFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSQKDLVNEFPGLFVRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPQSSERFEQYGRVFSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTVSAIELEYDVKRRFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVEEIESPPPESNEEEQC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKRKKHVEEIESPPPES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKKHVEEIESPPPESNEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRKKHVEEIESPPPESN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRKKHVEEIESPPPESNEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEPSPREPLLPKPYSRY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKRKKHVEEIESPPPESNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePQSSERFEQYGRVFSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRKRKKHVEEIESPPPESN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePYSRYQYNTPIYYMRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKHVEEIESPPPESNEEEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKLASQKDLVNEFPGLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHVEEIESPPPESNEEEQC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPLQIYKTLLEYLQHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLRKRKKHVEEIESPPPES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAQEEWNMILHVLFEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKLASQKDLVNEFPGLFVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRKRKKHVEEIESPPPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLRKRKKHVEEIESPPPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHEPSPREPLLPKPYSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNNPQSSERFEQYGRVFSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFRTQLEDTRREVNNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLASQKDLVNEFPGLFVRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEIESPPPESNEEEQCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWKATVSAIELEYDVKRRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWKATVSAIELEYDVKR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRKKHVEEIESPPPESNEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRTQLEDTRREVNNLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKRKKHVEEIESPPPESNEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLRKRKKHVEEIESPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRKRKKHVEEIESPPPESNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLQIYKTLLEYLQHSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKHVEEIESPPPESNEEEQC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKVIRAHNFIQTIYGTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKKHVEEIESPPPESNEEEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePGTKNAYRNLVYYCEY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLRKRKKHVEEIESPPPESN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASQKDLVNEFPGLFVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKLASQKDLVNEFPGLFVRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATVSAIELEYDVKRRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLRKRKKHVEEIESPPPES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKHVEEIESPPPESNEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHVEEIESPPPESNEEEQCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKKHVEEIESPPPESNEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWKATVSAIELEYDVKRRFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRKKHVEEIESPPPESNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKRKKHVEEIESPPPESNEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKRKKHVEEIESPPPESN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRKRKKHVEEIESPPPESNEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePYSRYQYNTPIYYMRPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKKHVEEIESPPPESNEEEQC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRKRKKHVEEIESPPPES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLRKRKKHVEEIESPPPESNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHEPSPREPLLPKPYSRY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRKKHVEEIESPPPESNEEEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epp220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPLQIYKTLLEYLQHSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLRKRKKHVEEIESPPPESN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASQKDLVNEFPGLFVRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKHVEEIESPPPESNEEEQCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD2v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLRKRKKHVEEIESPPPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIKLASQKDLVNEFPGLFVRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00\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"},{"header":"Discussion","content":"\u003cp\u003eSince ASFV is a complex and lethal multi-antigen virus, it originated in Africa but has recently caused an emerging epidemic in Asia. With advances in computational biology and machine learning in the field of immunology, computational epitope prediction provides a new opportunity to improve ASFV multi-epitope vaccines. Several studies have demonstrated that ASFV enhances or modulates the host immune response through multiple proteins. Therefore, a recombinant multi-epitope vaccine has a potential to be an excellent ASFV vaccine.\u003c/p\u003e \u003cp\u003eIn this work, we have analyzed five proteins: CD2v, p30, p54, p72, and pp220. Variation in MHC polymorphisms would induce different immune responses (Opriessnig et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which plays an important role in identifying potential epitopes. Human epitopes have been widely used to build prediction models. However, very few swine epitopes and prediction models are available. To identify potential epitopes for swine vaccine, PFAS used state-of-the-art predictors with promising parameter setting to calculate T and B cell scores. Even when experimentally validated porcine epitopes were used for parameter validation, cross-species prediction models may still reduce prediction accuracy.\u003c/p\u003e \u003cp\u003eAlthough both the T and B cell immunogenicity are important, there must be a trade-off in identifying promising epitopes for conventional prediction methods. Therefore, the Pareto front method was proposed to cope with the bi-objective problem by converting both the T and B cell scores of a fragment into a single Pareto rank that vaccine designers can easily determine the number of promising epitopes for biological experiments. The validation of ASFV recombinant multi-epitope vaccines reported suggests that the Pareto front method would be a potentially useful approach to identifying promising epitopes in the design of multi-epitope vaccines against ASFV.\u003c/p\u003e \u003cp\u003eBecause ASFV has multiple antigens and complex immune interactions with the host immune system, ASFV recombinant multi-epitope vaccines require a multi-epitope combination. Therefore, we analyzed the protein features of the top-ranked epitopes in five potential proteins, and some results were consistent to those of the biological studies. Some studies have shown that CD2v exhibits serological specificity, participates in immune evasion, enhances viral replication, and damages lymphocyte functions (Sanna et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this work, we observed that CD2v had the highest average rank of extended fragments and the highest proportion (n\u0026thinsp;=\u0026thinsp;46) of the top 72 epitopes. These results are consistent to the findings of existing studies revealing that CD2v plays an important role in activating adaptive host immune responses (Jia et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). CD2v may be a potential protein candidate in the ASFV vaccine due to its high ranking. p30, a phosphoprotein involved in ASFV entry, is synthesized in the early phase and continues to be synthesized during the late phase of viral infection. Although p30 is an antigenic and conserved structural protein, the immune response triggered by p30 alone is insufficient for antibody-mediated protection. However, combining it with other proteins, such as hemagglutinin, can increase humoral and cellular responses (Argilaguet et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this work, p30 had the second highest average rank, and p30 had two of the top 72 epitopes. It appears that p30 can be an important part of multi-epitope vaccine. p54 is important for the recruitment of envelope precursors to assembly factories and induces apoptosis during the early phase of infection (Hernaez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Rodriguez et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). p54 is an antigenic structural protein that induces the production of specific antibodies. However, protein variability analysis demonstrated that there is a highly variable region in the C-terminus of the p54 protein. Accordingly, p54 had the lowest average rank among the five proteins in this work, and these show that the p54 epitope selection may depend on the target swine species and predominant MHC type. p72 is conserved and essential for viral icosahedron formation during viral infection (Cobbold and Wileman \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e); therefore, p72 has the characteristics of high antigenicity and immunogenicity and is enriched and assembled in the ER during late-stage expression of infection. This study (Neilan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) showed that p72 may produce high levels of p72-specific IgG antibodies, but there exists partial protection when using p72 epitopes alone. In this work, p72 had the third highest Pareto front and 10 p72 epitopes were selected from the top 72 epitopes, the data show that p72 may increase antibody production in multi-epitope vaccine. The ASFV polyprotein precursor pp220 is highly conserved in the viral genome, and pp220 is cleaved by proteases to produce the mature virion proteins p150, p37, p14, and p34, which account for approximately 30% of the total viral protein mass and play an important role in the assembly process of the viral capsids and viral infection (Andres et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this work, pp220 had seven epitopes in the top three fronts, and pp220 had the second highest proportion (n\u0026thinsp;=\u0026thinsp;14) among the top 72 promising epitopes. These results indicate that pp220 can be an important component of multi-epitope vaccine.\u003c/p\u003e \u003cp\u003eHowever, in the swine computational studies, the prediction models were trained mainly using human datasets and small amounts of animal data. In addition, the percentage of immune cell populations and the function of T cells differ between pigs and humans (Gerner et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rubic-Schneider et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and variations in MHC polymorphisms induce different immune responses (Opriessnig et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For computational prediction, identifying individual predictors is important to improve swine epitope prediction.\u003c/p\u003e \u003cp\u003eAlthough cross-species epitope prediction increases the uncertainty of the results, this study has demonstrated a relationship between Pareto rank and swine survival days based on the Pareto front approach. These findings support the hypothesis that accurate predictors with the Pareto front method may reduce vaccine development time and costs when applied to human vaccine development.\u003c/p\u003e \u003cp\u003eIn this study, we use the Pareto front method to consider T and B cell immunogenicity simultaneously and ranked the epitopes with Pareto ranks. This procedure involved state-of-the-art computational methods and confirmed parameters. In addition, the evaluation of the experimental epitope ranks for the vaccination study had a significant coefficient of determination, demonstrating that the Pareto front method has effective screening efficiency. Finally, promising epitopes based on fragment extension and the peptide sequences with Pareto ranks were provided for biological experimental verification and confirmation. Overall, our study has proposed a computational prediction method based on the Pareto front method, provides Pareto rank of all fragments, promising epitopes, and may contribute to the development of recombinant multi-epitope vaccines for ASFV. The method may be used for human or cross-species promising epitope identification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with animals performed by any of the authors.\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData availability statements\u003c/h2\u003e \u003cp\u003eThe data that download from prediction tools are available from the corresponding author upon request. The MATLAB codes of the Pareto front method are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/NYCU-ICLAB/PFAS\u003c/span\u003e\u003cspan address=\"https://github.com/NYCU-ICLAB/PFAS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The validated epitopes and experimental results are available at supplementary information. The promising epitopes are available at supplementary information.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003ePei-Yin Wue and Chia-Jung Chang are employed by the Reber Genetics Co.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe work was supported by grants from National Science and Technology Council, Taiwan (110-2221-E-A49-099-MY3, 112-2740-B-400-005-), and was financially supported by the \u0026ldquo;Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B)\u0026rdquo; from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eTC, CC, SH, and PW conceived the study. TC and SH designed the experiments. TC, YH and FK performed the experiments and analyzed the data. CC and PW performed the formal analysis and technical assistance. TC drafted the original manuscript. SH and CC validated, supervised, and proof-read the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank National Core Facility for Biopharmaceuticals (NCFB, 111-2740-B-492-001) and National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs) of Taiwan for providing computational resources and storage resources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamczyk-Poplawska M, Markowicz S, Jagusztyn-Krynicka EK (2011) Proteomics for development of vaccine. J Proteomics 74(12):2596-616 doi:10.1016/j.jprot.2011.01.019\u003c/li\u003e\n\u003cli\u003eAlejo A, Matamoros T, Guerra M, Andres G (2018) A Proteomic Atlas of the African Swine Fever Virus Particle. J Virol 92(23) doi:10.1128/JVI.01293-18\u003c/li\u003e\n\u003cli\u003eAndres G, Alejo A, Salas J, Salas ML (2002) African swine fever virus polyproteins pp220 and pp62 assemble into the core shell. J Virol 76(24):12473-82 doi:10.1128/jvi.76.24.12473-12482.2002\u003c/li\u003e\n\u003cli\u003eArgilaguet JM, Perez-Martin E, Nofrarias M, Gallardo C, Accensi F, Lacasta A, Mora M, Ballester M, Galindo-Cardiel I, Lopez-Soria S, Escribano JM, Reche PA, Rodriguez F (2012) DNA vaccination partially protects against African swine fever virus lethal challenge in the absence of antibodies. PLoS One 7(9):e40942 doi:10.1371/journal.pone.0040942\u003c/li\u003e\n\u003cli\u003eBandrick M, Gutierrez AH, Desai P, Rincon G, Martin WD, Terry FE, De Groot AS, Foss DL (2020) T cell epitope content comparison (EpiCC) analysis demonstrates a bivalent PCV2 vaccine has greater T cell epitope overlap with field strains than monovalent PCV2 vaccines. Vet Immunol Immunopathol 223:110034 doi:10.1016/j.vetimm.2020.110034\u003c/li\u003e\n\u003cli\u003eBaratelli M, Morgan S, Hemmink JD, Reid E, Carr BV, Lefevre E, Montaner-Tarbes S, Charleston B, Fraile L, Tchilian E, Montoya M (2020) Identification of a Newly Conserved SLA-II Epitope in a Structural Protein of Swine Influenza Virus. Front Immunol 11:2083 doi:10.3389/fimmu.2020.02083\u003c/li\u003e\n\u003cli\u003eBhasin M, Raghava GP (2004) Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13(3):596-607 doi:10.1110/ps.03373104\u003c/li\u003e\n\u003cli\u003eBlome S, Franzke K, Beer M (2020) African swine fever - A review of current knowledge. Virus Res 287:198099 doi:10.1016/j.virusres.2020.198099\u003c/li\u003e\n\u003cli\u003eBosch-Camos L, Lopez E, Navas MJ, Pina-Pedrero S, Accensi F, Correa-Fiz F, Park C, Carrascal M, Dominguez J, Salas ML, Nikolin V, Collado J, Rodriguez F (2021) Identification of Promiscuous African Swine Fever Virus T-Cell Determinants Using a Multiple Technical Approach. Vaccines (Basel) 9(1) doi:10.3390/vaccines9010029\u003c/li\u003e\n\u003cli\u003eBosch-Camos L, Lopez E, Rodriguez F (2020) African swine fever vaccines: a promising work still in progress. Porcine Health Manag 6:17 doi:10.1186/s40813-020-00154-2\u003c/li\u003e\n\u003cli\u003eBurmakina G, Malogolovkin A, Tulman ER, Xu W, Delhon G, Kolbasov D, Rock DL (2019) Identification of T-cell epitopes in African swine fever virus CD2v and C-type lectin proteins. J Gen Virol 100(2):259-265 doi:10.1099/jgv.0.001195\u003c/li\u003e\n\u003cli\u003eCalis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, Kesmir C, Peters B (2013) Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 9(10):e1003266 doi:10.1371/journal.pcbi.1003266\u003c/li\u003e\n\u003cli\u003eClem AS (2011) Fundamentals of vaccine immunology. J Glob Infect Dis 3(1):73-8 doi:10.4103/0974-777X.77299\u003c/li\u003e\n\u003cli\u003eCobbold C, Wileman T (1998) The major structural protein of African swine fever virus, p73, is packaged into large structures, indicative of viral capsid or matrix precursors, on the endoplasmic reticulum. J Virol 72(6):5215-23 doi:10.1128/JVI.72.6.5215-5223.1998\u003c/li\u003e\n\u003cli\u003eDorigatti E, Schubert B (2020) Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLoS Comput Biol 16(10):e1008237 doi:10.1371/journal.pcbi.1008237\u003c/li\u003e\n\u003cli\u003eFan S, Wang Y, Wang X, Huang L, Zhang Y, Liu X, Zhu W (2018) Analysis of the affinity of influenza A virus protein epitopes for swine MHC I by a modified in vitro refolding method indicated cross-reactivity between swine and human MHC I specificities. Immunogenetics 70(10):671-680 doi:10.1007/s00251-018-1070-6\u003c/li\u003e\n\u003cli\u003eGao Z, Shao JJ, Zhang GL, Ge SD, Chang YY, Xiao L, Chang HY (2021) Development of an indirect ELISA to specifically detect antibodies against African swine fever virus: bioinformatics approaches. Virol J 18(1):97 doi:10.1186/s12985-021-01568-2\u003c/li\u003e\n\u003cli\u003eGarcia-Boronat M, Diez-Rivero CM, Reinherz EL, Reche PA (2008) PVS: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery. Nucleic Acids Res 36(Web Server issue):W35-41 doi:10.1093/nar/gkn211\u003c/li\u003e\n\u003cli\u003eGaudreault NN, Richt JA (2019) Subunit Vaccine Approaches for African Swine Fever Virus. Vaccines (Basel) 7(2) doi:10.3390/vaccines7020056\u003c/li\u003e\n\u003cli\u003eGerner W, Talker SC, Koinig HC, Sedlak C, Mair KH, Saalmuller A (2015) Phenotypic and functional differentiation of porcine alphabeta T cells: current knowledge and available tools. Mol Immunol 66(1):3-13 doi:10.1016/j.molimm.2014.10.025\u003c/li\u003e\n\u003cli\u003eGomez-Puertas P, Rodriguez F, Oviedo JM, Brun A, Alonso C, Escribano JM (1998) The African swine fever virus proteins p54 and p30 are involved in two distinct steps of virus attachment and both contribute to the antibody-mediated protective immune response. Virology 243(2):461-71 doi:10.1006/viro.1998.9068\u003c/li\u003e\n\u003cli\u003eGomez-Puertas P, Rodriguez F, Oviedo JM, Ramiro-Ibanez F, Ruiz-Gonzalvo F, Alonso C, Escribano JM (1996) Neutralizing antibodies to different proteins of African swine fever virus inhibit both virus attachment and internalization. J Virol 70(8):5689-94 doi:10.1128/JVI.70.8.5689-5694.1996\u003c/li\u003e\n\u003cli\u003eGuo F, Tang Y, Zhang W, Yuan H, Xiang J, Teng W, Lei A, Li R, Dai G (2022) DnaJ, a promising vaccine candidate against Ureaplasma urealyticum infection. Appl Microbiol Biotechnol 106(22):7643-7659 doi:10.1007/s00253-022-12230-4\u003c/li\u003e\n\u003cli\u003eGupta S, Ansari HR, Gautam A, Open Source Drug Discovery C, Raghava GP (2013) Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol Direct 8:27 doi:10.1186/1745-6150-8-27\u003c/li\u003e\n\u003cli\u003eHajialibeigi A, Amani J, Gargari SLM (2021) Identification and evaluation of novel vaccine candidates against Shigella flexneri through reverse vaccinology approach. Appl Microbiol Biotechnol 105(3):1159-1173 doi:10.1007/s00253-020-11054-4\u003c/li\u003e\n\u003cli\u003eHernaez B, Diaz-Gil G, Garcia-Gallo M, Ignacio Quetglas J, Rodriguez-Crespo I, Dixon L, Escribano JM, Alonso C (2004) The African swine fever virus dynein-binding protein p54 induces infected cell apoptosis. FEBS Lett 569(1-3):224-8 doi:10.1016/j.febslet.2004.06.001\u003c/li\u003e\n\u003cli\u003eIvanov V, Efremov EE, Novikov BV, Balyshev VM, Tsibanov S, Kalinovsky T, Kolbasov DV, Niedzwiecki A, Rath M (2011) Vaccination with viral protein-mimicking peptides postpones mortality in domestic pigs infected by African swine fever virus. Molecular medicine reports 4(3):395-401 doi:10.3892/mmr.2011.454\u003c/li\u003e\n\u003cli\u003eJach ME, Baj T, Juda M, Swider R, Mickowska B, Malm A (2020) Statistical evaluation of growth parameters in biofuel waste as a culture medium for improved production of single cell protein and amino acids by Yarrowia lipolytica. AMB Express 10(1):35 doi:10.1186/s13568-020-00968-x\u003c/li\u003e\n\u003cli\u003eJia N, Ou Y, Pejsak Z, Zhang Y, Zhang J (2017) Roles of African Swine Fever Virus Structural Proteins in Viral Infection. J Vet Res 61(2):135-143 doi:10.1515/jvetres-2017-0017\u003c/li\u003e\n\u003cli\u003eKessler C, Forth JH, Keil GM, Mettenleiter TC, Blome S, Karger A (2018) The intracellular proteome of African swine fever virus. Sci Rep 8(1):14714 doi:10.1038/s41598-018-32985-z\u003c/li\u003e\n\u003cli\u003eKibria KMK, Faruque MO, Islam MSB, Ullah H, Mahmud S, Miah M, Saleh AA (2022) A conserved subunit vaccine designed against SARS-CoV-2 variants showed evidence in neutralizing the virus. Appl Microbiol Biotechnol 106(11):4091-4114 doi:10.1007/s00253-022-11988-x\u003c/li\u003e\n\u003cli\u003eLarsen MV, 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 doi:10.1186/1471-2105-8-424\u003c/li\u003e\n\u003cli\u003eLiu Q, Ma B, Qian N, Zhang F, Tan X, Lei J, Xiang Y (2019) Structure of the African swine fever virus major capsid protein p72. Cell Res 29(11):953-955 doi:10.1038/s41422-019-0232-x\u003c/li\u003e\n\u003cli\u003eLokhandwala S, Petrovan V, Popescu L, Sangewar N, Elijah C, Stoian A, Olcha M, Ennen L, Bray J, Bishop RP, Waghela SD, Sheahan M, Rowland RRR, Mwangi W (2019) Adenovirus-vectored African Swine Fever Virus antigen cocktails are immunogenic but not protective against intranasal challenge with Georgia 2007/1 isolate. Vet Microbiol 235:10-20 doi:10.1016/j.vetmic.2019.06.006\u003c/li\u003e\n\u003cli\u003eLopera-Madrid J, Osorio JE, He Y, Xiang Z, Adams LG, Laughlin RC, Mwangi W, Subramanya S, Neilan J, Brake D, Burrage TG, Brown WC, Clavijo A, Bounpheng MA (2017) Safety and immunogenicity of mammalian cell derived and Modified Vaccinia Ankara vectored African swine fever subunit antigens in swine. Vet Immunol Immunopathol 185:20-33 doi:10.1016/j.vetimm.2017.01.004\u003c/li\u003e\n\u003cli\u003eManavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G (2018) iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction. Front Immunol 9:1695 doi:10.3389/fimmu.2018.01695\u003c/li\u003e\n\u003cli\u003eMasoudi-Sobhanzadeh Y, Jafari B, Parvizpour S, Pourseif MM, Omidi Y (2021) A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset. Comput Biol Med 138:104896 doi:10.1016/j.compbiomed.2021.104896\u003c/li\u003e\n\u003cli\u003eNeilan JG, Zsak L, Lu Z, Burrage TG, Kutish GF, Rock DL (2004) Neutralizing antibodies to African swine fever virus proteins p30, p54, and p72 are not sufficient for antibody-mediated protection. Virology 319(2):337-42 doi:10.1016/j.virol.2003.11.011\u003c/li\u003e\n\u003cli\u003eOpriessnig T, Mattei AA, Karuppannan AK, Halbur PG (2021) Future perspectives on swine viral vaccines: where are we headed? Porcine Health Manag 7(1):1 doi:10.1186/s40813-020-00179-7\u003c/li\u003e\n\u003cli\u003eReynisson B, Alvarez B, Paul S, Peters B, Nielsen M (2020) NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 48(W1):W449-W454 doi:10.1093/nar/gkaa379\u003c/li\u003e\n\u003cli\u003eRodriguez F, Ley V, Gomez-Puertas P, Garcia R, Rodriguez JF, Escribano JM (1996) The structural protein p54 is essential for African swine fever virus viability. Virus Res 40(2):161-7 doi:10.1016/0168-1702(95)01268-0\u003c/li\u003e\n\u003cli\u003eRos-Lucas A, Correa-Fiz F, Bosch-Camos L, Rodriguez F, Alonso-Padilla J (2020) Computational Analysis of African Swine Fever Virus Protein Space for the Design of an Epitope-Based Vaccine Ensemble. Pathogens 9(12) doi:10.3390/pathogens9121078\u003c/li\u003e\n\u003cli\u003eRubic-Schneider T, Christen B, Brees D, Kammuller M (2016) Minipigs in Translational Immunosafety Sciences: A Perspective. Toxicol Pathol 44(3):315-24 doi:10.1177/0192623315621628\u003c/li\u003e\n\u003cli\u003eSaha S, Raghava GP (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40-8 doi:10.1002/prot.21078\u003c/li\u003e\n\u003cli\u003eSanna G, Dei Giudici S, Bacciu D, Angioi PP, Giammarioli M, De Mia GM, Oggiano A (2017) Improved Strategy for Molecular Characterization of African Swine Fever Viruses from Sardinia, Based on Analysis of p30, CD2V and I73R/I329L Variable Regions. Transbound Emerg Dis 64(4):1280-1286 doi:10.1111/tbed.12504\u003c/li\u003e\n\u003cli\u003eSingh H, Ansari HR, Raghava GP (2013) Improved method for linear B-cell epitope prediction using antigen\u0026apos;s primary sequence. PLoS One 8(5):e62216 doi:10.1371/journal.pone.0062216\u003c/li\u003e\n\u003cli\u003eTeklue T, Sun Y, Abid M, Luo Y, Qiu HJ (2020) Current status and evolving approaches to African swine fever vaccine development. Transbound Emerg Dis 67(2):529-542 doi:10.1111/tbed.13364\u003c/li\u003e\n\u003cli\u003eYu M, Morrissy CJ, Westbury HA (1996) Strong sequence conservation of African swine fever virus p72 protein provides the molecular basis for its antigenic stability. Arch Virol 141(9):1795-802 doi:10.1007/BF01718302\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"amb-express","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ambe","sideBox":"Learn more about [AMB Express](http://amb-express.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/AMBE/default.aspx","title":"AMB Express","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"African swine fever virus, immunogenicity, Pareto front, promising epitope, T and B cell activation, vaccine design","lastPublishedDoi":"10.21203/rs.3.rs-3784481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3784481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eT and B cell activation are equally important in triggering and orchestrating adaptive host responses to design multi-epitope African swine fever virus (ASFV) vaccines. However, few design methods have considered the trade-off between T and B cell immunogenicity when identifying promising ASFV epitopes. This work proposed a novel Pareto front-based ASFV screening method PFAS to identify promising epitopes for designing multi-epitope vaccines utilizing five ASFV Georgia 2007/1 sequences. To accurately predict T cell immunogenicity, four scoring methods were used to estimate the T cell activation in the four stages, including proteasomal cleavage probability, transporter associated with antigen processing transport efficiency, class I binding affinity of the major histocompatibility complex, and CD8 + cytotoxic T cell immunogenicity. PFAS ranked promising epitopes using a Pareto front method considering T and B cell immunogenicity. The coefficient of determination between the Pareto ranks of multi-epitope vaccines and survival days of swine vaccinations was R\u003csup\u003e2\u003c/sup\u003e = 0.95. Consequently, PFAS scored complete epitope profiles and identified 72 promising top-ranked epitopes, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. PFAS is the first method of using the Pareto front approach to identify promising epitopes that considers the objectives of maximizing both T and B cell immunogenicity. The top-ranked promising epitopes can be cost-effectively validated \u003cem\u003ein vitro\u003c/em\u003e. The Pareto front approach can be adaptively applied to various epitope predictors for bacterial, viral and cancer vaccine developments. The MATLAB code of the Pareto front method was available at https://github.com/NYCU-ICLAB/PFAS.\u003c/p\u003e","manuscriptTitle":"Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-01 14:13:31","doi":"10.21203/rs.3.rs-3784481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2024-07-21T19:48:55+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-01-24T10:52:46+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-29T02:23:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-25T04:47:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"AMB Express","date":"2023-12-19T23:22:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"amb-express","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ambe","sideBox":"Learn more about [AMB Express](http://amb-express.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/AMBE/default.aspx","title":"AMB Express","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"265277a4-def2-4e9e-80a2-0d6874195858","owner":[],"postedDate":"January 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T16:03:04+00:00","versionOfRecord":{"articleIdentity":"rs-3784481","link":"https://doi.org/10.1186/s13568-024-01749-6","journal":{"identity":"amb-express","isVorOnly":false,"title":"AMB Express"},"publishedOn":"2024-08-31 15:57:39","publishedOnDateReadable":"August 31st, 2024"},"versionCreatedAt":"2024-01-01 14:13:31","video":"","vorDoi":"10.1186/s13568-024-01749-6","vorDoiUrl":"https://doi.org/10.1186/s13568-024-01749-6","workflowStages":[]},"version":"v1","identity":"rs-3784481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3784481","identity":"rs-3784481","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}