In silico Identification of Novel Drug Targets in Erwinia amylovora: A Step Toward Fire Blight Control in pear and apple trees

In silico Identification of Novel Drug Targets in Erwinia amylovora: A Step Toward Fire Blight Control in pear and apple trees | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article In silico Identification of Novel Drug Targets in Erwinia amylovora : A Step Toward Fire Blight Control in pear and apple trees Wesam Elkholy, Nermeen Salah, Hayam Allam, Yasmin Abdelrehem, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7381667/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fire blight, a destructive disease affecting apple and pear orchards, is caused by the Gram-negative pathogen Erwinia amylovora , leading to considerable economic losses worldwide. Current control strategies using copper-based bactericides and antibiotics are becoming increasingly ineffective due to resistance development, highlighting the need for novel therapeutic interventions. In this study, we applied a robust subtractive proteomics pipeline to identify potential drug targets unique to E. amylovora . The proteome was systematically filtered by length, redundancy, and host homology against Malus domestica and Pyrus communis , followed by screening for virulence and essentiality using VFDB and DEG. Subcellular localization, Pfam domain annotation, druggability analysis via DrugBank, and Gene Ontology enrichment were performed. Two high-confidence targets were identified. The first harbors the CheB methylesterase domain (PF01339), a key regulator of bacterial chemotaxis. The second is a cyclic-di-GMP signaling protein, essential for bacterial lifestyle transitions and biofilm regulation, and contains the GGDEF (PF13426), EAL (PF00989), and PilZ (PF08448) domains. These domain architectures underscore their roles in intracellular signaling and motility control. Structural modeling using AlphaFold, coupled with active site prediction, enabled virtual screening against ~ 9,500 bioactive compounds from Life Chemicals. Nine compounds demonstrated strong binding affinities with the predicted active sites via AutoDock Vina. These findings support their potential as inhibitors, although experimental validation is required. This integrative in silico pipeline offers promising candidates for the development of targeted therapeutics against E. amylovora , contributing to sustainable fire blight management. Fire blight Erwinia amylovora Apple Pear Molecular docking Figures Figure 1 Figure 2 Figure 3 I. Introduction Erwinia amylovora is a Gram-negative bacterium responsible for fire blight, a highly destructive disease affecting various members of the Rosaceae family, particularly apple ( Malus domestica ) and pear ( Pyrus communis ) trees. This pathogen is accountable for substantial economic losses worldwide, with estimates indicating approximately $ 100 million in damages annually in the United States alone [ 1 ]. In Egypt, fire blight has similarly devastating effects, with reported losses ranging from 75–90% in affected pear orchards since its introduction in the early 1960s. The disease manifests through symptoms such as wilting, blackening of blossoms and shoots, and the formation of cankers, which can lead to tree death if not managed effectively [ 2 , 3 ]. Traditional cultural practices, such as pruning infected twigs, controlling nitrogen fertilization to limit vegetative growth, and avoiding overhead irrigation, constitute the first line of defense [ 4 ]. While essential, these practices are labor-intensive, time-consuming, and require early detection of symptoms to be effective, which is often challenging in large-scale orchards. Moreover, improper pruning can exacerbate disease spread through contaminated tools. Chemical control, particularly with antibiotics such as streptomycin and oxytetracycline, has been a mainstay in managing fire blight due to their ability to suppress bacterial growth. However, the extensive use of these antibiotics has led to the emergence of resistant E. amylovora strains in several regions, reducing their efficacy [ 5 ]. Moreover, the extensive use has led to the emergence of antibiotic-resistant strains of E. amylovora , raising concerns about their long-term efficacy and environmental impact [ 6 , 7 ]. Furthermore, regulatory restrictions on antibiotic use in agriculture, especially in the European Union, have limited their availability. Copper-based bactericides are an alternative, but they are generally less effective and can cause phytotoxicity, leading to leaf damage and reduced fruit quality [ 8 ]. Biological control strategies have received increasing attention due to their environmentally friendly nature. These involve the use of non-pathogenic bacteria, such as Pantoea agglomerans or Bacillus subtilis , which compete with E. amylovora for space and nutrients on floral surfaces. While promising in controlled environments, these biocontrol agents often exhibit inconsistent efficacy under field conditions due to environmental variability and competition with native microbial communities [ 9 ]. Furthermore, registration and commercialization of such products are often constrained by regulatory and economic hurdles. Host resistance through the development and cultivation of fire blight-resistant apple and pear varieties offers a potentially sustainable solution. Breeding programs have identified quantitative trait loci (QTLs) associated with resistance, such as those derived from Malus robusta and Malus fusca [ 10 ]. However, the inheritance of resistance traits is often complex and polygenic, and breeding new cultivars is a long-term process that may compromise desirable horticultural traits such as fruit quality and yield. Bacteriophage therapy has emerged as an innovative and highly specific biocontrol strategy against E. amylovora . Bacteriophages, which are viruses that infect and lyse specific bacterial hosts, are ideal candidates for the targeted suppression of fire blight without adversely affecting beneficial microflora. Several lytic bacteriophages targeting E. amylovora have been identified and demonstrated to reduce disease incidence in laboratory and greenhouse settings [ 11 , 12 ]. Phage-based products, such as AgriPhage, have been developed for field application. However, their effectiveness can be constrained by factors such as UV degradation, narrow host range, and the development of bacterial resistance to phages. Additionally, regulatory approval processes and public perception of viral applications in agriculture remain obstacles to widespread adoption. In light of the limitations associated with traditional control methods, the current study investigates a subtractive proteomics approach to identify novel targets for controlling E. amylovora . This technique involves comparing the proteomes of the pathogen and its host plants to identify proteins essential for bacterial survival but absent in the host. This innovative strategy not only promises to enhance the specificity and efficacy of treatments against fire blight but also contributes to the broader goal of sustainable agricultural practices by minimizing chemical inputs and their associated risks. In conclusion, while traditional methods for managing fire blight have been foundational in agricultural practices, their limitations necessitate the exploration of advanced techniques like subtractive proteomics, which may pave the way for more effective and environmentally friendly solutions. II. Materials and methods The reference proteome of the pathogen was obtained from the NCBI RefSeq database. Protein sequences were filtered to retain only those with lengths ranging from 100 to 1000 amino acids, thereby excluding potentially incomplete or low-complexity sequences. Redundancy was minimized using CD-HIT with a 40% sequence identity threshold to ensure a representative and non-redundant dataset [ 13 ]. To eliminate potential host homologs, using an E-value cutoff of 1e -5 , BLASTP searches were conducted against the proteomes of apple ( Malus domestica ) and pear ( Pyrus communis ), and proteins exhibiting significant similarity were excluded from further analysis. The resulting pathogen-specific protein set was screened for virulence factors by performing BLASTP against the core dataset of the Virulence Factors Database (VFDB) using an E-value cutoff of 1e -5 [ 14 ]. Essential proteins were identified through BLASTP searches against the Database of Essential Genes (DEG) with the same E-value threshold [ 15 ]. Subcellular localization of the proteins was predicted using DeepLoc-1.0, and only proteins localized to the cytoplasm or cell membrane were retained [ 16 ]. Functional domains were characterized using HMMER with the Pfam-A database [ 17 ]. Druggability was assessed by querying the refined protein set against the DrugBank database to identify matches with known drug targets [ 18 ]. Gene Ontology (GO) terms were assigned using Panzer2 via the SANSparallel server [ 19 ]. Homology to known Erwinia-derived pathogenic proteins was assessed using BLASTP against PHI-base (v. 5) to identify proteins potentially involved in host-pathogen interactions [ 20 ]. The three-dimensional structures of the final candidate proteins were retrieved from the AlphaFold Protein Structure Database (v. 4) [ 21 ], and their active sites were predicted using the DeepSite server [ 22 ]. Subsequently, AutoDock Vina [ 23 ] was employed to evaluate the binding affinity of these targets against a library of approximately 9,500 bioactive compounds obtained from Life Chemicals ( https://lifechemicals.com/screening-libraries/biologically-active-compound-library ). III. Results and discussion The reference proteome of the pathogen was initially retrieved and filtered to retain protein sequences ranging from 100 to 1000 amino acids in length, resulting in 2,875 sequences. Redundancy reduction at a 40% identity threshold using CD-HIT yielded a non-redundant dataset of 2,697 sequences. To eliminate potential host homologs, BLASTP was conducted against the proteomes of apple ( Malus domestica ) and pear ( Pyrus communis ), and 1,651 pathogen-specific proteins were retained following the removal of homologous sequences. Subsequent screening for virulence factors using the VFDB database (E-value ≤ 1e-5) identified 526 proteins, while 374 of these were also predicted as essential proteins via comparison with the DEG database. DeepLoc-based subcellular localization analysis revealed 166 proteins localized to either the cytoplasm or the cell membrane. Functional domain analysis using Pfam classified 107 proteins with identifiable domain architectures. Screening against DrugBank identified 44 proteins with potential druggability. GO annotation via Panzer2 assigned relevant terms to 31 proteins, and a final homology check against 123 Erwinia -derived sequences from PHI-base identified 2 proteins with significant similarity (E-value ≤ 1e-5) as mentioned at Table 1 . Table 1 Potential drug targets in Erwinia amylovora Accession no. Pfam domains Subcellular location Go term WP_004160380.1 PF00563; PF00990; PF13426; PF13426; PF13426; PF00989; PF00989; PF08448 Cytoplasm GO:0071111; GO:0006355; GO:0016301; GO:0016829; GO:0016020 WP_004158032.1 PF01339; PF00072 Cytoplasm GO:0008984; GO:0050568; GO:0000156; GO:0006935; GO:0000160; GO:0005737 Table 2 presents the top five ligands identified through molecular docking against the target proteins WP_004160380.1 and WP_004158032.1, which are predicted to possess cyclic-guanylate-specific phosphodiesterase and protein-glutamate methylesterase activities, respectively. These ligands were selected based on their binding affinities, with more negative binding energy values indicating stronger predicted interactions. The docking results provide insights into potential inhibitory molecules that may modulate the function of these bacterial signaling proteins, supporting their relevance as candidate targets for antimicrobial development. Table 2 Top five ligands docked against the potential drug targets of Erwinia amylovora Drug target Ligand Binding affinity (kcal/mol) WP_004160380.1 CHEMBL2093312 -11.647 CHEMBL1304704 -10.857 CHEMBL1097223 -10.837 CHEMBL1539970 -10.816 CHEMBL1566748 -10.595 WP_004158032.1 CHEMBL2093312 -11.050 CHEMBL1306632 -11.019 CHEMBL1485976 -10.965 CHEMBL1489036 -10.879 CHEMBL1328470 -10.763 To better understand the structural characteristics and binding modes of the top-performing ligands, both 2D and 3D representations were generated (Fig. 1 , 2 , 3 ). The 2D depictions allowed for comparison of key chemical features such as ring systems, functional groups, and overall scaffold diversity. In parallel, 3D docking poses illustrated the spatial orientation of the ligands within the binding site of both targets, highlighting key interactions including hydrogen bonds, hydrophobic contacts, and potential π–π stacking. These visualizations provided insights into the structural basis of binding affinity and supported the selection of promising candidates for further analysis. This study utilized a comprehensive subtractive proteomic methodology to identify potential therapeutic targets within the pathogen's proteome. Starting with the reference proteome, a series of rigorous filtration and prioritization steps—including sequence length selection, redundancy elimination, host homology exclusion, and functional annotation—refined the dataset from thousands of proteins to a concise set of candidates. The identification of virulence-associated, essential, and pathogen-specific proteins localized to critical cellular compartments underscores the robustness of the pipeline. Additionally, the integration of domain analysis, druggability screening, and GO annotation provided further functional insights. Notably, two proteins exhibited significant similarity to experimentally validated virulence factors in Erwinia species, underscoring their potential as key therapeutic targets. These findings are discussed below in the context of their biological relevance and potential applicability in antimicrobial development. The two annotated protein targets from Erwinia amylovora , WP_004160380.1 and WP_004158032.1, demonstrate distinct but complementary regulatory functions, based on their conserved Pfam domains and enriched GO terms, which underscore their potential as novel antimicrobial targets. WP_004160380.1 possesses a composite structure consisting of PF00563 (EAL domain), PF00990 (GGDEF domain), and multiple PAS-related domains (PF13426, PF00989, PF08448). This domain configuration is characteristic of cyclic-di-GMP signaling proteins, which act as pivotal regulators of bacterial lifestyle transitions including motility, biofilm formation, and virulence [ 24 ]. The GGDEF domain confers diguanylate cyclase activity, while the EAL domain supports cyclic-guanylate-specific phosphodiesterase activity (GO:0071111), enabling the dynamic synthesis and degradation of cyclic-di-GMP (c-di-GMP), a ubiquitous bacterial second messenger. The presence of PAS and PAS-fold domains indicates potential regulatory control through environmental sensing, as these domains often modulate enzymatic function in response to redox potential, oxygen, or small ligands. The assigned GO terms support this multifunctionality, including regulation of DNA-templated transcription (GO:0006355), kinase activity (GO:0016301), and lyase activity (GO:0016829). While the protein is localized to the cytoplasm, the inclusion of membrane (GO:0016020) suggests peripheral membrane association or interaction with transmembrane complexes that facilitate signaling integration. In contrast, the second protein, WP_004158032.1, features PF01339 (CheB methylesterase domain) and PF00072 (response regulator receiver domain), components classically associated with two-component signal transduction systems, particularly in chemotaxis [ 25 ]. CheB is a cytoplasmic response regulator that plays a pivotal role in bacterial chemotaxis through its involvement in the phosphorelay signal transduction system (GO:0000160). It exhibits dual enzymatic functions, including phosphorelay response regulator activity (GO:0000156) and protein-glutamate methylesterase activity (GO:0008984), enabling the demethylation of methyl-accepting chemotaxis proteins (MCPs) to modulate signal sensitivity. Additionally, it possesses protein-glutamine glutaminase activity (GO:0050568), which contributes to the adaptation of the chemotactic response (GO:0006935). These biochemical functions collectively regulate bacterial movement in response to chemical gradients, with CheB operating primarily within the cytoplasm (GO:0005737) as part of a finely tuned signaling network. Together, these two proteins represent key regulatory nodes in E. amylovora 's adaptive responses, with WP_004160380.1 governing intracellular signaling and transcriptional regulation via c-di-GMP dynamics, while WP_004158032.1 fine-tunes environmental navigation via chemotactic signal processing. Their disruption could impair essential virulence and survival mechanisms, offering promising avenues for structure-based drug targeting against this economically significant phytopathogen. IV. Conclusion This study employed a comprehensive subtractive proteomics approach to identify potential drug targets in Erwinia by integrating comparative genomics, functional annotation, subcellular localization, and structure-based screening. From an initial proteome of 2,875 proteins, a refined set of 44 pathogen-specific, druggable, and functionally relevant candidates was identified. Further prioritization based on virulence, essentiality, and homology to known pathogenic factors revealed two high-confidence proteins with potential roles in host-pathogen interactions. These targets, supported by structural modeling and binding site prediction, provide promising starting points for the development of novel antibacterial agents against Erwinia -related diseases. Abbreviations QTLs Quantitative Trait Loci UV Ultraviolet NCBI RefSeq National Center for Biotechnology Information Reference Sequence CD-HIT Cluster Database at High Identity with Tolerance BLASTP Basic Local Alignment Search Tool for Proteins VFDB Virulence Factors Database DEG Database of Essential Genes HMMER Biosequence Analysis Using Profile Hidden Markov Models Pfam Protein Families Database GO term Gene Ontology term PHI-base Pathogen–Host Interaction Database 2D Two-dimensional 3D Three-dimensional EAL domain Glutamate–Alanine–Leucine domain GGDEF domain Glycine–Glycine–Aspartate–Glutamate–Phenylalanine domain PAS-related domains Per–Arnt–Sim related domains CheB methylesterase domain Chemotaxis-specific methylesterase domain MCPs Methyl-accepting Chemotaxis Proteins Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing Interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution W.E., N.S., H.A., Y.A., N.M., and A.F.R. conceived and designed the study. W.E., N.S., H.A., Y.A., and N.M. performed the in silico analyses. A.F.R. supervised the project. W.E. and A.F.R. wrote the main manuscript text. N.S., H.A., Y.A., and N.M. prepared the figures and tables. All authors reviewed and approved the final manuscript. Acknowledgement The authors acknowledge Dr. Aly AbdelMoteleb for his valuable comments and insightful suggestions that contributed to the improvement of this work. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Wang D, Korban SS, Pusey PL, Zhao Y. AmyR is a novel negative regulator of amylovoran production in Erwinia amylovora. PLoS ONE. 2012;7:e45038. https://doi.org/10.1371/journal.pone.0045038 . El-Nasr S, Hamdy I, Ali MH, FIRE BLIGHT INCIDENCE IN, PEAR ORCHARDS AND ITS CONTROL IN EGYPT. Acta Hortic. 1990;392–392. https://doi.org/10.17660/ActaHortic.1990.273.59 . Abo-El-Dahab MK, El-Goorani MA, El-Kasheir HM, Shoeib AA, SEVERE OUTBREAKS OF FIRE BLIGHT IN EGYPT DURING 1982 AND 1983 SEASONS. Acta Hortic. 1984;341–8. https://doi.org/10.17660/ActaHortic.1984.151.51 . Van Tom DZ, Beer SV, Cornell. university., United States. Fire blight–its nature, prevention, and control : a practical guide to integrated disease management /. Washington, DC : U.S. Dept. of Agriculture :; 1999. https://doi.org/10.5962/bhl.title.134796 McManus PS, Stockwell VO, Sundin GW, Jones AL. Antibiotic use in plant agriculture. Annu Rev Phytopathol. 2002;40:443–65. https://doi.org/10.1146/annurev.phyto.40.120301.093927 . Barakat F, Mikhail M, Tawfik A, Shahin H. Effect of some Plant Extracts on the Growth of Streptomycin Resistant and Sensitive Isolates of Erwinia amylovora. Egypt J Phytopathol. 2009;37:29–43. https://doi.org/10.21608/ejp.2009.234976 . Ke D, Luo J, Liu P, Shou L, Ijaz M, Ahmed T, et al. Advancements in Bacteriophages for the Fire Blight Pathogen Erwinia amylovora. Viruses. 2024;16:1619. https://doi.org/10.3390/v16101619 . Gür A, Baştaş KK. Effectiveness of Phosphorous acid, Bacillus subtilis and Copper Compounds on Apple cv. Gala with M9 Rootstock in the Control of Fire Blight. Turkish JAF SciTech. 2023;11:2595–600. https://doi.org/10.24925/turjaf.v11is1.2595-2600.6533 . Stockwell VO, Johnson KB, Sugar D, Loper JE. Mechanistically compatible mixtures of bacterial antagonists improve biological control of fire blight of pear. Phytopathology. 2011;101:113–23. https://doi.org/10.1094/PHYTO-03-10-0098 . Peil A, Garcia-Libreros T, Richter K, Trognitz FC, Trognitz B, Hanke M, ‐V., et al. Strong evidence for a fire blight resistance gene of Malus robusta located on linkage group 3. Plant Breeding. 2007;126:470–5. https://doi.org/10.1111/j.1439-0523.2007.01408.x . Gill JJ, Svircev AM, Smith R, Castle AJ. Bacteriophages of Erwinia amylovora. Appl Environ Microbiol. 2003;69:2133–8. https://doi.org/10.1128/AEM.69.4.2133-2138.2003 . Mehla K, Ramana J. Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study. OMICS. 2015;19:393–406. https://doi.org/10.1089/omi.2015.0046 . Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9. https://doi.org/10.1093/bioinformatics/btl158 . Liu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 2022;50:D912–7. https://doi.org/10.1093/nar/gkab1107 . Zhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res. 2009. https://doi.org/10.1093/nar/gkn858 . 37 Database issue:D455-458. Moreno J, Nielsen H, Winther O, Teufel F. Predicting the subcellular location of prokaryotic proteins with DeepLocPro. 2024;:2024.01.04.574157. https://doi.org/10.1101/2024.01.04.574157 Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021;49:D412–9. https://doi.org/10.1093/nar/gkaa913 . Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82. https://doi.org/10.1093/nar/gkx1037 . Törönen P, Medlar A, Holm L. PANNZER2: a rapid functional annotation web server. Nucleic Acids Res. 2018;46:W84–8. https://doi.org/10.1093/nar/gky350 . Urban M, Cuzick A, Seager J, Wood V, Rutherford K, Venkatesh SY, et al. PHI-base in 2022: a multi-species phenotype database for Pathogen–Host Interactions. Nucleic Acids Res. 2022;50:D837–47. https://doi.org/10.1093/nar/gkab1037 . Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9. https://doi.org/10.1038/s41586-021-03819-2 . Jiménez J, Doerr S, Martínez-Rosell G, Rose AS, De Fabritiis G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics. 2017;33:3036–42. https://doi.org/10.1093/bioinformatics/btx350 . Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61:3891–8. Chou S-H, Galperin MY. Diversity of Cyclic Di-GMP-Binding Proteins and Mechanisms. J Bacteriol. 2016;198:32–46. https://doi.org/10.1128/JB.00333-15 . Skerker JM, Prasol MS, Perchuk BS, Biondi EG, Laub MT. Two-component signal transduction pathways regulating growth and cell cycle progression in a bacterium: a system-level analysis. PLoS Biol. 2005;3:e334. https://doi.org/10.1371/journal.pbio.0030334 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7381667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508014387,"identity":"a998a0d4-0a89-49b3-abcb-05ef67457b0c","order_by":0,"name":"Wesam Elkholy","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Wesam","middleName":"","lastName":"Elkholy","suffix":""},{"id":508014388,"identity":"3a4dbe67-f217-4809-be7c-8879213f1542","order_by":1,"name":"Nermeen Salah","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Nermeen","middleName":"","lastName":"Salah","suffix":""},{"id":508014390,"identity":"78bb8ee8-0a09-4e6e-b50f-2fe3fb4ecc95","order_by":2,"name":"Hayam Allam","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Hayam","middleName":"","lastName":"Allam","suffix":""},{"id":508014393,"identity":"ee5a3f47-0114-4294-b7c0-2245633677ae","order_by":3,"name":"Yasmin Abdelrehem","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Yasmin","middleName":"","lastName":"Abdelrehem","suffix":""},{"id":508014394,"identity":"ee89bef8-4b88-4a59-a859-dbb8c3d4876f","order_by":4,"name":"Yara Ahmad","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Yara","middleName":"","lastName":"Ahmad","suffix":""},{"id":508014396,"identity":"7376cc4e-dd1c-4bdb-acc1-b62d76934fc7","order_by":5,"name":"Nada Mostafa","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Nada","middleName":"","lastName":"Mostafa","suffix":""},{"id":508014398,"identity":"fd9240a0-0bb8-4cda-85b3-dc9b5aa158f8","order_by":6,"name":"Ahmed F. 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Polar interactions are shown in red; nonpolar interactions in sky blue.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7381667/v1/a4a633dda327757259236371.png"},{"id":90326789,"identity":"dac51f20-924a-4611-9fd9-e4c32f2f8a39","added_by":"auto","created_at":"2025-09-01 12:20:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":609087,"visible":true,"origin":"","legend":"\u003cp\u003e2D structures of the top five ligands docked to WP_004160380.1. The 2D representations were generated using Maestro software.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7381667/v1/f6df5e941e1e6115fe4c8b2c.png"},{"id":90327935,"identity":"ceac896f-8d91-452a-9c3d-f06c5d597fb3","added_by":"auto","created_at":"2025-09-01 12:28:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":480721,"visible":true,"origin":"","legend":"\u003cp\u003e2D structures of the top live Ligands docked to WP_004158032.1. The 2D representations were generated using Maestro software.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7381667/v1/a8129bb0c12dd479936a9945.png"},{"id":90783879,"identity":"e9fd472e-0787-4eef-b7a7-5e66cd0b1587","added_by":"auto","created_at":"2025-09-08 06:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18551133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7381667/v1/0c0e9d3c-73a3-48e7-897f-2518f10e95f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIn silico Identification of Novel Drug Targets in \u003cem\u003eErwinia amylovora\u003c/em\u003e: A Step Toward Fire Blight Control in pear and apple trees\u003c/p\u003e","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003e\u003cem\u003eErwinia amylovora\u003c/em\u003e is a Gram-negative bacterium responsible for fire blight, a highly destructive disease affecting various members of the Rosaceae family, particularly apple (\u003cem\u003eMalus domestica\u003c/em\u003e) and pear (\u003cem\u003ePyrus communis\u003c/em\u003e) trees. This pathogen is accountable for substantial economic losses worldwide, with estimates indicating approximately \u003cspan\u003e$\u003c/span\u003e100\u0026nbsp;million in damages annually in the United States alone [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Egypt, fire blight has similarly devastating effects, with reported losses ranging from 75\u0026ndash;90% in affected pear orchards since its introduction in the early 1960s. The disease manifests through symptoms such as wilting, blackening of blossoms and shoots, and the formation of cankers, which can lead to tree death if not managed effectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Traditional cultural practices, such as pruning infected twigs, controlling nitrogen fertilization to limit vegetative growth, and avoiding overhead irrigation, constitute the first line of defense [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While essential, these practices are labor-intensive, time-consuming, and require early detection of symptoms to be effective, which is often challenging in large-scale orchards. Moreover, improper pruning can exacerbate disease spread through contaminated tools.\u003c/p\u003e\u003cp\u003eChemical control, particularly with antibiotics such as streptomycin and oxytetracycline, has been a mainstay in managing fire blight due to their ability to suppress bacterial growth. However, the extensive use of these antibiotics has led to the emergence of resistant \u003cem\u003eE. amylovora\u003c/em\u003e strains in several regions, reducing their efficacy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, the extensive use has led to the emergence of antibiotic-resistant strains of \u003cem\u003eE. amylovora\u003c/em\u003e, raising concerns about their long-term efficacy and environmental impact [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, regulatory restrictions on antibiotic use in agriculture, especially in the European Union, have limited their availability. Copper-based bactericides are an alternative, but they are generally less effective and can cause phytotoxicity, leading to leaf damage and reduced fruit quality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBiological control strategies have received increasing attention due to their environmentally friendly nature. These involve the use of non-pathogenic bacteria, such as \u003cem\u003ePantoea agglomerans\u003c/em\u003e or \u003cem\u003eBacillus subtilis\u003c/em\u003e, which compete with \u003cem\u003eE. amylovora\u003c/em\u003e for space and nutrients on floral surfaces. While promising in controlled environments, these biocontrol agents often exhibit inconsistent efficacy under field conditions due to environmental variability and competition with native microbial communities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, registration and commercialization of such products are often constrained by regulatory and economic hurdles. Host resistance through the development and cultivation of fire blight-resistant apple and pear varieties offers a potentially sustainable solution. Breeding programs have identified quantitative trait loci (QTLs) associated with resistance, such as those derived from \u003cem\u003eMalus robusta\u003c/em\u003e and \u003cem\u003eMalus fusca\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the inheritance of resistance traits is often complex and polygenic, and breeding new cultivars is a long-term process that may compromise desirable horticultural traits such as fruit quality and yield.\u003c/p\u003e\u003cp\u003eBacteriophage therapy has emerged as an innovative and highly specific biocontrol strategy against \u003cem\u003eE. amylovora\u003c/em\u003e. Bacteriophages, which are viruses that infect and lyse specific bacterial hosts, are ideal candidates for the targeted suppression of fire blight without adversely affecting beneficial microflora. Several lytic bacteriophages targeting \u003cem\u003eE. amylovora\u003c/em\u003e have been identified and demonstrated to reduce disease incidence in laboratory and greenhouse settings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Phage-based products, such as AgriPhage, have been developed for field application. However, their effectiveness can be constrained by factors such as UV degradation, narrow host range, and the development of bacterial resistance to phages. Additionally, regulatory approval processes and public perception of viral applications in agriculture remain obstacles to widespread adoption.\u003c/p\u003e\u003cp\u003eIn light of the limitations associated with traditional control methods, the current study investigates a subtractive proteomics approach to identify novel targets for controlling \u003cem\u003eE. amylovora\u003c/em\u003e. This technique involves comparing the proteomes of the pathogen and its host plants to identify proteins essential for bacterial survival but absent in the host. This innovative strategy not only promises to enhance the specificity and efficacy of treatments against fire blight but also contributes to the broader goal of sustainable agricultural practices by minimizing chemical inputs and their associated risks. In conclusion, while traditional methods for managing fire blight have been foundational in agricultural practices, their limitations necessitate the exploration of advanced techniques like subtractive proteomics, which may pave the way for more effective and environmentally friendly solutions.\u003c/p\u003e"},{"header":"II. Materials and methods","content":"\u003cp\u003eThe reference proteome of the pathogen was obtained from the NCBI RefSeq database. Protein sequences were filtered to retain only those with lengths ranging from 100 to 1000 amino acids, thereby excluding potentially incomplete or low-complexity sequences. Redundancy was minimized using CD-HIT with a 40% sequence identity threshold to ensure a representative and non-redundant dataset [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To eliminate potential host homologs, using an E-value cutoff of 1e\u003csup\u003e-5\u003c/sup\u003e, BLASTP searches were conducted against the proteomes of apple (\u003cem\u003eMalus domestica\u003c/em\u003e) and pear (\u003cem\u003ePyrus communis\u003c/em\u003e), and proteins exhibiting significant similarity were excluded from further analysis. The resulting pathogen-specific protein set was screened for virulence factors by performing BLASTP against the core dataset of the Virulence Factors Database (VFDB) using an E-value cutoff of 1e\u003csup\u003e-5\u003c/sup\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Essential proteins were identified through BLASTP searches against the Database of Essential Genes (DEG) with the same E-value threshold [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Subcellular localization of the proteins was predicted using DeepLoc-1.0, and only proteins localized to the cytoplasm or cell membrane were retained [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Functional domains were characterized using HMMER with the Pfam-A database [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Druggability was assessed by querying the refined protein set against the DrugBank database to identify matches with known drug targets [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Gene Ontology (GO) terms were assigned using Panzer2 via the SANSparallel server [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Homology to known Erwinia-derived pathogenic proteins was assessed using BLASTP against PHI-base (v. 5) to identify proteins potentially involved in host-pathogen interactions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The three-dimensional structures of the final candidate proteins were retrieved from the AlphaFold Protein Structure Database (v. 4) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and their active sites were predicted using the DeepSite server [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Subsequently, AutoDock Vina [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was employed to evaluate the binding affinity of these targets against a library of approximately 9,500 bioactive compounds obtained from Life Chemicals (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lifechemicals.com/screening-libraries/biologically-active-compound-library\u003c/span\u003e\u003cspan address=\"https://lifechemicals.com/screening-libraries/biologically-active-compound-library\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"III. Results and discussion","content":"\u003cp\u003eThe reference proteome of the pathogen was initially retrieved and filtered to retain protein sequences ranging from 100 to 1000 amino acids in length, resulting in 2,875 sequences. Redundancy reduction at a 40% identity threshold using CD-HIT yielded a non-redundant dataset of 2,697 sequences. To eliminate potential host homologs, BLASTP was conducted against the proteomes of apple (\u003cem\u003eMalus domestica\u003c/em\u003e) and pear (\u003cem\u003ePyrus communis\u003c/em\u003e), and 1,651 pathogen-specific proteins were retained following the removal of homologous sequences.\u003c/p\u003e\u003cp\u003eSubsequent screening for virulence factors using the VFDB database (E-value\u0026thinsp;\u0026le;\u0026thinsp;1e-5) identified 526 proteins, while 374 of these were also predicted as essential proteins via comparison with the DEG database. DeepLoc-based subcellular localization analysis revealed 166 proteins localized to either the cytoplasm or the cell membrane. Functional domain analysis using Pfam classified 107 proteins with identifiable domain architectures. Screening against DrugBank identified 44 proteins with potential druggability. GO annotation via Panzer2 assigned relevant terms to 31 proteins, and a final homology check against 123 \u003cem\u003eErwinia\u003c/em\u003e-derived sequences from PHI-base identified 2 proteins with significant similarity (E-value\u0026thinsp;\u0026le;\u0026thinsp;1e-5) as mentioned at Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003ePotential drug targets in \u003cem\u003eErwinia amylovora\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccession no.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePfam domains\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubcellular location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGo term\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_004160380.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePF00563;\u003c/p\u003e\u003cp\u003ePF00990;\u003c/p\u003e\u003cp\u003ePF13426;\u003c/p\u003e\u003cp\u003ePF13426;\u003c/p\u003e\u003cp\u003ePF13426;\u003c/p\u003e\u003cp\u003ePF00989;\u003c/p\u003e\u003cp\u003ePF00989;\u003c/p\u003e\u003cp\u003ePF08448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCytoplasm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGO:0071111; GO:0006355; GO:0016301; GO:0016829; GO:0016020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_004158032.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePF01339;\u003c/p\u003e\u003cp\u003ePF00072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCytoplasm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGO:0008984; GO:0050568; GO:0000156; GO:0006935; GO:0000160; GO:0005737\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the top five ligands identified through molecular docking against the target proteins WP_004160380.1 and WP_004158032.1, which are predicted to possess cyclic-guanylate-specific phosphodiesterase and protein-glutamate methylesterase activities, respectively. These ligands were selected based on their binding affinities, with more negative binding energy values indicating stronger predicted interactions. The docking results provide insights into potential inhibitory molecules that may modulate the function of these bacterial signaling proteins, supporting their relevance as candidate targets for antimicrobial development.\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\u003eTop five ligands docked against the potential drug targets of \u003cem\u003eErwinia amylovora\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug target\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLigand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBinding affinity (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eWP_004160380.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL2093312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-11.647\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1304704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.857\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1097223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1539970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1566748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eWP_004158032.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL2093312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-11.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1306632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-11.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1485976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1489036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHEMBL1328470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.763\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\u003eTo better understand the structural characteristics and binding modes of the top-performing ligands, both 2D and 3D representations were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The 2D depictions allowed for comparison of key chemical features such as ring systems, functional groups, and overall scaffold diversity. In parallel, 3D docking poses illustrated the spatial orientation of the ligands within the binding site of both targets, highlighting key interactions including hydrogen bonds, hydrophobic contacts, and potential π\u0026ndash;π stacking. These visualizations provided insights into the structural basis of binding affinity and supported the selection of promising candidates for further analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study utilized a comprehensive subtractive proteomic methodology to identify potential therapeutic targets within the pathogen's proteome. Starting with the reference proteome, a series of rigorous filtration and prioritization steps\u0026mdash;including sequence length selection, redundancy elimination, host homology exclusion, and functional annotation\u0026mdash;refined the dataset from thousands of proteins to a concise set of candidates. The identification of virulence-associated, essential, and pathogen-specific proteins localized to critical cellular compartments underscores the robustness of the pipeline. Additionally, the integration of domain analysis, druggability screening, and GO annotation provided further functional insights. Notably, two proteins exhibited significant similarity to experimentally validated virulence factors in \u003cem\u003eErwinia\u003c/em\u003e species, underscoring their potential as key therapeutic targets. These findings are discussed below in the context of their biological relevance and potential applicability in antimicrobial development.\u003c/p\u003e\u003cp\u003eThe two annotated protein targets from \u003cem\u003eErwinia amylovora\u003c/em\u003e, WP_004160380.1 and WP_004158032.1, demonstrate distinct but complementary regulatory functions, based on their conserved Pfam domains and enriched GO terms, which underscore their potential as novel antimicrobial targets. WP_004160380.1 possesses a composite structure consisting of PF00563 (EAL domain), PF00990 (GGDEF domain), and multiple PAS-related domains (PF13426, PF00989, PF08448). This domain configuration is characteristic of cyclic-di-GMP signaling proteins, which act as pivotal regulators of bacterial lifestyle transitions including motility, biofilm formation, and virulence [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The GGDEF domain confers diguanylate cyclase activity, while the EAL domain supports cyclic-guanylate-specific phosphodiesterase activity (GO:0071111), enabling the dynamic synthesis and degradation of cyclic-di-GMP (c-di-GMP), a ubiquitous bacterial second messenger. The presence of PAS and PAS-fold domains indicates potential regulatory control through environmental sensing, as these domains often modulate enzymatic function in response to redox potential, oxygen, or small ligands. The assigned GO terms support this multifunctionality, including regulation of DNA-templated transcription (GO:0006355), kinase activity (GO:0016301), and lyase activity (GO:0016829). While the protein is localized to the cytoplasm, the inclusion of membrane (GO:0016020) suggests peripheral membrane association or interaction with transmembrane complexes that facilitate signaling integration.\u003c/p\u003e\u003cp\u003eIn contrast, the second protein, WP_004158032.1, features PF01339 (CheB methylesterase domain) and PF00072 (response regulator receiver domain), components classically associated with two-component signal transduction systems, particularly in chemotaxis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCheB is a cytoplasmic response regulator that plays a pivotal role in bacterial chemotaxis through its involvement in the phosphorelay signal transduction system (GO:0000160). It exhibits dual enzymatic functions, including phosphorelay response regulator activity (GO:0000156) and protein-glutamate methylesterase activity (GO:0008984), enabling the demethylation of methyl-accepting chemotaxis proteins (MCPs) to modulate signal sensitivity. Additionally, it possesses protein-glutamine glutaminase activity (GO:0050568), which contributes to the adaptation of the chemotactic response (GO:0006935). These biochemical functions collectively regulate bacterial movement in response to chemical gradients, with CheB operating primarily within the cytoplasm (GO:0005737) as part of a finely tuned signaling network.\u003c/p\u003e\u003cp\u003eTogether, these two proteins represent key regulatory nodes in \u003cem\u003eE. amylovora\u003c/em\u003e's adaptive responses, with WP_004160380.1 governing intracellular signaling and transcriptional regulation via c-di-GMP dynamics, while WP_004158032.1 fine-tunes environmental navigation via chemotactic signal processing. Their disruption could impair essential virulence and survival mechanisms, offering promising avenues for structure-based drug targeting against this economically significant phytopathogen.\u003c/p\u003e"},{"header":"IV. Conclusion","content":"\u003cp\u003eThis study employed a comprehensive subtractive proteomics approach to identify potential drug targets in \u003cem\u003eErwinia\u003c/em\u003e by integrating comparative genomics, functional annotation, subcellular localization, and structure-based screening. From an initial proteome of 2,875 proteins, a refined set of 44 pathogen-specific, druggable, and functionally relevant candidates was identified. Further prioritization based on virulence, essentiality, and homology to known pathogenic factors revealed two high-confidence proteins with potential roles in host-pathogen interactions. These targets, supported by structural modeling and binding site prediction, provide promising starting points for the development of novel antibacterial agents against \u003cem\u003eErwinia\u003c/em\u003e-related diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQTLs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative Trait Loci\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUltraviolet\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCBI RefSeq\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Center for Biotechnology Information Reference Sequence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCD-HIT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCluster Database at High Identity with Tolerance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLASTP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBasic Local Alignment Search Tool for Proteins\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVFDB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVirulence Factors Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDatabase of Essential Genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHMMER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBiosequence Analysis Using Profile Hidden Markov Models\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePfam\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProtein Families Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO term\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Ontology term\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePHI-base\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePathogen\u0026ndash;Host Interaction Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e2D\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTwo-dimensional\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e3D\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThree-dimensional\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEAL domain\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlutamate\u0026ndash;Alanine\u0026ndash;Leucine domain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGGDEF domain\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlycine\u0026ndash;Glycine\u0026ndash;Aspartate\u0026ndash;Glutamate\u0026ndash;Phenylalanine domain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAS-related domains\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePer\u0026ndash;Arnt\u0026ndash;Sim related domains\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCheB methylesterase domain\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChemotaxis-specific methylesterase domain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCPs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMethyl-accepting Chemotaxis Proteins\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.E., N.S., H.A., Y.A., N.M., and A.F.R. conceived and designed the study. W.E., N.S., H.A., Y.A., and N.M. performed the in silico analyses. A.F.R. supervised the project. W.E. and A.F.R. wrote the main manuscript text. N.S., H.A., Y.A., and N.M. prepared the figures and tables. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge Dr. Aly AbdelMoteleb for his valuable comments and insightful suggestions that contributed to the improvement of this work.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang D, Korban SS, Pusey PL, Zhao Y. AmyR is a novel negative regulator of amylovoran production in Erwinia amylovora. PLoS ONE. 2012;7:e45038. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0045038\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0045038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl-Nasr S, Hamdy I, Ali MH, FIRE BLIGHT INCIDENCE IN, PEAR ORCHARDS AND ITS CONTROL IN EGYPT. Acta Hortic. 1990;392\u0026ndash;392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17660/ActaHortic.1990.273.59\u003c/span\u003e\u003cspan address=\"10.17660/ActaHortic.1990.273.59\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbo-El-Dahab MK, El-Goorani MA, El-Kasheir HM, Shoeib AA, SEVERE OUTBREAKS OF FIRE BLIGHT IN EGYPT DURING 1982 AND 1983 SEASONS. Acta Hortic. 1984;341\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17660/ActaHortic.1984.151.51\u003c/span\u003e\u003cspan address=\"10.17660/ActaHortic.1984.151.51\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Tom DZ, Beer SV, Cornell. university., United States. Fire blight\u0026ndash;its nature, prevention, and control : a practical guide to integrated disease management /. Washington, DC : U.S. Dept. of Agriculture :; 1999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5962/bhl.title.134796\u003c/span\u003e\u003cspan address=\"10.5962/bhl.title.134796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcManus PS, Stockwell VO, Sundin GW, Jones AL. Antibiotic use in plant agriculture. Annu Rev Phytopathol. 2002;40:443\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.phyto.40.120301.093927\u003c/span\u003e\u003cspan address=\"10.1146/annurev.phyto.40.120301.093927\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarakat F, Mikhail M, Tawfik A, Shahin H. Effect of some Plant Extracts on the Growth of Streptomycin Resistant and Sensitive Isolates of Erwinia amylovora. Egypt J Phytopathol. 2009;37:29\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21608/ejp.2009.234976\u003c/span\u003e\u003cspan address=\"10.21608/ejp.2009.234976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKe D, Luo J, Liu P, Shou L, Ijaz M, Ahmed T, et al. Advancements in Bacteriophages for the Fire Blight Pathogen Erwinia amylovora. Viruses. 2024;16:1619. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/v16101619\u003c/span\u003e\u003cspan address=\"10.3390/v16101619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026uuml;r A, Baştaş KK. Effectiveness of Phosphorous acid, Bacillus subtilis and Copper Compounds on Apple cv. Gala with M9 Rootstock in the Control of Fire Blight. Turkish JAF SciTech. 2023;11:2595\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24925/turjaf.v11is1.2595-2600.6533\u003c/span\u003e\u003cspan address=\"10.24925/turjaf.v11is1.2595-2600.6533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStockwell VO, Johnson KB, Sugar D, Loper JE. Mechanistically compatible mixtures of bacterial antagonists improve biological control of fire blight of pear. Phytopathology. 2011;101:113\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-03-10-0098\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-03-10-0098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeil A, Garcia-Libreros T, Richter K, Trognitz FC, Trognitz B, Hanke M, ‐V., et al. Strong evidence for a fire blight resistance gene of \u003cem\u003eMalus robusta\u003c/em\u003e located on linkage group 3. Plant Breeding. 2007;126:470\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1439-0523.2007.01408.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0523.2007.01408.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGill JJ, Svircev AM, Smith R, Castle AJ. Bacteriophages of Erwinia amylovora. Appl Environ Microbiol. 2003;69:2133\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.69.4.2133-2138.2003\u003c/span\u003e\u003cspan address=\"10.1128/AEM.69.4.2133-2138.2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMehla K, Ramana J. Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study. OMICS. 2015;19:393\u0026ndash;406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/omi.2015.0046\u003c/span\u003e\u003cspan address=\"10.1089/omi.2015.0046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btl158\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btl158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 2022;50:D912\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkab1107\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkab1107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res. 2009. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkn858\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkn858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 37 Database issue:D455-458.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoreno J, Nielsen H, Winther O, Teufel F. Predicting the subcellular location of prokaryotic proteins with DeepLocPro. 2024;:2024.01.04.574157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.01.04.574157\u003c/span\u003e\u003cspan address=\"10.1101/2024.01.04.574157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021;49:D412\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkaa913\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkx1037\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkx1037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eT\u0026ouml;r\u0026ouml;nen P, Medlar A, Holm L. PANNZER2: a rapid functional annotation web server. Nucleic Acids Res. 2018;46:W84\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gky350\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUrban M, Cuzick A, Seager J, Wood V, Rutherford K, Venkatesh SY, et al. PHI-base in 2022: a multi-species phenotype database for Pathogen\u0026ndash;Host Interactions. Nucleic Acids Res. 2022;50:D837\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkab1037\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkab1037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-021-03819-2\u003c/span\u003e\u003cspan address=\"10.1038/s41586-021-03819-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJim\u0026eacute;nez J, Doerr S, Mart\u0026iacute;nez-Rosell G, Rose AS, De Fabritiis G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics. 2017;33:3036\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btx350\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btx350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61:3891\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChou S-H, Galperin MY. Diversity of Cyclic Di-GMP-Binding Proteins and Mechanisms. J Bacteriol. 2016;198:32\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/JB.00333-15\u003c/span\u003e\u003cspan address=\"10.1128/JB.00333-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkerker JM, Prasol MS, Perchuk BS, Biondi EG, Laub MT. Two-component signal transduction pathways regulating growth and cell cycle progression in a bacterium: a system-level analysis. PLoS Biol. 2005;3:e334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pbio.0030334\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.0030334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fire blight, Erwinia amylovora, Apple, Pear, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-7381667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7381667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFire blight, a destructive disease affecting apple and pear orchards, is caused by the Gram-negative pathogen \u003cem\u003eErwinia amylovora\u003c/em\u003e, leading to considerable economic losses worldwide. Current control strategies using copper-based bactericides and antibiotics are becoming increasingly ineffective due to resistance development, highlighting the need for novel therapeutic interventions. In this study, we applied a robust subtractive proteomics pipeline to identify potential drug targets unique to \u003cem\u003eE. amylovora\u003c/em\u003e. The proteome was systematically filtered by length, redundancy, and host homology against \u003cem\u003eMalus domestica\u003c/em\u003e and \u003cem\u003ePyrus communis\u003c/em\u003e, followed by screening for virulence and essentiality using VFDB and DEG. Subcellular localization, Pfam domain annotation, druggability analysis via DrugBank, and Gene Ontology enrichment were performed. Two high-confidence targets were identified. The first harbors the CheB methylesterase domain (PF01339), a key regulator of bacterial chemotaxis. The second is a cyclic-di-GMP signaling protein, essential for bacterial lifestyle transitions and biofilm regulation, and contains the GGDEF (PF13426), EAL (PF00989), and PilZ (PF08448) domains. These domain architectures underscore their roles in intracellular signaling and motility control. Structural modeling using AlphaFold, coupled with active site prediction, enabled virtual screening against ~\u0026thinsp;9,500 bioactive compounds from Life Chemicals. Nine compounds demonstrated strong binding affinities with the predicted active sites via AutoDock Vina. These findings support their potential as inhibitors, although experimental validation is required. This integrative in silico pipeline offers promising candidates for the development of targeted therapeutics against \u003cem\u003eE. amylovora\u003c/em\u003e, contributing to sustainable fire blight management.\u003c/p\u003e","manuscriptTitle":"In silico Identification of Novel Drug Targets in Erwinia amylovora: A Step Toward Fire Blight Control in pear and apple trees","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 12:20:15","doi":"10.21203/rs.3.rs-7381667/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5086b0b5-79f1-467d-9d03-75707514ed70","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T06:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 12:20:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7381667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7381667","identity":"rs-7381667","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}