[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117236-en":3,"doc-seo-117236-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117236,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Integrating machine learning to advance epitope mapping - Review","Identifying epitopes—protein segments that bind antibodies—is central to vaccine design, immunotherapeutics, and diagnostics. This review explains how experimental mapping methods differ in accuracy, throughput, cost, and feasibility, and how integrating machine learning can improve specificity and prediction accuracy through better data selection, feature design, and algorithm choice. It also addresses limitations of current approaches and outlines machine learning strategies to refine epitope prediction for polyreactive antibodies and conformational epitopes, improving interpretability and practical implementation.","TYPE Review  \nPUBLISHED 30 September 2024 DOI 10.3389/fimmu.2024.1463931  \nOPEN ACCESS  \nEDITED BY  \nAnastas Dimitrov Pashov,  \nBulgarian Academy of Sciences (BAS), Bulgaria  \nREVIEWED BY  \nAnjali Dhall,  \nNational Cancer Institute (NIH), United States Minh Nguyen,  \nBioinformatics Institute (A*STAR), Singapore  \n*CORRESPONDENCE  \nStephanie K. Yanow  \n[yanow@ualberta.ca](yanow@ualberta.ca)  \nRECEIVED 12 July 2024  \nACCEPTED 09 September 2024  \nPUBLISHED 30 September 2024  \nCITATION  \nGrewal S, Hegde N and Yanow SK (2024) Integrating machine learning to advance epitope mapping.  \nFront. Immunol. 15:1463931 .  \ndoi: 10.3389/fimmu.2024.1463931  \nCOPYRIGHT  \n© 2024 Grewal, Hegde and Yanow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIntegrating machine learning to advance epitope mapping  \nSimranjit Grewal 1, Nidhi Hegde 2 and Stephanie K. Yanow 1,3*  \n1 Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada, 2 Department of Computing Science, University of Alberta, Edmonton, AB, Canada, 3School of Public Health, University of Alberta, Edmonton, AB, Canada  \nIdentifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and diagnostics. In vaccine design, the intent is to identify the minimal epitope of an antigen that can elicit an immune response and avoid off-target effects. For prognostics and diagnostics, the epitope-antibody interaction is exploited to measure antigens associated with disease outcomes . Experimental methods such as X-ray crystallography, cryo-electron microscopy, and peptide arrays are used widely to map epitopes but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epito pe mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations of current methods and the potential for machine learning to deepen interpretation and increase feasibility of these methods. We also propose how machine learning can be employed to refine epitope prediction to address the apparent promiscuity of polyreactive antibodies and the challenge of defining conformational epitopes. We highlight the impact of machine learning on our current understanding of epitopes and its potential to guide the design of therapeutic interventions with more predictable outcomes.  \nKEYWORDS  \nmachine learning, epitope, B-cell, algorithm, features, databases, toolboxes, vaccine  \n1 Introduction  \nVaccines are among the most successful and cost-effective public health interventions, particularly to protect against infectious diseases. This was never more evident than during the COVID-19 pandemic where vaccines were the most valuable intervention to protect vulnerable populations from hospitalization and death (1, 2) . All the SARS-CoV-2 vaccines were based on the spike protein as the vaccine antigen, either expressed from DNA, mRNA, or as a recombinant protein, and elicited immune responses against dominant epitopes (or segments) within the protein. In response, new variants ofthe virus emerged with different amino acid sequences in these epitopes, impairing the efficacy of the first-generation vaccines and requiring the design of new variant-specific ones. The ongoing management of SARS-CoV-2 depends on our preparedness against emerging variants; this can be  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nfacilitated by designing vaccines that focus imm","cbCaisdFFKy8PLyC","https://ap.wps.com/l/cbCaisdFFKy8PLyC","pdf",1415959,1,14,"English","en",105,"# Introduction\n## Importance of epitope mapping\n## Role of machine learning in epitope mapping\n# In vitro epitope mapping methods\n## T-cell epitopes vs B-cell epitopes\n## Experimental methods: strengths and trade-offs","[{\"question\":\"Why is epitope mapping critical for vaccines and antibody-based applications?\",\"answer\":\"Epitope mapping supports vaccine design by identifying minimal antigen segments that elicit immune responses while limiting off-target effects. It also enables prognostics, diagnostics, and immunotherapeutics by leveraging epitope–antibody interactions.\"},{\"question\":\"How do T-cell and B-cell epitopes differ in structure and length?\",\"answer\":\"T-cell epitopes are typically presented as linear peptide segments recognized by MHC class I or II molecules. B-cell epitopes are usually conformational, formed by amino acids close in the protein’s 3D structure, making them variable in length and structure.\"},{\"question\":\"What factors determine the performance of machine learning tools for epitope mapping?\",\"answer\":\"The review emphasizes data selection, feature design, and algorithm choice as key determinants of specificity and prediction accuracy.\"}]","Integrating machine learning to advance epitope mapping - Review | PDF",1785674598,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-machine-learning-to-advance-epitope-mapping-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integrating-machine-learning-to-advance-epitope-mapping-review/117236/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is epitope mapping critical for vaccines and antibody-based applications?","Question",{"text":75,"@type":76},"Epitope mapping supports vaccine design by identifying minimal antigen segments that elicit immune responses while limiting off-target effects. It also enables prognostics, diagnostics, and immunotherapeutics by leveraging epitope–antibody interactions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do T-cell and B-cell epitopes differ in structure and length?",{"text":80,"@type":76},"T-cell epitopes are typically presented as linear peptide segments recognized by MHC class I or II molecules. B-cell epitopes are usually conformational, formed by amino acids close in the protein’s 3D structure, making them variable in length and structure.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors determine the performance of machine learning tools for epitope mapping?",{"text":84,"@type":76},"The review emphasizes data selection, feature design, and algorithm choice as key determinants of specificity and prediction accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]