[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125836-en":3,"doc-seo-125836-105":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},125836,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Prediction of polyspecificity from antibody sequence data by machine learning","Diversity of antibody repertoires enables binding to specific antigen targets, but polyspecific antibodies may also bind unrelated molecules, causing side effects and reduced therapeutic efficacy. A neural-network-based model is developed to predict polyspecificity using the antibody heavy-chain variable region sequence as input. The work includes enriching antibodies from immunization campaigns for antigen-specific or polyspecific binding, generating sequencing datasets for training and cross-validation, and analyzing model behavior to identify influential physico-chemical features. The approach supports therapeutic antibody development.","TYPE Original Research PUBLISHED 08 April 2024  \nDOI 10.3389/fbinf.2023.1286883  \nOPEN ACCESS  \nEDITED BY  \nHuixiao Hong,  \nUnited States Food and Drug Administration, United States  \nREVIEWED BY  \nVictor Greiff,  \nUniversity of Oslo, Norway Yariv Wine,  \nTel Aviv University, Israel  \n*CORRESPONDENCE  \nSzabolcs Éliás,  \n [sz.e@outlook.com](sz.e@outlook.com)[ ](sz.e@outlook.com)Francesca Ros,  \n [francesca. ros@roche.com](francesca. ros@roche.com)  \nRECEIVED 31 August 2023  \nACCEPTED 06 November 2023  \nPUBLISHED 08 April 2024  \nCITATION  \nÉliás S, Wrzodek C, Deane CM, Tissot AC,  \nKlostermann S and Ros F (2024), Prediction of polyspecificity from antibody sequence data by machine learning.  \nFront. Bioinform. 3:1286883 .  \ndoi: 10.3389/fbinf.2023.1286883  \nCOPYRIGHT  \n© 2024 Éliás, Wrzodek, Deane, Tissot, Klostermann and Ros. This is an openaccess 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.  \nPrediction of polyspecificity from antibody sequence data by machine learning  \nSzabolcs Éliás 1*, Clemens Wrzodek 1, Charlotte M. Deane 2, Alain C. Tissot 3, Stefan Klostermann 1 and Francesca Ros 3*  \n1Roche Pharma Research and Early Development Informatics, Roche Innovation Center Munich, Penzberg, Germany, 2Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom, 3Roche Pharmaceutical Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany  \nAntibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and crossvalidation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.  \nKEYWORDS  \nneural network, immunoglobulin, immune repertoire, polyspecificity, antibody, therapeutic antibodies, deep learning, machine learning  \n1 Introduction  \nAntibodies produced by B lymphocytes are a crucial part of the adaptive immune system. They are large proteins recognizing certain structures in their cognate antigen, and specific antibodies are generated during a germinal center reaction in secondary lymphoid organs [reviewed in (Victora and Nussenzweig, 2012)] . Antibody binding tags a pathogenic structure, ultimately leading to its neutralization and/or elimination by a complex interplay of several immune cells and pathways.  \nGenes encoding antibodies contain variable segments which through gene segment rearrangements, iterative somatic mutations and subsequent selections of antigen-binding antibodies enable the creation of a large variety of antibodies recog","cbCaiaL9GhXMGMkK","https://ap.wps.com/l/cbCaiaL9GhXMGMkK","pdf",2669371,1,17,"English","en",105,"# Introduction\n# Methods\n## Model training and cross-validation\n## Sequencing dataset generation\n# Results\n## Physico-chemical feature analysis\n# Discussion","[{\"question\":\"What problem does the study address in therapeutic antibody development?\",\"answer\":\"Polyspecific antibodies can bind structurally unrelated molecules, which may produce off-target effects, side effects, and decreased efficacy.\"},{\"question\":\"How is polyspecificity predicted in this work?\",\"answer\":\"A neural-network-based model predicts polyspecificity using the antibody heavy chain variable region sequence as input.\"},{\"question\":\"What data strategy is used to train and validate the model?\",\"answer\":\"Antibodies are enriched from immunization campaigns for antigen-specific or polyspecific binding, followed by generation of a large sequencing dataset for training and cross-validation.\"}]","Prediction of polyspecificity from antibody sequence data by machine learning | 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problem does the study address in therapeutic antibody development?","Question",{"text":75,"@type":76},"Polyspecific antibodies can bind structurally unrelated molecules, which may produce off-target effects, side effects, and decreased efficacy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is polyspecificity predicted in this work?",{"text":80,"@type":76},"A neural-network-based model predicts polyspecificity using the antibody heavy chain variable region sequence as input.",{"name":82,"@type":73,"acceptedAnswer":83},"What data strategy is used to train and validate the model?",{"text":84,"@type":76},"Antibodies are enriched from immunization campaigns for antigen-specific or polyspecific binding, followed by generation of a large sequencing dataset for training and 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