[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119150-en":3,"doc-seo-119150-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119150,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine learning for predicting antibody-antigen interaction from amino acid sequences","The mammalian immune system generates antibodies against diverse antigens, yet antigen specificity prediction from sequence data remains limited by the lack of high-throughput, sequence-based methods. This thesis compares multiple machine learning approaches for predicting antibody-antigen binding using a curated antibody-antigen pair dataset. Training and testing data are extracted from PDB and Cov-AbDab, with additional pairs generated via molecular docking.","Machine learning for predicting antibody-antigen interaction from amino acid sequences  \nAuthor:  \nYe , Chao  \nPublication Date:  \n2024  \nDOI:  \n[https://doi.org/10.26190/unsworks/30143](https://doi.org/10.26190/unsworks/30143)  \nLicense:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nLink to license to see what you are allowed to do with this resource.  \nDownloaded from [http://hdl.handle. net/1959.4/102207](http://hdl.handle. net/1959.4/102207) in [https://](https://)[ ](https://)[unsworks. unsw.edu.au](unsworks. unsw.edu.au) on 2024-10-11  \nMachine learning for predicting antibody-antigen interaction from amino acid sequences  \nChao Ye  \nA thesis submitted for the degree of Doctor of Philosophy  \nSchool of Computer Science and Engineering Faculty of Engineering Feb 2024  \nAbstract  \nBackground:  \nThe mammalian immune system is able to generate antibodies against a huge variety of antigens including bacteria, viruses and toxins. “Ultra-deep” DNA sequencing of rearranged immunoglobulin genes has considerable potential in furthering our understanding of the immune response, but is limited by the lack of high-throughput, sequence-based method for predicting the antigen(s) a given immunoglobulin will recognize.  \nObjective:  \nAs a step towards the prediction of antibody-antigen binding from sequence data alone, we aimed to compare the application of a range of machine learning approaches to a collated dataset of antibody-antigen pairs in order to predict antibody-antigen binding from sequence data.  \nMethods:  \nData for training and testing were extracted from the PDB and Cov-AbDab databases, and additional antibody-antigen pair data were generated using a molecular docking protocol. Several machine learning methods including weighted nearest neighbor, nearest neighbor with BLOSUM62 matrices and random forests were applied to the problem.  \nResults:  \nThe final dataset contained 1157 antibodies and 57 antigens combined in 5041 Ab-Ag pairs. The best performance for prediction of interactions was obtained using nearest neighbor with BLOSUM62 matrices which allowed around 82% accuracy on the full dataset. These results provide a useful frame of reference as well as protocols and considerations for machine learning and dataset creation in this area.  \nConclusions:  \nSeveral machine learning approaches were compared to predict antibody- antigen interaction from protein sequences. Both the dataset (in csv format) and the machine learning program (coded in python) are freely available for download at [https://github.com/jessye123/ab-ag-seq-machine-learning](https://github.com/jessye123/ab-ag-seq-machine-learning)  \nAcknowledgements  \nI would like to express my sincere gratitude to the many people who have made this thesis possible.  \nFirst and foremost, I am immensely thankful to my supervisor, Dr. Bruno Gaeta, for his unwavering support throughout my PhD journey. His patience, motivation, enthusiasm, and profound knowledge have been invaluable to me. Without Bruno's guidance and assistance in nearly every aspect of my research, I would not have been able to accomplish this work. I couldn't have asked for a better supervisor, and I am truly fortunate to have had Bruno by my side.  \nI would also like to extend my thanks to the members of my panel team for their assistance in addressing challenges in immunology, thesis and publication writing, and their collaboration in the research. Additionally, I am grateful to my co-supervisor, Dr. Mike Bain, for his enlightening lectures on machine learning. Special thanks go to Wenxing Hu for his invaluable assistance with the COVID dataset, which greatly contributed to my research.  \nI want to acknowledge my friend Ann, who encouraged me to pursue a PhD at a later stage in life, as well as all my other friends who supported me throughout my doctoral studies.  \nLastly, I extend my heartfelt thanks to my parents and my sister. Your happiness in seein","cbCaiaciVa93ZF6e","https://ap.wps.com/l/cbCaiaciVa93ZF6e","pdf",5686751,1,150,"English","en",105,"# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusions\n# Acknowledgements\n# Publications resulting from this thesis\n# List of tables","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To compare machine learning approaches for predicting antibody-antigen binding using sequence data alone.\"},{\"question\":\"How were the training and test data generated?\",\"answer\":\"Antibody-antigen data were extracted from PDB and Cov-AbDab, and additional pairs were generated using a molecular docking protocol.\"},{\"question\":\"Which method achieved the best prediction performance and why is it important?\",\"answer\":\"Nearest neighbor with BLOSUM62 matrices produced the best performance, reaching about 82% accuracy on the full dataset, providing practical reference protocols for future dataset and model creation.\"}]","Machine learning for predicting antibody-antigen interaction from amino acid sequences | 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is the main objective of the study?","Question",{"text":76,"@type":77},"To compare machine learning approaches for predicting antibody-antigen binding using sequence data alone.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the training and test data generated?",{"text":81,"@type":77},"Antibody-antigen data were extracted from PDB and Cov-AbDab, and additional pairs were generated using a molecular docking protocol.",{"name":83,"@type":74,"acceptedAnswer":84},"Which method achieved the best prediction performance and why is it important?",{"text":85,"@type":77},"Nearest neighbor with BLOSUM62 matrices produced the best performance, reaching about 82% accuracy on the full dataset, providing practical reference protocols for future dataset and model 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