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SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs
Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR),...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320061/ https://www.ncbi.nlm.nih.gov/pubmed/37216611 http://dx.doi.org/10.1093/nar/gkad427 |
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author | Qiu, Tianyi Zhang, Lu Chen, Zikun Wang, Yuan Mao, Tiantian Wang, Caicui Cun, Yewei Zheng, Genhui Yan, Deyu Zhou, Mengdi Tang, Kailin Cao, Zhiwei |
author_facet | Qiu, Tianyi Zhang, Lu Chen, Zikun Wang, Yuan Mao, Tiantian Wang, Caicui Cun, Yewei Zheng, Genhui Yan, Deyu Zhou, Mengdi Tang, Kailin Cao, Zhiwei |
author_sort | Qiu, Tianyi |
collection | PubMed |
description | Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/. |
format | Online Article Text |
id | pubmed-10320061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103200612023-07-06 SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs Qiu, Tianyi Zhang, Lu Chen, Zikun Wang, Yuan Mao, Tiantian Wang, Caicui Cun, Yewei Zheng, Genhui Yan, Deyu Zhou, Mengdi Tang, Kailin Cao, Zhiwei Nucleic Acids Res Web Server Issue Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/. Oxford University Press 2023-05-22 /pmc/articles/PMC10320061/ /pubmed/37216611 http://dx.doi.org/10.1093/nar/gkad427 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server Issue Qiu, Tianyi Zhang, Lu Chen, Zikun Wang, Yuan Mao, Tiantian Wang, Caicui Cun, Yewei Zheng, Genhui Yan, Deyu Zhou, Mengdi Tang, Kailin Cao, Zhiwei SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs |
title | SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs |
title_full | SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs |
title_fullStr | SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs |
title_full_unstemmed | SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs |
title_short | SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs |
title_sort | seppa-mab: spatial epitope prediction of protein antigens for mabs |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320061/ https://www.ncbi.nlm.nih.gov/pubmed/37216611 http://dx.doi.org/10.1093/nar/gkad427 |
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