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Simultaneous prediction of antibody backbone and side-chain conformations with deep learning
Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution mo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200299/ https://www.ncbi.nlm.nih.gov/pubmed/35704640 http://dx.doi.org/10.1371/journal.pone.0258173 |
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author | Akpinaroglu, Deniz Ruffolo, Jeffrey A. Mahajan, Sai Pooja Gray, Jeffrey J. |
author_facet | Akpinaroglu, Deniz Ruffolo, Jeffrey A. Mahajan, Sai Pooja Gray, Jeffrey J. |
author_sort | Akpinaroglu, Deniz |
collection | PubMed |
description | Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side-chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side-chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering it particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of the model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side-chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side-chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side-chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences. |
format | Online Article Text |
id | pubmed-9200299 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92002992022-06-16 Simultaneous prediction of antibody backbone and side-chain conformations with deep learning Akpinaroglu, Deniz Ruffolo, Jeffrey A. Mahajan, Sai Pooja Gray, Jeffrey J. PLoS One Research Article Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side-chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side-chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering it particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of the model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side-chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side-chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side-chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences. Public Library of Science 2022-06-15 /pmc/articles/PMC9200299/ /pubmed/35704640 http://dx.doi.org/10.1371/journal.pone.0258173 Text en © 2022 Akpinaroglu et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Akpinaroglu, Deniz Ruffolo, Jeffrey A. Mahajan, Sai Pooja Gray, Jeffrey J. Simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
title | Simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
title_full | Simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
title_fullStr | Simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
title_full_unstemmed | Simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
title_short | Simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
title_sort | simultaneous prediction of antibody backbone and side-chain conformations with deep learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200299/ https://www.ncbi.nlm.nih.gov/pubmed/35704640 http://dx.doi.org/10.1371/journal.pone.0258173 |
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