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Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets
Understanding the structural determinants of a protein’s biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140102/ https://www.ncbi.nlm.nih.gov/pubmed/34021153 http://dx.doi.org/10.1038/s41467-021-23246-1 |
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author | Ward, Michael D. Zimmerman, Maxwell I. Meller, Artur Chung, Moses Swamidass, S. J. Bowman, Gregory R. |
author_facet | Ward, Michael D. Zimmerman, Maxwell I. Meller, Artur Chung, Moses Swamidass, S. J. Bowman, Gregory R. |
author_sort | Ward, Michael D. |
collection | PubMed |
description | Understanding the structural determinants of a protein’s biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding. |
format | Online Article Text |
id | pubmed-8140102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81401022021-06-03 Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets Ward, Michael D. Zimmerman, Maxwell I. Meller, Artur Chung, Moses Swamidass, S. J. Bowman, Gregory R. Nat Commun Article Understanding the structural determinants of a protein’s biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding. Nature Publishing Group UK 2021-05-21 /pmc/articles/PMC8140102/ /pubmed/34021153 http://dx.doi.org/10.1038/s41467-021-23246-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ward, Michael D. Zimmerman, Maxwell I. Meller, Artur Chung, Moses Swamidass, S. J. Bowman, Gregory R. Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets |
title | Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets |
title_full | Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets |
title_fullStr | Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets |
title_full_unstemmed | Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets |
title_short | Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets |
title_sort | deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with diffnets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140102/ https://www.ncbi.nlm.nih.gov/pubmed/34021153 http://dx.doi.org/10.1038/s41467-021-23246-1 |
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