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Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties
Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9978208/ https://www.ncbi.nlm.nih.gov/pubmed/36876042 http://dx.doi.org/10.3389/fmolb.2023.1121962 |
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author | Sala, Davide Hildebrand, Peter W. Meiler, Jens |
author_facet | Sala, Davide Hildebrand, Peter W. Meiler, Jens |
author_sort | Sala, Davide |
collection | PubMed |
description | Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion. |
format | Online Article Text |
id | pubmed-9978208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99782082023-03-03 Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties Sala, Davide Hildebrand, Peter W. Meiler, Jens Front Mol Biosci Molecular Biosciences Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion. Frontiers Media S.A. 2023-02-16 /pmc/articles/PMC9978208/ /pubmed/36876042 http://dx.doi.org/10.3389/fmolb.2023.1121962 Text en Copyright © 2023 Sala, Hildebrand and Meiler. https://creativecommons.org/licenses/by/4.0/This is an open-access 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. |
spellingShingle | Molecular Biosciences Sala, Davide Hildebrand, Peter W. Meiler, Jens Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties |
title | Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties |
title_full | Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties |
title_fullStr | Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties |
title_full_unstemmed | Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties |
title_short | Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties |
title_sort | biasing alphafold2 to predict gpcrs and kinases with user-defined functional or structural properties |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9978208/ https://www.ncbi.nlm.nih.gov/pubmed/36876042 http://dx.doi.org/10.3389/fmolb.2023.1121962 |
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