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OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies...

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Detalles Bibliográficos
Autores principales: Xu, Gang, Wang, Qinghua, Ma, Jianpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769891/
https://www.ncbi.nlm.nih.gov/pubmed/34905769
http://dx.doi.org/10.1093/bib/bbab529
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author Xu, Gang
Wang, Qinghua
Ma, Jianpeng
author_facet Xu, Gang
Wang, Qinghua
Ma, Jianpeng
author_sort Xu, Gang
collection PubMed
description Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).
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spelling pubmed-87698912022-01-20 OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors Xu, Gang Wang, Qinghua Ma, Jianpeng Brief Bioinform Problem Solving Protocol Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4). Oxford University Press 2021-12-15 /pmc/articles/PMC8769891/ /pubmed/34905769 http://dx.doi.org/10.1093/bib/bbab529 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Problem Solving Protocol
Xu, Gang
Wang, Qinghua
Ma, Jianpeng
OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
title OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
title_full OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
title_fullStr OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
title_full_unstemmed OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
title_short OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
title_sort opus-rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769891/
https://www.ncbi.nlm.nih.gov/pubmed/34905769
http://dx.doi.org/10.1093/bib/bbab529
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