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Deep learning approaches for conformational flexibility and switching properties in protein design

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibilit...

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Detalles Bibliográficos
Autores principales: Rudden, Lucas S. P., Hijazi, Mahdi, Barth, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399439/
https://www.ncbi.nlm.nih.gov/pubmed/36032687
http://dx.doi.org/10.3389/fmolb.2022.928534
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author Rudden, Lucas S. P.
Hijazi, Mahdi
Barth, Patrick
author_facet Rudden, Lucas S. P.
Hijazi, Mahdi
Barth, Patrick
author_sort Rudden, Lucas S. P.
collection PubMed
description Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.
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spelling pubmed-93994392022-08-25 Deep learning approaches for conformational flexibility and switching properties in protein design Rudden, Lucas S. P. Hijazi, Mahdi Barth, Patrick Front Mol Biosci Molecular Biosciences Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399439/ /pubmed/36032687 http://dx.doi.org/10.3389/fmolb.2022.928534 Text en Copyright © 2022 Rudden, Hijazi and Barth. 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
Rudden, Lucas S. P.
Hijazi, Mahdi
Barth, Patrick
Deep learning approaches for conformational flexibility and switching properties in protein design
title Deep learning approaches for conformational flexibility and switching properties in protein design
title_full Deep learning approaches for conformational flexibility and switching properties in protein design
title_fullStr Deep learning approaches for conformational flexibility and switching properties in protein design
title_full_unstemmed Deep learning approaches for conformational flexibility and switching properties in protein design
title_short Deep learning approaches for conformational flexibility and switching properties in protein design
title_sort deep learning approaches for conformational flexibility and switching properties in protein design
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399439/
https://www.ncbi.nlm.nih.gov/pubmed/36032687
http://dx.doi.org/10.3389/fmolb.2022.928534
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