Explore Protein Conformational Space With Variational Autoencoder

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational a...

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Autores principales:	Hao Tian, Xi Jiang, Francesco Trozzi, Sian Xiao, Eric C. Larson, Peng Tao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	protein system conformational space variational autoencoder molecular dynamics deep learning Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0dd63e9213564b10acf17ca8968be3b0
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id	oai:doaj.org-article:0dd63e9213564b10acf17ca8968be3b0
record_format	dspace
spelling	oai:doaj.org-article:0dd63e9213564b10acf17ca8968be3b02021-11-12T06:43:44ZExplore Protein Conformational Space With Variational Autoencoder2296-889X10.3389/fmolb.2021.781635https://doaj.org/article/0dd63e9213564b10acf17ca8968be3b02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmolb.2021.781635/fullhttps://doaj.org/toc/2296-889XMolecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.Hao TianXi JiangFrancesco TrozziSian XiaoEric C. LarsonPeng TaoFrontiers Media S.A.articleprotein systemconformational spacevariational autoencodermolecular dynamicsdeep learningBiology (General)QH301-705.5ENFrontiers in Molecular Biosciences, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein system conformational space variational autoencoder molecular dynamics deep learning Biology (General) QH301-705.5
spellingShingle	protein system conformational space variational autoencoder molecular dynamics deep learning Biology (General) QH301-705.5 Hao Tian Xi Jiang Francesco Trozzi Sian Xiao Eric C. Larson Peng Tao Explore Protein Conformational Space With Variational Autoencoder
description	Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.
format	article
author	Hao Tian Xi Jiang Francesco Trozzi Sian Xiao Eric C. Larson Peng Tao
author_facet	Hao Tian Xi Jiang Francesco Trozzi Sian Xiao Eric C. Larson Peng Tao
author_sort	Hao Tian
title	Explore Protein Conformational Space With Variational Autoencoder
title_short	Explore Protein Conformational Space With Variational Autoencoder
title_full	Explore Protein Conformational Space With Variational Autoencoder
title_fullStr	Explore Protein Conformational Space With Variational Autoencoder
title_full_unstemmed	Explore Protein Conformational Space With Variational Autoencoder
title_sort	explore protein conformational space with variational autoencoder
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/0dd63e9213564b10acf17ca8968be3b0
work_keys_str_mv	AT haotian exploreproteinconformationalspacewithvariationalautoencoder AT xijiang exploreproteinconformationalspacewithvariationalautoencoder AT francescotrozzi exploreproteinconformationalspacewithvariationalautoencoder AT sianxiao exploreproteinconformationalspacewithvariationalautoencoder AT ericclarson exploreproteinconformationalspacewithvariationalautoencoder AT pengtao exploreproteinconformationalspacewithvariationalautoencoder
_version_	1718431092174749696

Explore Protein Conformational Space With Variational Autoencoder

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