Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks...

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Autores principales:	Yang Li, Chengxin Zhang, Eric W Bell, Wei Zheng, Xiaogen Zhou, Dong-Jun Yu, Yang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ebd691bba94f4b35990eb6e0cb86a802
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id	oai:doaj.org-article:ebd691bba94f4b35990eb6e0cb86a802
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spelling	oai:doaj.org-article:ebd691bba94f4b35990eb6e0cb86a8022021-12-02T19:58:14ZDeducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.1553-734X1553-735810.1371/journal.pcbi.1008865https://doaj.org/article/ebd691bba94f4b35990eb6e0cb86a8022021-03-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008865https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.Yang LiChengxin ZhangEric W BellWei ZhengXiaogen ZhouDong-Jun YuYang ZhangPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 3, p e1008865 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yang Li Chengxin Zhang Eric W Bell Wei Zheng Xiaogen Zhou Dong-Jun Yu Yang Zhang Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
description	The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.
format	article
author	Yang Li Chengxin Zhang Eric W Bell Wei Zheng Xiaogen Zhou Dong-Jun Yu Yang Zhang
author_facet	Yang Li Chengxin Zhang Eric W Bell Wei Zheng Xiaogen Zhou Dong-Jun Yu Yang Zhang
author_sort	Yang Li
title	Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
title_short	Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
title_full	Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
title_fullStr	Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
title_full_unstemmed	Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
title_sort	deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ebd691bba94f4b35990eb6e0cb86a802
work_keys_str_mv	AT yangli deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks AT chengxinzhang deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks AT ericwbell deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks AT weizheng deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks AT xiaogenzhou deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks AT dongjunyu deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks AT yangzhang deducinghighaccuracyproteincontactmapsfromatripletofcoevolutionarymatricesthroughdeepresidualconvolutionalnetworks
_version_	1718375813719523328

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

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