Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, d...

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Autores principales:	Xiao Chen, Jian Liu, Zhiye Guo, Tianqi Wu, Jie Hou, Jianlin Cheng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7cc61f3613084b268a5cf7d582f47d0e
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spelling	oai:doaj.org-article:7cc61f3613084b268a5cf7d582f47d0e2021-12-02T15:00:59ZProtein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP1410.1038/s41598-021-90303-62045-2322https://doaj.org/article/7cc61f3613084b268a5cf7d582f47d0e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90303-6https://doaj.org/toc/2045-2322Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.Xiao ChenJian LiuZhiye GuoTianqi WuJie HouJianlin ChengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiao Chen Jian Liu Zhiye Guo Tianqi Wu Jie Hou Jianlin Cheng Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14
description	Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.
format	article
author	Xiao Chen Jian Liu Zhiye Guo Tianqi Wu Jie Hou Jianlin Cheng
author_facet	Xiao Chen Jian Liu Zhiye Guo Tianqi Wu Jie Hou Jianlin Cheng
author_sort	Xiao Chen
title	Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14
title_short	Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14
title_full	Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14
title_fullStr	Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14
title_full_unstemmed	Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14
title_sort	protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in casp14
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7cc61f3613084b268a5cf7d582f47d0e
work_keys_str_mv	AT xiaochen proteinmodelaccuracyestimationempoweredbydeeplearningandinterresiduedistancepredictionincasp14 AT jianliu proteinmodelaccuracyestimationempoweredbydeeplearningandinterresiduedistancepredictionincasp14 AT zhiyeguo proteinmodelaccuracyestimationempoweredbydeeplearningandinterresiduedistancepredictionincasp14 AT tianqiwu proteinmodelaccuracyestimationempoweredbydeeplearningandinterresiduedistancepredictionincasp14 AT jiehou proteinmodelaccuracyestimationempoweredbydeeplearningandinterresiduedistancepredictionincasp14 AT jianlincheng proteinmodelaccuracyestimationempoweredbydeeplearningandinterresiduedistancepredictionincasp14
_version_	1718389146065567744

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

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