Amino acid torsion angles enable prediction of protein fold classification

Abstract Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used...

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Autores principales:	Kun Tian, Xin Zhao, Xiaogeng Wan, Stephen S.-T. Yau
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/199937e8b1a14984ab6c0c6f66e11993
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spelling	oai:doaj.org-article:199937e8b1a14984ab6c0c6f66e119932021-12-02T16:18:07ZAmino acid torsion angles enable prediction of protein fold classification10.1038/s41598-020-78465-12045-2322https://doaj.org/article/199937e8b1a14984ab6c0c6f66e119932020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78465-1https://doaj.org/toc/2045-2322Abstract Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.Kun TianXin ZhaoXiaogeng WanStephen S.-T. YauNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kun Tian Xin Zhao Xiaogeng Wan Stephen S.-T. Yau Amino acid torsion angles enable prediction of protein fold classification
description	Abstract Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.
format	article
author	Kun Tian Xin Zhao Xiaogeng Wan Stephen S.-T. Yau
author_facet	Kun Tian Xin Zhao Xiaogeng Wan Stephen S.-T. Yau
author_sort	Kun Tian
title	Amino acid torsion angles enable prediction of protein fold classification
title_short	Amino acid torsion angles enable prediction of protein fold classification
title_full	Amino acid torsion angles enable prediction of protein fold classification
title_fullStr	Amino acid torsion angles enable prediction of protein fold classification
title_full_unstemmed	Amino acid torsion angles enable prediction of protein fold classification
title_sort	amino acid torsion angles enable prediction of protein fold classification
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/199937e8b1a14984ab6c0c6f66e11993
work_keys_str_mv	AT kuntian aminoacidtorsionanglesenablepredictionofproteinfoldclassification AT xinzhao aminoacidtorsionanglesenablepredictionofproteinfoldclassification AT xiaogengwan aminoacidtorsionanglesenablepredictionofproteinfoldclassification AT stephenstyau aminoacidtorsionanglesenablepredictionofproteinfoldclassification
_version_	1718384156551938048

Amino acid torsion angles enable prediction of protein fold classification

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