Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information

The subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences...

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Autores principales:	Ge Wang, Yu-Jia Zhai, Zhen-Zhen Xue, Ying-Ying Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	subcellular location prediction protein structure deep learning graph neural network protein data bank Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/37461aa05ec542d2b8645e8d2331ce59
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id	oai:doaj.org-article:37461aa05ec542d2b8645e8d2331ce59
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spelling	oai:doaj.org-article:37461aa05ec542d2b8645e8d2331ce592021-11-25T16:52:45ZImproving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information10.3390/biom111116072218-273Xhttps://doaj.org/article/37461aa05ec542d2b8645e8d2331ce592021-10-01T00:00:00Zhttps://www.mdpi.com/2218-273X/11/11/1607https://doaj.org/toc/2218-273XThe subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences as the data source. Only a few works focused on other protein data types. For example, three-dimensional structures, which contain far more functional protein information than sequences, remain to be explored. In this work, we extracted various handcrafted features to describe the protein structures from physical, chemical, and topological aspects, as well as the learned features obtained by deep neural networks. We then used these features to classify the protein subcellular locations. Our experimental results demonstrated that some of these structural features have a certain effect on the protein location classification, and can help improve the performance of sequence-based location predictors. Our method provides a new view for the analysis of protein spatial distribution, and is anticipated to be used in revealing the relationships between protein structures and functions.Ge WangYu-Jia ZhaiZhen-Zhen XueYing-Ying XuMDPI AGarticlesubcellular location predictionprotein structuredeep learninggraph neural networkprotein data bankMicrobiologyQR1-502ENBiomolecules, Vol 11, Iss 1607, p 1607 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	subcellular location prediction protein structure deep learning graph neural network protein data bank Microbiology QR1-502
spellingShingle	subcellular location prediction protein structure deep learning graph neural network protein data bank Microbiology QR1-502 Ge Wang Yu-Jia Zhai Zhen-Zhen Xue Ying-Ying Xu Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information
description	The subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences as the data source. Only a few works focused on other protein data types. For example, three-dimensional structures, which contain far more functional protein information than sequences, remain to be explored. In this work, we extracted various handcrafted features to describe the protein structures from physical, chemical, and topological aspects, as well as the learned features obtained by deep neural networks. We then used these features to classify the protein subcellular locations. Our experimental results demonstrated that some of these structural features have a certain effect on the protein location classification, and can help improve the performance of sequence-based location predictors. Our method provides a new view for the analysis of protein spatial distribution, and is anticipated to be used in revealing the relationships between protein structures and functions.
format	article
author	Ge Wang Yu-Jia Zhai Zhen-Zhen Xue Ying-Ying Xu
author_facet	Ge Wang Yu-Jia Zhai Zhen-Zhen Xue Ying-Ying Xu
author_sort	Ge Wang
title	Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information
title_short	Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information
title_full	Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information
title_fullStr	Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information
title_full_unstemmed	Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information
title_sort	improving protein subcellular location classification by incorporating three-dimensional structure information
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/37461aa05ec542d2b8645e8d2331ce59
work_keys_str_mv	AT gewang improvingproteinsubcellularlocationclassificationbyincorporatingthreedimensionalstructureinformation AT yujiazhai improvingproteinsubcellularlocationclassificationbyincorporatingthreedimensionalstructureinformation AT zhenzhenxue improvingproteinsubcellularlocationclassificationbyincorporatingthreedimensionalstructureinformation AT yingyingxu improvingproteinsubcellularlocationclassificationbyincorporatingthreedimensionalstructureinformation
_version_	1718412886886318080

Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information

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