Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with...

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Autores principales:	Tao Huang, Xiao-He Shi, Ping Wang, Zhisong He, Kai-Yan Feng, Lele Hu, Xiangyin Kong, Yi-Xue Li, Yu-Dong Cai, Kuo-Chen Chou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6c59599778ed4033b2c8aafbe1e8615e
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spelling	oai:doaj.org-article:6c59599778ed4033b2c8aafbe1e8615e2021-12-02T20:21:09ZAnalysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.1932-620310.1371/journal.pone.0010972https://doaj.org/article/6c59599778ed4033b2c8aafbe1e8615e2010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20532046/?tool=EBIhttps://doaj.org/toc/1932-6203The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: "short", "medium", "long", and "extra-long" half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.Tao HuangXiao-He ShiPing WangZhisong HeKai-Yan FengLele HuXiangyin KongYi-Xue LiYu-Dong CaiKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 6, p e10972 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tao Huang Xiao-He Shi Ping Wang Zhisong He Kai-Yan Feng Lele Hu Xiangyin Kong Yi-Xue Li Yu-Dong Cai Kuo-Chen Chou Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
description	The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: "short", "medium", "long", and "extra-long" half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.
format	article
author	Tao Huang Xiao-He Shi Ping Wang Zhisong He Kai-Yan Feng Lele Hu Xiangyin Kong Yi-Xue Li Yu-Dong Cai Kuo-Chen Chou
author_facet	Tao Huang Xiao-He Shi Ping Wang Zhisong He Kai-Yan Feng Lele Hu Xiangyin Kong Yi-Xue Li Yu-Dong Cai Kuo-Chen Chou
author_sort	Tao Huang
title	Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
title_short	Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
title_full	Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
title_fullStr	Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
title_full_unstemmed	Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
title_sort	analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/6c59599778ed4033b2c8aafbe1e8615e
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Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.

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