Literature DB >> 24862400

Human proteins characterization with subcellular localizations.

Lei Yang1, Yingli Lv1, Tao Li2, Yongchun Zuo3, Wei Jiang4.   

Abstract

Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell's survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Biological properties; Codon usage bias; Expression level; Physicochemical properties; Topological properties

Mesh:

Substances:

Year:  2014        PMID: 24862400     DOI: 10.1016/j.jtbi.2014.05.008

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  3 in total

1.  IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy.

Authors:  Hao Wang; Qilemuge Xi; Pengfei Liang; Lei Zheng; Yan Hong; Yongchun Zuo
Journal:  Amino Acids       Date:  2021-01-23       Impact factor: 3.520

2.  Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization.

Authors:  Hanhan Cong; Hong Liu; Yuehui Chen; Yi Cao
Journal:  Med Biol Eng Comput       Date:  2020-10-20       Impact factor: 2.602

3.  Multiple-Localization and Hub Proteins.

Authors:  Motonori Ota; Hideki Gonja; Ryotaro Koike; Satoshi Fukuchi
Journal:  PLoS One       Date:  2016-06-10       Impact factor: 3.240

  3 in total

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