Literature DB >> 23154188

Protein space: a natural method for realizing the nature of protein universe.

Chenglong Yu1, Mo Deng, Shiu-Yuen Cheng, Shek-Chung Yau, Rong L He, Stephen S-T Yau.   

Abstract

Current methods cannot tell us what the nature of the protein universe is concretely. They are based on different models of amino acid substitution and multiple sequence alignment which is an NP-hard problem and requires manual intervention. Protein structural analysis also gives a direction for mapping the protein universe. Unfortunately, now only a minuscule fraction of proteins' 3-dimensional structures are known. Furthermore, the phylogenetic tree representations are not unique for any existing tree construction methods. Here we develop a novel method to realize the nature of protein universe. We show the protein universe can be realized as a protein space in 60-dimensional Euclidean space using a distance based on a normalized distribution of amino acids. Every protein is in one-to-one correspondence with a point in protein space, where proteins with similar properties stay close together. Thus the distance between two points in protein space represents the biological distance of the corresponding two proteins. We also propose a natural graphical representation for inferring phylogenies. The representation is natural and unique based on the biological distances of proteins in protein space. This will solve the fundamental question of how proteins are distributed in the protein universe.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 23154188     DOI: 10.1016/j.jtbi.2012.11.005

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


  18 in total

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2.  An information-based network approach for protein classification.

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3.  Distinguishing proteins from arbitrary amino acid sequences.

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Journal:  Sci Rep       Date:  2015-01-22       Impact factor: 4.379

4.  3D representations of amino acids-applications to protein sequence comparison and classification.

Authors:  Jie Li; Patrice Koehl
Journal:  Comput Struct Biotechnol J       Date:  2014-09-06       Impact factor: 7.271

5.  Virus Database and Online Inquiry System Based on Natural Vectors.

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Journal:  Evol Bioinform Online       Date:  2017-12-17       Impact factor: 1.625

6.  Establishing the phylogeny of Prochlorococcus with a new alignment-free method.

Authors:  Xin Zhao; Kun Tian; Rong L He; Stephen S-T Yau
Journal:  Ecol Evol       Date:  2017-11-15       Impact factor: 2.912

7.  A latent genetic subtype of major depression identified by whole-exome genotyping data in a Mexican-American cohort.

Authors:  C Yu; M Arcos-Burgos; J Licinio; M-L Wong
Journal:  Transl Psychiatry       Date:  2017-05-16       Impact factor: 6.222

8.  FEGS: a novel feature extraction model for protein sequences and its applications.

Authors:  Zengchao Mu; Ting Yu; Xiaoping Liu; Hongyu Zheng; Leyi Wei; Juntao Liu
Journal:  BMC Bioinformatics       Date:  2021-06-03       Impact factor: 3.169

9.  Two Dimensional Yau-Hausdorff Distance with Applications on Comparison of DNA and Protein Sequences.

Authors:  Kun Tian; Xiaoqian Yang; Qin Kong; Changchuan Yin; Rong L He; Stephen S-T Yau
Journal:  PLoS One       Date:  2015-09-18       Impact factor: 3.240

10.  DFA7, a new method to distinguish between intron-containing and intronless genes.

Authors:  Chenglong Yu; Mo Deng; Lu Zheng; Rong Lucy He; Jie Yang; Stephen S-T Yau
Journal:  PLoS One       Date:  2014-07-18       Impact factor: 3.240

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