Literature DB >> 22165974

Applications of graph theory in protein structure identification.

Yan Yan1, Shenggui Zhang, Fang-Xiang Wu.   

Abstract

There is a growing interest in the identification of proteins on the proteome wide scale. Among different kinds of protein structure identification methods, graph-theoretic methods are very sharp ones. Due to their lower costs, higher effectiveness and many other advantages, they have drawn more and more researchers' attention nowadays. Specifically, graph-theoretic methods have been widely used in homology identification, side-chain cluster identification, peptide sequencing and so on. This paper reviews several methods in solving protein structure identification problems using graph theory. We mainly introduce classical methods and mathematical models including homology modeling based on clique finding, identification of side-chain clusters in protein structures upon graph spectrum, and de novo peptide sequencing via tandem mass spectrometry using the spectrum graph model. In addition, concluding remarks and future priorities of each method are given.

Entities:  

Year:  2011        PMID: 22165974      PMCID: PMC3289078          DOI: 10.1186/1477-5956-9-S1-S17

Source DB:  PubMed          Journal:  Proteome Sci        ISSN: 1477-5956            Impact factor:   2.480


  46 in total

1.  A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry.

Authors:  T Chen; M Y Kao; M Tepel; J Rush; G M Church
Journal:  J Comput Biol       Date:  2001       Impact factor: 1.479

2.  A graph-theoretic approach for the separation of b and y ions in tandem mass spectra.

Authors:  Bo Yan; Chongle Pan; Victor N Olman; Robert L Hettich; Ying Xu
Journal:  Bioinformatics       Date:  2004-09-28       Impact factor: 6.937

Review 3.  Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book.

Authors:  Rovshan G Sadygov; Daniel Cociorva; John R Yates
Journal:  Nat Methods       Date:  2004-12       Impact factor: 28.547

4.  PepNovo: de novo peptide sequencing via probabilistic network modeling.

Authors:  Ari Frank; Pavel Pevzner
Journal:  Anal Chem       Date:  2005-02-15       Impact factor: 6.986

Review 5.  Proteomics, networks and connectivity indices.

Authors:  Humberto González-Díaz; Yenny González-Díaz; Lourdes Santana; Florencio M Ubeira; Eugenio Uriarte
Journal:  Proteomics       Date:  2008-02       Impact factor: 3.984

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Authors:  J Skolnick; A Kolinski
Journal:  Science       Date:  1990-11-23       Impact factor: 47.728

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Authors:  J W Ponder; F M Richards
Journal:  J Mol Biol       Date:  1987-02-20       Impact factor: 5.469

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Authors:  G Némethy; H A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  1979-12       Impact factor: 11.205

9.  Structure of an electron transfer complex: methylamine dehydrogenase, amicyanin, and cytochrome c551i.

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Journal:  Science       Date:  1994-04-01       Impact factor: 47.728

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Authors:  A Pertsemlidis; J W Fondon
Journal:  Genome Biol       Date:  2001-09-27       Impact factor: 13.583

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  4 in total

1.  Network representation of protein interactions: Theory of graph description and analysis.

Authors:  Dennis Kurzbach
Journal:  Protein Sci       Date:  2016-06-19       Impact factor: 6.725

2.  Weighted protein residue networks based on joint recurrences between residues.

Authors:  Wael I Karain; Nael I Qaraeen
Journal:  BMC Bioinformatics       Date:  2015-05-26       Impact factor: 3.169

3.  Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination.

Authors:  Rachael A Mansbach; Srirupa Chakraborty; Timothy Travers; S Gnanakaran
Journal:  Mar Drugs       Date:  2020-05-14       Impact factor: 5.118

Review 4.  Graph representation learning for structural proteomics.

Authors:  Romanos Fasoulis; Georgios Paliouras; Lydia E Kavraki
Journal:  Emerg Top Life Sci       Date:  2021-12-21
  4 in total

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