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Applications of graph theory in protein structure identification

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...

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Detalles Bibliográficos
Autores principales: Yan, Yan, Zhang, Shenggui, Wu, Fang-Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289078/
https://www.ncbi.nlm.nih.gov/pubmed/22165974
http://dx.doi.org/10.1186/1477-5956-9-S1-S17
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author Yan, Yan
Zhang, Shenggui
Wu, Fang-Xiang
author_facet Yan, Yan
Zhang, Shenggui
Wu, Fang-Xiang
author_sort Yan, Yan
collection PubMed
description 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.
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spelling pubmed-32890782012-02-29 Applications of graph theory in protein structure identification Yan, Yan Zhang, Shenggui Wu, Fang-Xiang Proteome Sci Proceedings 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. BioMed Central 2011-10-14 /pmc/articles/PMC3289078/ /pubmed/22165974 http://dx.doi.org/10.1186/1477-5956-9-S1-S17 Text en Copyright ©2011 Yan et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Yan, Yan
Zhang, Shenggui
Wu, Fang-Xiang
Applications of graph theory in protein structure identification
title Applications of graph theory in protein structure identification
title_full Applications of graph theory in protein structure identification
title_fullStr Applications of graph theory in protein structure identification
title_full_unstemmed Applications of graph theory in protein structure identification
title_short Applications of graph theory in protein structure identification
title_sort applications of graph theory in protein structure identification
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289078/
https://www.ncbi.nlm.nih.gov/pubmed/22165974
http://dx.doi.org/10.1186/1477-5956-9-S1-S17
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