应用图论分析与最优化理论来数据挖掘大规模水牛普里昂蛋白结构数据

doi:10.15960/j.cnki.issn.1007-6093.2017.02.009

运筹学学报

应用图论分析与最优化理论来数据挖掘大规模水牛普里昂蛋白结构数据

张家普^1,2,*CHATTERJEE Subhojyoti²王凤²

1. 澳大利亚联邦大学信息与应用最优化研究中心, 巴拉瑞特 Vic3353, 澳大利亚; 2. 澳大利亚斯文本科技大学分子模型开发实验室, 墨尔本 Vic3122, 澳大利亚

收稿日期:2017-04-13 出版日期:2017-06-15 发布日期:2017-06-15
通讯作者: 张家普 E-mail: j.zhang@federation.edu.au

Using graph theory and optimization theory to do data mining the large scale buffalo prion protein structure database

ZHANG Jiapu^1,2,* CHATTERJEE Subhojyoti² WANG Feng²

1. Centre of Informatics and Applied Optimisation, The Federation University Australia, Ballarat Vic3353, Australia; 2. Molecular Model Discovery Laboratory, Swinburne University of Technology, Melbourne Vic3122, Australia

Received:2017-04-13 Online:2017-06-15 Published:2017-06-15
Supported by:
This research was supported by a Melbourne Bioinformatics grant numbered FED0001 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government (Australia)

摘要/Abstract

摘要：

图论、最优化理论显然在蛋白质结构的研究中大有用场. 首先, 调查/回顾了研究蛋白质结构的所有图论模型. 其后, 建立了一个图论模型: 让蛋白质的侧链来作为图的顶点, 应用图论的诸如团、 $k$-团、社群、枢纽、聚类等概念来建立图的边. 然后, 应用数学最优化的现代摩登数据挖掘算法/方法来分析水牛普里昂蛋白结构的大数据. 成功与令人耳目一新的数值结果将展示给朋友们.

关键词: 图论, 最优化理论, 数据挖掘, 应用于大数据

Abstract:

Graph theory and optimization theory are clearly very useful in the study of protein structures. Firstly, this paper is to research/review graph theory models in studies of protein structures. Secondly, we build a graph theory model to let the side-chain of a protein as a node, in the use of graph theory concepts such as clique, k-clique, community, hub, and cluster to build the edges. Thirdly, we solve the graph theory model built, using optimization theory/modern data mining algorithms/methods. Successful and fresh numerical results of data mining the large scale buffalo prion protein database will be illuminated.

Key words: graph theory, optimization theory, data mining, application to large scale databases

张家普, CHATTERJEE Subhojyoti, 王凤. 应用图论分析与最优化理论来数据挖掘大规模水牛普里昂蛋白结构数据[J]. 运筹学学报, doi: 10.15960/j.cnki.issn.1007-6093.2017.02.009.

ZHANG Jiapu, CHATTERJEE Subhojyoti, WANG Feng.

Using graph theory and optimization theory to do data mining the large scale buffalo prion protein structure database

[J]. Operations Research Transactions, doi: 10.15960/j.cnki.issn.1007-6093.2017.02.009.

参考文献

[1] Bondy J A, Murty U S R. Graph Theory [M]. London: Springer, 2008.

[2] Bondy J A, Murty U S R. Graph Theory with Applications [M]. New York: North-Holland, 1976.

[3] Niknam N, Khakzad H, Arab S S, et al. PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory [J]. Computers in Biology & Medicine, 2016, 72: 151-160.

[4] Chakrabarty B, Parekh N. PRIGSA: Protein repeat identification by graph spectral analysis [J]. Journal of Bioinformatics & Computational Biology, 2014, 12(6): 117-161.

[5] Namboodiri S K K. SpecP: A tool for spectral partitioning of protein contact graph [J]. Bioinformation, 2013, 9(10): 54-58.

[6] Miao Z, Cao Y, Jiang T. RASP: rapid modeling of protein side chain conformations [J]. Bioinformatics, 2011, 27(22): 17-22.

[7] Vijayabaskar M S, Niranjan V, Vishveshwara S. GraProStr graphs of protein structures: a tool for constructing the graphs and generating graph parameters for protein structures [J]. Open Bioinformatics Journal, 2011, 5(1): 53-58.

[8] Yan Y, Zhang S, Wu FX. Applications of graph theory in protein structure identification [J]. Proteome Science, 2011, 9 (Suppl 1)(1): S17.

[9] Tiwari A, Tiwari V. HBNG: Graph theory based visualization of hydrogen bond networks in protein structures [J]. Bioinformation,} 2007, 2(1): 28-30.

[10] Canutescu A A, Shelenkov A A, Dunbrack Jr R L. A graph-theory algorithm for rapid protein side-chain prediction [J]. Protein Science, 2003, 12(9): 2001-2014.

[11] Patra SM, Vishveshwara S. Backbone cluster identification in proteins by a graph theoretical method [J]. Biophysical Chemistry, 2000, 84(1): 13-25.

[12] Samudrala R, Moult J. A graph-theoretic algorithm for comparative modeling of protein structure [J]. Journal of Molecular Biology, 1998, 279(1): 287-302.

[13] Samudrala R, Moult J. Handling context-sensitivity in protein structures using graph theory: bona fide prediction [J]. Proteins,
1997, (Suppl 1): 43-49.

[14] van Geerestein-Ujah EC, Mariani M, Vis H. Use of graph theory for secondary structure recognition and sequential assignment in heteronuclear (13C, 15N) NMR spectra: application to HU protein from Bacillus stearothermophilus [J]. Biopolymers, 1996, 39(5): 691-707.

[15] Mitchell E M, Artymiuk P J, Rice D W, et al. Use of techniques derived from graph theory to compare secondary structure motifs in proteins [J]. Journal of Molecular Biology, 1990, 212(1): 151-166.

[16] Vishveshwara S, Brinda K V, Kannan N. Protein structure: insights from graph theory [J]. Journal of Theoretical & Computational Chemistry, 2002, 1(1): 187-211.

[17] Chang C C, Lin C J. LIBSVM: A library for support vector machines [J]. ACM Transactions on Intelligent Systems & Technology, 2011, 2(3): 1-27.

[18] Zhang J, Wang F, Chatterjee S. Molecular dynamics studies on the buffalo prion protein [J]. Journal of Biomolecular Structure & Dynamics, 2016, 34(4): 762-777.

[19] Chatterjee S, Ghosh S, Vishveshwara S. Network properties of decoys and CASP predicted models: a comparison with native protein structures [J]. Molecular Biosystems, 2013, 9: 1774-1788.

[20] Ghosh S, Vishveshwara S. Ranking the quality of protein structure models using sidechain based network properties [J]. F1000 Res, 2014, 3: 17-27.

[21] Kannan N, Vishveshwara S. Identification of side-chain clusters in protein structures by a graph spectral method [J]. Journal of Molecular Biology, 1999, 292: 441-464.

[22] Bhattacharyya M, Upadhyay R, Vishveshwara S. Interaction signatures stabilizing the NAD(P)-binding Rossmann fold: A structure network approach [J]. Plos One, 2012, 7: e51676-e51676.

[23] Sukhwal A, Bhattacharyya M, Vishveshwara S. Network approach for capturing ligand-induced subtle global changes in protein structures [J]. Acta Crystallographica, 2011, 67: 429-439.

[24] Palla G, Derenyi I, Farkas I, et al. Uncovering the overlapping community structure of complex networks in nature and society [J]. Nature, 2005, 435: 814-818.

[25] Bhattacharyya M, Ghosh A, Hansia P, et al. Allostery and conformational free energy changes in human tryptophanyl-tRNA synthetase from essential dynamics and structure networks [J]. Proteins, 2009, 78: 506-517.

[26] Deb D, Vishveshwara S, Vishveshwara S. Understanding protein structure from a percolation perspective [J]. Biophysical Journal, 2009, 97: 1787-1794.

应用图论分析与最优化理论来数据挖掘大规模水牛普里昂蛋白结构数据

Using graph theory and optimization theory to do data mining the large scale buffalo prion protein structure database

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价