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基于遗传算法的G蛋白偶联受体(GPCR)家族的分类预测研究 被引量：1

Classification prediction of G protein-coupled receptor(GPCR) family based on genetic algorithm
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摘要 G蛋白偶联受体广泛参与各类生理活动的调控,目前市场上1/2的小分子药物均是以GPCR为药物靶标。由于G蛋白偶联受体晶体结构缺乏,采用理论方法对G蛋白受体耦合特异性进行分类预测在药物研发领域有着重要的学术和应用价值。因此,本文采用模式识别方法,基于GPCR序列,以伪氨基酸算法以及遗传算法为基础,用支持向量机方法建立了G蛋白偶联受体耦合特异性的预测模型,取得了可达82.5%的较高的预测准确度。 G protein-coupled receptor（GPCR） has widely participated in the regulation of various physiological functions. GPCR has been drug target of most drug moleeules on the market. Due to lack of crystal structures of GPCR,it is important to use computation- al method to predict the coupling selectivity of GPCRs in the drug design field. Thereby, in the work, the pseudo-amino acids algo- rithm,the genetic algorithm and the Support Vector Machine method are used to carry out classification prediction of GPCR, by means of the protein sequence information. The prediction accuracy of the elassification model reaches up to 82. 5%.
作者 王敏琦 张力耘 田雪 蒲雪梅 李梦龙
机构地区 四川大学化学学院
出处 《化学研究与应用》 CAS CSCD 北大核心 2012年第10期1534-1539,共6页 Chemical Research and Application
基金 2012年"全国大学生创新创业训练计划"四川大学国家级项目(201210610046)资助 2012年"全国大学生创新创业训练计划"四川大学校级项目(20120166)资助
关键词 G蛋白偶联受体 模式识别 伪氨基酸算法 遗传算法 G protein-coupled receptor pattern recognition pseudo-amino acids algorithm genetic algorithm
分类号 O604 [理学—化学]
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参考文献18

  • 1Hebert T E, Bouvier M. Structural and functional aspects of G protein-coupled receptor oligomerization[ J]. Biochem Cell Biol, 1998,76 ( 1 ) : 1-11.
  • 2Schoneberg T, Schulz A, Biebermann H, et al. Mutant G- protein-coupled receptors as a cause of human diseases [J]. Pharmacol Ther,2004,104(3) :173-206.
  • 3Klabunde T, Hessler G. Drug design strategies for targeting G-protein-coupled receptors [ J ]. Chem Bio Chem, 2002,3 (10) :928-944.
  • 4Simon M I, Strathmann M P, Gautam N. Diversity of G pro- reins in signal transduction [ J ]. Science, 1991, 252 (5007) :802-808.
  • 5YanzhiGuo, MenglongLi, MinchunLu, et al. Predicting G- protein coupled receptors-G-protein coupling specificity based on autocross-covariance transform [ J ]. PROTEINS : Struct , Funct , and Bioinf ,2006 , 65 ( 1 ) :55-60.
  • 6Ji T H, Grossmann M, Ji I. G-protein coupled receptors [ J ]. J Biol Chem, 1998,273 (28) : 17299-17302.
  • 7Altschul S F, Madden T L, Schaffer A A, et al. Gapped BLAST and PSI-BLAST:a new generation of protein data- base search programs [ J]. Nucleic Acids Res, 1997,25 (17) :3389-3402.
  • 8Pearson W R. Flexible sequence similarity searching with the FASTA3 program package [ J ]. Methods Mol Biol, 2000,132(2) : 185-219.
  • 9Papasaikas P K, Bagos P G, Litou Z I, et al. A novel meth- od for GPCR recognition and family classification from se- quence alone using signatures derived from profile hidden Markov models[ J ]. SAR QSAR Environ Res, 2003,14 ( 5- 6) :413-420.
  • 10Karchin R, Karplus K, Haussler D. Classifying G-protein coupled receptors with support vector machines [ J ]. Bioinformatics ,2002,18( 1 ) : 147-159.

同被引文献11

  • 1Sowa M E, He W, Slep K C, et al. Prediction and confirma- tion of a site critical for effector regulation of RGS domain activity[ J]. Nat Struct Biol,2001,8 (3) :234-237.
  • 2Tunebag N, Gursoy A, Nussinov R, et al. Predicting pro- rein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM[J]. Nat Protoc ,2011,6 ( 9 ) :1341-1354.
  • 3Liu X Y, Liu B, Huang Z M, et al. SPPS : a sequence-based method for predicting probability of protein-protein interac- tion partners[J]. Plos One,2012,7( 1 ) :e30938.
  • 4Zahiri J, Yaghoubi O, Noori M M, et al. PPIevo : protein- protein interaction prediction from PSSM based evolution- ary information[ J ]. Genom/cs ,2013,102:237-242.
  • 5]Guo Y Z, Yu L Z, Wen Z N, et al. Using support vector machine combined with auto covariance to predict protein- protein interactions from protein sequences [ J ]. Nucleic Acids Res ,2008,36 (9) :3025-3030.
  • 6]Li Z C, Zhou X, Dai Z, et al. Classification of G-protein coupled receptors based on support vector machine with maximum relevant minimum redundancy and genetic algo- rithm[J].BMC Bioinformatics, 11:325.
  • 7Dubchak I, Muchnik I. Holbrook S R, et al. Prediction of protein folding class using global description of amino acid sequence[ J ]. Proc Natl Acad Sci USA, 1995,92 : 8700- 8704.
  • 8Han L Y, Cai C Z, Ji Z L, et al. Predicting functional fami- ly of novel enzyme irrespective of sequence similarity[ J]. Nucleic Acids Res ,2004,32:6437-64.44.
  • 9Peng H, Long F, Ding C. Feature selection based on mutu- al information : criteria of max-dependency, max-relevance, and rain-redundancy [ J ]. IEEE Trans Pattern Anal Mach lntell,2005,27 ( 8 ) : 1226-1238.
  • 10Chang C H,Lin C J. LIBSVM:a library for support vector machines. Software available at http://www, csie. ntu. edu. tw/-cjlin/libsvm.

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化学研究与应用
2012年 第10期

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