Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model  

作　　者：LIANG GuiZhao & ZHAO Wei Key Laboratory of Biorheological Science and Technology(Chongqing University),Ministry of Education Bioengineering College,Chongqing University,Chongqing 400044,China 

出　　处：《Science China Chemistry》2010年第5期1160-1166,共7页中国科学（化学英文版）

基　　金：supported by the National Natural Science Foundation of China(10901169);Innovation Ability Training Foundation of Chongqing University(CDCX008)

摘　　要：This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,separately.Our results are compared with the previously best known β-turn prediction methods and are shown to give comparative performance.Most significantly,the SVM model provides some information related to β-turn residues in proteins.The results demonstrate that the present combination approach may be used in the prediction of protein structures.This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,separately.Our results are compared with the previously best known β-turn prediction methods and are shown to give comparative performance.Most significantly,the SVM model provides some information related to β-turn residues in proteins.The results demonstrate that the present combination approach may be used in the prediction of protein structures.

关 键 词：β-turns factor analysis scales of generalized AMINO ACID INFORMATION support VECTOR machine 

分 类 号：Q517[生物学—生物化学]