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Swfoldrate: Predicting protein folding rates from amino acid sequence with sliding window method
Author(s) -
Cheng Xiang,
Xiao Xuan,
Wu Zhicheng,
Wang Pu,
Lin Weizhong
Publication year - 2013
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.24171
Subject(s) - pseudo amino acid composition , jackknife resampling , protein folding , support vector machine , folding (dsp implementation) , sliding window protocol , feature selection , biological system , matthews correlation coefficient , protein sequencing , sequence (biology) , feature (linguistics) , cross validation , computer science , algorithm , chemistry , artificial intelligence , amino acid , peptide sequence , mathematics , window (computing) , statistics , biology , biochemistry , engineering , philosophy , estimator , linguistics , operating system , dipeptide , electrical engineering , gene
Protein folding is the process by which a protein processes from its denatured state to its specific biologically active conformation. Understanding the relationship between sequences and the folding rates of proteins remains an important challenge. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. In this study, the long-range and short-range contact in protein were used to derive extended version of the pseudo amino acid composition based on sliding window method. This method is capable of predicting the protein folding rates just from the amino acid sequence without the aid of any structural class information. We systematically studied the contributions of individual features to folding rate prediction. The optimal feature selection procedures are adopted by means of combining the forward feature selection and sequential backward selection method. Using the jackknife cross validation test, the method was demonstrated on the large dataset. The predictor was achieved on the basis of multitudinous physicochemical features and statistical features from protein using nonlinear support vector machine (SVM) regression model, the method obtained an excellent agreement between predicted and experimentally observed folding rates of proteins. The correlation coefficient is 0.9313 and the standard error is 2.2692. The prediction server is freely available at http://www.jci-bioinfo.cn/swfrate/input.jsp.