Prediction of S-nitrosylation modification sites based on kernel sparse representation classification and mRMR algorithm.

ABSTRACT: Protein S-nitrosylation plays a very important role in a wide variety of cellular biological activities. Hitherto, accurate prediction of S-nitrosylation sites is still of great challenge. In this paper, we presented a framework to computationally predict S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm. As much as 666 features derived from five categories of amino acid properties and one protein structure feature are used for numerical representation of proteins. A total of 529 protein sequences collected from the open-access databases and published literatures are used to train and test our predictor. Computational results show that our predictor achieves Matthews' correlation coefficients of 0.1634 and 0.2919 f

SUBMITTER: Huang G 

PROVIDER: S-EPMC4145740 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature