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Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

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DOI: 10.4236/jbise.2009.21009    5,002 Downloads   9,573 Views   Citations

ABSTRACT

The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information; PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Xiao, R. , Guo, Y. , Zeng, Y. , Tan, H. , Tan, H. , Pu, X. and Li, M. (2009) Using position specific scoring matrix and auto covariance to predict protein subnuclear localization. Journal of Biomedical Science and Engineering, 2, 51-56. doi: 10.4236/jbise.2009.21009.

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