HIV-1蛋白酶切割位点的最新预测技术

State of the art prediction of HIV-1 protease cleavage sites.

作者信息

Rögnvaldsson Thorsteinn, You Liwen, Garwicz Daniel

机构信息

CAISR, School of Information Science, Computer and Electrical Engineering, Halmstad University, Halmstad, Sweden and Division of Clinical Chemistry and Pharmacology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.

出版信息

Bioinformatics. 2015 Apr 15;31(8):1204-10. doi: 10.1093/bioinformatics/btu810. Epub 2014 Dec 9.

DOI:10.1093/bioinformatics/btu810

PMID:25504647

Abstract

MOTIVATION

Understanding the substrate specificity of human immunodeficiency virus (HIV)-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.

RESULTS

The linear support vector machine with orthogonal encoding is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor services. It is also found that schemes using physicochemical properties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed.

AVAILABILITY AND IMPLEMENTATION

The datasets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available.