Abstract
The HIV-1 protease inhibitor plays an important role in the therapy of AIDS. The research on HIV-1 protease’s cleavage site will be useful to found new therapeutic targets. To predict the HIV-1 protease specific site, we apply Amino Acid Index(AAIndex)’s 531 amino acid’s parameter of chemical and physical to present the structure of peptide sample. And based on two stage feature selection method , 57 features are selected from original 4248 features. By using four kernel function of support vector machine(SVM), HIV-1 protease specific site’s model is built. Our research showed the modeling by the kernel function of NormalizePolyKernel had the higher prediction rate than other three kernel function. As a result, the accuracy rate of prediction achieves 93.947% and 93.684% for corss validation test and an independent set test, respectively.
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