Cleavage knowledge extraction in HIV-1 protease using Hidden Markov Model

Abstract

Inactive HIV is a poly protein precursor. This protein chain has to be cleaved at 9 specific positions to produce individual functional mature proteins responsible far making up a new active virus. Cleavage knowledge extraction in HIV Protease will assist in designing effective inhibitors used in the treatment of AIDS. Although much progress has been made in sequencing the viral protease, little progress has been made in understanding the specificity. Several machine learning techniques have been used in understanding the specificity of HIV-1 protease with the highest prediction rate being 92%. In this paper the Hidden Markov Model is used for analyzing the specificity of HIV-1 protease. The..