Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides

Abstract

The world is entering the third decade of the acquired immunodeficiency syndrome (AIDS) pandemic. The primary cause of the disease has known to be human immunodeficiency virus type I (HIV-1). Recently, peptides are shown to have high potency as drugs in the treatment of AIDS. Therefore, in the present study, we have developed a method to predict anti-HIV-1 peptides using support vectormachine (SVM) as a powerful machine learning algorithm. Peptide descriptors were represented based on the concept of Chou's pseudo-amino acid composition (PseAAC). HIV-1 P24-derived peptides were examined to predict anti-HIV-1 activity among them. The efficacy of the prediction was then validated in vitro. The mutagenic effect of validated anti-HIV-1 peptides was further investigated by the Ames test. Computational classification using SVMshowed the accuracy and sensitivity of 96.76% and 98.1%, respectively. Based on SVM classification algorithm, 3 out of 22 P24-derived peptides were predicted to be anti-HIV-1, while the restwere estimated to be inactive. HIV-1 replicationwas inhibited by the three predicted anti-HIV-1 peptides as revealed in vitro, while the results of the same test on two of non-anti-HIV-1 peptides showed complete inactivity. The three anti-HIV-1 peptides were shown to be not mutagenic because of the Ames test results. These data suggest that the proposed computational method is highly efficient for predicting the anti-HIV-1 activity of any unknown peptide having only its amino acid sequence. Moreover, further experimental studies can be performed on the mentioned peptides, which may lead to new anti-HIV-1 peptide therapeutics candidates.