Virtual Screening for Potential Substances for the Prophylaxis of HIV Infection in Libraries of Commercially Available Organic Compounds

HIV reverse transcriptase (RT) inhibitors targeting the early stages of virus-host interactions are of great interest to scientists. Acquired HIV RT resistance happens due to mutations in a particular region of the pol gene encoding the HIV RT amino acid sequence. We propose an application of the previously developed PASS algorithm for prediction of amino acid substitutions potentially involved in the resistance of HIV-1 based on open data. In our work, we used more than 3200 HIV-1 RT variants from the publicly available Stanford HIV RT and protease sequence database already tested for 10 anti-HIV drugs including both nucleoside and non-nucleoside RT inhibitors. We used a particular amino acid residue and its position to describe primary structure-resistance relationships. The average balanced accuracy of the prediction obtained in 20-fold cross-validation for the Phenosense dataset was about 88% and for the Antivirogram dataset was about 79%. Thus, the PASS-based algorithm may be used for prediction of the amino acid substitutions associated with the resistance of HIV-1 based on open data. The computational approach for the prediction of HIV-1 associated resistance can be useful for the selection of RT inhibitors for the treatment of HIV infected patients in the clinical practice. Prediction of the HIV-1 RT associated resistance can be useful for the development of new anti-HIV drugs active against the resistant variants of RT. Therefore, we propose that this study can be potentially useful for anti-HIV drug development.

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Virtual Screening for Potential Substances for the Prophylaxis of HIV Infection in Libraries of Commercially Available Organic Compounds

Cited by 3 publications

References 41 publications

Anti-HIV Agents: Current Status and Recent Trends

Anti-HIV Agents: Current Status and Recent Trends

Web Resources for Discovery and Development of New Medicines

PASS-based approach to predict HIV-1 reverse transcriptase resistance

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