Journal of Computational Chemistry
Guest editorial: Computational intelligence and machine learning in bioinformatics
Artificial Intelligence in Medicine
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Objective: Computational proteomics analysis of biomolecular interactions is proposed to determine molecular signatures of the HIV-1 protease inhibitors. A comparative microscopic analysis is conducted for a panel of inhibitors which exemplify a diversity of the HIV-1 PR binding mechanisms, from the active site inhibition to intervening with the protease folding and dimerization. Methods and materials: Replica-exchange Monte Carlo simulations with the conformational ensembles of the HIV-1 PR dimer and monomer structures enable a molecular analysis underlying diversity of the HIV-1 PR binding mechanisms. Results: We have investigated the molecular basis underlying diversity of the HIV-1 PR binding mechanisms. The molecular basis of the HIV-1 PR active site and dimerization inhibition mechanisms has been analyzed for an active site tripeptide inhibitor and a tetrapeptide inhibitor, which can act as both a dimerization inhibitor and a competitive active site inhibitor. We have also simulated a structural mimicry mechanism of the HIV-1 PR folding inhibition and dimerization, according to which the folding inhibitor targets the conserved HIV-1 PR regions by mimicking the interaction network of the active dimer. Conclusions: We have shown that binding interfaces of the studied dimerization and folding HIV-1 PR inhibitors may enable structural mimicry with the hot spot residues of the HIV-1 PR dimer. The proposed structural models of intervening with the HIV-1 PR dimerization and folding support the mechanism of structural mimicry, which may alleviate drug resistance effects.