Computer-Aided Algorithmic Approaches to Drug Development for Multi-Mutant HIV-1 Reverse Transcriptase.

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) inhibit the activity of the reverse transcriptase enzyme in HIV, representing a significant advancement in antiviral therapy. The emergence of antiviral-resistant strains of HIV-1 poses a substantial challenge in the treatment of HIV. This study presents an innovative virtual screening method that integrates a drug screening approach based on molecular structure to identify potential inhibitors for drug-resistant HIV-1 strains. Wild-type reverse transcriptase and a proposed multi-mutant variant were identified as target proteins for structure-based virtual screening. For better interpretation, selected compounds were used for molecular docking and molecular dynamics simulation. Six compounds with strong binding affinities were identified from the Comprehensive Marine Natural Products Database (CMNPD) as potential NNRTI candidates. In CMNPD database six compounds were identifed that have potential activity against the multi-mutant reverse transcriptase enzyme of HIV-1. Molecular modeling studies revealed that the highest-ranking compound (CMNPD370) binds persistently and with significant affinity to the multi-mutant HIV-RT. Molecular mechanics/generalized born surface area analysis revealed CMNPD370 binds more strongly to the mutant reverse transcriptase (RT) compared to the wild-type, as indicated by a more negative total binding free energy (ΔG_bind) of -17697.64 kcal/mol versus -15503.75 kcal/mol. The results demonstrate that our proposed method is feasible, reliable, and effective. Our findings may facilitate the development of novel NNRTIs targeting drug-resistant strains and offer new insights for identifying natural therapies for HIV.