Molecular dynamic simulations to investigate the structural impact of known drug resistance mutations on HIV-1C Integrase-Dolutegravir binding.

Resistance associated mutations (RAMs) threaten the long-term success of combination antiretroviral therapy (cART) outcomes for HIV-1 treatment. HIV-1 Integrase (IN) strand transfer inhibitors (INSTIs) have proven to be a viable option for highly specific HIV-1 therapy. The INSTI, Dolutegravir is recommended by the World Health Organization for use as first-line cART. This study aims to understand how RAMs affect the stability of IN, as well as the binding of the drug Dolutegravir to the catalytic pocket of the protein. A homology model of HIV-1 subtype C IN was successfully constructed and validated. The site directed mutator webserver was used to predict destabilizing and/or stabilizing effects of known RAMs while FoldX confirmed any changes in protein energy upon introduction of mutation. Also, interaction analysis was performed between neighbouring residues. Three mutations known to be associated with Raltegravir, Elvitegravir and Dolutegravir resistance were selected; E92Q, G140S and Y143R, for molecular dynamics simulations. The structural quality assessment indicated high reliability of the HIV-1C IN tetrameric structure, with more than 90% confidence in modelled regions. Change in free energy for the three mutants indicated different effects, while simulation analysis showed G140S to have the largest affect on protein stability and flexibility. This was further supported by weaker non-bonded pairwise interaction energy and binding free energy values between the drug DTG and E92Q, Y143R and G140S mutants suggesting reduced binding affinity, as indicated by interaction analysis in comparison to the WT. Our findings suggest the G140S mutant has the strongest effect on the HIV-1C IN protein structure and Dolutegravir binding. To the best of our knowledge, this is the first study that uses the consensus wild type HIV-1C IN sequence to build an accurate 3D model to understand the effect of three known mutations on DTG drug binding in a South Africa context.