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Drug escapes due to mutations in the HIV-1 virus genome have been a global menace over the last few decades. A major focus in dealing with this issue has revolved around taking advantage of the structural view of the virus to investigate how these mutations affect target drugs. The role of comparative molecular dynamics (MD), which provides valuable insights on motions involved in the functional interactions and dynamic fluctuations of the atoms in the amino acid residues on the protein chains. Although less explored than structural analyses of drug-target interactions, this study provides substantial and critical information needed to fully understand, trace, and identify the significant sites involved in HIV-1 drug escapes.
In this project, we focused on the HIV-1 reverse transcriptase (RT), which is the most drug-targeted enzyme for HIV-1 treatments partly due to its mechanism and location inside the host cell. The Babbitt lab in RIT, which is focused on the biophysical function of proteins, DNA/RNA with and without ligands, provided the newly developed site-wise machine learning-assisted method of comparative molecular dynamic analysis software, ATOMDANCE. This was used to run the molecular dynamic simulations of both wild type and mutant RTs, with and without the drug candidates after generating trajectory and topology files from DRIODS. We focused on the Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) drug candidates, which act as allosteric inhibitors and are known to exhibit major conformational changes of the enzyme in space. The software validated most of the already experimentally validated sites involved in drug escape, which are hot spots for mutations on the HIV-1 RT, and predicted some interesting sites worth taking into consideration in the future design of drug candidates able to resist most current mutations.