Acquired drug resistance is a major clinical problem in diseases such as HIV where targets such as HIV protease rapidly mutate. Inhibitors can be designed to mimic the substrate by occupying the consensus volume of substrate, the “substrate envelope”. However, there are some designed drugs that obey this principle but have significantly weak binding affinities to clinical HIV protease mutants. We hypothesize that drugs gain too much binding energy from specific regions of the target, and therefore one strategy would be to avoid certain interactions. In this SuperUROP I will analyze binding profiles of designed drugs and develop a filter to select drugs that minimize contacts with certain regions, which should present a new framework for designing drugs that combat resistance mutations.

Previously I’ve worked at Oregon State University designing Markov models of Coho salmon movement and at the Fraenkel Lab at MIT finding putative miRNA-transcription factor interactions in type 2 diabetes. This SuperUROP is another opportunity to learn about the applications of computer science to biology, this case in the context of enzyme-substrate interactions and drug design.