Abstract
HIV-1 is one of the most rapidly evolving pathogens known, and its survival strategy is built on mutation. Changes in the envelope glycoproteins (Envs) — the proteins that stud the viral surface and mediate entry into host cells — drive immune evasion and resistance to therapeutics. The CD4-binding site and the high-mannose patch on the Env are among the most critical targets for broadly neutralizing antibodies and other treatments. When mutations accumulate at these sites, therapeutics lose efficacy. The question is whether these mutational patterns can be predicted — and if so, whether therapeutic design can get ahead of the virus rather than perpetually chasing it.
This research program, conducted in collaboration with Dr. Hillel Haim at the University of Iowa and with ViiV Healthcare, has pursued that predictive goal across several studies. The foundational hypothesis was that variance at a given Env position could be predicted from the variance at adjacent positions — that mutational change has spatial structure rather than being random. To test this, I developed a novel classification model using dynamic ensemble selection techniques, which we applied to the CD4-binding site and high-mannose patch. The model demonstrated superior performance in predicting variance patterns compared to existing approaches, confirming that spatial mutational structure exists and can be exploited computationally. This work was published in Algorithms for Molecular Biology (2023).
Building on this foundation, we developed a broader framework for comprehensive profiling of the escape paths by which HIV-1 evolves resistance to specific therapeutics. Focusing on the antiretroviral Temsavir, I designed a statistical procedure to rank rare but potentially high-impact positions on the molecule and developed an AI-based predictive framework to estimate the Half-maximal Inhibitory Concentration (IC50) — a key measure of drug efficacy. The study identified the set of positions contributing toward Temsavir resistance in treated patients, providing a roadmap of the virus’s most likely evolutionary escape routes. This manuscript is under review at Cell Reports.
The earliest study in this line used a stacking-based classification approach to predict HIV-1 Env volatility, presented at the INFORMS International Conference on Service Science (2020).