Coupling Quantum Mechanical Modeling and Molecular Dynamics on Heterogeneous Supercomputers for Studying Distal Mutation Effects on Drug Binding in HIV-1

TL;DR

This study presents a scalable workflow combining molecular dynamics and quantum mechanics to analyze how distal mutations influence drug resistance in HIV-1 protease.

Contribution

It introduces a coupled simulation approach leveraging GPU-accelerated MD and linear-scaling DFT for detailed electronic analysis of protein-ligand interactions.

Findings

Identified electronic interaction networks affected by mutations.

Demonstrated the workflow's scalability on supercomputers.

Provided insights into mutation-induced drug resistance mechanisms.

Abstract

Predicting how protein mutations affect drug binding remains a major challenge, particularly when the mutations are distal from the binding site. In this study, we introduce a coupled simulation workflow that combines long-time-scale molecular dynamics (MD) with high-throughput quantum mechanical (QM) analysis to reveal the electronic structure signatures of mutation induced drug resistance in the HIV-1 protease. Our workflow leverages GPU-accelerated MD to generate conformational ensembles, and performs in-operando linear-scaling density functional theory (DFT) calculations on selected frames parallelized on a coupled partition of CPU nodes. This design enables efficient, massively parallel quantum analysis of protein-ligand complexes at atomic resolution. Using this approach, we investigate resistance to the antiviral Darunavir in a multi-mutant HIV-1 protease variant. By mapping the network of electronic interactions across the binding interface, our results highlight the critical role of conformational sampling and quantum insight in understanding distal mutation effects, and demonstrate a scalable computational strategy for studying complex biophysical mechanisms of drug resistance. We argue that such kind of analysis may pave the way for designing inhibitors that maintain binding stability against systemic, mutation-induced destabilization.