(330f) Drug Resistance Predictions Based on Non-Equilibrium Alchemical Calculations

Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy. Protein mutations that directly impair drug binding are frequently involved in resistance, and the ability to anticipate these mutations would be beneficial in drug development and clinical practice. While atomistic simulation of drug binding events is computationally challenging and often intractable in practice, the use of alchemical (i.e., non-physical) thermodynamic cycles enables the efficient computation of thermodynamic properties, such as ligand binding affinity. In this talk, I will introduce pmx, a Python tool for the setup and analysis of alchemical calculations in Gromacs, and present the results of a retrospective study in which we tested the ability of a non-equilibrium protocol to predict protein mutations associated with inhibitor resistance in the cancer target Abl kinase. Results obtained with data-driven, structure-based approaches like Rosetta and machine learning will also be presented. Finally, I will show the results of a prospective evaluation of these approaches against novel Abl kinase mutations.

Reference: Aldeghi, M., Gapsys, V. & de Groot, B. L. Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches. ACS Cent. Sci. 5, 1468–1474 (2019).