Characterizing Protein–Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV‑1 Protease

• Published in: Journal of chemical theory and computation 2020-02, Vol.16 (2), p.1284-1299
• Main Authors: Whitfield, Troy W, Ragland, Debra A, Zeldovich, Konstantin B, Schiffer, Celia A
• Format: Article
• Language: English
• Subjects: Amino acids; Binding; Biomolecules; Computer simulation; Drug Resistance, Viral; HIV; HIV Protease - chemistry; HIV Protease - genetics; HIV Protease - metabolism; HIV Protease Inhibitors - chemistry; HIV Protease Inhibitors - metabolism; HIV-1 - enzymology; Human immunodeficiency virus; Humans; Hydrogen Bonding; Ligands; Machine Learning; Molecular dynamics; Molecular Dynamics Simulation; Monte Carlo Method; Mutation; Protease; Protein Binding; Proteins; Simulation; Static Electricity; Test procedures; Trajectory analysis; Viruses
• ISSN: 1549-9618; 1549-9626
• DOI: 10.1021/acs.jctc.9b00781

Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few closely related systems has guided our understanding of the mechanisms by which changes such as ligand binding or mutation can alter the function. The general problem of detecting and interpreting such mechanisms from simulations of many related systems, however, remains a challenge. This problem is addressed here by applying supervised and unsupervised machine learning techniques to a variety of thermodynamic observables extracted from molecular dynamics simulations of different systems. As an important test case, these methods are applied to understand the evasion by human immunodeficiency virus type-1 (HIV-1) protease of darunavir, a potent inhibitor to which resistance can develop via the simultaneous mutation of multiple amino acids. Complex mutational patterns have been observed among resistant strains, presenting a challenge to developing a mechanistic picture of resistance in the protease. In order to dissect these patterns and gain mechanistic insight into the role of specific mutations, molecular dynamics simulations were carried out on a collection of HIV-1 protease variants, chosen to include highly resistant strains and susceptible controls, in complex with darunavir. Using a machine learning approach that takes advantage of the hierarchical nature in the relationships among the sequence, structure, and function, an integrative analysis of these trajectories reveals key details of the resistance mechanism, including changes in the protein structure, hydrogen bonding, and protein–ligand contacts.