Department of Medicine; Department of Biochemistry and Molecular Pharmacology; Program in Bioinformatics and Integrative Biology; Schiffer Lab
Biochemistry | Bioinformatics | Enzymes and Coenzymes | Integrative Biology | Medicinal Chemistry and Pharmaceutics | Medicinal-Pharmaceutical Chemistry | Molecular Biology | Structural Biology | Virology
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 like ligand binding or mutation can alter 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 understanding the evasion by 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 on 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 sequence, structure and function, an integrative analysis of these trajectories reveals key details of the resistance mechanism, including changes in protein structure, hydrogen bonding and protein-ligand contacts.
biomolecules, molecular dynamics, machine learning, HIV-1 protease, resistance
Rights and Permissions
Copyright © 2019 American Chemical Society. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jctc.9b00781. Accepted manuscript posted after 12 months as allowed by publisher's Journal Publishing Agreement User’s Guide at https://pubs.acs.org/userimages/ContentEditor/1285231362937/jpa_user_guide.pdf.
DOI of Published Version
Whitfield TW, Ragland DA, Zeldovich KB, Schiffer CA. Characterizing protein-ligand binding using atomistic simulation and machine learning: Application to drug resistance in HIV-1 protease. J Chem Theory Comput. 2019 Dec 26;10.1021/acs.jctc.9b00781. doi: 10.1021/acs.jctc.9b00781. [Epub ahead of print]. PMID: 31877249. Link to article on publisher's website
Journal of Chemical Theory and Computation
Whitfield TW, Ragland DA, Zeldovich KB, Schiffer CA. (2019). Characterizing protein-ligand binding using atomistic simulation and machine learning: Application to drug resistance in HIV-1 protease. Schiffer Lab Publications. https://doi.org/10.1021/acs.jctc.9b00781. Retrieved from https://escholarship.umassmed.edu/schiffer/41
Available for download on Saturday, December 26, 2020
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