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Evolving Mutational Buildup in HIV-1 Protease Shifts Conformational Dynamics to Gain Drug Resistance.

Authors :
Souffrant M
Yao XQ
Hamelberg D
Source :
Journal of chemical information and modeling [J Chem Inf Model] 2023 Jun 26; Vol. 63 (12), pp. 3892-3902. Date of Electronic Publication: 2023 Jun 07.
Publication Year :
2023

Abstract

Drug resistance in antiviral treatments is a serious public health problem. Viral proteins mutate very fast, giving them a way to escape drugs by lowering drug binding affinity but with compromised function. Human immunodeficiency virus type I (HIV-1) protease, a critical antiretroviral therapeutic target, represents a model for such viral regulation under inhibition. Drug inhibitors of HIV-1 protease lose effectiveness as the protein evolves through several variants to become more resistant. However, the detailed mechanism of drug resistance in HIV-1 protease is still unclear. Here, we test the hypothesis that mutations throughout the protease alter the protein conformational ensemble to weaken protein-inhibitor binding, resulting in an inefficient protease but still viable virus. Comparing conformational ensembles between variants and the wild type helps detect these function-related dynamical changes. All analyses of over 30 μs simulations converge to the conclusion that conformational dynamics of more drug-resistant variants are more different from that of the wild type. Distinct roles of mutations during viral evolution are discussed, including a mutation predominantly contributing to the increase of drug resistance and a mutation that is responsible (synergistically) for restoring catalytic efficiency. Drug resistance is mainly due to altered flap dynamics that hinder the access to the active site. The mutant variant showing the highest drug resistance has the most ″collapsed″ active-site pocket and hence the largest magnitude of hindrance of drug binding. An enhanced difference contact network community analysis is applied to understand allosteric communications. The method summarizes multiple conformational ensembles in one community network and can be used in future studies to detect function-related dynamics in proteins.

Details

Language :
English
ISSN :
1549-960X
Volume :
63
Issue :
12
Database :
MEDLINE
Journal :
Journal of chemical information and modeling
Publication Type :
Academic Journal
Accession number :
37285207
Full Text :
https://doi.org/10.1021/acs.jcim.3c00535