期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 17, 期 9, 页码 5896-5906出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.1c00325
关键词
-
资金
- National Institutes of Health [R01GM130794]
The study revealed persistent differences in the ACE2 structure upon binding, validated using machine learning methods. The research also identified the compatibility of various compounds with ACE2 structure.
The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are apo-like versus complex-like and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.
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