The human angiotensin-converting enzyme 2 (ACE2) serves as a critical first recognition point of the SARS-CoV-2 coronavirus, as well as SARS-CoV and HCoV-NL63. In all of these cases, there is a distinct binding interface between the extracellular domain of ACE2 and the S1 tailspike protein of the virus. Although these interfaces have been characterized by X-ray crystallography, the structures do not reveal significant differences in ACE2 structure upon S1 protein binding. This is unfortunate, as structural differences could be used to motivate the design of allosteric inhibitors that shift the ACE2 structural equilibrium towards apo-like conformations. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in ACE2 structure upon SARS-CoV-2 S1 protein binding by using the linear discriminant analysis (LDA) machine learning method. These differences are also validated using independent test datasets, and include long trajectories generated by D.E. Shaw Research (DESRES) on the Anton 2 supercomputer. Additionally, long trajectories for 78 potent ACE2-binding compounds, also generated by DESRES, 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-generating” vs “complex-generating”, as well as to pinpoint long-range ligand-induced allosteric changes of ACE2 structure.

Learning the differences between apo- and SARS-CoV-2-bound ACE2 structures

ACS Fall 2021

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