Perturbation of ACE2 structural ensembles by SARS-CoV-2 spike protein binding
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Abstract
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 and Cryo-EM, these structures do not reveal significant differences in ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in 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” vs “complex-like”, as well as to pinpoint long-range ligand-induced allosteric changes of ACE2 structure.
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SciScore for 10.1101/2021.03.02.433608: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Simulations were run using the CHARMM36 forcefield33–35,63 and OpenMM version 7.5.0.64 We use the NPT ensemble, achieved with a Monte Carlo barostat set at 1 atm with volume moves attempted every 100 steps. OpenMMsuggested: (OpenMM, RRID:SCR_000436)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Res…
SciScore for 10.1101/2021.03.02.433608: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Simulations were run using the CHARMM36 forcefield33–35,63 and OpenMM version 7.5.0.64 We use the NPT ensemble, achieved with a Monte Carlo barostat set at 1 atm with volume moves attempted every 100 steps. OpenMMsuggested: (OpenMM, RRID:SCR_000436)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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