Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein
Quantitative Biology
; 9(1):61-72, 2021.
Article
in English
| ProQuest Central | ID: covidwho-1876232
ABSTRACT
Background:
A novel coronavirus (the SARS-CoV-2) has been identified in January 2020 as the causal pathogen for COVID-19 , a pandemic started near the end of 2019. The Angiotensin converting enzyme 2 protein (ACE2) utilized by the SARS-CoV as a receptor was found to facilitate the infection of SARS-CoV-2, initiated by the binding of the spike protein to human ACE2.Methods:
Using homology modeling and molecular dynamics (MD) simulation methods, we report here the detailed structure and dynamics of the ACE2 in complex with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein.Results:
The predicted model is highly consistent with the experimentally determined structures, validating the homology modeling results. Besides the binding interface reported in the crystal structures, novel binding poses are revealed from all-atom MD simulations. The simulation data are used to identify critical residues at the complex interface and provide more details about the interactions between the SARS-CoV-2 RBD and human ACE2.Conclusion:
Simulations reveal that RBD binds to both open and closed state of ACE2. Two human ACE2 mutants and rat ACE2 are modeled to study the mutation effects on RBD binding to ACE2. The simulations show that the N-terminal helix and the K353 are very important for the tight binding of the complex, the mutants are found to alter the binding modes of the CoV2-RBD to ACE2.
Biology; SARS-CoV-2; COVID-19; ACE2; mutation; molecular dynamics simulations; Coronaviruses; Simulation; Mutants; Severe acute respiratory syndrome coronavirus 2; Angiotensin-converting enzyme 2--ACE2; Spike protein; Computer applications; Peptidyl-dipeptidase A; Homology; Molecular dynamics; Angiotensin; Proteins
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Quantitative Biology
Year:
2021
Document Type:
Article
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