Peptide modelling and screening against human ACE2 and spike glycoprotein RBD of SARS-CoV-2

Shravan B Rathod (Lead / Corresponding author), Pravin B. Prajapati, Lata B. Punjabi, Kuntal N. Prajapati, Neha Chauhan, Mohmedyasin F. Mansuri

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Outbreak of Coronavirus Disease 2019 (COVID-19) has become a great challenge for scientific community globally. Virus enters cell through spike glycoprotein fusion with ACE2 (Angiotensin-Converting Enzyme 2) human receptor. Hence, spike glycoprotein of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a potential target for diagnostics, vaccines, and antibodies. Also, virus entry can be prevented by blocking ACE2 thus, ACE2 can be considered potential target for therapeutics. As being highly specific, safe and efficacious, peptides hold their place in therapeutics. In present study, we retrieved sequence of 70 peptides from Antiviral Peptide Database (AVPdb), modelled them using 3D structure predicting web tool and docked them with receptor binding domain (RBD) of spike protein and human host receptor ACE2 using peptide-protein docking. It was observed that peptides have more affinity towards ACE2 in comparison with spike RBD. Interestingly it was noticed that most of the peptides bind to RBM (residue binding motif) which is responsible for ACE2 binding at the interface of RBD while, for ACE2, peptides prefer to bind the core cavity rather than RBD binding interface. To further investigate how peptides at the interface of RBD or ACE2 alter the binding between RBD and ACE2, protein-protein docking of RBD and ACE2 with and without peptides was performed. Peptides, AVP0671 at RBD and AVP1244 at ACE2 interfaces significantly reduce the binding affinity and change the orientation of RBD and ACE2 binding. This finding suggests that peptides can be used as a drug to inhibit virus entry in cells to stop COVID-19 pandemic in the future after experimental evidences.

    Original languageEnglish
    Article number3
    Number of pages9
    Journal In Silico Pharmacology
    Volume8
    DOIs
    Publication statusPublished - 9 Nov 2020

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