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Abhinand CS, Prabhakaran AA, Krishnamurthy A, Raju R, Keshava Prasad TS, Nair AS, Rajasekharan KN, Oommen OV, Sudhakaran PR. SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches. J Biomol Struct Dyn 2023; 41:11166-11177. [PMID: 36572420 DOI: 10.1080/07391102.2022.2160819] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022]
Abstract
The outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) has created a public health emergency globally. SARS-CoV-2 enters the human cell through the binding of the spike protein to human angiotensin converting enzyme 2 (ACE2) receptor. Significant changes have been reported in the mutational landscape of SARS-CoV-2 in the receptor binding domain (RBD) of S protein, subsequent to evolution of the pandemic. The present study examines the correlation between the binding affinity of mutated S-proteins and the rate of viral infectivity. For this, the binding affinity of SARS-CoV and variants of SARS-CoV-2 towards ACE2 was computationally determined. Subsequently, the RBD mutations were classified on the basis of the number of strains identified with respect to each mutation and the resulting variation in the binding affinity was computationally examined. The molecular docking studies indicated a significant correlation between the Z-Rank score of mutated S proteins and the rate of infectivity, suitable for predicting SARS-CoV-2 infectivity. Accordingly, a 30-mer peptide was designed and the inhibitory properties were computationally analyzed. Single amino acid-wise mutation was performed subsequently to identify the peptide with the highest binding affinity. Molecular dynamics and free energy calculations were then performed to examine the stability of the peptide-protein complexes. Additionally, selected peptides were synthesized and screened using a colorimetric assay. Together, this study developed a model to predict the rate of infectivity of SARS-CoV-2 variants and propose a potential peptide that can be used as an inhibitor for the viral entry to human.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Chandran S Abhinand
- Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, India
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Athira A Prabhakaran
- Inter-University Centre for Genomics and Gene Technology, University of Kerala, Thiruvananthapuram, Kerala, India
| | | | - Rajesh Raju
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
- Center for Integrative Omics Data Science, Yenepoya (Deemed to be University), Mangalore, India
| | | | - Achuthsankar S Nair
- Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, India
| | | | - Oommen V Oommen
- Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Perumana R Sudhakaran
- Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, India
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