Drug repurposing and sequence analysis in S-glycoprotein variants reveals critical signature patterns and destabilization of receptor-binding domain in omicron variant

Abstract

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus since its emergence in 2019 has yielded several new viral variants with varied infectivity, disease severity, and antigenicity. Although most mutations are expected to be relatively neutral, mutations at the Spike region of the genome have shown to have a major impact on the viral transmission and infection in humans. Therefore, it is crucial to survey the structures of spike protein across the global virus population to contextualize the rate of therapeutic success against these variants. In this study, high-frequency mutational variants from different geographic regions were pooled in order to study the structural evolution of the spike protein through drug docking and MD simulations. We investigated the mutational burden in the spike subregions and have observed that the different variants harbour unique signature patterns in the spike subregions, with certain domains being highly prone to mutations. Further, the MD simulations and docking study revealed that different variants show differential stability when docked for the same set of drug targets. This work sheds light on the mutational burden and the stability landscape of the spike protein across the variants from different geographical regions.

Communicated by Ramaswamy H. Sarma

Keywords:

• Spike protein
• SARS-COV-2
• COVID 19 variants
• mutational frequency
• drug docking
• md simulation
• spike evolution
• geographical spike variants

Acknowledgements

We thank GISAID (Acknowledgement table in the supplementary file 1) for the database of SARS-CoV-2 genome sequences. The authors would like to thank Ishita Agarwal and Swaroopa Nakkeeran from Indian Institute of Science Education and Research, Mohali for their help with data mining and literature review.

Disclosure statement

The authors declare no conflict of interest. Funding: The authors did not receive funding from any source for this work.

Author’s contributions

AKJ and AKP conceptualized and designed the project. ME and MS retrieved the data from the GISAID. DHL performed mutation frequency and variant analysis. AKP performed the docking analysis and the drug interaction study. NMA, AKJ and DB worked on MD simulation and its analysis. AKP, NMA, DHL, AKJ and ME wrote the manuscript. AKJ and AKP provided intellectual support in interpreting the results and editing the manuscript.

Additional information

Funding

DHL, NMA, MS and DB are supported by the INSPIRE scholarship (https://online-inspire.gov.in/). A.J. is supported by UKRI-BBSRC grant BB/M011224/1, Clarendon Fund (University of Oxford), and Oxford Interdisciplinary Bioscience DTP. AKP is supported by the DFG Grant from TUM and GSN LMU Munich.