Structural aspects of SARS-CoV-2 mutations: Implications to plausible infectivity with ACE-2 using computational modeling approach

Abstract

Some of the SARS-CoV-2 variants are said to be more infectious than the previous others and are causing panic around the globe. Cases related to Delta plus (δ+) and omicron (ο) variants are on the rise worldwide. This sudden surge warrants an investigation into the reasons for its binding with ACE-2. The present study attempts to find out the structural basis of binding interactions of SARS-CoV-2 mutants based on computational modeling and comparative analysis. In silico strategies including protein-protein docking, mutation analysis, molecular dynamics, and binding energy calculations were used to study the binding of the 'receptor binding domain' (RBD) of the seven ‘variants of concern’ which include Alpha (α), Beta (β), Gamma (γ), Kappa (κ), Delta (δ), Delta plus (δ+) and omicron (ο) with ACE-2 (human angiotensin-converting enzyme-2) and with antibodies. Among all the variants dealt with in this study, Delta plus and omicron were found to be binding more strongly to ACE-2 than others due to inherent mutations and the consequent change in the hydrophilic and hydrophobic environment of the binding site. Furthermore, molecular dynamic (MD) simulations and subsequent MM/PBSA calculations provided useful structural insights into key residues participating in the interaction. Infectivity of a virus could be dependent on the interplay of evading antibodies and simultaneously attaching strongly with the host receptor. A cross-correlation between mutant spike proteins' binding with ACE-2 and antibodies provides a holistic assessment of the binding nature of these mutants vis-à-vis native virus and offers opportunities for designing potential therapeutics against these new mutants.

Communicated by Ramaswamy H. Sarma

Keywords:

• ACE-2
• binding interactions
• SARS-CoV-2
• structural analysis
• variants of concern

Acknowledgment

We acknowledge Dr. Girinath G. Pillai for his help with Google Colab scripts (available on GitHub).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Dr. Prateek Pandya gratefully acknowledges the ICMR for the project grant number ISRM/12(06)/2017. The high-end computer system purchased under the project was used for mutation analysis, molecular docking, and analysis of molecular dynamics trajectories.