Computational analysis of affinity dynamics between the variants of SARS-CoV-2 spike protein (RBD) and human ACE-2 receptor

Abstract

The novel coronavirus SARS-CoV-2 resulted in a significant worldwide health emergency known as the COVID-19 pandemic. This crisis has been marked by the widespread of various variants, with certain ones causing notable apprehension. In this study, we harnessed computational techniques to scrutinize these Variants of Concern (VOCs), including various Omicron subvariants. Our approach involved the use of protein structure prediction algorithms and molecular docking techniques, we have investigated the effects of mutations within the Receptor Binding Domain (RBD) of SARS-CoV-2 and how these mutations influence its interactions with the human angiotensin-converting enzyme 2 (hACE-2) receptor. Further we have predicted the structural alterations in the RBD of naturally occurring SARS-CoV-2 variants using the tr-Rosetta algorithm. Subsequent docking and binding analysis employing HADDOCK and PRODIGY illuminated crucial interactions occurring at the Receptor-Binding Motif (RBM). Our findings revealed a hierarchy of increased binding affinity between the human ACE2 receptor and the various RBDs, in the order of wild type (Wuhan-strain) < Beta < Alpha < Gamma < Omicron-B.1.1.529 < Delta < Omicron-BA.2.12.1 < Omicron-BA.5.2.1 < Omicron-BA.1.1. Notably, Omicron-BA.1.1 demonstrated the highest binding affinity of -17.4 kcal mol^-1 to the hACE2 receptor when compared to all the mutant complexes. Additionally, our examination indicated that mutations occurring in active residues of the Receptor Binding Domain (RBD) consistently improved the binding affinity and intermolecular interactions in all mutant complexes. Analysis of the differences among variants has laid a foundation for the structure-based drug design targeting the RBD region of SARS-CoV-2.

Keywords: HADDOCK; SARS-CoV-2; Variants of concern (VOCs); h-ACE2; trRosetta.

Fig. 1

The stability changes of each mutation of different variant of concern and the omicron subvariants and their mean stability values

Fig. 2

A-a The trRosetta-generated Receptor-Binding Domain (RBD) structures of all SARS CoV 2 variants were super-imposed on 6M0J. A-b Structural changes between the RBM of 6M0J and the Delta sequence. A-c Structural changes of the RBM between 6M0J and the Omicron sequence. B-a RBM of 6M0J; (1), (2), (3), (4) are the changes observed. B-b Structural changes of the RBM between 6M0J and the Delta sequence. B-c Structural changes of the RBM between 6M0J and the Omicron sequence

Fig. 3

a Represents the binding interface of mutant complexes and a surface representation. b Offers the binding interface and stick model of the fundamental hydrogen bonding interactions of the mutant. (Chain A represents hACE2R, and chain B represents the RBD of each variant). Original Wuhan strain (1), Alpha variant(2), Beta variant(3), Gamma variant(4), Omicron variant BA.1.1 which has the highest binding affinity among all variants in USA (5), Omicron variant BA.1.1 which has the highest binding affinity among all variants in India (6), Delta variant B.1.617.2 which has the highest binding affinity among all variants in France (7), Omicron variant BA.1.1 which has the highest binding affinity among all variants in Germany (8), Omicron variant BA.1.1 which has the highest binding affinity among all variants in Brazil (9)

Fig. 4

RBD's 2D DIMPOT interactions representation, including hydrogen interactions in mutant complexes. Original Wuhan strain (A), Omicron variant BA.1.1 which has the highest binding affinity among all variants in USA (B), Omicron variant BA.1.1 which has the highest binding affinity among all variants in India (C), Delta variant B.1.617.2 which has the highest binding affinity among all variants in France (D), Omicron variant BA.1.1 which has the highest binding affinity among all variants in Germany (E), Omicron variant BA.1.1 which has the highest binding affinity among all variants in Brazil (F)