An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD)

Sanyal, Dwipanjan ; Banerjee, Suharto ; Bej, Aritra ; Chowdhury, Vaidehi Roy ; Uversky, Vladimir N. ; Chowdhury, Sourav ; Chattopadhyay, Krishnananda (2022) An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD) International Journal of Biological Macromolecules, 217 . pp. 492-505. ISSN 0141-8130

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Official URL: http://doi.org/10.1016/j.ijbiomac.2022.07.022

Related URL: http://dx.doi.org/10.1016/j.ijbiomac.2022.07.022

Abstract

Conventional drug development strategies typically use pocket in protein structures as drug-target sites. They overlook the plausible effects of protein evolvability and resistant mutations on protein structure which in turn may impair protein-drug interaction. In this study, we used an integrated evolution and structure guided strategy to develop potential evolutionary-escape resistant therapeutics using receptor binding domain (RBD) of SARS-CoV-2 spike-protein/S-protein as a model. Deploying an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence-space. Guided by molecular simulation and structure network analysis we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility. Using fuzzy C-means clustering we combined evolutionary and structural features of RBD and identified a critical region. Subsequently, we used computational drug screening using a library of 1615 small molecules and identified one lead molecule, which is expected to target the identified region, critical for evolvability and structural stability of RBD. This integrated evolution-structure guided strategy to develop evolutionary-escape resistant lead molecules have potential general applications beyond SARS-CoV-2.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Covid-19; SARS-CoV-2; Receptor Binding Domain; Sequence Space Analysis; Co-Evolution; Deep Mutation Scan; Molecular Dynamic Simulation; Structure Network Analysis; Fuzzy C-Means Clustering; Druggability; Machine Learning
ID Code:137131
Deposited On:02 Sep 2025 07:24
Last Modified:02 Sep 2025 07:24

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