Surveying the side-chain network approach to protein structure and dynamics: The SARS-CoV-2 spike protein as an illustrative case
Anushka Halder, Arinnia Anto, Varsha Subramanyan, Moitrayee, Bhattacharyya, Smitha Vishveshwara, Saraswathi Vishveshwara

TL;DR
This paper reviews network theory-based methods for analyzing protein structures, emphasizing side-chain networks, and demonstrates their application to the SARS-CoV-2 spike protein to reveal conformational changes relevant for viral entry.
Contribution
It introduces a comprehensive review of side-chain network analysis techniques and applies them to SARS-CoV-2 spike protein, highlighting their advantages over backbone measurements.
Findings
Side-chain network metrics reveal global conformational changes.
Differences at strategic locations relate to protein function.
Side-chain analysis enhances understanding of allostery and drug targeting.
Abstract
Network theory-based approaches provide valuable insights into the variations in global structural connectivity between differing dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational change between the closed and partially open states of the SARS-CoV-2 spike protein. This conformational change in the…
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