4.7 Article

Computational analysis of the sequence-structure relation in SARS-CoV-2 spike protein using protein contact networks

Journal

SCIENTIFIC REPORTS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41598-023-30052-w

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The structure of proteins determines their function, and mutations can lead to structural changes and functional modifications. In this study, we investigate the relationship between sequence mutations and structure variations of the SARS-CoV-2 Spike protein to understand the structural consequences of mutated amino acid residues in different strains of the virus. We propose the use of protein contact network (PCN) formalism to compare sequence and structure, and we found that the Omicron variant has a unique mutational pattern resulting in distinct structural implications compared to other strains.
The structure of proteins impacts directly on the function they perform. Mutations in the primary sequence can provoke structural changes with consequent modification of functional properties. SARS-CoV-2 proteins have been extensively studied during the pandemic. This wide dataset, related to sequence and structure, has enabled joint sequence-structure analysis. In this work, we focus on the SARS-CoV-2 S (Spike) protein and the relations between sequence mutations and structure variations, in order to shed light on the structural changes stemming from the position of mutated amino acid residues in three different SARS-CoV-2 strains. We propose the use of protein contact network (PCN) formalism to: (i) obtain a global metric space and compare various molecular entities, (ii) give a structural explanation of the observed phenotype, and (iii) provide context dependent descriptors of single mutations. PCNs have been used to compare sequence and structure of the Alpha, Delta, and Omicron SARS-CoV-2 variants, and we found that omicron has a unique mutational pattern leading to different structural consequences from mutations of other strains. The non-random distribution of changes in network centrality along the chain has allowed to shed light on the structural (and functional) consequences of mutations.

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