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

Abstract

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.

SARS-CoV-2 protein structure and sequence mutations: evolutionary analysis and effects on virus variants

“…All the considered parameters confirm that there is a remarkable change in centrality values. In particular, the difference in terms of network invariants among alpha, beta, delta, and Omicron has been studied also in [38]. The presented results can also be considered as further extension of previous results presented in [38] in two ways.…”

“…In particular, the difference in terms of network invariants among alpha, beta, delta, and Omicron has been studied also in [38]. The presented results can also be considered as further extension of previous results presented in [38] in two ways. First, we confirm that the difference is not limited to network invariants, but also other protein structural measures, i.e.…”

“…We studied a set of variants, including the VOC and other ones. We analysed the impact of these mutations on the structure of the S protein by the PCN formalism [27].…”

“…We focus on a subset of variants which cause modifications to the S protein structure, and with structural model description available at the exascale consortium database https://www.exscalate4cov.eu/ [22]. We study the impact of sequence changes on Spike protein structure by means of the Protein Contact Network (PCN) formalism [23][24][25][26][27]. PCNs are graphs whose nodes represent the C − α atoms of the backbone of proteins, while edges represent a relative spatial distance of between 4 and 8 angstroms among residues.Topological descriptors of PCNs, such as node centrality measures, are used to discover protein properties, even at the sub-molecular level [25,26].…”

Improving model performance of shortest‐path‐based centrality measures in network models through scale space

“…4 To analyze these differences, CC and BC measures were used for Omicron variants. 5 Protein-protein interactions vary by node degree, for example, high-high or high-low. Different clusters in the network were shown to form depending on this variation to study the network structure.…”