Mande, S. | bioRxiv Science

bioRxiv2021

Convergent evolution of SARS-CoV-2 spike mutations, L452R, E484Q and P681R, in the second wave of COVID-19 in Maharashtra, India

As the global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic expands, genomic epidemiology and whole genome sequencing are being constantly used to investigate its transmissions and evolution. In the backdrop of the global emergence of "variants of concern" (VOCs) during December 2020 and an upsurge in a state in the western part of India since January 2021, whole genome sequencing and analysis of spike protein mutations using sequence and structural approaches was undertaken to identify possible new variants and gauge the fitness of current circulating strains. Phylogenetic analysis revealed that the predominant clade in circulation was a distinct newly identified lineage B.1.617 possessing common signature mutations D111D, G142D, L452R, E484Q, D614G and P681R, in the spike protein including within the receptor binding domain (RBD). Of these, the mutations at residue positions 452, 484 and 681 have been reported in other globally circulating lineages. The structural analysis of RBD mutations L452R and E484Q along with P681R in the furin cleavage site, revealed that these may possibly result in increased ACE2 binding and rate of S1-S2 cleavage resulting in better transmissibility. The same two RBD mutations indicated decreased binding to select monoclonal antibodies (mAbs) and may affect their neutralization potential. Experimental validation against a wider panel of mAbs, sera from vaccinees and those that recovered from natural infection needs to be studied. The emergence of such local variants through the accumulation of convergent mutations during the COVID-19 second wave needs to be further investigated for their public health impact in the rest of the country and its possibility of becoming a VOC.

molecular biology↗

bioRxiv2019

CoRNeA: A pipeline to decrypt the protein-protein interaction from amino acid sequence information

Computational methods have been devised in the past to predict the interface residues using amino acid sequence information but have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence are different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here we report a new hybrid pipeline for the prediction of protein-protein interaction interfaces from the amino acid sequence information alone based on the framework of Co-evolution, machine learning (Random forest) and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We incorporate the intra contact information of the individual proteins to eliminate false positives from the predictions as the amino acid sequence also holds information for its own folding along with the interface propensities. Our prediction on various case studies shows that CoRNeA can successfully identify minimal interacting regions of two partner proteins with higher precision and recall.

bioinformatics↗