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The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale. [ABSTRACT FROM AUTHOR]

Abstract Objective Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs. Results We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

Objective: Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs. Results: We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff. [ABSTRACT FROM AUTHOR]