Human Cytokine and Coronavirus Nucleocapsid Protein Interactivity Using Large-Scale Virtual Screens

Abstract

In the battle against the ever-changing SARS-CoV-2 landscape, understanding the interactions between viral proteins and the human immune system is paramount as it helps to explain potential factors contributing to diverse immunological responses in infected individuals. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 coronavirus nucleocapsid proteins. Our comprehensivein silicoanalyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based residue assessment function (G-RAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

“…To complete the 42 docking experiments, this took approximately 3 hours on ten 36-core nodes. This scalable docking process closely follows methods reported in Tomezsko and Ford et al 2023 (32) and the published antibody docking protocol from the Bonvin Lab at Utrecht University (30, 33, 34).…”

“…Previous studies by our team (32, 40, 41) and others (42– 44) have shown the utility of computational screens for understanding the interaction of antibodies with protein targets as well as the identification of therapeutic targets of interest. Combining this capability with protein diffusion, we can increase the throughput of computational drug design through high-performance computing and automated complex evaluation, as we’ve demonstrated in this preliminary work.…”

“…Since it appears that some of the biochemical features of these diffused sequences may be unusual as compared to naturally-derived antibodies, it is still unknown how this will affect the viability of the candidates, both from a therapeutic perspective and from a manufacturability perspective. Previous studies by our team (32,40,41) and others (42)(43)(44) have shown the utility of computational screens for understanding the interaction of antibodies with protein targets as well as the identification of therapeutic targets of interest. Combining this capability with protein diffusion, we can increase the throughput of computational drug design through high-performance computing and automated complex evaluation, as we've demonstrated in this preliminary work.…”