Human cytokine and coronavirus nucleocapsid protein interactivity using large-scale virtual screens.

Autor:	Tomezsko PJ; MIT Lincoln Laboratory, Lexington, MA, United States., Ford CT; Tuple LLC, Charlotte, NC, United States.; University of North Carolina at Charlotte, Department of Bioinformatics and Genomics, Charlotte, NC, United States.; University of North Carolina at Charlotte, Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), Charlotte, NC, United States., Meyer AE; MIT Lincoln Laboratory, Lexington, MA, United States., Michaleas AM; MIT Lincoln Laboratory, Lexington, MA, United States., Jaimes R 3rd; MIT Lincoln Laboratory, Lexington, MA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in bioinformatics [Front Bioinform] 2024 May 24; Vol. 4, pp. 1397968. Date of Electronic Publication: 2024 May 24 (Print Publication: 2024).
DOI:	10.3389/fbinf.2024.1397968
Abstrakt:	Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. 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 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses 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 interface residue assessment function (GIRAF). 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. Competing Interests: Author CF was employed by Tuple LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Tomezsko, Ford, Meyer, Michaleas and Jaimes.)
Databáze:	MEDLINE
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