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Comparative assessment of favipiravir and remdesivir against human coronavirus NL63 in molecular docking and cell culture models.

ABSTRACT: Human coronavirus NL63 (HCoV-NL63) mainly affects young children and immunocompromised patients, causing morbidity and mortality in a subset of patients. Since no specific treatment is available, this study aims to explore the anti-SARS-CoV-2 agents including favipiravir and remdesivir for treating HCoV-NL63 infection. We first successfully modelled the 3D structure of HCoV-NL63 RNA-dependent RNA polymerase (RdRp) based on the experimentally solved SARS-CoV-2 RdRp structure. Molecular docking indicated that favipiravir has similar binding affinities to SARS-CoV-2 and HCoV-NL63 RdRp with LibDock scores of 75 and 74, respectively. The LibDock scores of remdesivir to SARS-CoV-2 and HCoV-NL63 were 135 and 151, suggesting that remdesivir may have a higher affinity to HCoV-NL63 compared to SARS-CoV-2 RdRp. In cell culture models infected with HCoV-NL63, both favipiravir and remdesivir significantly inhibited viral replication and production of infectious viruses. Overall, remdesivir compared to favipiravir is more potent in inhibiting HCoV-NL63 in cell culture. Importantly, there is no evidence of resistance development upon long-term exposure to remdesivir. Furthermore, combining favipiravir or remdesivir with the clinically used antiviral cytokine interferon-alpha resulted in synergistic effects. These findings provided a proof-of-concept that anti-SARS-CoV-2 drugs, in particular remdesivir, have the potential to be repurposed for treating HCoV-NL63 infection.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8648821 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10(3) fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.

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Comparative assessment of favipiravir and remdesivir against human coronavirus NL63 in molecular docking and cell culture models.

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