![Author ORCID: We display the ORCID iD icon alongside authors names on our website to acknowledge that the ORCiD has been authenticated when entered by the user. To view the users ORCiD record click the icon. [opens in a new tab]](https://chemrxiv.org/engage/assets/public/chemrxiv/images/logos/orcid.png)
Abstract
The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest to discover novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study in which, by a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied an in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and for deeply investigating the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.
Supplementary materials
Title
Supplementary of Combinatorial library screening of quinadoline B derivatives against SARS-CoV-2 RNA-dependent RNA polymerase
Description
Figure S1: Superposition between the docked pose of Q3 obtained by AutoDock and by Glide into RdRp binding site; Figure S2: RMSF calculation for each complex, selected by docking studies, after 100 ns of MD simulation; Figure S3: Compound 4 monitored during the simulation. The contacts can be grouped by type and summarized, as shown in the plots. Grouping protein-ligand interactions into four types: H-bonds, hydrophobic, ionic, and water bridges; Figure S4: Compound 5 monitored during the simulation. The contacts can be grouped by type and summarized, as shown in the plots. Grouping protein-ligand interactions into four types: H-bonds, hydrophobic, ionic, and water bridges; Table S1: Structure of selected compounds reported as SMILES string.
Actions
