article · Frontiers in Chemistry
Targeting the papain-like protease of SARS-CoV-2 is an important strategy for halting viral replication. Because the binding site that accommodates small molecules is identical between SARS-CoV and SARS-CoV-2, researchers used known small-molecule binders from SARS-CoV to construct a tailored DEKOIS 2.0 benchmark set. A homology model was generated to facilitate computational screening in the absence of an available ligand-bound SARS-CoV-2 crystal structure. Three publicly accessible docking programs, FRED, AutoDock Vina, and PLANTS, were evaluated against this model. While all three performed better than random, FRED demonstrated the highest accuracy and effectively enriched the most potent active molecules in early ranks. Validation against subsequent X-ray crystal structures confirmed these findings. Consequently, FRED was deployed in a prospective virtual screening of the DrugBank database to identify potential therapeutic candidates.
Identifying effective drugs against rapidly spreading viruses requires reliable computational methods to screen thousands of compounds quickly. By proving that existing structural data from earlier coronaviruses can guide accurate docking models, this work helps researchers select the best software tools for finding candidate antiviral medicines, reducing wasted effort and speeding up early-stage drug discovery.
This research is at an early computational stage. It provides a validated benchmarking workflow and identified docking protocol that pharmaceutical companies, biotechnology firms, and academic drug discovery teams can use to screen compound collections against SARS-CoV-2. The immediate application is accelerating in silico hit-finding from repurposing databases such as DrugBank, though prospective candidates still require subsequent chemical synthesis, in vitro testing, and clinical validation before reaching practical use.
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The coronavirus disease 19 (COVID-19) is a rapidly growing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its papain-like protease (SARS-CoV-2 PLpro) is a crucial target to halt virus replication. SARS-CoV PLpro and SARS-CoV-2 PLpro share an 82.9% sequence identity and a 100% sequence identity for the binding site reported to accommodate small molecules in SARS-CoV. The flexible key binding site residues Tyr269 and Gln270 for small-molecule recognition in SARS-CoV PLpro exist also in SARS-CoV-2 PLpro. This inspired us to use the reported small-molecule binders to SARS-CoV PLpro to generate a high-quality DEKOIS 2.0 benchmark set. Accordingly, we used them in a cross-benchmarking study against SARS-CoV-2 PLpro. As there is no SARS-CoV-2 PLpro structure complexed with a small-molecule ligand publicly available at the time of manuscript submission, we built a homology model based on the ligand-bound SARS-CoV structure for benchmarking and docking purposes. Three publicly available docking tools FRED, AutoDock Vina, and PLANTS were benchmarked. All showed better-than-random performances, with FRED performing best against the built model. Detailed performance analysis via pROC-Chemotype plots showed a strong enrichment of the most potent bioactives in the early docking ranks. Cross-benchmarking against the X-ray structure complexed with a peptide-like inhibitor confirmed that FRED is the best-performing tool. Furthermore, we performed cross-benchmarking against the newly introduced X-ray structure complexed with a small-molecule ligand. Interestingly, its benchmarking profile and chemotype enrichment were comparable to the built model. Accordingly, we used FRED in a prospective virtual screen of the DrugBank database. In conclusion, this study provides an example of how to harness a custom-made DEKOIS 2.0 benchmark set as an approach to enhance the virtual screening success rate against a vital target of the rapidly emerging pandemic.
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DOI: 10.3389/fchem.2020.592289
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