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Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.

ABSTRACT: As an extension of our research against COVID-19, a multiphase in silico approach was applied in the selection of the three most common inhibitors (Glycyrrhizoflavone (76), Arctigenin (94), and Thiangazole (298)) against papain-like protease, PLpro (PDB ID: 4OW0), among 310 metabolites of natural origin. All compounds of the exam set were reported as antivirals. The structural similarity between the examined compound set and S88, the co-crystallized ligand of PLpro, was examined through structural similarity and fingerprint studies. The two experiments pointed to Brevicollin (28), Cryptopleurine (41), Columbamine (46), Palmatine (47), Glycyrrhizoflavone (76), Licochalcone A (87), Arctigenin (94), Termilignan (98), Anolignan B (99), 4,5-dihydroxy-6″-deoxybromotopsentin (192), Dercitin (193), Tryptanthrin (200), 6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole (211), Thiangazole (298), and Phenoxan (300). The binding ability against PLpro was screened through molecular docking, disclosing the favorable binding modes of six metabolites. ADMET studies expected molecules 28, 76, 94, 200, and 298 as the most favorable metabolites. Then, molecules 76, 94, and 298 were chosen through in silico toxicity studies. Finally, DFT studies were carried out on glycyrrhizoflavone (76) and indicated a high level of similarity in the molecular orbital analysis. The obtained data can be used in further in vitro and in vivo studies to examine and confirm the inhibitory effect of the filtered metabolites against PLpro and SARS-CoV-2.

SUBMITTER: Elkaeed EB

PROVIDER: S-EPMC9505301 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.

Elkaeed Eslam B EB Metwaly Ahmed M AM Alesawy Mohamed S MS Saleh Abdulrahman M AM Alsfouk Aisha A AA Eissa Ibrahim H IH

Life (Basel, Switzerland) 20220909 9

As an extension of our research against COVID-19, a multiphase in silico approach was applied in the selection of the three most common inhibitors (Glycyrrhizoflavone (76), Arctigenin (94), and Thiangazole (298)) against papain-like protease, PLpro (PDB ID: 4OW0), among 310 metabolites of natural origin. All compounds of the exam set were reported as antivirals. The structural similarity between the examined compound set and S88, the co-crystallized liga ...[more]

PMID: 36143445

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Project description: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.

Project description:BackgroundThe derivatives of quercetin is known for their immune-modulating antiviral, anti-blood clotting, antioxidant, and also for its anti-inflammatory efficacy. The current study was therefore conducted to examine the noted novel derivatives of quercetin present in plant sources as an immune modulator and as an antiviral molecule in the COVID-19 disease and also to study their affinity of binding with potential three targets reported for coronavirus, i.e., papain-like protease, spike protein receptor-binding domain, and 3C-like protease. Based on the high-positive drug-likeness score, the reported derivatives of quercetin obtained from an open-source database were further filtered. Compounds with positive and high drug-likeness scores were further predicted for their potential targets using DIGEP-Pred software, and STRING was used to evaluate the interaction between modulated proteins. The associated pathways were recorded based on the Kyoto Encyclopedia of Genes and Genomes pathway database. Docking was performed finally using PyRx having AutoDock Vina to identify the efficacy of binding between quercetin derivatives with papain-like protease, spike protein receptor-binding domain, and 3C-like protease. The ligand that scored minimum binding energy was chosen to visualize the interaction between protein and ligand. Normal mode analysis in internal coordinates was done with normal mode analysis to evaluate the physical movement and stability of the best protein-ligand complexes using the iMODS server.ResultsForty bioactive compounds with the highest positive drug-likeness scores were identified. These 40 bioactives were responsible for regulating different pathways associated with antiviral activity and modulation of immunity. Finally, three lead molecules were identified based on the molecular docking and dynamics simulation studies with the highest anti-COVID-19 and immunomodulatory potentials. Standard antiviral drug remdesivir on docking showed a binding affinity of - 5.8 kcal/mol with PLpro, - 6.4 kcal/mol with 3CLpro, and - 8.6 kcal/mol with spike protein receptor-binding domain of SARS-CoV-2, the discovered hit molecules quercetin 3-O-arabinoside 7-O-rhamnoside showed binding affinity of - 8.2 kcal/mol with PLpro, whereas quercetin 3-[rhamnosyl-(1- > 2)-alpha-L-arabinopyranoside] and quercetin-3-neohesperidoside-7-rhamnoside was predicted to have a binding affinity of - 8.5 kcal/mol and - 8.8 kcal/mol with spike protein receptor-binding domain and 3CLpro respectively CONCLUSION: Docking study revealed quercetin 3-O-arabinoside 7-O-rhamnoside to possess the highest binding affinity with papain-like protease, quercetin 3-[rhamnosyl-(1- > 2)-alpha-L-arabinopyranoside] with spike protein receptor-binding domain, and quercetin-3-neohesperidoside-7-rhamnoside with 3C-like protease and all the protein-ligand complexes were found to be stable after performing the normal mode analysis of the complexes in internal coordinates.

Dataset Information

Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.

Publications

Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.

Similar Datasets

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