AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

Tang, Bowen; He, Fengming; Liu, Dongpeng; He, Fei; Wu, Tong; Fang, Meijuan; Niu, Zhangming; Wu, Zhen; Xu, Dong

doi:10.3390/biom12060746

Cited by 49 publications

(24 citation statements)

References 56 publications

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“…Throughout the molecular docking protocols, docked ligands showed relevant anchoring at the M PRO binding site ( Figure 4 A). The SARS-CoV-2 protease is of similar topology as any M PRO protease enzyme, where the substrate-binding site comprises four important subsites, S1′, S2, S3, and S4, correlating to the peptide-based substrate residues (P1′, P2, P3, and P4, respective) [ 56 ]. Both 6-paradol (PAD) and 6-gingerol (GNG) depicted common conformation/orientation, with their substituted aromatic scaffold being settled at the S1 subsite while their tail was extended across the other subsites, at the end reaching towards the S3 subsite.…”

Section: Resultsmentioning

confidence: 99%

Bio-Guided Isolation of SARS-CoV-2 Main Protease Inhibitors from Medicinal Plants: In Vitro Assay and Molecular Dynamics

Abdallah

El-Halawany

Darwish

et al. 2022

Plants

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Since the emergence of the pandemic of the coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the discovery of antiviral phytoconstituents from medicinal plants against SARS-CoV-2 has been comprehensively researched. In this study, thirty-three plants belonging to seventeen different families used traditionally in Saudi Arabia were tested in vitro for their ability to inhibit the SARS-CoV-2 main protease (MPRO). Major constituents of the bio-active extracts were isolated and tested for their inhibition potential against this enzyme; in addition, their antiviral activity against the SARS-CoV-2 Egyptian strain was assessed. Further, the thermodynamic stability of the best active compounds was studied through focused comparative insights for the active metabolites regarding ligand–target binding characteristics at the molecular level. Additionally, the obtained computational findings provided useful directions for future drug optimization and development. The results revealed that Psiadia punctulata, Aframomum melegueta, and Nigella sativa extracts showed a high percentage of inhibition of 66.4, 58.7, and 31.5%, against SARS-CoV-2 MPRO, respectively. The major isolated constituents of these plants were identified as gardenins A and B (from P. punctulata), 6-gingerol and 6-paradol (from A. melegueta), and thymoquinone (from N. sativa). These compounds are the first to be tested invitro against SARS-CoV-2 MPRO. Among the isolated compounds, only thymoquinone (THY), gardenin A (GDA), 6-gingerol (GNG), and 6-paradol (PAD) inhibited the SARS-CoV-2 MPRO enzyme with inhibition percentages of 63.21, 73.80, 65.2, and 71.8%, respectively. In vitro assessment of SARS-CoV-2 (hCoV-19/Egypt/NRC-03/2020 (accession number on GSAID: EPI_ISL_430820) revealed a strong-to-low antiviral activity of the isolated compounds. THY showed relatively high cytotoxicity and was anti-SARS-CoV-2, while PAD demonstrated a cytotoxic effect on the tested VERO cells with a selectivity index of CC50/IC50 = 1.33 and CC50/IC50 = 0.6, respectively. Moreover, GNG had moderate activity at non-cytotoxic concentrations in vitro with a selectivity index of CC50/IC50 = 101.3/43.45 = 2.3. Meanwhile, GDA showed weak activity with a selectivity index of CC50/IC50 = 246.5/83.77 = 2.9. The thermodynamic stability of top-active compounds revealed preferential stability and SARS-CoV-2 MPRO binding affinity for PAD through molecular-docking-coupled molecular dynamics simulation. The obtained results suggest the treating potential of these plants and/or their active metabolites for COVID-19. However, further in-vivo and clinical investigations are required to establish the potential preventive and treatment effectiveness of these plants and/or their bio-active compounds in COVID-19.

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Section: Resultsmentioning

confidence: 99%

Bio-Guided Isolation of SARS-CoV-2 Main Protease Inhibitors from Medicinal Plants: In Vitro Assay and Molecular Dynamics

Abdallah

El-Halawany

Darwish

et al. 2022

Plants

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“…Although many researchers have achieved excellence in drug design and discovery, and artificial intelligence combined with computer-aided drug design has accelerated this process, discovery of novel backbones and critical targets is still a challenge ( 33 , 34 ). Furthermore, the development of new drugs involves huge costs and years of continuous trials, and their subsequent inappropriate use may increase the onset and development of drug resistance and shorten the effective life of the drug ( 35 ).…”

Section: Discussionmentioning

confidence: 99%

Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics

Wang

et al. 2023

Microbiol Spectr

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“…Zhavoronkov et al ( 92 ) developed a generative chemistry pipeline based on the knowledge of protein, molecule structures, and homology models strategies to identify new drugs related to SARS-CoV-2. Tang et al ( 93 ) have built processes based on deep learning (DL) algorithms to design new antivirus drugs of a chemical or peptide nature based on the information available in the literature and different chemical rules.…”

Section: Machine Learning Application To Covid-19mentioning

confidence: 99%

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

Varón

González-Puelma

Medina-Ortiz

et al. 2023

Front. Public Health

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The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

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AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

Cited by 49 publications

References 56 publications

Bio-Guided Isolation of SARS-CoV-2 Main Protease Inhibitors from Medicinal Plants: In Vitro Assay and Molecular Dynamics

Bio-Guided Isolation of SARS-CoV-2 Main Protease Inhibitors from Medicinal Plants: In Vitro Assay and Molecular Dynamics

Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

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