Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

Zhavoronkov, Alex; Zagribelnyy, Bogdan; Zhebrak, Alexander; Vladimir, Aladinskiy; Victor, Terentiev; Vanhaelen, Quentin; S, Bezrukov Dmitry; Polykovskiy, Daniil; Shayakhmetov, Rim; Filimonov, Andrey; Michael, Bishop; McCloskey, Steve; Leija, Edgardo; Deborah, Bright; Keita, Funakawa; Lin, Yen‐Chu; Shih-Hsien, Huang; Hsuan-Jen, Liao; Alex, Aliper; Ivanenkov, Yan A.

doi:10.26434/chemrxiv.12301457.v1

Cited by 30 publications

(27 citation statements)

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“…Three-dimensional X-ray crystallographic structure of SARS-CoV-2 M pro was downloaded from the RCSB Protein Data Bank (PDB). The SARS-CoV M pro bound to a noncovalent inhibitor with PDB ID: 6W63 (resolution 2.1 Å) (Zhavoronkov et al., 2020 ) was used for this study. Before the virtual screening, the protein structures were prepared using the ‘Protein Preparation Wizard’ workflow in the Schrodinger suite.…”

Section: Methodsmentioning

confidence: 99%

“…These inhibitors can be used as guidance to design drugs for SARS-CoV-2 (Zhavoronkov et al. 2020 ). Active compounds have been obtained by covalent bonding with the cysteine at the catalytic site.…”

Section: Introductionmentioning

confidence: 99%

“…However, side effects and toxicity can often be caused by covalent bonding ( Zhavoronkov et al. 2020 ). Thus, prevents their use for clinic therapy.…”

Section: Introductionmentioning

confidence: 99%

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Natural phyto, compounds as possible noncovalent inhibitors against SARS-CoV2 protease: computational approach

Ogidigo

Iwuchukwu

Ibeji

et al. 2020

Journal of Biomolecular Structure and Dynamics

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At present, there is no cure or vaccine for the devastating new highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has affected people globally. Herein, we identified potent phytocompounds from two antiviral plants Momordica charantia L. and Azadirachta indica used locally for the treatment of viral and parasitic infections. Structure-based virtual screening and molecular dynamics (MD) simulation have been employed to study their inhibitory potential against the main protease (M pro ) SARS-CoV-2. A total of 86 compounds from M. charantia L. and A. indica were identified. The top six phytocompounds; momordicine, deacetylnimninene, margolonone, momordiciode F2, nimbandiol, 17-hydroxyazadiradione were examined and when compared with three FDA reference drugs (remdesivir, hydroxychloroquine and ribavirin). The top six ranked compounds and FDA drugs were then subjected to MD simulation and pharmacokinetic studies. These phytocompounds showed strong and stable interactions with the active site amino acid residues of SARS-CoV-2 Mpro similar to the reference compound. Results obtained from this study showed that momordicine and momordiciode F2 exhibited good inhibition potential (best MMGBA-binding energies; −41.1 and −43.4 kcal/mol) against the M pro of SARS-CoV-2 when compared with FDA reference anti-viral drugs (Ribavirin, remdesivir and hydroxychloroquine). Per-residue analysis, root mean square deviation and solvent-accessible surface area revealed that compounds interacted with key amino acid residues at the active site of the enzyme and showed good system stability. The results obtained in this study show that these phytocompounds could emerge as promising therapeutic inhibitors for the M pro of SARS-CoV-2. However, urgent trials should be conducted to validate this outcome. Communicated by Ramaswamy H. Sarma

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Natural phyto, compounds as possible noncovalent inhibitors against SARS-CoV2 protease: computational approach

Ogidigo

Iwuchukwu

Ibeji

et al. 2020

Journal of Biomolecular Structure and Dynamics

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“…In [35], Tang et al proposed a novel fragment-based drug design architecture based on a new deep Q-learning network for producing and predicting lead compounds targeting 3CLpro of SARS-CoV-2. Besides, in [36], Zhavoronkov et al employed three generative chemistry approaches for generating drug-like compounds (i.e., ligand-based generation, homology modelling-based generation, and crystal-derived pocked-based) as a potential antiviral for SARS-CoV-2. Gao et al [37] showed that the binding sites corresponding to protease inhibitor of SARS-CoV-2 and SARS-CoV are very similar, and adopted a new deep generative network complex to learn affinity scores for pairs of drug-protein interaction to identify the optimal antiviral suppressors for COVID-19 spread.…”

Section: B Sars-cov-2 Drug Repurposingmentioning

confidence: 99%

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing

et al. 2020

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The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.

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“…By the end of April 2020, additional Mpro crystal structures had been released on the Protein Data Bank website, but only one of them (PDB: 6W63) contained X77, as a non-covalent inhibitor of Mpro [22] . Within the scientific community, most of the current research efforts focus on finding potential SARS-CoV-2 Mpro covalent inhibitors while the possibilities to identify non-covalent inhibitors remain less investigated [23] The present study focused on the SAR-COV-2 Mpro as potential target proteins for Dexamethasone in COVID- To investigate the binding mode of Dexamethasone within SARS-COV2 main protease, we examined the binding interaction of Dexamethasone in comparison with the Remdesivir using multiple computational methods. In first step, we benchmark few molecular docking methods for selecting the best molecular docking approach.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Binding Mechanism of Dexamethasone Against SARS-CoV-2 Main Protease: Computational Molecular Modelling Approach

Khan

Htar²

2020

Preprint

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<p>At present, there are no proven agents for the treatment of 2019 coronavirus disease (COVID-19). The available evidence has not allowed guidelines to clearly recommend any drugs outside the context of clinical trials. One of the most important SARS-CoV-2 protein targets for therapeutics is the 3C-like protease (main protease, Mpro). Here in this study we utilize the recently published 6W63 crystal structure of Mpro complexed with a non-covalent inhibitor X77. Various docking methods FRED, HYBRID, CDOCKER and LEADFINDER tools were benchmark to optimally re-dock the co-crystal ligand within the active site of SARS-COV-2 Mpro. This study was restricted to molecular docking without validation by molecular dynamics simulations. CDOCKER was found to depict the exact binding of co-crystal ligand having lowest RMSD of less than 2 A. Interactions with the SARS-COV-2 Mpro may play a key role in fighting against viruses. Dexamethasone was found to bind with a high affinity to the same sites of the SARS-COV-2 Mpro than the Remdesivir. Dexamethasone was forming six hydrogen bonds compared to the three hydrogen bonds formed by Remdesivir within the active site of SARS-COV-2 Mpro. LEU141, GLY143, HIS163, GLU166, GLN192 were the key amino acid residue of SAR-COV-2 Mpro involved in stabilizing the complex between Dexamethasone and SARS-COV-2 Mpro. The results suggest the effectiveness of Dexamethasone as potent drugs against SARS-CoV-2 since it bind tightly to its Mpro. In addition, the results also suggest that dexamethasone as top antiviral treatments option than the Remdesivir with high potential to fight the SARS-CoV-2.</p>

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Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

Cited by 30 publications

References 1 publication

Natural phyto, compounds as possible noncovalent inhibitors against SARS-CoV2 protease: computational approach

Natural phyto, compounds as possible noncovalent inhibitors against SARS-CoV2 protease: computational approach

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing

Deciphering the Binding Mechanism of Dexamethasone Against SARS-CoV-2 Main Protease: Computational Molecular Modelling Approach

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