High-throughput Screening and Experimental Identification of Potent Drugs Targeting SARS-CoV-2 Main Protease

Li, Yan; Zhang, Jinyong; Wang, Ning; Zhang, Yanjing; Yang, Yongjun; Yuan, Yue; Jing, Huaiqi; Liu, Xin; Wu, Sisi; Luo, Ping; Zhang, Weijun; Lu, Dongshui; Zeng, Hao; Guo, Gang; Zou, Quanming

doi:10.21203/rs.3.rs-40014/v1

Cited by 3 publications

(4 citation statements)

References 31 publications

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“… 35 , 36 Among the docking-selected candidates, 3, 3, and 4 are in the A, MA, I categories, respectively. The A-category drugs are atovaquone (IC 50 = 1.5 μM), 37 midostaurin ( K D = 43.5 μM), 35 and tadalafil ( K D = 52.2 μM). 35 The MA-category drugs are dihydroergotamine ( K D = 107.6), 35 simeprevir (IC 50 = 13.74 μM), 36 and mefloquine (IC 50 = 14.1 μM).…”

Section: Resultsmentioning

confidence: 99%

“…We found 10 of the 62 candidates with reported IC 50 or K D data against M PRO and divided the 10 into three categories according to efficacy: active (A) with IC 50 < 10 μM or K D < 100 μM; moderately active (MA) with 10 μM < IC 50 < 20 μM or 100 μM < K D < 200 μM; and inactive (I) with IC 50 > 20 μM or K D > 200 μM. , Among the docking-selected candidates, 3, 3, and 4 are in the A, MA, I categories, respectively. The A-category drugs are atovaquone (IC 50 = 1.5 μM), midostaurin ( K D = 43.5 μM), and tadalafil ( K D = 52.2 μM) . The MA-category drugs are dihydroergotamine ( K D = 107.6), simeprevir (IC 50 = 13.74 μM), and mefloquine (IC 50 = 14.1 μM) .…”

Section: Resultsmentioning

confidence: 99%

“…The A-category drugs are atovaquone (IC 50 = 1.5 μM), midostaurin ( K D = 43.5 μM), and tadalafil ( K D = 52.2 μM) . The MA-category drugs are dihydroergotamine ( K D = 107.6), simeprevir (IC 50 = 13.74 μM), and mefloquine (IC 50 = 14.1 μM) . The I-category drugs are pimozide (IC 50 = 42 μM), itraconazole (IC 50 = 111 μM), amphotericin B (reported as “did not inhibit SARS-CoV-2 infection”), and azelastine ( IC 50 = 20–100 μM) …”

Section: Resultsmentioning

confidence: 99%

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Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

Gupta

Zhou

2020

ACS Comb. Sci.

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The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SARS-CoV-2 main protease M PRO . The workflow started with docking (using Autodock Vina) each of 1615 FDA-approved drugs to the M PRO active site. This step selected 62 candidates with docking energies lower than −8.5 kcal/mol. Then, the 62 docked protein–drug complexes were subjected to 100 ns of molecular dynamics (MD) simulations in a molecular mechanics (MM) force field (CHARMM36). This step reduced the candidate pool to 26, based on the root-mean-square-deviations (RMSDs) of the drug molecules in the trajectories. Finally, we modeled the 26 drug molecules by a pseudoquantum mechanical (ANI) force field and ran 5 ns hybrid ANI/MM MD simulations of the 26 protein–drug complexes. ANI was trained by neural network models on quantum mechanical density functional theory (wB97X/6-31G(d)) data points. An RMSD cutoff winnowed down the pool to 12, and free energy analysis (MM/PBSA) produced the final selection of 9 drugs: dihydroergotamine, midostaurin, ziprasidone, etoposide, apixaban, fluorescein, tadalafil, rolapitant, and palbociclib. Of these, three are found to be active in literature reports of experimental studies. To provide physical insight into their mechanism of action, the interactions of the drug molecules with the protein are presented as 2D-interaction maps. These findings and mappings of drug–protein interactions may be potentially used to guide rational drug discovery against COVID-19.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

Gupta

Zhou

2020

ACS Comb. Sci.

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“…In addition, an X-ray crystal structure of boceprevir complexed with M pro is available on PDB (PDB: 6WNP). Some of the top 10 molecules on Additional file 1: Table S2 have been described in different in silico analysis showing their potential to bind to M pro , such as Novobiocin [61], Saquinavir [62,63], Aprepitant [64] and Leucovorin [65]. Nevertheless, confirmatory screens on these predicted hits are still lacking.…”

Section: Predicting the Bioactivity Of Generated Moleculesmentioning

confidence: 99%

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Santana

Silva

2021

BMC Chemistry

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The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of putative inhibitors of SARS-CoV-2 main protease (Mpro). Our methodology consists of 3 main steps: (1) training and validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical space of SARS-CoV- Mpro inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The generated molecules showed a good overlap with Mpro chemical space, displaying similar physicochemical properties and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking displaying binding poses and interactions similar to experimentally validated inhibitors.

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Perspectives on SARS-CoV-2 Main Protease Inhibitors

et al. 2021

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The main protease (Mpro) plays a crucial role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication and is highly conserved, rendering it one of the most attractive therapeutic targets for SARS-CoV-2 inhibition. Currently, although two drug candidates targeting SARS-CoV-2 Mpro designed by Pfizer are under clinical trials, no SARS-CoV-2 medication is approved due to the long period of drug development. Here, we collect a comprehensive list of 817 available SARS-CoV-2 and SARS-CoV Mpro inhibitors from the literature or databases and analyze their molecular mechanisms of action. The structure–activity relationships (SARs) among each series of inhibitors are discussed. Additionally, we broadly examine available antiviral activity, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and animal tests of these inhibitors. We comment on their druggability or drawbacks that prevent them from becoming drugs. This Perspective sheds light on the future development of Mpro inhibitors for SARS-CoV-2 and future coronavirus diseases.

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High-throughput Screening and Experimental Identification of Potent Drugs Targeting SARS-CoV-2 Main Protease

Cited by 3 publications

References 31 publications

Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Perspectives on SARS-CoV-2 Main Protease Inhibitors

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High-throughput Screening and Experimental Identification of Potent Drugs Targeting SARS-CoV-2 Main Protease

Cited by 3 publications

References 31 publications

Profiling SARS-CoV-2 Main Protease (MPRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

Profiling SARS-CoV-2 Main Protease (MPRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Perspectives on SARS-CoV-2 Main Protease Inhibitors

Contact Info

Product

Resources

About

Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields