TMPRSS2 inhibitor discovery facilitated through an<i>in silico</i>and biochemical screening platform

Al, Peiffer; Jm, Garlick; Wu, Yuzhang; Mb, Soellner; Cl, Brooks; Ak, Mapp

doi:10.1101/2021.03.22.436465

Cited by 12 publications

(15 citation statements)

References 64 publications

(71 reference statements)

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“…The TMPRSS2 enzymatic activity assay (Table 4, Assay ID-8) is speci c to measure the TMPRSS2 inhibitory potential of compounds. Recent studies have highlighted the importance of human TMPRSS2 as an antiviral drug target and made attempts to screen the molecules with inhibitory potential against TMPRSS2 19,26 .…”

Section: Methodsmentioning

confidence: 99%

ASCoVPred: a machine learning-based platform for quantitative prediction of anti-SARS-CoV-2 activity and human cell toxicity of molecules

Sharma¹,

Chaturvedi²,

Gupta³

2021

Preprint

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There is an urgent need to accelerate the discovery of effective drugs for COVID-19. We have developed machine learning models for rapid discovery of molecules potentially inhibitory to SARS-CoV-2 and negligible or no human cell toxicity. The machine learning (ML) QSAR models were trained and optimized with features (descriptors and fingerprints) of the experimentally validated SARS-CoV-2 inhibitory compounds. Several molecular descriptors and fingerprints were calculated to select the decisive ones for the training and evaluation of thousands of ML models. The best-optimized models are deployed as ASCoVPred webserver and standalone software, that provides easy and free access to the models. The feature selection for selecting the best descriptors for ML models training helped identify a set of decisive descriptors and fingerprints that correlate positively or negatively with the anti-SARS-CoV-2 activity and toxicity of the compounds. Systematic prediction and optimization of compounds with the help of ASCoVPred can facilitate the discovery of novel anti-SARS-CoV-2 compounds. The ASCoVPred web server and standalone software are freely available at http://14.139.62.220/ascovpred/.

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Section: Methodsmentioning

confidence: 99%

ASCoVPred: a machine learning-based platform for quantitative prediction of anti-SARS-CoV-2 activity and human cell toxicity of molecules

Sharma¹,

Chaturvedi²,

Gupta³

2021

Preprint

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“… 117 , 119 Five shortlisted drugs were identified by combining computational and cell-based screenings: nafamostat (EC 50 = 0.04 μM), camostat mesylate (EC 50 = 0.64 μM), lopinavir (EC 50 = 5.73 μM), olaparib (EC 50 = 10.34 μM), and midostaurin (EC 50 = 6.47 μM). 25 , 26 , 36 , 52 , 84 , 119 , 120 , 122 …”

Section: Target-based Sars-cov-2 Repositioning Screeningsmentioning

confidence: 99%

Section: Target-based Sars-cov-2 Repositioning Screeningsmentioning

confidence: 99%

“…Camostat mesylate was the only drug predicted by multiple in silico studies. , The drug is a trypsin-like serine protease inhibitor and blocked coronaviral cell entry via TMPRSS2 inhibition . Recent biochemical HTS reported camostat mesylate activities of IC 50 = 6.7 and 2.7 nM . Similarly, biochemical HTS identified nafamostat, FOY 251, and gabaxate with low nanomolar potency ranges. , Five shortlisted drugs were identified by combining computational and cell-based screenings: nafamostat (EC 50 = 0.04 μM), camostat mesylate (EC 50 = 0.64 μM), lopinavir (EC 50 = 5.73 μM), olaparib (EC 50 = 10.34 μM), and midostaurin (EC 50 = 6.47 μM). ,,,,,,, …”

Section: Target-based Sars-cov-2 Repositioning Screeningsmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Consensus Analysis of SARS-CoV-2 Drug Repurposing Campaigns

Mslati

Gentile

Perez

et al. 2021

J. Chem. Inf. Model.

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The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices—including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PL pro , and M pro ) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results.

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“…Several research groups have considered TMPRSS2 as the molecular target for identifying small molecules that can bind and inhibit the proteolytic process [Hu et al, 2021, Huggins, 2020, Peiffer et al, 2021. For example, nafamostat and camostat, are in clinical trials as inhibitors of TMPRSS2 .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Virtual Screening Efficiency in the Discovery of Human TMPRSS2 Inhibitor for Combating COVID-19

Vuai

Ogedjo

Onoka

et al. 2021

Preprint

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As the coronavirus disease 19 (COVID-19) pandemic continues to pose a health and economic crisis worldwide, the quest for drugs and/or vaccines against the virus continues. The human transmembrase protease serines 2 (TMPRSS2) has attracted attention as a target for drug discovery, as inhibition of its catalytic reaction would results in the inactivation of the proteolytic cleavage of the SARS-CoV-2 S protein. As a result, the inactivation prevents viral cell entry to the host’s cell. In this work, we screened and identified two potent molecules that interact and inhibit the catalytic reaction by using computational approaches. Two docking screening experiments were performed utilizing the crystal structure and holo ensemble structure obtained from molecular dynamics in bound form. There is enhancement and sensitivity of docking results to the holo ensemble as compared to the crystal structure. Compound 1 binds more stable than nafamostat by interacting with catalytic triad residue His296 and Ser441, thereby disrupting the already establish hydrogen bond interaction. The stability of the ligand-TMPRSS2 complexes was studied by molecular dynamics simulation, and the binding energy was re-scored by using MM-PBSA binding free energy. The obtained compounds may serve as initial point towards the discovery of potent TMPRSS2 inhibitors upon further in vivo validation.

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TMPRSS2 inhibitor discovery facilitated through anin silicoand biochemical screening platform

Cited by 12 publications

References 64 publications

ASCoVPred: a machine learning-based platform for quantitative prediction of anti-SARS-CoV-2 activity and human cell toxicity of molecules

ASCoVPred: a machine learning-based platform for quantitative prediction of anti-SARS-CoV-2 activity and human cell toxicity of molecules

Comprehensive Consensus Analysis of SARS-CoV-2 Drug Repurposing Campaigns

Enhancing Virtual Screening Efficiency in the Discovery of Human TMPRSS2 Inhibitor for Combating COVID-19

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