scholarly journals Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1325
Author(s):  
Yoonjung Choi ◽  
Bonggun Shin ◽  
Keunsoo Kang ◽  
Sungsoo Park ◽  
Bo Ram Beck
Keyword(s):  
Deep Learning ◽  
Angiotensin Converting Enzyme ◽  
Drug Target ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Target Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

scholarly journals Virtual screening as therapeutic strategy of COVID-19 targeting angiotensin-converting enzyme 2

2021 ◽  
Vol 2 (1) ◽  
pp. 16-27
Author(s):  
Zahra Sharifinia ◽  
◽  
Samira Asadi ◽  
Mahyar Irani ◽  
Abdollah Allahverdi ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Angiotensin Converting Enzyme ◽  
Host Cells ◽  
Spike Protein ◽  
Potential Drug ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Approved Drugs ◽  
Fda Approved Drugs

Objective: The receptor-binding domain (RBD) of the S1 domain of the SARS-CoV- 2 Spike protein performs a key role in the interaction with Angiotensin-converting enzyme 2 (ACE2), leading to both subsequent S2 domain-mediated membrane fusion and incorporation of viral RNA in host cells. Methods: In this study, we investigated the inhibitor’s targeted compounds through existing human ACE2 drugs to use as a future viral invasion. 54 FDA approved drugs were selected to assess their binding affinity to the ACE2 receptor. The structurebased methods via computational ones have been used for virtual screening of the best drugs from the drug database. Key Findings: The ligands “Cinacalcet” and “Levomefolic acid” highaffinity scores can be a potential drug preventing Spike protein of SARS-CoV-2 and human ACE2 interaction. Levomefolic acid from vitamin B family was proved to be a potential drug as a spike protein inhibitor in previous clinical and computational studies. Besides that, in this study, the capability of Levomefolic acid to avoid ACE2 and Spike protein of SARS-CoV-2 interaction is indicated. Therefore, it is worth to consider this drug for more in vitro investigations as ACE2 and Spike protein inhibition candidate. Conclusion: The two Cinacalcet and Levomefolic acid are the two ligands that have highest energy binding for human ACE2 blocking among 54 FDA approved drugs.

scholarly journals Prediction of Off-Target Effects on Angiotensin-Converting Enzyme 2

2011 ◽  
Vol 16 (8) ◽  
pp. 878-885 ◽  
Author(s):  
Lidia V. Kulemina ◽  
David A. Ostrov
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Catalytic Efficiency ◽  
Converting Enzyme ◽  
Chemical Library ◽  
Angiotensin Converting Enzyme 2 ◽  
Specific Proteins ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Target Effects

The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)–approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs that enhance catalytic efficiency of ACE2 in vitro. These data demonstrate that libraries of approved drugs can be rapidly screened to identify potential side effects due to interactions with specific proteins other than the intended targets.

scholarly journals In-Silico FDA-Approved Drug Repurposing to Find the Possible Treatment of Coronavirus Disease-19 (COVID-19)

2020 ◽  
Author(s):  
Kumar Sharp ◽  
Dr. Shubhangi Dange
Keyword(s):  
In Silico ◽  
Drug Target ◽  
Drug Repurposing ◽  
Target Interaction ◽  
Compound Libraries ◽  
Anti Cancer ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Large Compound ◽  
Better Than

Identification of potential drug-target interaction for approved drugs serves as the basis of repurposing drugs. Studies have shown polypharmacology as common phenomenon. In-silico approaches help in screening large compound libraries at once which could take years in a laboratory. We screened a library of 1050 FDA-approved drugs against spike glycoprotein of SARS-CoV2 in-silico. Anti-cancer drugs have shown good binding affinity which is much better than hydroxychloroquine and arbidol. We have also introduced a hypothesis named “Bump” hypothesis which and be developed further in field of computational biology.

scholarly journals In-Silico FDA-Approved Drug Repurposing to Find the Possible Treatment of Coronavirus Disease-19 (COVID-19)

2020 ◽  
Author(s):  
Kumar Sharp ◽  
Dr. Shubhangi Dange
Keyword(s):  
In Silico ◽  
Drug Target ◽  
Drug Repurposing ◽  
Target Interaction ◽  
Compound Libraries ◽  
Anti Cancer ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Large Compound ◽  
Better Than

Identification of potential drug-target interaction for approved drugs serves as the basis of repurposing drugs. Studies have shown polypharmacology as common phenomenon. In-silico approaches help in screening large compound libraries at once which could take years in a laboratory. We screened a library of 1050 FDA-approved drugs against spike glycoprotein of SARS-CoV2 in-silico. Anti-cancer drugs have shown good binding affinity which is much better than hydroxychloroquine and arbidol. We have also introduced a hypothesis named “Bump” hypothesis which and be developed further in field of computational biology.

scholarly journals Computational drug repurposing strategy predicted peptide-based drugs that can potentially inhibit the interaction of SARS-CoV-2 spike protein with its target (humanACE2)

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245258
Author(s):  
Samuel Egieyeh ◽  
Elizabeth Egieyeh ◽  
Sarel Malan ◽  
Alan Christofells ◽  
Burtram Fielding
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Protein Interaction ◽  
Drug Repurposing ◽  
Spike Protein ◽  
Converting Enzyme ◽  
Peptide Drugs ◽  
Angiotensin Converting Enzyme 2 ◽  
Protein Protein Interaction ◽  
Potential Inhibitors

Drug repurposing for COVID-19 has several potential benefits including shorter development time, reduced costs and regulatory support for faster time to market for treatment that can alleviate the current pandemic. The current study used molecular docking, molecular dynamics and protein-protein interaction simulations to predict drugs from the Drug Bank that can bind to the SARS-CoV-2 spike protein interacting surface on the human angiotensin-converting enzyme 2 (hACE2) receptor. The study predicted a number of peptide-based drugs, including Sar9 Met (O2)11-Substance P and BV2, that might bind sufficiently to the hACE2 receptor to modulate the protein-protein interaction required for infection by the SARS-CoV-2 virus. Such drugs could be validated in vitro or in vivo as potential inhibitors of the interaction of SARS-CoV-2 spike protein with the human angiotensin-converting enzyme 2 (hACE2) in the airway. Exploration of the proposed and current pharmacological indications of the peptide drugs predicted as potential inhibitors of the interaction between the spike protein and hACE2 receptor revealed that some of the predicted peptide drugs have been investigated for the treatment of acute respiratory distress syndrome (ARDS), viral infection, inflammation and angioedema, and to stimulate the immune system, and potentiate antiviral agents against influenza virus. Furthermore, these predicted drug hits may be used as a basis to design new peptide or peptidomimetic drugs with better affinity and specificity for the hACE2 receptor that may prevent interaction between SARS-CoV-2 spike protein and hACE2 that is prerequisite to the infection by the SARS-CoV-2 virus.

scholarly journals A new computational drug repurposing method using established disease–drug pair knowledge

2019 ◽  
Vol 35 (19) ◽  
pp. 3672-3678 ◽  
Author(s):  
Nafiseh Saberian ◽  
Azam Peyvandipour ◽  
Michele Donato ◽  
Sahar Ansari ◽  
Sorin Draghici
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Large Scale ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Supervised Machine Learning ◽  
Supplementary Information ◽  
Sources Of Information ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Motivation Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. Results We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. Availability and implementation The R scripts are available on demand from the authors. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

2020 ◽  
Author(s):  
Muhammad Umer Anwar ◽  
Farjad Adnan ◽  
Asma Abro ◽  
Muhammad Rayyan Khan ◽  
Asad Ur Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Docking ◽  
Active Sites ◽  
Drug Repurposing ◽  
Viral Proteins ◽  
Penicillin G ◽  
Computational Approaches ◽  
Docking Simulations ◽  
Approved Drugs ◽  
Fda Approved Drugs

<p></p><p>The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.</p><p></p>

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