scholarly journals A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity

2021 ◽  
Author(s):  
Raghvendra Mall ◽  
Abdurrahman Elbasir ◽  
Hossam Almeer ◽  
Zeyaul Islam ◽  
Prasanna R Kolatkar ◽  
...  
Keyword(s):  
Viral Protein ◽  
Mean Squared Error ◽  
De Novo ◽  
Pearson Correlation ◽  
Drug Repurposing ◽  
Viral Proteins ◽  
Binding Energies ◽  
Supplementary Information ◽  
Protein Activity ◽  
Docking Simulations

Abstract Motivation A global effort is underway to identify compounds for the treatment of COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. Model We propose a machine learning representation framework that uses deep learning induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model in-turn uses a consensus framework to rank approved compounds against viral proteins of interest. Results Our consensus framework achieves a highmean Pearson correlation of 0.916, mean R2 of 0.840 and a low mean squared error of 0.313 for the task of compound-viral protein activity prediction on an independent test set. As a use case, we identify a ranked list of 47 compounds common to three main proteins of SARS-COV-2 virus (PL-PRO, 3CL-PRO and Spike protein) as potential targets including 21 antivirals, 15 anticancer, 5 antibiotics and 6 other investigationalhuman compounds.We performadditional molecular docking simulations to demonstrate thatmajority of these compounds have low binding energies and thus high binding affinity with the potential to be effective against the SARS-COV-2 virus. Availability All the source code and data is available at: https://github.com/raghvendra5688/Drug-Repurposing and https://dx.doi.org/10.17632/8rrwnbcgmx.3. We also implemented a web-server at: https://machinelearning-protein.qcri.org/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Data-Driven Drug Repurposing for COVID-19

2020 ◽  
Author(s):  
Raghvendra Mall ◽  
Abdurrahman Elbasir ◽  
Hossam Al Meer ◽  
Sanjay Chawla ◽  
Ehsan Ullah
Keyword(s):  
Clinical Trials ◽  
De Novo ◽  
Drug Repurposing ◽  
Binding Energies ◽  
Protein Activity ◽  
De Novo Drug Design ◽  
Docking Simulations ◽  
Antibiotic Drugs ◽  
Approved Drugs ◽  
Ranked List

<div>Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be</div><div>repurposed for COVID-19.</div><div><br></div><div>Model: We propose a machine learning representation framework that uses deep learning induced vector embeddings of drugs and viral proteins as features to predict drug-viral protein activity. The prediction model in-turn is used to build an ensemble framework to rank approved drugs based on their ability to inhibit the three main proteases (enzymes) of the SARS-COV-2 virus.</div><div><br></div><div>Results: We identify a ranked list of 19 drugs as potential targets including 7 antivirals, 6 anticancer, 3 antibiotics, 2 antimalarial, and 1 antifungal. Several drugs, such as Remdesivir, Lopinavir, Ritonavir, and Hydroxychloroquine, in our ranked list, are currently in clinical trials. Moreover, through molecular docking simulations, we demonstrate that majority of the anticancer and antibiotic drugs in our ranked list have low binding energies and thus high binding affinity with the 3CL-pro protease of SARS-COV-2 virus.</div><div><br></div><div>Disclaimer: Our models are computational and the drugs suggested should not be taken for treating COVID-19 without a doctor's advice, as further wet-lab research and clinical trials are essential to elucidate their efficacy for this purpose.</div>

scholarly journals Data-Driven Drug Repurposing for COVID-19

2020 ◽  
Author(s):  
Raghvendra Mall ◽  
Abdurrahman Elbasir ◽  
Hossam Al Meer ◽  
Sanjay Chawla ◽  
Ehsan Ullah
Keyword(s):  
Clinical Trials ◽  
De Novo ◽  
Drug Repurposing ◽  
Binding Energies ◽  
Protein Activity ◽  
De Novo Drug Design ◽  
Docking Simulations ◽  
Antibiotic Drugs ◽  
Approved Drugs ◽  
Ranked List

<div>Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be</div><div>repurposed for COVID-19.</div><div><br></div><div>Model: We propose a machine learning representation framework that uses deep learning induced vector embeddings of drugs and viral proteins as features to predict drug-viral protein activity. The prediction model in-turn is used to build an ensemble framework to rank approved drugs based on their ability to inhibit the three main proteases (enzymes) of the SARS-COV-2 virus.</div><div><br></div><div>Results: We identify a ranked list of 19 drugs as potential targets including 7 antivirals, 6 anticancer, 3 antibiotics, 2 antimalarial, and 1 antifungal. Several drugs, such as Remdesivir, Lopinavir, Ritonavir, and Hydroxychloroquine, in our ranked list, are currently in clinical trials. Moreover, through molecular docking simulations, we demonstrate that majority of the anticancer and antibiotic drugs in our ranked list have low binding energies and thus high binding affinity with the 3CL-pro protease of SARS-COV-2 virus.</div><div><br></div><div>Disclaimer: Our models are computational and the drugs suggested should not be taken for treating COVID-19 without a doctor's advice, as further wet-lab research and clinical trials are essential to elucidate their efficacy for this purpose.</div>

scholarly journals An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

2020 ◽  
Author(s):  
Alfonso Trezza ◽  
Daniele Iovinelli ◽  
Filippo Prischi ◽  
Annalisa Santucci ◽  
Ottavia Spiga
Keyword(s):  
Host Cell ◽  
Viral Entry ◽  
In Silico ◽  
De Novo ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Spike Protein ◽  
Effective Vaccine ◽  
Docking Simulations ◽  
Approved Drugs

Abstract The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Nilotinib and Imatinib bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.

scholarly journals Screening of Clinically Approved and Investigation Drugs as Potential Inhibitors of COVID-19 Main Protease: A Virtual Drug Repurposing Study

2020 ◽  
Author(s):  
Serdar Durdagi ◽  
Busecan Aksoydan ◽  
Berna Dogan ◽  
Kader Sahin ◽  
Aida Shahraki
Keyword(s):  
Virtual Screening ◽  
Clinical Investigation ◽  
Drug Repurposing ◽  
Md Simulations ◽  
Binding Energies ◽  
New Drugs ◽  
Docking Simulations ◽  
New Drug ◽  
Main Protease ◽  
Glide Docking

<div>There is an urgent need for a new drug against COVID-19. Since designing a new drug and testing its pharmacokinetics and pharmacodynamics properties may take years, here we used a physics-driven high throughput virtual screening drug re-purposing approach to identify new compounds against COVID-19. As the molecules considered in repurposing studies passed through several stages and have well-defined profiles, they would not require prolonged preclinical studies and hence, they would be excellent candidates in the cases of disease emergencies or outbreaks. While the spike protein is the key for the virus to enter the cell though the interaction with ACE2, enzymes such as main protease are crucial for the life cycle of the virus. This protein is one of the most attractive targets for the development of new drugs against</div><div>COVID-19 due to its pivotal role in the replication and transcription of the virus. We used 7922 FDA approved small molecule drugs as well as compounds in clinical investigation from NIH Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC) database in our drug repurposing study. Both apo and holo forms of target protein COVID-19 main proteases were used in virtual screening. Target proteins were retrieved from protein data bank (PDB IDs, 6M03 and 6LU7). Standard Precision (SP) protocol of Glide docking program of Maestro was used in docking. Compounds were then ranked based on their docking scores that represents binding energies. Top-30 compounds from each docking simulations were considered initially in short (10-ns) molecular dynamics (MD) simulations and their average binding energies using collected 1000 trajectories throughout the MD simulations were calculated by Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method. Selected promising hit compounds based on average MM/GBSA scores were then used in long (100-ns) MD simulations. These numerical calculations showed that the following 6 compounds can be considered as COVID-19 Main Protease inhibitors: Lasinavir, Brecanavir, Telinavir, Rotigaptide, 1,3-Bis-(2-ethoxycarbonylchromon-5-yloxy)-2-(lysyloxy)propane and Pimelautide.</div>

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>

scholarly journals An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

2020 ◽  
Author(s):  
Alfonso Trezza ◽  
Daniele Iovinelli ◽  
Filippo Prischi ◽  
Annalisa Santucci ◽  
Ottavia Spiga
Keyword(s):  
Host Cell ◽  
Viral Entry ◽  
In Silico ◽  
De Novo ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Spike Protein ◽  
Effective Vaccine ◽  
Docking Simulations ◽  
Approved Drugs

Abstract The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.Authors Alfonso Trezza and Daniele Iovinelli contributed equally to this work.

scholarly journals Screening of Clinically Approved and Investigation Drugs as Potential Inhibitors of SARS-CoV-2 Main Protease and Spike Receptor-Binding Domain Bound with ACE2 COVID19 Target Proteins: A Virtual Drug Repurposing Study

2020 ◽  
Author(s):  
Serdar Durdagi ◽  
Busecan Aksoydan ◽  
Berna Dogan ◽  
Kader Sahin ◽  
Aida Shahraki ◽  
...  
Keyword(s):  
Clinical Investigation ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Binding Energies ◽  
Protein Domain ◽  
Spike Protein ◽  
Docking Simulations ◽  
Main Protease

In this virtual drug repurposing study, we used 7922 FDA approved drugs and compounds in clinical investigation from NPC database. Both apo and holo forms of SARS-CoV-2 Main Protease as well as Spike Protein/ACE2 were used for virtual screening. Initially, docking was performed for these compounds at target binding sites. The compounds were then sorted according to their docking scores which represent binding energies. The first 100 compounds from each docking simulations were initially subjected to short (10 ns) MD simulations (in total 300 ligand-bound complexes), and average binding energies during MD simulations were calculated using the MM/GBSA method. Then, the selected promising hit compounds based on average MM/GBSA scores were used in long (100-ns and 500-ns) MD simulations. In total around 15 µs MD simulations were performed in this study. Both docking and MD simulations binding free energy calculations showed that holo form of the target protein is more appropriate choice for virtual drug screening studies. These numerical calculations have shown that the following 8 compounds can be considered as SARS-CoV-2 Main Protease inhibitors: Pimelautide, Rotigaptide, Telinavir, Ritonavir, Pinokalant, Terlakiren, Cefotiam and Cefpiramide. In addition, following 5 compounds were identified as potential SARS-CoV-2 ACE-2/Spike protein domain inhibitors: Denopamine, Bometolol, Naminterol, Rotigaptide and Benzquercin. These compounds can be clinically tested and if the simulation results validated, they may be considered to be used as treatment for COVID-19.

scholarly journals Screening of Clinically Approved and Investigation Drugs as Potential Inhibitors of SARS-CoV-2 Main Protease and Spike Receptor-Binding Domain Bound with ACE2 COVID19 Target Proteins: A Virtual Drug Repurposing Study

2020 ◽  
Author(s):  
Serdar Durdagi ◽  
Busecan Aksoydan ◽  
Berna Dogan ◽  
Kader Sahin ◽  
Aida Shahraki ◽  
...  
Keyword(s):  
Clinical Investigation ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Binding Energies ◽  
Protein Domain ◽  
Spike Protein ◽  
Docking Simulations ◽  
Main Protease

In this virtual drug repurposing study, we used 7922 FDA approved drugs and compounds in clinical investigation from NPC database. Both apo and holo forms of SARS-CoV-2 Main Protease as well as Spike Protein/ACE2 were used for virtual screening. Initially, docking was performed for these compounds at target binding sites. The compounds were then sorted according to their docking scores which represent binding energies. The first 100 compounds from each docking simulations were initially subjected to short (10 ns) MD simulations (in total 300 ligand-bound complexes), and average binding energies during MD simulations were calculated using the MM/GBSA method. Then, the selected promising hit compounds based on average MM/GBSA scores were used in long (100-ns and 500-ns) MD simulations. In total around 15 µs MD simulations were performed in this study. Both docking and MD simulations binding free energy calculations showed that holo form of the target protein is more appropriate choice for virtual drug screening studies. These numerical calculations have shown that the following 8 compounds can be considered as SARS-CoV-2 Main Protease inhibitors: Pimelautide, Rotigaptide, Telinavir, Ritonavir, Pinokalant, Terlakiren, Cefotiam and Cefpiramide. In addition, following 5 compounds were identified as potential SARS-CoV-2 ACE-2/Spike protein domain inhibitors: Denopamine, Bometolol, Naminterol, Rotigaptide and Benzquercin. These compounds can be clinically tested and if the simulation results validated, they may be considered to be used as treatment for COVID-19.

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>

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

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