scholarly journals Repurposing of RdRp Inhibitors against SARS-CoV-2 through Molecular Docking Tools

Coronaviruses ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 108-116 ◽  
Author(s):  
Rohit Bhatia ◽  
Raj Kumar Narang ◽  
Ravindra Kumar Rawal
Keyword(s):  
Amino Acids ◽  
Molecular Docking ◽  
Drug Repurposing ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Rna Dependent Rna Polymerase ◽  
Hydrogen Bonding Interactions ◽  
Docking Protocol ◽  
Approved Drugs ◽  
2D And 3D

In the present hour, the COVID-19 pandemic needs no introduction. There is continuous and keen research in progress in order to discover or develop a suitable therapeutic candidate/vaccine against the fatal, severe acute respiratory syndrome causing coronavirus (SARS-CoV-2). Drug repurposing is an approach of utilizing the therapeutic potentials of previously approved drugs against some new targets or pharmacological responses. In the presented work, we have evaluated the RNA dependent RNA polymerase (RdRp) inhibitory potentials of FDA approved anti-viral drugs remdesivir, ribavirin, sofosbuvir and galidesivir through molecular docking. The studies were carried out using MOE 2019.0102 software against RdRp (PDB ID:7BTF, released on 8th April, 2020). All four drugs displayed good docking scores and significant binding interactions with the amino acids of the receptor. The docking protocol was validated by redocking of the ligands and the root mean square deviation (RMSD) value was found to be less than 2. The 2D and 3D binding patterns of the drugs were studied and evaluated with the help of poses. The drugs displayed excellent hydrogen bonding interactions within the cavity of the receptor and displayed comparable docking scores. These drugs may serve as new therapeutic candidates or leads against SARS-CoV-2.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

Repurposing Nonnucleoside Antivirals Against SARS-CoV2 NSP12 (RNA Dependent RNA Polymerase) and Identification of Domain Specific Interactions

2020 ◽  
Author(s):  
Feroza Begum ◽  
UPASANA RAY
Keyword(s):  
Rna Polymerase ◽  
Particle Production ◽  
Drug Repurposing ◽  
Viral Rna ◽  
Rna Dependent Rna Polymerase ◽  
Domain Specific ◽  
Virus Rna ◽  
Promising Area ◽  
Screening And Identification ◽  
Approved Drugs

<p>The pandemic of SARS-CoV-2 has necessitated expedited research efforts towards finding potential antiviral targets and drug development measures. While new drug discovery is time consuming, drug repurposing has been a promising area for elaborate virtual screening and identification of existing FDA approved drugs that could possibly be used for targeting against functions of various proteins of SARS-CoV-2 virus. RNA dependent RNA polymerase (RdRp) is an important enzyme for the virus that mediates replication of the viral RNA. Inhibition of RdRp could inhibit viral RNA replication and thus new virus particle production. Here, we screened non-nucleoside antivirals and found three out of them to be strongest in binding to RdRp. We propose these three drugs as potential RdRp inhibitors based on the site of binding. </p>

POTENSI DAUN ASAM JAWA (Tamarindus indica L.) SEBAGAI ALTERNATIF ANTIINFLAMASI: STUDI IN SILICO

2019 ◽  
pp. 42-50
Author(s):  
Erma Yunita ◽  
Siti Fatimah ◽  
Deni Yulianto ◽  
Vedy Trikuncahyo ◽  
Zihan Khodijah
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
In Silico ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Tamarindus Indica ◽  
Protein Target ◽  
Cox 2 ◽  
Cox 1

  Daun asam jawa (Tamarindus indica L.) merupakan tanaman yang memiliki banyak khasiat. Kandungan senyawa kimia yang terkandung salah satunya Kuersetin. Kuersetin merupakan senyawa flavonoid yang dapat digunakan sebagai anti inflamasi. Penelitian ini bertujuan untuk mengetahui potensi aktivitas Kuersetin dari daun asam jawa sebagai anti inflamasi terhadap protein COX-1 dan COX-2 secara in silico. Ekstrak daun asam jawa diperoleh dengan maserasi bertingkat menggunakan heksan dan etanol. Kadar Kuersetinnya dihitung secara spektrofotometri UVVis. Konfirmasi aktivitas antiinflamasi dilakukan secara in silico. Protein yang digunakan adalah 6COX, 3PGH, dan 1EQH. Kuersetin sebagai senyawa aktif sedangkan Aspirin digunakan sebagai zat pembanding. Preparasi ligan Kuersetin menggunakan MarvinSketch kemudian preparasi protein target 6COX, 1EQH, dan 3PGH menggunakan YASARA. Selanjutnya melakukan molecular docking menggunakan program PLANTS. Parameter evaluasi validasi dapat dilihat dari nilai Root Mean Square Deviation (RMSD), dimana nilai RMSD yang diterima adalah kurang dari 2Å. Kadar Kuersetin yang diperoleh dalam ekstrak dalam daun asam jawa sebesar 31,26 mg/g. Hasil docking menunjukkan bahwa Kuersetin mampu berinteraksi dengan 1EQH, 3PGH, dan 6COX dimana skor dockingnya masing-masing adalah -77,6195; -75,1344; dan -82,2454, sedangkan hasil docking Aspirin masing-masing adalah -69,8784; -75,2421; dan - 72,0884. Kuersetin memiliki potensi sebagai anti inflamasi yang lebih baik dibandingkan dengan Aspirin namun memiliki resiko lebih tinggi menyebabkan ulkus lambung dibanding Aspirin.

scholarly journals Repurposing the Natural Compound for Antiviral During an Epidemic -a Case Study on the Drug Repurpose of Natural Compounds to Treat COVID-19

2020 ◽  
Author(s):  
Zhihao Wang ◽  
Chi Xu ◽  
Bing Liu ◽  
Nan Qiao
Keyword(s):  
Molecular Docking ◽  
In Silico ◽  
Natural Compound ◽  
Early Stage ◽  
Drug Repurposing ◽  
The United States ◽  
Natural Compounds ◽  
Global Scale ◽  
Novel Coronavirus ◽  
Approved Drugs

<p>The pandemic caused by the novel coronavirus SARS-CoV-2 is rapidly spreading and infecting the population on the global scale, it is a global health threat due to its high infection rate, high mortality and the lack of clinically approved drugs and vaccines for treating the disease (COVID-19). Utilising the published structures and homologue remodelling for proteins from SARS-CoV-2, an <i>in silico</i> molecular docking based screening was conducted and deposited in the Shennong project database. The results from the screening could be used to explain the clinical observation of repurposing the Ritonavir and Lopinavir to treat patients in the early stage of COVID-19 infection, and the prescription of Remdisivir in the United States as the therapy. Additionally, this molecular docking identified natural compound candidates for drug repurposing. This <i>in silico </i>molecular docking screen may be used for the initatial evaluation and rationalisation for drug repurposing of other potential candidates, especially other natural compounds from traditional Chinese medicines.</p>

scholarly journals Identification of Novel Anthracycline Resistance Genes and Their Inhibitors

2021 ◽  
Vol 14 (10) ◽  
pp. 1051
Author(s):  
Onat Kadioglu ◽  
Mohamed Elbadawi ◽  
Edmond Fleischer ◽  
Thomas Efferth
Keyword(s):  
Drug Resistance ◽  
Molecular Docking ◽  
Drug Repurposing ◽  
Multidrug Resistant ◽  
Resistance Mechanisms ◽  
Hek293 Cells ◽  
Transfected Cells ◽  
Anthracycline Resistance ◽  
Approved Drugs ◽  
Sensitizing Effect

Differentially expressed genes have been previously identified by us in multidrug-resistant tumor cells mainly resistant to doxorubicin. In the present study, we exemplarily focused on some of these genes to investigate their causative relationship with drug resistance. HMOX1, NEIL2, and PRKCA were overexpressed by lentiviral-plasmid-based transfection of HEK293 cells. An in silico drug repurposing approach was applied using virtual screening and molecular docking of FDA-approved drugs to identify inhibitors of these new drug-resistant genes. Overexpression of the selected genes conferred resistance to doxorubicin and daunorubicin but not to vincristine, docetaxel, and cisplatin, indicating the involvement of these genes in resistance to anthracyclines but not to a broader MDR phenotype. Using virtual drug screening and molecular docking analyses, we identified FDA-approved compounds (conivaptan, bexarotene, and desloratadine) that were interacting with HMOX1 and PRKCA at even stronger binding affinities than 1-(adamantan-1-yl)-2-(1H-imidazol-1-yl)ethenone and ellagic acid as known inhibitors of HMOX1 and PRKCA, respectively. Conivaptan treatment increased doxorubicin sensitivity of both HMOX1- and PRKCA-transfected cell lines. Bexarotene treatment had a comparable doxorubicin-sensitizing effect in HMOX1-transfected cells and desloratadine in PRKCA-transfected cells. Novel drug resistance mechanisms independent of ABC transporters have been identified that contribute to anthracycline resistance in MDR cells.

scholarly journals Repurposing Nonnucleoside Antivirals Against SARS-CoV2 NSP12 (RNA Dependent RNA Polymerase) and Identification of Domain Specific Interactions

2020 ◽  
Author(s):  
Feroza Begum ◽  
UPASANA RAY
Keyword(s):  
Rna Polymerase ◽  
Particle Production ◽  
Drug Repurposing ◽  
Viral Rna ◽  
Rna Dependent Rna Polymerase ◽  
Domain Specific ◽  
Virus Rna ◽  
Promising Area ◽  
Screening And Identification ◽  
Approved Drugs

<p>The pandemic of SARS-CoV-2 has necessitated expedited research efforts towards finding potential antiviral targets and drug development measures. While new drug discovery is time consuming, drug repurposing has been a promising area for elaborate virtual screening and identification of existing FDA approved drugs that could possibly be used for targeting against functions of various proteins of SARS-CoV-2 virus. RNA dependent RNA polymerase (RdRp) is an important enzyme for the virus that mediates replication of the viral RNA. Inhibition of RdRp could inhibit viral RNA replication and thus new virus particle production. Here, we screened non-nucleoside antivirals and found three out of them to be strongest in binding to RdRp. We propose these three drugs as potential RdRp inhibitors based on the site of binding. </p>

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

In Silico Identification of Active Phytochemicals against COVID-19 by Targeting the SARS-CoV-2 Spike Glycoprotein Through Molecular Docking: A Drug Repurposing Approach

2021 ◽  
Vol 8 (2) ◽  
pp. 72-87
Author(s):  
Rathan Kumar
Keyword(s):  
Molecular Docking ◽  
Antiviral Treatment ◽  
Drug Repurposing ◽  
Modern Human ◽  
Binding Affinities ◽  
In Silico Identification ◽  
Adme Properties ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Natural Inhibitors

The spread of coronavirus disease (COVID-19) has become one of the most significant pandemics in modern human history, affecting more than 70 million people worldwide. Currently, only a few fda-approved drugs have suggested fighting the infection, in the absence of a specific antiviral treatment. Thus, repurposing the presently available drugs or using plant-based bioactive compounds can be the fastest possible solution. In this study, the computational methodology of molecular docking techniques was performed to screen and identify the viable potent inhibitors against the SARS-CoV-2 spike protein from a library of 200 active phytochemicals, based on their highest binding affinity towards the target protein. Later, the binding affinities of these phytochemicals were compared with that of the fda-approved drug fluvoxamine, which is currently in use against the mild COVID-19 patients. Out of these, 86 phytochemicals that exhibited better binding energy of value ≤-7.00kcal/mol, is selected for adme (absorption, distribution, metabolism, and excretion) analysis and drug likeliness studies to check the feasibility of these compounds. Wherein, 79 out of 86 phytochemicals showed a better theoretical affinity with sufficiently bearable adme properties. Thus, they can be the lead molecule for further investigation and validation processes towards developing natural inhibitors against the SARS-CoV-2 virus.

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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