scholarly journals Deep learning-based computational drug discovery to inhibit the RNA Dependent RNA Polymerase: application to SARS-CoV and COVID-19

2020 ◽  
Author(s):  
Sayalee Patankar
Keyword(s):  
Deep Learning ◽  
Rna Polymerase ◽  
Short Term Memory ◽  
Comparison Group ◽  
Binding Energies ◽  
Therapeutic Options ◽  
Rna Dependent Rna Polymerase ◽  
Drug Candidates ◽  
Zinc Database ◽  
Binding Data

There is an urgency to find drugs and vaccines for the 2019 coronavirus disease (COVID-19). Therapeutic options include repurposing existing drugs or finding new ones. One approach is to target the RNA-dependent RNA polymerase (RdRp) and block viral RNA synthesis. Currently clinical trials to repurpose remdesivir, a RdRp targeting pro-drug for Ebola, to COVID-19 is under way. More such potential drugs need to be identified to efficiently find best therapeutic options. To address this need, a Long Short Term Memory (LSTM) model from literature was trained to read the SMILES fingerprint of a molecule and predict the IC50 of the molecule when binding to an RdRp. This model was trained using IC50 binding data from the PDB database. 310,000 drug-like compounds from the ZINC database were then screened using the trained LSTM model. Additionally, the 310,000 molecules with their predicted IC50s were used to train a generative Semi-Supervised Variational AutoEncoder (SSVAE) model from literature. Although not trained by actual experimental data (sufficient data are not available), the SSVAE model was used to generate 10 new molecules by sampling from the latent space to demonstrate its utility. These 10 molecules and the 1025 molecules with the lowest predicted IC50s from the LSTM model were docked onto the SARS coronavirus (a virus similar to COVID-19) RdRp using AutoDock Vina. Top four most stable inhibitors from the screened ZINC database compounds had binding energies of less than -33.89 kJ/mol. These binding energies were less than the binding energies of the comparison group consisting of prior drugs remdesivir, favipiravir, and galidesivir. Among the ten new molecules generated by the SSVAE model, the most stable new molecule had binding energy lower than the comparison group of prior drugs. The low binding energies of these molecules indicate they could potentially be good drug candidates for the SARS CoV and COVID-19. These results also show the utility of deep learning-based models in screening existing compound and generating new molecules to find drugs for COVID-19.

scholarly journals Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sakshi Piplani ◽  
Puneet Kumar Singh ◽  
David A. Winkler ◽  
Nikolai Petrovsky
Keyword(s):  
Natural Products ◽  
Rna Polymerase ◽  
Kinase Inhibitor ◽  
Binding Energies ◽  
Dynamics Simulation ◽  
Rna Dependent Rna Polymerase ◽  
Starting Point ◽  
Potential Activity ◽  
Repurposed Drugs ◽  
Multiple Drugs

AbstractRepurposing of existing drugs and drug candidates is an ideal approach to identify new potential therapies for SARS-CoV-2 that can be tested without delay in human trials of infected patients. Here we applied a virtual screening approach using Autodock Vina and molecular dynamics simulation in tandem to calculate binding energies for repurposed drugs against the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We thereby identified 80 promising compounds with potential activity against SARS-Cov2, consisting of a mixture of antiviral drugs, natural products and drugs with diverse modes of action. A substantial proportion of the top 80 compounds identified in this study had been shown by others to have SARS-CoV-2 antiviral effects in vitro or in vivo, thereby validating our approach. Amongst our top hits not previously reported to have SARS-CoV-2 activity, were eribulin, a macrocyclic ketone analogue of the marine compound halichondrin B and an anticancer drug, the AXL receptor tyrosine kinase inhibitor bemcentinib. Our top hits from our RdRp drug screen may not only have utility in treating COVID-19 but may provide a useful starting point for therapeutics against other coronaviruses. Hence, our modelling approach successfully identified multiple drugs with potential activity against SARS-CoV-2 RdRp.

scholarly journals Working Mechanism of Remdesivir and Discovering New Potential Inhibitors to SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) using High-Throughput Virtual Screening and Molecular Dynamics Simulations .

2021 ◽  
Author(s):  
Dylan Brunt ◽  
Phillip Lakernick ◽  
CHUN WU
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Virtual Screening ◽  
Rna Polymerase ◽  
High Throughput ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Rna Dependent Rna Polymerase ◽  
Zinc Database ◽  
High Throughput Virtual Screening

Abstract RNA-dependent RNA polymerase (RdRp), is an enzyme essential component in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to RdRp over adenosine-triphosphate (ATP) and active-form remdesivir-triphosphate (RTP) using high-throughput virtual screening (HTVS) coupled with extensive (total 4800 ns) molecular dynamics (MD) simulations with using the ZINC compounds database against SARS-CoV-2 RdRp (PDB: 7BV2). We found that the simulations with both ATP and RTP remained stable for the duration of their trajectories, and it was revealed that the phosphate tail of RTP was stabilized by a positive amino acid pocket near the entry channel of RTP and magnesium ions containing residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.

scholarly journals Structural analogues of existing anti-viral drugs inhibit SARS-CoV-2 RNA dependent RNA polymerase: A computational hierarchical investigation

2020 ◽  
Author(s):  
Md. Kamrul Hasan ◽  
Mohammad Kamruzzaman ◽  
Omar Hamza Bin Manjur ◽  
Araf Mahmud ◽  
Nazmul Hussain ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Inhibitory Effects ◽  
Rna Dependent Rna Polymerase ◽  
Drug Candidates ◽  
Structural Analogues ◽  
Repurposed Drugs ◽  
Potential Inhibitors ◽  
Structural Homologues ◽  
Prime Target ◽  
Potential Therapeutics

Abstract It’s been more than 8 months since COVID-19 became a pandemic and scientists all over the world are struggling to find suitable solutions to combat it. Multiple repurposed drugs have already been in several trials or recently completed. However, none of them shows any promising effect in combating COVID-19. Therefore, developing an effective drug is an unmet global need. RdRP (RNA dependent RNA polymerase) plays a pivotal role in viral replication therefore, it is considered as a prime target of drugs that may treat COVID-19. In this study, we have screened a library of compounds, containing approved RdRP inhibitor drugs in use to treat other viruses (Favipiravir, Sofosbuvir, Ribavirin, Lopinavir, Tenofovir, Ritonavir, Galidesivir and Remdesivir) and their structural homologues, in order to identify potential inhibitors of SARS-Cov-2 RdRP. Extensive screening, molecular docking and molecular dynamics show that five structural analogues have notable inhibitory effects against RdRP of SARS-Cov-2. Importantly, comparative protein-antagonists interaction revealed that these compounds fit well in the pocket of RdRP. ADMET analysis of these compounds suggests their potency as drug candidates. Our identified compounds may serve as potential therapeutics for COVID-19.

scholarly journals Finding inhibitors for PCSK9 using computational methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255523
Author(s):  
Rida Zainab ◽  
Afshan Kaleem ◽  
Michał B. Ponczek ◽  
Roheena Abdullah ◽  
Mehwish Iqtedar ◽  
...  
Keyword(s):  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Pharmacological Action ◽  
Binding Energies ◽  
Ligand Interaction ◽  
Drug Candidates ◽  
Zinc Database ◽  
Actual Mechanism ◽  
Density Lipoprotein Receptor

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is one of the key targets for atherosclerosis drug development as its binding with low-density lipoprotein receptor leads to atherosclerosis. The protein-ligand interaction helps to understand the actual mechanism for the pharmacological action. This research aims to discover the best inhibitory candidates targeting PCSK9. To start with, reported ACE inhibitors were incorporated into pharmacophore designing using PharmaGist to produce pharmacophore models. Selected models were later screened against the ZINC database using ZINCPHARMER to define potential drug candidates that were docked with the target protein to understand their interactions. Molecular docking revealed the top 10 drug candidates against PCSK9, with binding energies ranging from -9.8 kcal·mol-1 to -8.2 kcal·mol-1, which were analyzed for their pharmacokinetic properties and oral bioavailability. Some compounds were identified as plant-derived compounds like (S)-canadine, hesperetin or labetalol (an antihypertensive drug). Molecular dynamics results showed that these substances formed stable protein-ligand complexes. (S)-canadine-PCSK9 complex was the most stable with the lowest RMSD. It was concluded that (S)-canadine may act as a potential inhibitor against atherosclerosis for the development of new PCSK9 inhibitory drugs in future in vitro research.

scholarly journals Alkaloids from Cryptolepis sanguinolenta as Potential Inhibitors of SARS-CoV-2 Viral Proteins: An In Silico Study

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lawrence Sheringham Borquaye ◽  
Edward Ntim Gasu ◽  
Gilbert Boadu Ampomah ◽  
Lois Kwane Kyei ◽  
Margaret Amerley Amarh ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Rna Polymerase ◽  
In Silico ◽  
Viral Proteins ◽  
Binding Energies ◽  
New Drugs ◽  
Binding Potential ◽  
Rna Dependent Rna Polymerase ◽  
Cryptolepis Sanguinolenta ◽  
Main Protease

The ongoing global pandemic caused by the human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions of people and claimed hundreds of thousands of lives. The absence of approved therapeutics to combat this disease threatens the health of all persons on earth and could cause catastrophic damage to society. New drugs are therefore urgently required to bring relief to people everywhere. In addition to repurposing existing drugs, natural products provide an interesting alternative due to their widespread use in all cultures of the world. In this study, alkaloids from Cryptolepis sanguinolenta have been investigated for their ability to inhibit two of the main proteins in SARS-CoV-2, the main protease and the RNA-dependent RNA polymerase, using in silico methods. Molecular docking was used to assess binding potential of the alkaloids to the viral proteins whereas molecular dynamics was used to evaluate stability of the binding event. The results of the study indicate that all 13 alkaloids bind strongly to the main protease and RNA-dependent RNA polymerase with binding energies ranging from -6.7 to -10.6 kcal/mol. In particular, cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine exhibited very strong inhibitory potential towards both proteins. Results from the molecular dynamics study revealed that a stable protein-ligand complex is formed upon binding. Alkaloids from Cryptolepis sanguinolenta therefore represent a promising class of compounds that could serve as lead compounds in the search for a cure for the corona virus disease.

scholarly journals Suramin, Penciclovir and Anidulafungin bind nsp12, which governs the RNA-dependent-RNA polymerase activity of SARS-CoV-2, with higher interaction energy than Remdesivir, indicating potential in the treatment of Covid-19

2020 ◽  
Author(s):  
Sanjay Kumar Dey ◽  
Manisha Saini ◽  
Chetna Dhembla ◽  
Shruti Bhatt ◽  
A. Sai Rajesh ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Molecular Mechanisms ◽  
Therapeutic Potential ◽  
Drug Repositioning ◽  
Polymerase Activity ◽  
Binding Energies ◽  
Dynamics Simulation ◽  
Dimensional Structure ◽  
Rna Dependent Rna Polymerase ◽  
Rna Polymerase Activity

Structured abstract:Introduction: COVID-19, for which no vaccine or confirmed therapeutic agents are available, has claimed over 7,30,000 lives globally. A feasible and quicker method to resolve this problem may be ‘drug repositioning’.Areas covered: We investigated selected FDA and WHO-EML approved drugs based on their previously promising potential as antivirals, antibacterials or antifungals. These drugs were docked onto the three-dimensional structure of nsp12 protein, which reigns the RNA-dependent RNA polymerase activity of SARS-CoV-2 and is one of the major therapeutic targets for corona viruses. Inhibitor-protein complexes were also subjected to molecular dynamics simulation. The binding energies and the mode of interaction of the active site of the protein with the drugs were evaluated.Results: Suramin, Penciclovir and Anidulafungin were found to bind to nsp12 with similar binding energies as that of Remdesivir, which is currently being used in the treatment of COVID-19. In addition, recent experimental evidences indicate that these drugs exhibit antiviral efficacy against SARS-CoV-2. Thus, they might have a prospective therapeutic potential against the key viral enzyme.Expert opinion: Repurposed drugs will provide viable options for the treatment of COVID-19 and insight into the molecular mechanisms by which these potential drug candidates exhibit anti-SARSCoV-2 activity.

scholarly journals Inhibition of COVID-19 RNA-Dependent RNA Polymerase by Natural Bioactive Compounds: Molecular Docking Analysis

2020 ◽  
Author(s):  
Nourhan M. Abd El-Aziz ◽  
Mohamed G. Shehata ◽  
Olfat M. Eldin Awad ◽  
Sobhy A. El-Sohaimy
Keyword(s):  
Molecular Docking ◽  
Benzoic Acid ◽  
Rna Polymerase ◽  
Gallic Acid ◽  
Ellagic Acid ◽  
Binding Energies ◽  
Rna Dependent Rna Polymerase ◽  
Docking Analysis ◽  
Cell Defense ◽  
Pharmacokinetic Properties

Abstract Till now there is no approved treatment for COVID-19. Phenolic compounds are known to have antiviral activity against many viruses such as HCV and HIV, through their phenol rings interaction with viral proteins and/or RNA, or via its regulating MAP kinase signaling in host cell defense. The present study aimed to assess polyphenolic compounds (gallic acid, quercetin, caffeine, resveratrol, naringenin, benzoic acid, oleuropein and ellagic acid) as COVID-19 RNA-dependent RNA polymerase (PDB ID 6M71) inhibitors, using a molecular docking. Molecular docking of these polyphenols were performed using Autodock 4.0 and Chimera 1.8.1 programs. Drug likeness and polyphenols pharmacokinetic properties were calculated using SWISSADME prediction website (http://www.swissadme.ch/). Remdesivir and ribavirin were used as standard antiviral drugs for comparison. Docking analysis results, ranked by binding energy value (ΔG) of several tested ligands toward COVID-19 polymerase were; remdesivir > gallic acid > quercetin > caffeine > ribavirin > resveratrol > naringenin > benzoic acid > oleuropein > ellagic acid. The binding energies were -8.51, - 7.55, - 7.17, -6.10, - 6.01, - 5.79, - 5.69, - 5.54, - 4.94 and -4.59 kcal/mol, respectively. All tested polyphenols performed hydrogen bonds with one or two of the nucleotide triphosphate entry channel (NTP) amino acids in COVID-19 polymerase (ARG 555, ARG 555, LYS 545), except caffeine and oleuropein. Binding of polyphenols to NTP of COVID-19 polymerase may influence in the entry of the substrate and divalent cations into the central active site cavity, inhibiting the enzyme activity. It appears promising that, gallic acid and quercetin exhibited high binding affinity than ribavirin toward COVID-19 polymerase and expressed good drug likeness and pharmacokinetic properties. Therefore, gallic acid and quercetin may represent a potential treatment option for COVID-19. Further researches are urgently required to investigate the potential uses of these polyphenols in COVID-19 treatment. Additionally, resveratrol, naringenin, benzoic and ellagic acid seem to have the best potential to act as COVID-19 polymerase inhibitors.

scholarly journals Remdesivir MD Simulations Suggest a More Favourable Binding to SARS-CoV-2 RNA Dependent RNA Polymerase Mutant P323L Than Wild-Type

Biomolecules ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 919
Author(s):  
Anwar Mohammad ◽  
Fahd Al-Mulla ◽  
Dong-Qing Wei ◽  
Jehad Abubaker
Keyword(s):  
Rna Polymerase ◽  
Molecular Conformation ◽  
Antiviral Drug ◽  
Md Simulations ◽  
Binding Energies ◽  
Wild Type ◽  
Rna Dependent Rna Polymerase ◽  
Dynamic Motion ◽  
Similar Dynamic ◽  
Insight Into

SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) protein is the target for the antiviral drug Remdesivir (RDV). With RDV clinical trials on COVID-19 patients showing a reduced hospitalisation time. During the spread of the virus, the RdRp has developed several mutations, with the most frequent being A97V and P323L. The current study sought to investigate whether A97V and P323L mutations influence the binding of RDV to the RdRp of SARS-CoV-2 compared to wild-type (WT). The interaction of RDV with WT-, A97V-, and P323L-RdRp were measured using molecular dynamic (MD) simulations, and the free binding energies were extracted. Results showed that RDV that bound to WT- and A97V-RdRp had a similar dynamic motion and internal residue fluctuations, whereas RDV interaction with P323L-RdRp exhibited a tighter molecular conformation, with a high internal motion near the active site. This was further corroborated with RDV showing a higher binding affinity to P323L-RdRp (−24.1 kcal/mol) in comparison to WT-RdRp (−17.3 kcal/mol). This study provides insight into the potential significance of administering RDV to patients carrying the SARS-CoV-2 P323L-RdRp mutation, which may have a more favourable chance of alleviating the SARS-CoV-2 illness in comparison to WT-RdRp carriers, thereby suggesting further scientific consensus for the usage of Remdesivir as clinical candidate against COVID-19.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Sign in / Sign up

Export Citation Format

Share Document