scholarly journals Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5172
Author(s):  
Eduardo Tejera ◽  
Cristian R. Munteanu ◽  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
Yunierkis Pérez-Castillo
Keyword(s):  
Molecular Dynamics ◽  
Learning Strategy ◽  
Drug Repurposing ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Free Energies ◽  
Main Protease ◽  
Potential Inhibitors ◽  
Dynamics Analyses ◽  
Free Energy Of Binding

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

scholarly journals An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease

2021 ◽  
Vol 8 ◽  
Author(s):  
Tianhua Zhai ◽  
Fangyuan Zhang ◽  
Shozeb Haider ◽  
Daniel Kraut ◽  
Zuyi Huang
Keyword(s):  
Small Molecule ◽  
Small Molecule Inhibitors ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Ligand Docking ◽  
Enzyme Activity Assay ◽  
Computational Docking ◽  
Main Protease ◽  
Docking Approach ◽  
Potential Inhibitors

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS– CoV– 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS– CoV– 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1′-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50’s of 19 ± 3 μM and 38 ± 3 μM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro.

scholarly journals An Integrative in Silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease

2021 ◽  
Author(s):  
Nemanja Djokovic ◽  
Dusan Ruzic ◽  
Teodora Djikic ◽  
Sandra Cvijic ◽  
Jelisaveta Ignjatovic ◽  
...  
Keyword(s):  
In Silico ◽  
Drug Repurposing ◽  
Main Protease ◽  
Potential Inhibitors

scholarly journals Prioritization of Potential Drugs Targeting the SARS-CoV-2 Main Protease

2020 ◽  
Author(s):  
Yanjin Li ◽  
Yu Zhang ◽  
Yikai Han ◽  
Tengfei Zhang ◽  
Ranran Du
Keyword(s):  
Large Scale ◽  
Drug Repurposing ◽  
Drug Candidates ◽  
Neural Activities ◽  
Main Protease ◽  
Global Threat ◽  
Potential Inhibitors ◽  
Molecular Patterns ◽  
Effective Drugs ◽  
Large Scale Screening

<p> Since its outbreak in 2019, the acute respiratory syndrome caused by SARS-Cov-2 has become a severe global threat to human. The lack of effective drugs strongly limits the therapeutic treatment against this pandemic disease. Here we employed a computational approach to prioritize potential inhibitors that directly target the core enzyme of SARS-Cov-2, the main protease, which is responsible for processing the viral RNA-translated polyprotein into functional proteins for viral replication. Based on a large-scale screening of over 13, 000 drug-like molecules, we have identified the most potential drugs that may suffice drug repurposing for SARS-Cov-2. Importantly, the second top hit is Beclabuvir, a known replication inhibitor of hepatitis C virus (HCV), which is recently reported to inhibit SARS-Cov-2 as well. We also noted several neurotransmitter-related ligands among the top candidates, suggesting a novel molecular similarity between this respiratory syndrome and neural activities. Our approach not only provides a comprehensive list of prioritized drug candidates for SARS-Cov-2, but also reveals intriguing molecular patterns that are worth future explorations.</p>

scholarly journals Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

2020 ◽  
Author(s):  
Scott Bembenek
Keyword(s):  
Molecular Modeling ◽  
Therapeutic Targets ◽  
Drug Repurposing ◽  
Preclinical Research ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Usual Process ◽  
Potential Inhibitors ◽  
Computational Platform

<p>The recent<b> </b>outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (M<sup>pro</sup>, 3CL<sup>pro</sup>). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.</p>

scholarly journals Drug Repurposing Against SARS-CoV-2 Using E-Pharmacophore Based Virtual Screening and Molecular Docking with Main Protease as the Target

2020 ◽  
Author(s):  
arun kumar ◽  
Sharanya C.S ◽  
Abhithaj J ◽  
Dileep Francis ◽  
Sadasivan C
Keyword(s):  
Free Energy ◽  
Molecular Docking ◽  
Drug Target ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Energy Calculation ◽  
Viral Protease ◽  
Energy Estimation ◽  
Main Protease ◽  
Potential Inhibitors

Since its first report in December 2019 from China the COVID-19 pandemic caused by the beta-coronavirus SARS-CoV-2 has spread at an alarming pace infecting about 26 lakh, and claiming the lives of more than 1.8 lakh individuals across the globe. Although social quarantine measures have succeeded in containing the spread of the virus to some extent, the lack of a clinically approved vaccine or drug remains the biggest bottleneck in combating the pandemic. Drug repurposing can expedite the process of drug development by identifying known drugs which are effective against SARS-CoV-2. The SARS-CoV-2 main protease is a promising drug target due to its indispensable role in viral multiplication inside the host. In the present study an E-pharmacophore hypothesis was generated using the crystal structure of the viral protease in complex with an imidazole carbaximide inhibitor as the drug target. Drugs available in the superDRUG2 database were used to identify candidate drugs for repurposing. The hits were further screened using a structure based approach involving molecular docking at different precisions. The most promising drugs were subjected to binding free energy estimation using MM-GBSA. Among the 4600 drugs screened 17 drugs were identified as candidate inhibitors of the viral protease based on the glide scores obtained from molecular docking. Binding free energy calculation showed that six drugs viz, Binifibrate, Macimorelin acetate, Bamifylline, Rilmazafon, Afatinib and Ezetimibe can act as potential inhibitors of the viral protease.

scholarly journals Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy

2020 ◽  
Author(s):  
Muthu Kumar T ◽  
Rohini K ◽  
Nivya James ◽  
Shanthi V ◽  
Ramanathan Karuppasamy
Keyword(s):  
Machine Learning ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Screening Strategy ◽  
Antiviral Therapies ◽  
Drug Molecules ◽  
Main Protease ◽  
Antiviral Activities ◽  
Imminent Threat ◽  
Potential Inhibitors

<p>The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR respectively were developed using available small molecule inhibitors and utilized in the screening of DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir. </p>

scholarly journals Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli

2020 ◽  
Vol 13 (12) ◽  
pp. 431
Author(s):  
Beatriz Suay-Garcia ◽  
Antonio Falcó ◽  
J. Ignacio Bueso-Bordils ◽  
Gerardo M. Anton-Fos ◽  
M. Teresa Pérez-Gracia ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antibacterial Activity ◽  
Prediction Models ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Linear Discriminant ◽  
Drug Candidates ◽  
Close Relationship

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

2020 ◽  
Author(s):  
Scott Bembenek
Keyword(s):  
Molecular Modeling ◽  
Therapeutic Targets ◽  
Drug Repurposing ◽  
Preclinical Research ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Usual Process ◽  
Potential Inhibitors ◽  
Computational Platform

<p>The recent<b> </b>outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (M<sup>pro</sup>, 3CL<sup>pro</sup>). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.</p>

Sign in / Sign up

Export Citation Format

Share Document