- Protein–Ligand Docking: Virtual Screening and Applications to Drug Discovery

2015 ◽  
pp. 206-231
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Ligand Docking

scholarly journals Repurposing of FDA approved drugs targeting Main protease MPro for SARS-CoV-2

2020 ◽  
Author(s):  
Suryakant Tiwari ◽  
Raghav Jain ◽  
Indrani Banerjee
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Active Site ◽  
Ligand Docking ◽  
Kcal Mole ◽  
Main Protease ◽  
Alternative Approach ◽  
Repurposed Drugs ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract SARS-CoV-2 is one of the greatest pandemics in the history. There is no medicine or vaccine yet discovered to control the outbreak. The paper deals with repurposing existing drugs to control the outbreak of SARS-CoV-2 virus.Ten FDA-approved drugs namely Indinavir, Nelfinavir, Letermovir, Irinotecan, Elbasvir, Saquinavir, Darunavir, Raltegravir, Atazanavir and Amprenavir were studied. In silico methods for virtual screening of protein-ligand docking of these drugs against SARS-CoV-2 MPro was performed. The binding efficiency of the drugs against viral main protease MPro was significantly high to inhibit SARS-CoV-2.The results confirmed that Atazanavir, Nelfinavir, and Letermovir not only occupied the active site of Mpro but also showed increased binding affinity (-10.36 kcal/mole, -9.47 kcal/mole and -9.43 kcal/mole) even more than of control drugs of Lopinavir (-8.71 kcal/mole) and Ritonavir (-8.08 kcal/mole). These repurposed drugs can be used in combination or individually as an alternative approach for rapid drug discovery against SARS-CoV-2

Dose COVID-19 Uncovered a New Feature of Metronidazole Drug?

2020 ◽  
Author(s):  
Mohammad Seyedhamzeh ◽  
Bahareh Farasati Far ◽  
Mehdi Shafiee Ardestani ◽  
Shahrzad Javanshir ◽  
Fatemeh Aliabadi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Active Sites ◽  
Initial Plasma ◽  
New Feature ◽  
Metronidazole Benzoate ◽  
Global Health Problem ◽  
A Current ◽  
Pro Inflammatory Cytokines

Studies of coronavirus disease 2019 (COVID-19) as a current global health problem shown the initial plasma levels of most pro-inflammatory cytokines increased during the infection, which leads to patient countless complications. Previous studies also demonstrated that the metronidazole (MTZ) administration reduced related cytokines and improved treatment in patients. However, the effect of this drug on cytokines has not been determined. In the present study, the interaction of MTZ with cytokines was investigated using molecular docking as one of the principal methods in drug discovery and design. According to the obtained results, the IL12-metronidazole complex is more stable than other cytokines, and an increase in the surface and volume leads to prevent to bind to receptors. Moreover, ligand-based virtual screening of several libraries showed metronidazole phosphate, metronidazole benzoate, 1-[1-(2-Hydroxyethyl)-5- nitroimidazol-2-yl]-N-methylmethanimine oxide, acyclovir, and tetrahydrobiopterin (THB or BH4) like MTZ by changing the surface and volume prevents binding IL-12 to the receptor. Finally, the inhibition of the active sites of IL-12 occurred by modifying the position of the methyl and hydroxyl functional groups in MTZ. <br>

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Structure-Based Virtual Screening and Biochemical Evaluation for the Identification of Novel Trypanosoma brucei Aldolase Inhibitors

2018 ◽  
Vol 18 (5) ◽  
pp. 397-405 ◽  
Author(s):  
Leonardo L.G. Ferreira ◽  
Rafaela S. Ferreira ◽  
David L. Palomino ◽  
Adriano D. Andricopulo
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Trypanosoma Brucei ◽  
Enzyme Inhibitor ◽  
Practical Knowledge ◽  
Glycolytic Enzyme ◽  
Structure Based Drug Design ◽  
Biochemical Evaluation ◽  
Dynamics Simulations ◽  
Fructose 1,6 Bisphosphate

Introduction: The glycolytic enzyme fructose-1,6-bisphosphate aldolase is a validated molecular target in human African trypanosomiasis (HAT) drug discovery, a neglected tropical disease (NTD) caused by the protozoan Trypanosoma brucei. Herein, a structure-based virtual screening (SBVS) approach to the identification of novel T. brucei aldolase inhibitors is described. Distinct molecular docking algorithms were used to screen more than 500,000 compounds against the X-ray structure of the enzyme. This SBVS strategy led to the selection of a series of molecules which were evaluated for their activity on recombinant T. brucei aldolase. The effort led to the discovery of structurally new ligands able to inhibit the catalytic activity of the enzyme. Results: The predicted binding conformations were additionally investigated in molecular dynamics simulations, which provided useful insights into the enzyme-inhibitor intermolecular interactions. Conclusion: The molecular modeling results along with the enzyme inhibition data generated practical knowledge to be explored in further structure-based drug design efforts in HAT drug discovery.

scholarly journals Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2600
Author(s):  
Fábio G. Martins ◽  
André Melo ◽  
Sérgio F. Sousa
Keyword(s):  
Virtual Screening ◽  
Quorum Sensing ◽  
In Silico ◽  
Bacterial Infections ◽  
Extracellular Polymeric Substances ◽  
Free Energy Calculations ◽  
Ligand Docking ◽  
Screening Tests ◽  
Scoring Functions ◽  
Potential Inhibitors

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.

scholarly journals Structure-based high throughput virtual screening for drug discovery

2002 ◽  
Vol 58 (s1) ◽  
pp. c67-c67
Author(s):  
H. Jiang ◽  
J. Shen ◽  
X. Luo ◽  
H. Liu ◽  
F. Chen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Virtual Screening

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

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