Virtual Screening Based on Pharmacophore to Discover Host ER Alpha-Glucosidase II Inhibitor for Dengue Therapy

2020 ◽  
Vol 840 ◽  
pp. 221-229
Author(s):  
Elsafira Ariavianti ◽  
Filia Stephanie ◽  
Usman Sumo Friend Tambunan
Keyword(s):  
Endoplasmic Reticulum ◽  
Natural Products ◽  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Host Protein ◽  
Binding Interaction ◽  
Ligand Interaction ◽  
Docking Simulations ◽  
Glucosidase Ii

Dengue is one of the crucial diseases in human-caused by dengue virus (DENV) infection. However, the development of DENV antiviral is often facing a problem because no effective drug to treat infection caused by all DENV serotypes. The inhibition of host protein involved in DENV life cycle can be a potential approach in dengue drug discovery, and also avoiding antiviral resistance. Endoplasmic Reticulum (ER) α-glucosidase II is one of the target host protein in DENV endoplasmic reticulum that plays an important role in the maturation process of DENV envelope glycoprotein. Natural products have been known as an essential source of a lead compound for drug discovery due to their therapeutic potency. In this research, pharmacophore-based virtual screening and molecular docking simulations were performed to find ligand that has potential to inhibit α-glucosidase II activity. About 67,609 natural products from InterBioScreen (IBS) database were used in the simulation as ligands with α-glucosidase II as the protein target. After subjected to Lipinski’s Rule of Five, druglikeness, nasty functions, and toxicity screening using DataWarrior software, 17,462 ligands were obtained. The pharmacophore features for molecular docking simulation was obtained from Protein-Ligand Interaction Fingerprint (PLIF) analysis using eight α-glucosidase II protein with different ligands. Based on virtual screening, rigid, and flexible docking simulations using Molecular Operating Environment (MOE) software, 32 ligands have lower Gibbs free binding energy (ΔGbinding) compared to the standards. Two best ligands, namely STOCK1N-85545 and STOCK1N-86400 which belong alkaloid derivatives, showed the exceptional ligand interaction and had the lowest ΔGbinding of-11.204 and-10.276 kcal/mol, respectively. The ligands were identified to have a binding interaction with amino acid Asp564 and Asp640 in α-glucosidase II catalytic site. STOCK1N-85545 and STOCK1N-86400 were also identified to have a good pharmacological properties after subjected to ADME-tox test using Toxtree, SwissADME, admetSAR, and pkCSM software.

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>

scholarly journals In silicoidentification of putativeTrypanosoma cruzienolase inhibitors

2019 ◽  
Author(s):  
Edward A. Valera-Vera ◽  
Melisa Sayé ◽  
Chantal Reigada ◽  
Mariana R. Miranda ◽  
Claudio A. Pereira
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Active Site ◽  
Screening Strategy ◽  
Glycolytic Enzyme ◽  
Docking Simulations ◽  
Enzyme Active Site ◽  
Key Enzyme

AbstractEnolase is a glycolytic enzyme that catalyzes the interconversion between 2-phosphoglycerate and phosphoenolpyruvate. In trypanosomatids enolase was proposed as a key enzyme afterin silicoandin vivoanalysis and it was validated as a protein essential for the survival of the parasite. Therefore, enolase constitutes an interesting enzyme target for the identification of drugs against Chagas disease. In this work, a combined virtual screening strategy was implemented, employing similarity virtual screening, molecular docking and molecular dynamics. First, two known enolase inhibitors and the enzyme substrates were used as queries for the similarity screening on the Sweetlead database using five different algorithms. Compounds retrieved in the top 10 of at least three search algorithms were selected for further analysis, resulting in six compounds of medical use (etidronate, pamidronate, fosfomycin, acetohydroximate, triclofos, and aminohydroxybutyrate). Molecular docking simulations predicted acetohydroxamate and triclofos would not bind to the active site of the enzyme, and a re-scoring of the obtained poses signaled fosfomycin and aminohydroxybutyrate as bad enzyme binders. Docking poses obtained for etidronate, pamidronate, and PEP, were used for molecular dynamics calculations to describe their mode of binding. From the obtained results, we propose etidronate as a possibleTcENO inhibitor, and describe desirable and undesirable molecular motifs to be taken into account in the repurposing or design of drugs aiming this enzyme active site.

Advancements in Docking and Molecular Dynamics Simulations Towards Ligand-receptor Interactions and Structure-function Relationships

2018 ◽  
Vol 18 (20) ◽  
pp. 1755-1768 ◽  
Author(s):  
Ahmad Abu Turab Naqvi ◽  
Taj Mohammad ◽  
Gulam Mustafa Hasan ◽  
Md. Imtaiyaz Hassan
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Drug Discovery ◽  
Molecular Dynamics Simulations ◽  
Computational Method ◽  
Dynamics Simulation ◽  
Ligand Molecule ◽  
Ligand Interaction ◽  
Modern Drug ◽  
Dynamics Simulations

Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.

A primer on natural product-based virtual screening

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Eleni Koulouridi ◽  
Marilia Valli ◽  
Fidele Ntie-Kang ◽  
Vanderlan da Silva Bolzani
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
Natural Product ◽  
General Purpose ◽  
Target Type ◽  
Computational Techniques ◽  
Specific Subset ◽  
Virtual Libraries

Abstract Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

scholarly journals COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

2018 ◽  
Vol 8 (5) ◽  
pp. 504-509 ◽  
Author(s):  
Surabhi Surabhi ◽  
BK Singh
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Drug Discovery And Development ◽  
Research Areas ◽  
Computer Aided ◽  
Pharmaceutical Industries

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research. Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors

2019 ◽  
Vol 15 (3) ◽  
pp. 193-205 ◽  
Author(s):  
Wei-Neng Zhou ◽  
Yan-Min Zhang ◽  
Xin Qiao ◽  
Jing Pan ◽  
Ling-Feng Yin ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Bayesian Model ◽  
High Efficiency ◽  
Screening Strategy ◽  
Classification Model ◽  
Acetyl Coa ◽  
Ligand Interaction ◽  
Alcoholic Fatty Liver ◽  
Molecule Docking

Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.</P>

scholarly journals PHARMACOINFORMATICS APPROACH OF IDENTIFIED BIOLOGICAL COMPOUNDS FROM SELECTED PLECTRANTHUS (L.) SPECIES IN TAMIL NADU, INDIA: AN IN-SILICO APPROACH

2021 ◽  
Vol 12 (7) ◽  
pp. 14-21
Author(s):  
Selvarasuvasuki Manikandan ◽  
Sabeerali Ansarali ◽  
Manikandan Priyadharshini ◽  
Ganapathy Murugan Alagu Lakshmanan
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Tamil Nadu ◽  
Biological Activities ◽  
Ligand Interaction ◽  
Docking Analysis ◽  
Biological Compounds ◽  
Low Toxicity ◽  
Plectranthus Amboinicus ◽  
Molecular Docking Analysis

Aim: Plectranthus (Linn) is a typical genus of the Indian flora. It had been used in the folk medicines for its several medicinal properties. In this study, there are twenty-five major biological compounds were selected from Plectranthus forskohlii, Plectranthus coleoides, Plectranthus rotundifolius and Plectranthus vettiveroides for molecular docking analysis and find out the active compounds against Diabetic, Cancer and Tuberculosis diseases. Materials and methods: Biological compounds of Plectranthus Species were identifying and investigated by GC-MS and the biological activities of these compounds were studied with virtual screening, ADMET analysis, Protein ligand interaction through molecular docking analysis. Results: Twenty-five major biological compounds were selected for virtual screening analysis to find out the drug likeness activity. Out of these twenty-five compounds nine compounds are drug likeness in nature. Based on the ADMET analysis, Thymol beta D-Glucoside showed the low toxicity level and it represent Lipinski rule of five. The molecular docking results of Thymol beta D-Glucoside interact with different target proteins used in the study showed the maximum docking energy was obtained against tuberculosis protein -10.1846kcal/mol followed by diabetic protein -10.8736kcal/mol and cancer protein -11.4109kcal/mol. Conclusion: Plectranthus amboinicus leaves showed significant anti-diabetic, anti-cancer, anti-tuberculosis activity when compared to other studied species such as Plectranthus forskohlii, Plectranthus coleoides, Plectranthus rotundifolius and Plectranthus vettiveroides.

Methods for Docking and Drug Designing

2016 ◽  
pp. 39-53
Author(s):  
Ahmad Abu Turab Naqvi ◽  
Md. Imtaiyaz Hassan
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Force Field ◽  
De Novo ◽  
Scoring Functions ◽  
Receptor Molecule ◽  
Knowledge Based ◽  
Ligand Screening ◽  
Novel Drug

Molecular docking is the prediction of conformational complementarity between ligand and receptor molecule. The process of docking integrates two schematic approaches namely sampling of ligand conformations and ranking of selected conformations based on scoring functions. The authors have discussed established methodologies for molecular docking and well-known tools implementing these methods. A brief account of different classes of scoring functions such as force field based, empirical, knowledge based, and descriptor based scoring functions is given along with the exemplary implementations of these scoring functions. By replacing test and trial based ligand screening with structure based virtual screening, molecular docking has helped in shortening the duration of novel drug discovery up to some extent. However, the developments made in the field of drug discovery are assisted by the advances in the techniques of molecular docking, but there is strong need of enrichment in the techniques, especially in scoring functions, to tackle the inbound problems of de novo drug discovery.

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