In Silico Discovery and Virtual Screening of Multi-Target Inhibitors for Proteins in Mycobacterium tuberculosis

2012 ◽  
Vol 15 (8) ◽  
pp. 666-673 ◽  
Author(s):  
Alejandro Speck-Planche ◽  
Valeria V. Kleandrova ◽  
Feng Luan ◽  
M. Natalia D.S. Cordeiro
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
In Silico

scholarly journals in Silico Docking and Molecular Dynamic Simulation of 3-Dehydroquinate Dehydratase from Mycobacterium Tuberculosis Through Virtual Screening and Pharmacokinetics Studies

2020 ◽  
Author(s):  
Mustafa Alhaji Isa ◽  
Muhammad M Ibrahim
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
Molecular Dynamic ◽  
In Silico ◽  
Md Simulation ◽  
Binding Energies ◽  
In Silico Docking ◽  
Pharmacokinetic Properties

The 3-hydroquinate synthase (DHQase) is an enzyme that catalyzes the third step of the shikimate pathway in <i>Mycobacterium tuberculosis</i> (MTB), by converting 3-dehydroquinate into 3-dehydroshikimate. In this study, the novel inhibitors of DHQase from MTB was identified using in silico approach. The crystal structure of DHQase bound to 1,3,4-trihydroxy-5-(3-phenoxypropyl)-cyclohexane-1-carboxylic acid (CA) obtained from the Protein Data Bank (PDB ID: 3N76). The structure prepared through energy minimization and structure optimization. A total of 9699 compounds obtained from Zinc and PubChem databases capable of binding to DHQase and subjected to virtual screening through Lipinski’s rule of five and molecular docking analysis. Eight (8) compounds with good binding energies, ranged between ─8.99 to ─8.39kcal/mol were selected, better than the binding energy of ─4.93kcal/mol for CA and further filtered for pharmacokinetic properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity or ADMET). Five compounds (ZINC14981770, ZINC14741224, ZINC14743698, ZINC13165465, and ZINC8442077) which had desirable pharmacokinetic properties selected for molecular dynamic (MD) simulation and molecular generalized born surface area (MM-GBSA) analyses. The results of the analyses showed that all the compounds formed stable and rigid complexes after the 50ns MD simulation and also had a lower binding as compared to CA. Therefore, these compounds considered as good inhibitors of MTB after in vitro and in vivo validation.”

scholarly journals In Silico Characterization and Virtual Screening of GntR/HutC Family Transcriptional Regulator MoyR: A Potential Monooxygenase Regulator in Mycobacterium tuberculosis

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1241
Author(s):  
Thanusha Dhananji Abeywickrama ◽  
Inoka Chinthana Perera
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
In Silico ◽  
Streptomyces Coelicolor ◽  
Homology Modelling ◽  
Transcriptional Regulators ◽  
Physicochemical Analysis ◽  
Structure Stability ◽  
In Silico Characterization ◽  
Drug Leads

Mycobacterium tuberculosis is a well-known pathogen due to the emergence of drug resistance associated with it, where transcriptional regulators play a key role in infection, colonization and persistence. The genome of M. tuberculosis encodes many transcriptional regulators, and here we report an in-depth in silico characterization of a GntR regulator: MoyR, a possible monooxygenase regulator. Homology modelling provided a reliable structure for MoyR, showing homology with a HutC regulator DasR from Streptomyces coelicolor. In silico physicochemical analysis revealed that MoyR is a cytoplasmic protein with higher thermal stability and higher pI. Four highly probable binding pockets were determined in MoyR and the druggability was higher in the orthosteric binding site consisting of three conserved critical residues: TYR179, ARG223 and GLU234. Two highly conserved leucine residues were identified in the effector-binding region of MoyR and other HutC homologues, suggesting that these two residues can be crucial for structure stability and oligomerization. Virtual screening of drug leads resulted in four drug-like compounds with greater affinity to MoyR with potential inhibitory effects for MoyR. Our findings support that this regulator protein can be valuable as a therapeutic target that can be used for developing drug leads.

scholarly journals in Silico Docking and Molecular Dynamic Simulation of 3-Dehydroquinate Dehydratase from Mycobacterium Tuberculosis Through Virtual Screening and Pharmacokinetics Studies

2020 ◽  
Author(s):  
Mustafa Alhaji Isa ◽  
Muhammad M Ibrahim
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
Molecular Dynamic ◽  
In Silico ◽  
Md Simulation ◽  
Binding Energies ◽  
In Silico Docking ◽  
Pharmacokinetic Properties

The 3-hydroquinate synthase (DHQase) is an enzyme that catalyzes the third step of the shikimate pathway in <i>Mycobacterium tuberculosis</i> (MTB), by converting 3-dehydroquinate into 3-dehydroshikimate. In this study, the novel inhibitors of DHQase from MTB was identified using in silico approach. The crystal structure of DHQase bound to 1,3,4-trihydroxy-5-(3-phenoxypropyl)-cyclohexane-1-carboxylic acid (CA) obtained from the Protein Data Bank (PDB ID: 3N76). The structure prepared through energy minimization and structure optimization. A total of 9699 compounds obtained from Zinc and PubChem databases capable of binding to DHQase and subjected to virtual screening through Lipinski’s rule of five and molecular docking analysis. Eight (8) compounds with good binding energies, ranged between ─8.99 to ─8.39kcal/mol were selected, better than the binding energy of ─4.93kcal/mol for CA and further filtered for pharmacokinetic properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity or ADMET). Five compounds (ZINC14981770, ZINC14741224, ZINC14743698, ZINC13165465, and ZINC8442077) which had desirable pharmacokinetic properties selected for molecular dynamic (MD) simulation and molecular generalized born surface area (MM-GBSA) analyses. The results of the analyses showed that all the compounds formed stable and rigid complexes after the 50ns MD simulation and also had a lower binding as compared to CA. Therefore, these compounds considered as good inhibitors of MTB after in vitro and in vivo validation.”

Discovery of New AKT1 Inhibitors by Combination of In silico Structure Based Virtual Screening Approaches and Biological Evaluations

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla
Keyword(s):  
Virtual Screening ◽  
Success Rate ◽  
Protein Interactions ◽  
In Silico ◽  
Biological Data ◽  
In Vitro Binding ◽  
Vitro Binding ◽  
Screening Protocol ◽  
Screening Approaches

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

Promising Anti-stroke Signature of Voglibose: Investigation through In- Silico Molecular Docking and Virtual Screening in In-Vivo Animal Studies

2020 ◽  
Vol 20 (3) ◽  
pp. 223-235
Author(s):  
Pooja Shah ◽  
Vishal Chavda ◽  
Snehal Patel ◽  
Shraddha Bhadada ◽  
Ghulam Md. Ashraf
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
In Silico ◽  
Reductase Activity ◽  
Infarct Volume ◽  
Docking Studies ◽  
Serum Glucose ◽  
In Vivo Studies ◽  
In Silico Molecular Docking

Background: Postprandial hyperglycemia considered to be a major risk factor for cerebrovascular complications. Objective: The current study was designed to elucidate the beneficial role of voglibose via in-silico in vitro to in-vivo studies in improving the postprandial glycaemic state by protection against strokeprone type 2 diabetes. Material and Methods: In-Silico molecular docking and virtual screening were carried out with the help of iGEMDOCK+ Pymol+docking software and Protein Drug Bank database (PDB). Based on the results of docking studies, in-vivo investigation was carried out for possible neuroprotective action. T2DM was induced by a single injection of streptozotocin (90mg/kg, i.v.) to neonates. Six weeks after induction, voglibose was administered at the dose of 10mg/kg p.o. for two weeks. After eight weeks, diabetic rats were subjected to middle cerebral artery occlusion, and after 72 hours of surgery, neurological deficits were determined. The blood was collected for the determination of serum glucose, CK-MB, LDH and lipid levels. Brains were excised for determination of brain infarct volume, brain hemisphere weight difference, Na+-K+ ATPase activity, ROS parameters, NO levels, and aldose reductase activity. Results: In-silico docking studies showed good docking binding score for stroke associated proteins, which possibly hypotheses neuroprotective action of voglibose in stroke. In the present in-vivo study, pre-treatment with voglibose showed a significant decrease (p<0.05) in serum glucose and lipid levels. Voglibose has shown significant (p<0.05) reduction in neurological score, brain infarct volume, the difference in brain hemisphere weight. On biochemical evaluation, treatment with voglibose produced significant (p<0.05) decrease in CK-MB, LDH, and NO levels in blood and reduction in Na+-K+ ATPase, oxidative stress, and aldose reductase activity in brain homogenate. Conclusion: In-silico molecular docking and virtual screening studies and in-vivo studies in MCAo induced stroke, animal model outcomes support the strong anti-stroke signature for possible neuroprotective therapeutics.

Lead Molecule Prediction and Characterization for Designing MERS-CoV 3C-like Protease Inhibitors: An In silico Approach

2018 ◽  
Vol 15 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Md. Mostafijur Rahman ◽  
Md. Bayejid Hosen ◽  
M. Zakir Hossain Howlader ◽  
Yearul Kabir
Keyword(s):  
Binding Energy ◽  
Middle East ◽  
Virtual Screening ◽  
In Silico ◽  
Screening Method ◽  
Middle East Respiratory Syndrome ◽  
Cytochrome P450 Enzymes ◽  
Drug Molecules ◽  
Essential Enzyme ◽  
Reduced Toxicity

Background: 3C-like protease also called the main protease is an essential enzyme for the completion of the life cycle of Middle East Respiratory Syndrome Coronavirus. In our study we predicted compounds which are capable of inhibiting 3C-like protease, and thus inhibit the lifecycle of Middle East Respiratory Syndrome Coronavirus using in silico methods. </P><P> Methods: Lead like compounds and drug molecules which are capable of inhibiting 3C-like protease was identified by structure-based virtual screening and ligand-based virtual screening method. Further, the compounds were validated through absorption, distribution, metabolism and excretion filtering. Results: Based on binding energy, ADME properties, and toxicology analysis, we finally selected 3 compounds from structure-based virtual screening (ZINC ID: 75121653, 41131653, and 67266079) having binding energy -7.12, -7.1 and -7.08 Kcal/mol, respectively and 5 compounds from ligandbased virtual screening (ZINC ID: 05576502, 47654332, 04829153, 86434515 and 25626324) having binding energy -49.8, -54.9, -65.6, -61.1 and -66.7 Kcal/mol respectively. All these compounds have good ADME profile and reduced toxicity. Among eight compounds, one is soluble in water and remaining 7 compounds are highly soluble in water. All compounds have bioavailability 0.55 on the scale of 0 to 1. Among the 5 compounds from structure-based virtual screening, 2 compounds showed leadlikeness. All the compounds showed no inhibition of cytochrome P450 enzymes, no blood-brain barrier permeability and no toxic structure in medicinal chemistry profile. All the compounds are not a substrate of P-glycoprotein. Our predicted compounds may be capable of inhibiting 3C-like protease but need some further validation in wet lab.

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