scholarly journals Molecular Docking in Modern Drug Discovery: Principles and Recent Applications

2020 ◽  
Author(s):  
Aaftaab Sethi ◽  
Khusbhoo Joshi ◽  
K. Sasikala ◽  
Mallika Alvala
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Modern Drug

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

2016 ◽  
pp. 196-219
Author(s):  
Sanchaita Rajkhowa ◽  
Ramesh C. Deka
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
High Throughput Screening ◽  
Docking Studies ◽  
Stable Complex ◽  
Scoring Functions ◽  
Binding Modes ◽  
Structure Based Drug Design ◽  
Modern Drug

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

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.

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

Oncology ◽  
2017 ◽  
pp. 891-914
Author(s):  
Sanchaita Rajkhowa ◽  
Ramesh C. Deka
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
High Throughput Screening ◽  
Docking Studies ◽  
Stable Complex ◽  
Scoring Functions ◽  
Binding Modes ◽  
Structure Based Drug Design ◽  
Modern Drug

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

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>

Design, Synthesis and Docking Studies of Some Novel Fluoroquinolone Compounds with Antibacterial Activity

2018 ◽  
Vol 69 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Lucia Pintilie ◽  
Amalia Stefaniu ◽  
Alina Ioana Nicu ◽  
Maria Maganu ◽  
Miron Teodor Caproiu
Keyword(s):  
Antimicrobial Activity ◽  
Antibacterial Activity ◽  
Molecular Docking ◽  
Drug Discovery ◽  
Elemental Analysis ◽  
Docking Studies ◽  
Receptor Protein ◽  
Chemical Methods ◽  
Design Synthesis ◽  
Gram Negative

A new series of fluoroquinolone compounds have been obtained by Gould-Jacobs method. The compounds have been characterized by physic-chemical methods (elemental analysis, FTIR, NMR, UV-Vis) and by antimicrobial activity against Gram-positive and Gram-negative microorganisms. For the synthesized compounds have been performed calculations of characteristics and molecular properties, using Spartan�14 Software from Wavefunction, Inc. Irvine, CA. and molecular docking studies using CLC Drug Discovery Workbench 2.4 software, to identify and visualize the most likely interaction ligand (fluoroquinolone) with the receptor protein.

Molecular Docking and Drug Discovery in β-Adrenergic Receptors

2017 ◽  
Vol 24 (39) ◽  
Author(s):  
Santiago Vilar ◽  
Eduardo Sobarzo-Sanchez ◽  
Lourdes Santana ◽  
Eugenio Uriarte
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Adrenergic Receptors ◽  
Β Adrenergic Receptors

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

scholarly journals Prospect into therapeutic potentials of Moringa oleifera phytocompounds against cancer upsurge: de novo synthesis of test compounds, molecular docking, and ADMET studies

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
P. M. Aja ◽  
P. C. Agu ◽  
E. M. Ezeh ◽  
J. N. Awoke ◽  
H. A. Ogwoni ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Cancer Chemotherapy ◽  
Moringa Oleifera ◽  
De Novo ◽  
Gas Chromatography Mass Spectrometry ◽  
Molecular Docking Study ◽  
Cancer Proliferation ◽  
Target Proteins ◽  
Potential Inhibitors

Abstract Background Cancer chemotherapy is difficult because current medications for the treatment of cancer have been linked to a slew of side effects; as a result, researchers are tasked with developing greener cancer chemotherapies. Moringa oleifera has been reported with several bioactive compounds which confirm its application for various ailments by traditional practitioners. In this study, we aim to prospect the therapeutic potentials of M. oleifera phytocompounds against cancer proliferation as a step towards drug discovery using a computational approach. Target proteins: dihydrofolate reductase (DHFR) and B-Cell Lymphoid-2 (BCL-2), were retrieved from the RCSB PDB web server. Sixteen and five phytocompounds previously reported in M. oleifera leaves (ML) and seeds (MS), respectively, by gas chromatography–mass spectrometry were synthesized and used in the molecular docking study. For accurate prediction of binding sites of the target proteins; standard inhibitors, Methotrexate (MTX) for DHFR, and Venetoclax (VTC) for BCL-2, were docked together with the test compounds. We further predicted the ADMET profile of the potential inhibitors for an insight into their chance of success as candidates in drug discovery. Results Results for the binding affinities, docking poses, and the interactions showed that ML2, ML4-6, ML8-15, and MS1-5 are potential inhibitors of DHFR and BCL-2, respectively. In the ADMET profile, ML2 and ML4 showed the best drug-likeness by non-violation of Lipski Rule of Five. ML4-6, ML8, ML11, ML14-15, and MS1, MS3-5 exhibit high GI absorption; ML2, ML4-6, ML8, MS1, and MS5 are blood–brain barrier permeants. ML2, ML4, ML9, ML13, and MS2 do not interfere with any of the CYP450 isoforms. The toxicity profile showed that all the potential inhibitors are non-carcinogenic and non-hERG I (human ether-a-go-go related gene I) inhibitors. ML4, ML11, and MS4 are hepatotoxic and ML7, ML10, and MS4 are hERG II inhibitors. A plethora of insights on the toxic endpoints and lethal concentration values showed that ML5, ML13, and MS2 are comparatively less lethal than other potential inhibitors. Conclusion This study has demonstrated that M. oleifera phytocompounds are potential inhibitors of the disease proteins involved in cancer proliferation, thus, an invaluable step toward the discovery of cancer chemotherapy with lesser limitations.

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