Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

2020 ◽  
Vol 17 (2) ◽  
pp. 233-247
Author(s):  
Krishna A. Gajjar ◽  
Anuradha K. Gajjar
Keyword(s):  
Molecular Docking ◽  
Structural Information ◽  
Docking Studies ◽  
Structural Motif ◽  
Roc Curve Analysis ◽  
Ligand Complex ◽  
Discovery Studio ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Pharmacophore Models

Background: Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. Methods: DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software. Results and Conclusion: Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.

Using molecular docking and molecular dynamics to investigate protein-ligand interactions

2021 ◽  
Vol 35 (08) ◽  
pp. 2130002
Author(s):  
Connor J. Morris ◽  
Dennis Della Corte
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Drug Discovery ◽  
State Of The Art ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Art Methods ◽  
Ligand Interactions ◽  
Docking Calculations ◽  
Ligand Bond

Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

scholarly journals The structural diversity of ginsenosides affects their cholinesterase inhibitory potential

2020 ◽  
Vol 45 (2) ◽  
Author(s):  
Eda Özturan Özer ◽  
Oya Unsal Tan ◽  
Suna Turkoglu
Keyword(s):  
Molecular Docking ◽  
Enzyme Inhibitor ◽  
Direct Interaction ◽  
Structural Diversity ◽  
Docking Studies ◽  
Catalytic Triad ◽  
Active Components ◽  
Molecular Docking Studies ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

AbstractBackground/ObjectiveGinsenosides, the major active components of the ginseng, are known to have various effects on nervous systems. The present study aimed to clarify the inhibition potentials of ginsenosides Rb1, Rc, Re and Rg1 on acetylcholinesterase (AChE) and butrylcholinesterase (BChE) activities, and to evaluate the underlying mechanisms of inhibitions provided by protein-ligand interactions considering their probable candidates of prodrug.Materials and methodsThe inhibitory mechanisms of ginsenosides related with their structural diversity were analyzed kinetically and protein-ligand interactions for both enzymes were evaluated with most potent ginsenosides, by molecular docking studies.ResultsGinsenosides Re and Rg1, with sugar moieties attached to the C-6 and C-20 positions of core structure were found to possess the most powerful inhibitory effect on AChE and BChE activities. Molecular docking studies have been confirmed by kinetic studies. Ginsenosides having a direct interaction with amino acid residues belonging to the catalytic triad revealed the most powerful inhibition with lowest enzyme-inhibitor dissociation constant (Ki) values.ConclusionsGinsenosides Re and Rg1, either alone or in a specific combination, may provide beneficial effects on neurodegenerative pathologies in therapeutic terms.

scholarly journals Structure Based Design and Molecular Docking Studies for Phosphorylated Tau Inhibitors in Alzheimer’s Disease

Cells ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 260 ◽  
Author(s):  
Jangampalli Pradeepkiran ◽  
P. Reddy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Drug Target ◽  
Docking Studies ◽  
Toxicity Tests ◽  
Phosphorylated Tau ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Alternative Drug

The purpose of our study is to identify phosphorylated tau (p-tau) inhibitors. P-tau has recently received great interest as a potential drug target in Alzheimer’s disease (AD). The continuous failure of Aβ-targeted therapeutics recommends an alternative drug target to treat AD. There is increasing evidence and growing awareness of tau, which plays a central role in AD pathophysiology, including tangles formation, abnormal activation of phosphatases/kinases, leading p-tau aggregation in AD neurons. In the present study, we performed computational pharmacophore models, molecular docking, and simulation studies for p-tau in order to identify hyperphosphorylated sites. We found multiple serine sites that altered the R1/R2 repeats flanking sequences in the tau protein, affecting the microtubule binding ability of tau. The ligand molecules exhibited the p-O ester scaffolds with inhibitory and/or blocking actions against serine residues of p-tau. Our molecular docking results revealed five ligands that showed high docking scores and optimal protein-ligand interactions of p-tau. These five ligands showed the best pharmacokinetic and physicochemical properties, including good absorption, distribution, metabolism, and excretion (ADME) and admetSAR toxicity tests. The p-tau pharmacophore based drug discovery models provide the comprehensive and rapid drug interventions in AD, and tauopathies are expected to be the prospective future therapeutic approach in AD.

NMR Approaches To Understanding Protein Specificity

1997 ◽  
Author(s):  
G.C. K. Roberts ◽  
L.-Y. Lian
Keyword(s):  
Nmr Spectroscopy ◽  
Ligand Binding ◽  
Pathogenic Bacteria ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Protein Interactions ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Nuclear Overhauser

The biological functions of proteins all depend on their highly specific interactions with other molecules, and the understanding of the molecular basis of the specificity of these interactions is an important part of the effort to understand protein structure-function relationships. NMR spectroscopy can provide information on many different aspects of protein-ligand interactions, ranging from the determination of the complete structure of a protein-ligand complex to focussing on selected features of the interactions between the ligand and protein by using “reporter groups” on the ligand or the protein. It has two particular advantages: the ability to study the complex in solution, and the ability to provide not only structural, but also dynamic, kinetic and thermodynamic information on ligand binding. Early analyses of ligand binding (Jardetzky and Roberts, 1981) focused on measurements of relaxation times, chemical shifts and coupling constants, which gave relatively limited, although valuable, structural information. More recently, it has become possible to obtain much more detailed information, due to the extensive use of nuclear Overhauser effect measurements and isotope-labeled proteins and ligands; a number of reviews of this area are available (Feeney and Birdsall, 1993; Lian et al, 1994; Wand and Short, 1994; Petros and Fesik, 1994; Wemmer and Williams, 1994). In this article, we describe some recent work from our laboratory which illustrates the use of NMR spectroscopy to obtain structural and mechanistic information on relatively large enzyme-substrate and proteinprotein complexes. A number of species of pathogenic bacteria, notably Streptococci and Staphylococci, have proteins on their surface that bind immurioglobulins (reviewed in Boyle (1990)). Protein A from S. aureus and protein G from species of Streptococci are widely used as imrnunological tools and are the most extensively studied of these antibody-binding proteins. A detailed understanding of the binding mechanisms of these proteins is important, not only for providing us with the structural basis for their functions, but also as a contribution toward understanding the general rules of protein-protein interactions.

scholarly journals Layer-wise relevance propagation of InteractionNet explains protein–ligand interactions at the atom level

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hyeoncheol Cho ◽  
Eok Kyun Lee ◽  
Insung S. Choi
Keyword(s):  
Dissociation Constants ◽  
Structural Information ◽  
Three Dimensional ◽  
Graph Representation ◽  
Ligand Complex ◽  
Covalent Bonds ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Atom Level ◽  
Graph Neural Networks

AbstractDevelopment of deep-learning models for intermolecular noncovalent (NC) interactions between proteins and ligands has great potential in the chemical and pharmaceutical tasks, including structure–activity relationship and drug design. It still remains an open question how to convert the three-dimensional, structural information of a protein–ligand complex into a graph representation in the graph neural networks (GNNs). It is also difficult to know whether a trained GNN model learns the NC interactions properly. Herein, we propose a GNN architecture that learns two distinct graphs—one for the intramolecular covalent bonds in a protein and a ligand, and the other for the intermolecular NC interactions between the protein and the ligand—separately by the corresponding covalent and NC convolutional layers. The graph separation has some advantages, such as independent evaluation on the contribution of each convolutional step to the prediction of dissociation constants, and facile analysis of graph-building strategies for the NC interactions. In addition to its prediction performance that is comparable to that of a state-of-the art model, the analysis with an explainability strategy of layer-wise relevance propagation shows that our model successfully predicts the important characteristics of the NC interactions, especially in the aspect of hydrogen bonding, in the chemical interpretation of protein–ligand binding.

scholarly journals Mechanistic Validation of an Ergogenic T. arjuna Standardized Extract (Oxyjun®) Using a Molecular Docking Approach

2021 ◽  
pp. 54-63
Author(s):  
Kavita Pandey ◽  
Gursimran Kaur Uppal ◽  
Ratna Upadhyay
Keyword(s):  
Molecular Docking ◽  
Docking Studies ◽  
Terminalia Arjuna ◽  
Lipinski’S Rule ◽  
Protein Ligand Interactions ◽  
Potential Efficacy ◽  
Ligand Interactions ◽  
Docking Approach ◽  
Cardio Vascular ◽  
Botanical Extract

The bark of the tree Terminalia arjuna commonly referred as Arjuna is widely used in Ayurveda as a therapeutic agent for heart disease. More recently, a proprietary botanical extract of T. arjuna with tradename, Oxyjun®, demonstrated cardiotonic and ergogenic benefits for the first time in a younger and healthier population. However, the mechanism of action and biological actives of this novel sports ingredient were not clear. A molecular docking approach was adopted to understand the protein-ligand interactions and establish the most probable mechanism(s) of cardio vascular actions of the phytoconstituents of the T. arjuna standardized extract (TASE). Twenty-one phytochemicals (ligands) were chosen from Arjuna and their binding affinities against eight proteins serving cardiovascular functions (target proteins) were investigated. Autodock Vina was used to carry out the molecular docking studies. Potential efficacy in humans was assessed on the basis of ADMET properties and Lipinski’s Rule of 5. We found that arjunic acid, arjungenin, arjunetin, arjunglucoside1, chrysin, kaempferol, luteolin, rhamnetin and taxifolin demonstrated good docking scores and bioactivity.

scholarly journals Docking studies in targeting proteins involved in cardiovascular disorders using phytocompounds from Terminalia arjuna

2020 ◽  
Author(s):  
Vikas Kumar ◽  
Nitin Sharma ◽  
Anuradha Sourirajan ◽  
Prem Kumar Khosla ◽  
Kamal Dev
Keyword(s):  
Molecular Docking ◽  
3D Structure ◽  
Three Dimensional ◽  
Data Bank ◽  
Docking Studies ◽  
Terminalia Arjuna ◽  
Structure Based Drug Design ◽  
Lipinski’S Rule ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

AbstractTerminalia arjuna (Roxb.) Wight and Arnot (T. arjuna) commonly known as Arjuna has been known for its cardiotonic nature in heart failure, ischemic, cardiomyopathy, atherosclerosis, myocardium necrosis and also has been used in the treatment of different human disorders such as blood diseases, anaemia and viral diseases. Our focus has been on phytochemicals which do not exhibit any cytotoxicity and have significant cardioprotective activity. Since Protein-Ligand interactions play a key role in structure-based drug design, therefore with the help of molecular docking, we screened 19 phytochemicals present in T. arjuna and investigated their binding affinity against different cardiovascular target proteins. The three-dimensional (3D) structure of target cardiovascular proteins were retrieved from Protein Data Bank, and docked with 3D Pubchem structures of 19 phytochemicals using Autodock vina. Molecular docking and drug-likeness studies were made using ADMET properties while Lipinski’s rule of five was performed for the phytochemicals to evaluate their cardio protective activity. Among all selected phytocompounds, arjunic acid, arjungenin, and terminic acid were found to fulfill all ADMET rules, drug likeness, and are less toxic in nature. Our studies, therefore revealed that these three phytochemicals from T. arjuna can be used as promising candidates for developing broad spectrum drugs against cardiovascular diseases.

Homology Modeling of Mus Musculus CDK5 and Molecular Docking Studies with Flavonoids

2017 ◽  
Vol 9 (6) ◽  
Author(s):  
Sowmya Suri ◽  
Rumana Waseem ◽  
Seshagiri Bandi ◽  
Sania Shaik
Keyword(s):  
Molecular Docking ◽  
3D Model ◽  
Structural Features ◽  
Docking Studies ◽  
Amino Acid Residues ◽  
Cyclin Dependent Kinase ◽  
Ligand Complex ◽  
Ligand Interaction ◽  
Molecular Docking Studies ◽  
Cyclin Dependent Kinase 5

A 3D model of Cyclin-dependent kinase 5 (CDK5) (Accession Number: Q543f6) is generated based on crystal structure of P. falciparum PFPK5-indirubin-5-sulphonate ligand complex (PDB ID: 1V0O) at 2.30 Å resolution was used as template. Protein-ligand interaction studies were performed with flavonoids to explore structural features and binding mechanism of flavonoids as CDK5 (Cyclin-dependent kinase 5) inhibitors. The modelled structure was selected on the basis of least modeler objective function. The model was validated by PROCHECK. The predicted 3D model is reliable with 93.0% of amino acid residues in core region of the Ramachandran plot. Molecular docking studies with flavonoids viz., Diosmetin, Eriodictyol, Fortuneletin, Apigenin, Ayanin, Baicalein, Chrysoeriol and Chrysosplenol-D with modelled protein indicate that Diosmetin is the best inhibitor containing docking score of -8.23 kcal/mol. Cys83, Lys89, Asp84. The compound Diosmetin shows interactions with Cys83, Lys89, and Asp84.

scholarly journals Discovery of novel potential KIT inhibitors for the treatment of gastrointestinal stromal tumor

2021 ◽  
Vol 16 (1) ◽  
pp. 303-310
Author(s):  
Lili Jiang ◽  
Zhongmin Zhang ◽  
Zhen Wang ◽  
Yong Liu
Keyword(s):  
Molecular Docking ◽  
Gastrointestinal Stromal Tumor ◽  
Pharmacophore Modeling ◽  
Stromal Tumor ◽  
Strong Hydrogen Bond ◽  
Ligand Complex ◽  
Hydrogen Bond Interaction ◽  
Mutant Proteins ◽  
Docking Simulations ◽  
Pharmacophore Models

Abstract Numerous inhibitors of tyrosine-protein kinase KIT, a receptor tyrosine kinase, have been explored as a viable therapy for the treatment of gastrointestinal stromal tumor (GIST). However, drug resistance due to acquired mutations in KIT makes these drugs almost useless. The present study was designed to screen the novel inhibitors against the activity of the KIT mutants through pharmacophore modeling and molecular docking. The best two pharmacophore models were established using the KIT mutants’ crystal complexes and were used to screen the new compounds with possible KIT inhibitory activity against both activation loop and ATP-binding mutants. As a result, two compounds were identified as potential candidates from the virtual screening, which satisfied the potential binding capabilities, molecular modeling characteristics, and predicted absorption, distribution, metabolism, excretion, toxicity (ADMET) properties. Further molecular docking simulations showed that two compounds made strong hydrogen bond interaction with different KIT mutant proteins. Our results indicated that pharmacophore models based on the receptor–ligand complex had excellent ability to screen KIT inhibitors, and two compounds may have the potential to develop further as the future KIT inhibitors for GIST treatment.

Computational Investigation of Versatile Activity of Piperine

2019 ◽  
pp. 127-139
Author(s):  
Thenmozhi Marudhadurai ◽  
Navabshan Irfan
Keyword(s):  
Docking Study ◽  
Black Pepper ◽  
Docking Studies ◽  
Therapeutic Activity ◽  
Computational Docking ◽  
Target Proteins ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Anti Oxidant ◽  
Inhibition Property

Piperine is known for its versatile therapeutic activity. It has been used for various disease conditions (e.g., cold, cough, etc.). Piperine is an alkaloid found in black pepper. It possesses various pharmacological actions like anti-inflammatory, anti-oxidant, anti-cholinergic, and anti-cancerous. The above-mentioned properties will be studied by selecting target proteins COX-2 protein, angiotensin converting enzyme, acetylcholineesterases, and survivin using computational docking study. This chapter explains the inhibition property of piperine against selected target protein from the results of docking studies. Based on the docking scores and protein-ligand interactions, piperine was found to bind well in the active site of the selected target proteins. It ensures the binding efficacy of piperine against selected target proteins. Docking scores and protein-ligand interactions plays an important role in its therapeutic activity.

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