scholarly journals Mechanism of Selective Inhibition of Yohimbine and Its Derivatives in Adrenoceptorα2 Subtypes

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Liu Hai-Bo ◽  
Peng Yong ◽  
Huang Lu-qi ◽  
Xu Jun ◽  
Xiao Pei-Gen
Keyword(s):  
Molecular Dynamics ◽  
Selective Inhibition ◽  
Binding Mode ◽  
Mode Analysis ◽  
Inhibiting Activity ◽  
Binding Modes ◽  
Molecular Dynamics Methods ◽  
Subtype A ◽  
Key Residues ◽  
Homology Models

Some natural alkaloids from medicinal plants, such as yohimbine and its derivatives, have been reported with adrenoceptor (AR)α2 subtypes inhibiting activity. In trying to address the possible mechanism of the action, a set of homology models of ARα2 was built based on MOE. After that, docking and molecular dynamics methods were used to investigate the binding modes of yohimbine and its 2 derivatives in the active pocket of adrenoceptorα2 subtype A, B, and C. The key interactions between the 3 ligands and the 3 receptors were mapped. Binding mode analysis presents a strong identity in the key residues in each subtype. Only a few differences play the key role in modulating selectivity of yohimbine and its derivatives. These results can guide the design of new selective ARα2 inhibitors.

scholarly journals Theoretical Study of Sequence Selectivity and Preferred Binding Mode of Psoralen with DNA

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Patricia Saenz-Méndez ◽  
Rita C. Guedes ◽  
Daniel J. V. A. dos Santos ◽  
Leif A. Eriksson
Keyword(s):  
Molecular Dynamics ◽  
Theoretical Study ◽  
Binding Mode ◽  
Minor Groove Binding ◽  
Binding Modes ◽  
Groove Binding ◽  
Base Pairs ◽  
Sequence Selectivity ◽  
Molecular Dynamics Methods ◽  
Low Efficiency

Psoralen interaction with two models of DNA was investigated using molecular mechanics and molecular dynamics methods. Calculated energies of minor groove binding and intercalation were compared in order to define a preferred binding mode for the ligand. We found that both binding modes are possible, explaining the low efficiency for monoadduct formation from intercalated ligands. A comparison between the interaction energy for intercalation between different base pairs suggests that the observed sequence selectivity is due to favorable intercalation in 5′-TpA in (AT)n sequences.

scholarly journals Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

Biomolecules ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 686 ◽  
Author(s):  
Alexander Neumann ◽  
Viktor Engel ◽  
Andhika B. Mahardhika ◽  
Clara T. Schoeder ◽  
Vigneshwaran Namasivayam ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
G Protein ◽  
Phenyl Ring ◽  
Binding Mode ◽  
Dynamics Simulation ◽  
Simulation Studies ◽  
Binding Modes ◽  
G Protein Coupled Receptor ◽  
G Protein Coupled

GPR18 is an orphan G protein-coupled receptor (GPCR) expressed in cells of the immune system. It is activated by the cannabinoid receptor (CB) agonist ∆9-tetrahydrocannabinol (THC). Several further lipids have been proposed to act as GPR18 agonists, but these results still require unambiguous confirmation. In the present study, we constructed a homology model of the human GPR18 based on an ensemble of three GPCR crystal structures to investigate the binding modes of the agonist THC and the recently reported antagonists which feature an imidazothiazinone core to which a (substituted) phenyl ring is connected via a lipophilic linker. Docking and molecular dynamics simulation studies were performed. As a result, a hydrophobic binding pocket is predicted to accommodate the imidazothiazinone core, while the terminal phenyl ring projects towards an aromatic pocket. Hydrophobic interaction of Cys251 with substituents on the phenyl ring could explain the high potency of the most potent derivatives. Molecular dynamics simulation studies suggest that the binding of imidazothiazinone antagonists stabilizes transmembrane regions TM1, TM6 and TM7 of the receptor through a salt bridge between Asp118 and Lys133. The agonist THC is presumed to bind differently to GPR18 than to the distantly related CB receptors. This study provides insights into the binding mode of GPR18 agonists and antagonists which will facilitate future drug design for this promising potential drug target.

Pyrrolidine Derivatives as Plasmepsin Inhibitors: Binding Mode Analysis Assisted by Molecular Dynamics Simulations of a Highly Flexible Protein

ChemMedChem ◽  
2010 ◽  
Vol 5 (3) ◽  
pp. 443-454 ◽  
Author(s):  
Torsten Luksch ◽  
Andreas Blum ◽  
Nina Klee ◽  
Wibke E. Diederich ◽  
Christoph A. Sotriffer ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
Mode Analysis ◽  
Flexible Protein ◽  
Dynamics Simulations

scholarly journals Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations

Biomedicines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1197
Author(s):  
Vikas Kumar ◽  
Shraddha Parate ◽  
Gunjan Thakur ◽  
Gihwan Lee ◽  
Hyeon-Su Ro ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Cost Effective ◽  
Binding Mode ◽  
Kinase Domain ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Atp Binding Site ◽  
Dynamics Simulations

The cyclin-dependent kinase 7 (CDK7) plays a crucial role in regulating the cell cycle and RNA polymerase-based transcription. Overexpression of this kinase is linked with various cancers in humans due to its dual involvement in cell development. Furthermore, emerging evidence has revealed that inhibiting CDK7 has anti-cancer effects, driving the development of novel and more cost-effective inhibitors with enhanced selectivity for CDK7 over other CDKs. In the present investigation, a pharmacophore-based approach was utilized to identify potential hit compounds against CDK7. The generated pharmacophore models were validated and used as 3D queries to screen 55,578 natural drug-like compounds. The obtained compounds were then subjected to molecular docking and molecular dynamics simulations to predict their binding mode with CDK7. The molecular dynamics simulation trajectories were subsequently used to calculate binding affinity, revealing four hits—ZINC20392430, SN00112175, SN00004718, and SN00262261—having a better binding affinity towards CDK7 than the reference inhibitors (CT7001 and THZ1). The binding mode analysis displayed hydrogen bond interactions with the hinge region residues Met94 and Glu95, DFG motif residue Asp155, ATP-binding site residues Thr96, Asp97, and Gln141, and quintessential residue outside the kinase domain, Cys312 of CDK7. The in silico selectivity of the hits was further checked by docking with CDK2, the close homolog structure of CDK7. Additionally, the detailed pharmacokinetic properties were predicted, revealing that our hits have better properties than established CDK7 inhibitors CT7001 and THZ1. Hence, we argue that proposed hits may be crucial against CDK7-related malignancies.

scholarly journals Interaction of Coumarin Phytoestrogens with ERα and ERβ: A Molecular Dynamics Simulation Study

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1165 ◽  
Author(s):  
Ting Wang ◽  
Yunfei Wang ◽  
Xuming Zhuang ◽  
Feng Luan ◽  
Chunyan Zhao ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Free Energy ◽  
Human Life ◽  
Simulation Method ◽  
Binding Mode ◽  
Dynamics Simulation ◽  
Molecular Dynamics Methods ◽  
17Β Estradiol ◽  
The Stability

Coumarin phytoestrogens, as one of the important classes of phytoestrogens, have been proved to play an important role in various fields of human life. In this study, molecular simulation method including molecular docking and molecular dynamics methods were performed to explore the various effects between four classical coumarin phytoestrogens (coumestrol, 4-methoxycoumestrol, psoralen and isopsoralen), and estrogen receptors (ERα, ERβ), respectively. The calculated results not only proved that the four coumarin phytoestrogens have weaker affinity than 17β-estradiol to both ERα, and ERβ, but also pointed out that the selective affinity for ERβ is greater than ERα. In addition, the binding mode indicated that the formation of hydrogen bond and hydrophobic interaction have an important effect on the stability of the complexes. Further, the calculation and decomposition of binding free energy explored the main contribution interactions to the total free energy.

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

Binding mode analysis and enrichment studies on homology models of the human histamine H4 receptor

2008 ◽  
Vol 43 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
Róbert Kiss ◽  
Béla Noszál ◽  
Ákos Rácz ◽  
András Falus ◽  
Dániel Erős ◽  
...  
Keyword(s):  
Binding Mode ◽  
Mode Analysis ◽  
Histamine H4 Receptor ◽  
H4 Receptor ◽  
Human Histamine ◽  
Homology Models

scholarly journals Interaction of quercetin with transcriptional regulator LasR of Pseudomonas aeruginosa: Mechanistic insights of the inhibition of virulence through quorum sensing

2017 ◽  
Author(s):  
Hovakim Grabski ◽  
Lernik Hunanyan ◽  
Susanna Tiratsuyan ◽  
Hrachik Vardapetyan
Keyword(s):  
Molecular Dynamics ◽  
Pseudomonas Aeruginosa ◽  
Quorum Sensing ◽  
Biofilm Formation ◽  
Principal Component ◽  
Transcriptional Regulator ◽  
Binding Mode ◽  
World Health ◽  
Hydroxyl Group ◽  
Binding Modes

ABSTRACTBackgroundPseudomonas aeruginosais one of the most dangerous superbugs in the list of bacteria for which new antibiotics are urgently needed, which was published by World Health Organization.P. aeruginosais an antibiotic-resistant opportunistic human pathogen. It affects patients with AIDS, cystic fibrosis, cancer, burn victims and people with prosthetics and implants.P. aeruginosaalso forms biofilms. Biofilms increase resistance to antibiotics and host immune responses. Because of biofilms, current therapies are not effective. It is important to find new antibacterial treatment strategies againstP. aeruginosa. Biofilm formation is regulated through a system called quorum sensing. Thus disrupting this system is considered a promising strategy to combat bacterial pathogenicity. It is known that quercetin inhibitsPseudomonas aeruginosabiofilm formation, but the mechanism of action is unknown. In the present study, we tried to analyse the mode of interactions of LasR with quercetin.ResultsWe used a combination of molecular docking, molecular dynamics (MD) simulations and machine learning techniques for the study of the interaction of the LasR protein ofP. aeruginosawith quercetin. We assessed the conformational changes of the interaction and analysed the molecular details of the binding of quercetin with LasR. We show that quercetin has two binding modes. One binding mode is the interaction with ligand binding domain, this interaction is not competitive and it has also been shown experimentally. The second binding mode is the interaction with the bridge, it involves conservative amino acid interactions from LBD, SLR, and DBD and it is also not competitive. Experimental studies show hydroxyl group of ring A is necessary for inhibitory activity, in our model the hydroxyl group interacts with Leu177 during the second binding mode. This could explain the molecular mechanism of how quercetin inhibits LasR protein.ConclusionsThis study may offer insights on how quercetin inhibits quorum sensing circuitry by interacting with transcriptional regulator LasR. The capability of having two binding modes may explain why quercetin is effective at inhibiting biofilm formation and virulence gene expression.List of abbreviationsPDBProtein data bankMDMolecular DynamicsPCAPrincipal Component AnalysisPCPrincipal ComponentSLRShort Linker RegionBLASTBasic local alignment search toolDBIDavid-Bouldin IndexpsFpseudo-F statistic

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

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