NMR and Molecular Dynamics Study of the Binding Mode of Naphthalene-N-sulfonyl-d-glutamic Acid Derivatives: Novel MurD Ligase Inhibitors

2009 ◽  
Vol 52 (9) ◽  
pp. 2899-2908 ◽  
Author(s):  
Mihael Simčič ◽  
Milan Hodošček ◽  
Jan Humljan ◽  
Katja Kristan ◽  
Uroš Urleb ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Glutamic Acid ◽  
Binding Mode ◽  
Glutamic Acid Derivatives ◽  
Murd Ligase

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.

On the Manner of Cyclization of N-Acylated Aspartic and Glutamic Acid Derivatives

2011 ◽  
Vol 17 (2) ◽  
pp. 93-100
Author(s):  
Paweł Zajdel ◽  
Christine Enjalbal ◽  
Marek Żylewski ◽  
Gilles Subra
Keyword(s):  
Glutamic Acid ◽  
Glutamic Acid Derivatives

Ternary chromium(III)-nucleotide-amino acid complexes III. L-Glutamic acid derivatives

1989 ◽  
Vol 165 (1) ◽  
pp. 131-137 ◽  
Author(s):  
M. Vicens ◽  
J.J. Fiol ◽  
A. Terron ◽  
V. Moreno
Keyword(s):  
Amino Acid ◽  
Glutamic Acid ◽  
Amino Acid Complexes ◽  
Glutamic Acid Derivatives

WHICH IS THE PROTON-SHUTTLE IN ISOXANTHOPTERIN DEAMINASE? QM/MM MD UNDERSTANDING

2013 ◽  
Vol 12 (08) ◽  
pp. 1341002 ◽  
Author(s):  
XIN ZHANG ◽  
MING LEI
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Hydrogen Bonds ◽  
Proton Transfer ◽  
Glutamic Acid ◽  
Active Site ◽  
Nucleophilic Attack ◽  
Zinc Ions ◽  
Initial Model ◽  
Dynamics Simulations

The deamination process of isoxanthopterin catalyzed by isoxanthopterin deaminase was determined using the combined QM(PM3)/MM molecular dynamics simulations. In this paper, the updated PM3 parameters were employed for zinc ions and the initial model was built up based on the crystal structure. Proton transfer and following steps have been investigated in two paths: Asp336 and His285 serve as the proton shuttle, respectively. Our simulations showed that His285 is more effective than Aap336 in proton transfer for deamination of isoxanthopterin. As hydrogen bonds between the substrate and surrounding residues play a key role in nucleophilic attack, we suggested mutating Thr195 to glutamic acid, which could enhance the hydrogen bonds and help isoxanthopterin get close to the active site. The simulations which change the substrate to pterin 6-carboxylate also performed for comparison. Our results provide reference for understanding of the mechanism of deaminase and for enhancing the deamination rate of isoxanthopterin deaminase.

Solid-phase synthesis of Nα-benzyl-Nα-cinnamyl lysine and glutamic acid derivatives

2000 ◽  
Vol 41 (27) ◽  
pp. 5187-5191 ◽  
Author(s):  
Peter J Connolly ◽  
Kimberly N Beers ◽  
Steven K Wetter ◽  
William V Murray
Keyword(s):  
Glutamic Acid ◽  
Solid Phase Synthesis ◽  
Solid Phase ◽  
Phase Synthesis ◽  
Glutamic Acid Derivatives

Water Mapping and Scoring approaches to predict the role of Hydration sites in Binding Affinity of PAK1 inhibitors

2021 ◽  
Vol 24 ◽  
Author(s):  
Jayashree Biswal ◽  
Prajisha Jayaprakash ◽  
Suresh Kumar Rayala ◽  
Ganesh Venkatraman ◽  
Raghu Rangasamy ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Computational Model ◽  
Active Compound ◽  
Scoring Function ◽  
Binding Mode ◽  
Interaction Pattern ◽  
Dynamics Simulation ◽  
Interaction Patterns ◽  
Highly Active ◽  
Group I

Aim: This study aims to develop and establish a computational model that can identify potent molecules for p21-activating kinase 1 (PAK1). Background: PAK1 is a well-established drug target that has been explored for various therapeutic interventions. Control of this protein requires an indispensable inhibitor to curb the structural changes and subsequent activation of signalling effectors responsible for the progression of diseases, such as cancer, inflammatory, viral, and neurological disorders. Objective: To establish a computational model that could identify active molecules which will further provide a platform for developing potential PAK1 inhibitors. Method: A congeneric series of 27 compounds was considered for this study with Ki (nm) covering a minimum of 3 log range. The compounds were developed based on a previously reported Group-I PAK inhibitor, namely G-5555. The 27 compounds were subjected to the SP and XP mode of docking, to understand the binding mode, its conformation and interaction patterns. To understand the relevance of biological activity from computational approaches, the compounds were scored against generated water maps to obtain WM/MM ΔG binding energy. Moreover, molecular dynamics analysis was performed for the highly active compound, to understand the conformational variability and complex’s stability. We then evaluate the predictable binding pose obtained from the docking studies. Result: From the SP and XP modes of docking, the common interaction pattern with the amino acid residues Arg299 (cation-π), Glu345 (Aromatic hydrogen bond), hinge region Leu347, salt bridges Asp393 and Asp407 was observed, among the congeneric compounds. The interaction pattern was compared with the co-crystal inhibitor FRAX597 of the PAK1 crystal structure (PDB id: 4EQC). The correlation with different docking parameters in the SP and XP modes was insignificant and thereby revealed that the SP and XP’s scoring functions could not predict the active compounds. This was due to the limitations in the docking methodology that neglected the receptor flexibility and desolvation parameters. Hence, to recognise the desolvation and explicit solvent effects, as well as to study the Structure-Activity Relationships (SARs) extensively, WaterMap (WM) calculations were performed on the congeneric compounds. Based on displaceable unfavourable hydration sites (HS) and their associated thermodynamic properties, the WM calculations facilitated to understand the significance of correlation in the folds of activity of highly (19 and 17), moderate (16 and 21) and less active (26 and 25) compounds. Furthermore, the scoring function from WaterMap, namely WM/MM, led to a significant R2 value of 0.72, due to a coupled conjunction with MM treatment and displaced unfavourable waters at the binding site. To check the “optimal binding conformation”, molecular dynamics simulation was carried out with the highly active compound 19 to explain the binding mode, stability, interactions, solvent accessible area, etc., which could support the predicted conformation with bioactive conformation. Conclusion: This study determined the best scoring function, established SARs and predicted active molecules through a computational model. This will contribute towards development of the most potent PAK1 inhibitors.

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