NMR Spectroscopic Investigation of Mobility and Hydrogen Bonding of the Chromophore in the Binding Pocket of Phytochrome Proteins

ChemPhysChem ◽  
2010 ◽  
Vol 11 (6) ◽  
pp. 1248-1257 ◽  
Author(s):  
Marco Röben ◽  
Janina Hahn ◽  
Eva Klein ◽  
Tilman Lamparter ◽  
Georgios Psakis ◽  
...  
Keyword(s):  
Hydrogen Bonding ◽  
Spectroscopic Investigation ◽  
Binding Pocket

scholarly journals Improving the Substrate Affinity and Catalytic Efficiency of β-Glucosidase Bgl3A from Talaromyces leycettanus JCM12802 by Rational Design

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1882
Author(s):  
Wei Xia ◽  
Yingguo Bai ◽  
Pengjun Shi
Keyword(s):  
Hydrogen Bonding ◽  
Rational Design ◽  
Substrate Binding ◽  
Enzymatic Saccharification ◽  
Catalytic Efficiency ◽  
Binding Pocket ◽  
Glycoside Hydrolases ◽  
Cellulosic Biomass ◽  
Substrate Affinity ◽  
Hydrogen Bonding Networks

Improving the substrate affinity and catalytic efficiency of β-glucosidase is necessary for better performance in the enzymatic saccharification of cellulosic biomass because of its ability to prevent cellobiose inhibition on cellulases. Bgl3A from Talaromyces leycettanus JCM12802, identified in our previous work, was considered a suitable candidate enzyme for efficient cellulose saccharification with higher catalytic efficiency on the natural substrate cellobiose compared with other β-glucosidase but showed insufficient substrate affinity. In this work, hydrophobic stacking interaction and hydrogen-bonding networks in the active center of Bgl3A were analyzed and rationally designed to strengthen substrate binding. Three vital residues, Met36, Phe66, and Glu168, which were supposed to influence substrate binding by stabilizing adjacent binding site, were chosen for mutagenesis. The results indicated that strengthening the hydrophobic interaction between stacking aromatic residue and the substrate, and stabilizing the hydrogen-bonding networks in the binding pocket could contribute to the stabilized substrate combination. Four dominant mutants, M36E, M36N, F66Y, and E168Q with significantly lower Km values and 1.4–2.3-fold catalytic efficiencies, were obtained. These findings may provide a valuable reference for the design of other β-glucosidases and even glycoside hydrolases.

scholarly journals Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2322 ◽  
Author(s):  
Saw Simeon ◽  
Nuttapat Anuwongcharoen ◽  
Watshara Shoombuatong ◽  
Aijaz Ahmad Malik ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Hydrogen Bonding ◽  
Molecular Docking ◽  
Binding Pocket ◽  
Binding Energies ◽  
Acetylcholinesterase Inhibition ◽  
Qsar Modeling ◽  
Qsar Study ◽  
Data Set ◽  
Ache Inhibitors ◽  
Redundant Data

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds13,5and28exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding,π–πstacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

On-line 1H NMR spectroscopic investigation of hydrogen bonding in supercritical and near critical CO2–methanol up to 35MPa and 403K

2007 ◽  
Vol 43 (2) ◽  
pp. 267-275 ◽  
Author(s):  
Michael Maiwald ◽  
Hongping Li ◽  
Thorsten Schnabel ◽  
Kay Braun ◽  
Hans Hasse
Keyword(s):  
Hydrogen Bonding ◽  
1H Nmr ◽  
Spectroscopic Investigation ◽  
On Line

FT-IR and NIR spectroscopic investigation of hydrogen bonding in indole-ether systems

2017 ◽  
Vol 1144 ◽  
pp. 159-165 ◽  
Author(s):  
B. Kordić ◽  
M. Kovačević ◽  
T. Sloboda ◽  
A. Vidović ◽  
B. Jović
Keyword(s):  
Hydrogen Bonding ◽  
Spectroscopic Investigation ◽  
Ft Ir

scholarly journals OR24-01 Mutational Study of the GPR119 Receptor Binding Site

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Matthew D Rosales ◽  
Frank Dean ◽  
Evangelia Kotsikorou
Keyword(s):  
Hydrogen Bonding ◽  
Binding Site ◽  
Specific Activity ◽  
Md Simulations ◽  
Binding Pocket ◽  
Receptor Activation ◽  
Activation Mechanism ◽  
Receptor Model ◽  
Wild Type

Abstract The GPR119 receptor, a class A G-protein coupled receptor located in the pancreatic β cells, induces insulin production when activated. Due to its specific activity, the pharmaceutical industry has identified GPR119 as a target for the treatment for type 2 diabetes. The lack of a GRP119 crystal structure has hindered the study of the receptor so our laboratory developed GPR119 active and inactive homology models. Docking studies with the inactive receptor model indicated that two leucine residues facing the binding pocket, L5.43(169) and L6.52(242), may be involved in ligand activation. Additionally, a serine at the extracellular end of the pocket, S1.32(4), may help orient of the ligand in the binding pocket via hydrogen bonding. To gain further insight into the role of these residues and the receptor activation mechanism, molecular dynamics (MD) simulations and in vitro cAMP assays of the wild type and mutant receptors were employed. The software NAMD employing the CHARMM force field was used to carry out MD simulations of the active receptor model bound with the agonist AR231453 embedded in a hydrated lipid bilayer. Preliminary results indicate that L6.52(242), located on transmembrane helix (TMH) 6, does not face directly into the binding site and does not interact with the ligand, while L5.43(169), located on TMH5, does face into the binding site, potentially interacting directly with the ligand. Also, S1.32(4), because of its extracellular location, is solvated instead of interacting with the ligand. The in vitro studies overall support the MD simulations. The mutations L6.52(242)M and L6.52(242)A appear to have minimal to no effect on agonist-induced cAMP production, compared to the wild type. In contrast, the L5.43(169)M and L5.43(169)A mutations decrease the potency of activation by AR231453, indicating that L5.43(169) changes the shape of the binding pocket, affecting ligand binding and activation. Finally, the cAMP assays show that the S1.32(4)A mutant also shows decreased activity compared to the wild type, implying that the ligand may be losing a hydrogen bonding interaction when S1.32(4) is mutated to alanine.

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