scholarly journals Pairing interactions between nucleobases and ligands in aptamer:ligand complexes of riboswitches: Crystal structure analysis, classification, optimal structures and accurate interaction energies

2019 ◽  
Author(s):  
Preethi S. Prabhakar ◽  
Purshotam Sharma ◽  
Abhijit Mitra
Keyword(s):  
Hydrogen Bonding ◽  
Visual Inspection ◽  
Weak Interactions ◽  
Binding Pocket ◽  
Crystal Structure Analysis ◽  
Thiamine Pyrophosphate ◽  
Interaction Energies ◽  
Binding Pockets ◽  
Pairing Interactions

ABSTRACTIn the present work, sixty-seven crystal structures of the aptamer domains of RNA riboswitches, are chosen for analysis of the structure and strength of hydrogen bonding (pairing) interactions between nucleobases constituting the aptamer binding pockets and the bound ligands. A total of eighty unique base:ligand hydrogen-bonded pairs containing at least two hydrogen bonds were identified through visual inspection. Classification of these contacts in terms of the interacting edge of the aptamer nucleobase revealed that interactions involving the Watson-Crick edge are the most common, followed by the sugar edge of purines and the Hoogsteen edge of uracil. Alternatively, classification in terms of the chemical constitution of the ligand yields five unique classes of base:ligand pairs: base:base, base:amino acid, base:sugar, base:phosphate and base:other. Further, quantum mechanical (QM) geometry optimizations revealed that sixty seven out of eighty pairs exhibit stable geometries and optimal deviations from their macromolecular crystal occurrences. This indicates that these contacts are well-defined RNA aptamer:ligand interaction motifs. QM calculated interaction energies of base:ligand pairs reveal rich hydrogen bonding landscape, ranging from weak interactions (base:other, –3 kcal/mol) to strong (base:phosphate, –48 kcal/mol) contacts. The analysis was further extended to study the biological importance of base:ligand interactions in the binding pocket of the tetrahydrofolate riboswitch and thiamine pyrophosphate riboswitch. Overall, our study helps in understanding the structural and energetic features of base:ligand pairs in riboswitches, which could aid in developing meaningful hypotheses in context of RNA:ligand recognition. This can, in turn contribute towards current efforts to develop antimicrobials that target RNAs.

Hydrogen bonding vs. stacking interaction in the crystals of the simplest coumarin derivatives: a study from the energetic viewpoint

CrystEngComm ◽  
2019 ◽  
Vol 21 (45) ◽  
pp. 6945-6957 ◽  
Author(s):  
Svitlana V. Shishkina ◽  
Irina S. Konovalova ◽  
Sergiy M. Kovalenko ◽  
Pavlo V. Trostianko ◽  
Anna O. Geleverya ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Hydrogen Bonding ◽  
Structure Analysis ◽  
Crystal Structure Analysis ◽  
Pairwise Interaction ◽  
Interaction Energies ◽  
Coumarin Derivatives ◽  
Stacking Interaction ◽  
Close Interaction ◽  
Hydrogen Bonded

Hydrogen bonded and stacked dimers have very close interaction energies in the crystals of the simplest coumarin derivatives according to the data of the crystal structure analysis based on the comparison of pairwise interaction energies.

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.

scholarly journals Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features

2021 ◽  
pp. 2150016
Author(s):  
Wei Yan Peh ◽  
John Thomas ◽  
Elham Bagheri ◽  
Rima Chaudhari ◽  
Sagar Karia ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Visual Inspection ◽  
Detection System ◽  
Automated Classification ◽  
The Us ◽  
Electroencephalogram Eeg ◽  
Level Performance ◽  
Frequency Features ◽  
Channel Segment

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

scholarly journals Automated Negative Lightning Return Strokes Classification System

2021 ◽  
Vol 2107 (1) ◽  
pp. 012022
Author(s):  
F. Abdul Haris ◽  
M.Z.A. Ab Kadir ◽  
S. Sudin ◽  
D. Johari ◽  
J. Jasni ◽  
...  
Keyword(s):  
Classification System ◽  
Automatic System ◽  
Human Error ◽  
Processing Time ◽  
Visual Inspection ◽  
Automated System ◽  
Graphic User Interface ◽  
Data Comparison ◽  
Good Agreement

Abstract Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.

Fluorescent nanoparticle sensors with tailor-made recognition units and proximate fluorescent reporter groups

2018 ◽  
Vol 42 (12) ◽  
pp. 9377-9380 ◽  
Author(s):  
Xiaoyu Xing ◽  
Yan Zhao
Keyword(s):  
Molecular Imprinting ◽  
Binding Pocket ◽  
Fluorescent Sensors ◽  
Covalent Modification ◽  
Fluorescent Reporter ◽  
Binding Pockets ◽  
Fluorescent Nanoparticle

Molecular imprinting in micelles followed by covalent modification of the binding pocket yielded fluorescent sensors with precisely constructed binding pockets.

scholarly journals Exploring Multi‐Subsite Binding Pockets in Proteins: DEEP‐STD NMR Fingerprinting and Molecular Dynamics Unveil a Cryptic Subsite at the GM1 Binding Pocket of Cholera Toxin B

2020 ◽  
Vol 26 (44) ◽  
pp. 10024-10034
Author(s):  
Serena Monaco ◽  
Samuel Walpole ◽  
Hassan Doukani ◽  
Ridvan Nepravishta ◽  
Macarena Martínez‐Bailén ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Cholera Toxin ◽  
Binding Pocket ◽  
Toxin B ◽  
Cholera Toxin B ◽  
Binding Pockets ◽  
Std Nmr
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