scholarly journals Screening of hydrogen bond acceptor for the synthesis of low transition temperature mixtures with malic acid

2015 ◽  
Author(s):  
C. L. Yiin ◽  
S. Yusup ◽  
A. T. Quitain ◽  
Y. Uemura
Keyword(s):  
Hydrogen Bond ◽  
Transition Temperature ◽  
Malic Acid ◽  
Hydrogen Bond Acceptor

scholarly journals Prediction of solvation properties of low transition temperature mixtures (LTTMs) using COSMO-RS and NMR approach

2021 ◽  
Vol 1195 (1) ◽  
pp. 012006
Author(s):  
N R Yusuf ◽  
S Yusup ◽  
C L Yiin ◽  
P J Ratri ◽  
A A Halim ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Hydrogen Bonding ◽  
Transition Temperature ◽  
Environmental Science ◽  
Choline Chloride ◽  
Monosodium Glutamate ◽  
Experimental Studies ◽  
Green Solvent ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor

Abstract The concept of sustainable and green solvent has always highlighted in the field of energy and environmental science. The synthesis and application of natural-based Low Transition Temperature Mixture (LTTM) as a novel and green solvent for the lignocellulose biomass pre-treatment such as delignification of Oil-Palm Empty Fruit Bunch (EFB) have been greatly emphasized. In this present work, the investigation of LTTM efficiency as green solvent in delignification process was conducted using both theoretical and experimental studies. Initially, screening of solvation properties of different types of hydrogen bond acceptor (HBA) and predicted hydrogen bond donor (HBD) for synthesis of LTTMs was conducted using conductor-like screening model (COSMO-RS) software and formation of hydrogen bonding was evidenced using NMR spectroscopy analysis. Three types of HBA namely sucrose, choline chloride and monosodium glutamate were mixed with malic acids as HBD and their charge density distribution on the surface was determined through sigma profile (σ). The COSMO-RS results determined the σ profile of pure component malic acid to be 11.42, sucrose to be 25.37 and the total value of σ profile for mixtures is 14.19 as the best combination of LTTM composition compared to LTTM from choline chloride and monosodium glutamate (MSG). The reliability of the COSMO-RS predictions data was correlated with Nuclear Magnetic Resonance (NMR) analysis through determination of peaks with chemical shifts hydrogen bonding that suggested existence of potential interaction between malic acids and sucrose has occurred.

scholarly journals Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer’s Disease: Computational Refining and Biochemical Evaluation

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1946
Author(s):  
Nitin Chitranshi ◽  
Ashutosh Kumar ◽  
Samran Sheriff ◽  
Veer Gupta ◽  
Angela Godinez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hydrogen Bond ◽  
Virtual Screening ◽  
Cathepsin B ◽  
Amyloid Β ◽  
Proteolytic Degradation ◽  
Hydrogen Bond Acceptor ◽  
Starting Point ◽  
The Brain

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

On the potential role of the amino nitrogen atom as a hydrogen bond acceptor in macromolecules

1998 ◽  
Vol 279 (5) ◽  
pp. 1123-1136 ◽  
Author(s):  
Ben Luisi ◽  
Modesto Orozco ◽  
Jiri Sponer ◽  
Francisco J Luque ◽  
Zippora Shakked
Keyword(s):  
Hydrogen Bond ◽  
Nitrogen Atom ◽  
Potential Role ◽  
Amino Nitrogen ◽  
Hydrogen Bond Acceptor ◽  
Amino Nitrogen Atom

6,9-Dibromo-2a,10b-diphenyl-2a,5,10,10b-tetrahydro-2H,3H-2,3,4a,10a-tetraazabenzo[g]cyclopenta[cd]azulene-1,4-dione monohydrate

2006 ◽  
Vol 62 (5) ◽  
pp. o1754-o1755
Author(s):  
Neng-Fang She ◽  
Sheng-Li Hu ◽  
Hui-Zhen Guo ◽  
An-Xin Wu
Keyword(s):  
Crystal Structure ◽  
Hydrogen Bond ◽  
Hydrogen Bonds ◽  
Water Molecule ◽  
Title Compound ◽  
Supramolecular Structure ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor ◽  
Compound C ◽  
Bifurcated Hydrogen Bond

The title compound, C24H18Br2N4O2·H2O, forms a supramolecular structure via N—H...O, O—H...O and C—H...O hydrogen bonds. In the crystal structure, the water molecule serves as a bifurcated hydrogen-bond acceptor and as a hydrogen-bond donor.

Adsorption of 2-Chloroethyl Ethyl Sulfide on Silica: Binding Mechanism and Energy of a Bifunctional Hydrogen-Bond Acceptor at the Gas–Surface Interface

2014 ◽  
Vol 119 (1) ◽  
pp. 365-372 ◽  
Author(s):  
Joshua Abelard ◽  
Amanda R. Wilmsmeyer ◽  
Angela C. Edwards ◽  
Wesley O. Gordon ◽  
Erin M. Durke ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Binding Mechanism ◽  
Hydrogen Bond Acceptor

scholarly journals (2,3-Di-2-pyridylpyrazine-κ2 N 2,N 3)diiodidoplatinum(II)

2012 ◽  
Vol 68 (6) ◽  
pp. m834-m834 ◽  
Author(s):  
Kwang Ha
Keyword(s):  
Hydrogen Bond ◽  
Hydrogen Bonds ◽  
Least Squares ◽  
Dihedral Angle ◽  
Layer Structure ◽  
Title Complex ◽  
Maximum Deviation ◽  
Hydrogen Bond Acceptor ◽  
Pyrazine Ring ◽  
Square Planar

The PtII ion in the title complex, [PtI2(C14H10N4)], exists in a distorted square-planar environment defined by the two pyridine N atoms of the chelating 2,3-di-2-pyridylpyrazine ligand and two iodide anions. The pyridine rings are inclined to the least-squares plane of the PtI2N2 unit [maximum deviation = 0.070 (3) Å] at 66.1 (2) and 65.9 (2)°; the pyrazine ring is perpendicular to this plane [dihedral angle = 89.7 (2)°]. Two intermolecular C—H...I hydrogen bonds, both involving the same I atom as hydrogen-bond acceptor, generate a layer structure extending parallel to (001). Molecules are stacked in columns along the a axis. Along the b axis, successive molecules stack in opposite directions.

Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths

2018 ◽  
Vol 38 (4) ◽  
pp. 1800115 ◽  
Author(s):  
Christoph A. Bauer ◽  
Gisbert Schneider ◽  
Andreas H. Göller
Keyword(s):  
Hydrogen Bond ◽  
Gaussian Process ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Hydrogen Bond Acceptor

Gold setting the “gold standard” among transition metals as a hydrogen bond acceptor – a theoretical investigation

2017 ◽  
Vol 46 (15) ◽  
pp. 4960-4967 ◽  
Author(s):  
Ferdinand Groenewald ◽  
Helgard G. Raubenheimer ◽  
Jan Dillen ◽  
Catharine Esterhuysen
Keyword(s):  
Hydrogen Bond ◽  
Transition Metals ◽  
Theoretical Investigation ◽  
Gold Standard ◽  
Hydrogen Bond Acceptor ◽  
Hydrogen Bond Donors

MP2/aug-cc-pVTZ-pp calculations show that the Au(i) atom of dimethylaurate behaves as a hydrogen-bond acceptor to a range of hydrogen-bond donors.

scholarly journals Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph A. Bauer ◽  
Gisbert Schneider ◽  
Andreas H. Göller
Keyword(s):  
Machine Learning ◽  
Hydrogen Bond ◽  
Hydrogen Bonding ◽  
Training Data ◽  
Free Energies ◽  
Hydrogen Bond Donor ◽  
Chemical Application ◽  
Hydrogen Bond Acceptor ◽  
Donor And Acceptor ◽  
Target Values

Abstract We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol−1 (acceptors), and 2.3 kJ mol−1 (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding.

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