Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Theoretical prediction of hydrogen-bond basicity pKBHX using quantum chemical topology descriptors.

ABSTRACT: Hydrogen bonding plays an important role in the interaction of biological molecules and their local environment. Hydrogen-bond strengths have been described in terms of basicities by several different scales. The pKBHX scale has been developed with the interests of medicinal chemists in mind. The scale uses equilibrium constants of acid···base complexes to describe basicity and is therefore linked to Gibbs free energy. Site specific data for polyfunctional bases are also available. The pKBHX scale applies to all hydrogen-bond donors (HBDs) where the HBD functional group is either OH, NH, or NH+. It has been found that pKBHX can be described in terms of a descriptor defined by quantum chemical topology, ΔE(H), which is the change in atomic energy of the hydrogen atom upon complexation. Essentially the computed energy of the HBD hydrogen atom correlates with a set of 41 HBAs for five common HBDs, water (r2=0.96), methanol (r2=0.95), 4-fluorophenol (r2=0.91), serine (r2=0.93), and methylamine (r2=0.97). The connection between experiment and computation was strengthened with the finding that there is no relationship between ΔE(H) and pKBHX when hydrogen fluoride was used as the HBD. Using the methanol model, pKBHX predictions were made for an external set of bases yielding r2=0.90. Furthermore, the basicities of polyfunctional bases correlate with ΔE(H), giving r2=0.93. This model is promising for the future of computation in fragment-based drug design. Not only has a model been established that links computation to experiment, but the model may also be extrapolated to predict external experimental pKBHX values.

SUBMITTER: Green AJ

PROVIDER: S-EPMC4004274 | biostudies-other | 2014 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

HBcompare: Classifying Ligand Binding Preferences with Hydrogen Bond Topology.

Project description:This paper presents HBcompare, a method that classifies protein structures according to ligand binding preference categories by analyzing hydrogen bond topology. HBcompare excludes other characteristics of protein structure so that, in the event of accurate classification, it can implicate the involvement of hydrogen bonds in selective binding. This approach contrasts from methods that represent many aspects of protein structure because holistic representations cannot associate classification with just one characteristic. To our knowledge, HBcompare is the first technique with this capability. On five datasets of proteins that catalyze similar reactions with different preferred ligands, HBcompare correctly categorized proteins with similar ligand binding preferences 89.5% of the time. Using only hydrogen bond topology, classification accuracy with HBcompare surpassed standard structure-based comparison algorithms that use atomic coordinates. As a tool for implicating the role of hydrogen bonds in protein function categories, HBcompare represents a first step towards the automatic explanation of biochemical mechanisms.

| S-EPMC9687905 | biostudies-literature

DL_FFLUX: A Parallel, Quantum Chemical Topology Force Field.

Project description:DL_FFLUX is a force field based on quantum chemical topology that can perform molecular dynamics for flexible molecules endowed with polarizable atomic multipole moments (up to hexadecapole). Using the machine learning method kriging (aka Gaussian process regression), DL_FFLUX has access to atomic properties (energy, charge, dipole moment, etc.) with quantum mechanical accuracy. Newly optimized and parallelized using domain decomposition Message Passing Interface (MPI), DL_FFLUX is now able to deliver this rigorous methodology at scale while still in reasonable time frames. DL_FFLUX is delivered as an add-on to the widely distributed molecular dynamics code DL_POLY 4.08. For the systems studied here (103-105 atoms), DL_FFLUX is shown to add minimal computational cost to the standard DL_POLY package. In fact, the optimization of the electrostatics in DL_FFLUX means that, when high-rank multipole moments are enabled, DL_FFLUX is up to 1.25× faster than standard DL_POLY. The parallel DL_FFLUX preserves the quality of the scaling of MPI implementation in standard DL_POLY. For the first time, it is feasible to use the full capability of DL_FFLUX to study systems that are large enough to be of real-world interest. For example, a fully flexible, high-rank polarized (up to and including quadrupole moments) 1 ns simulation of a system of 10 125 atoms (3375 water molecules) takes 30 h (wall time) on 18 cores.

| S-EPMC8582247 | biostudies-literature

Depolarizing Effects in Hydrogen Bond Energy in 3<sub>10</sub>-Helices Revealed by Quantum Chemical Analysis.

Project description:Hydrogen-bond (H-bond) energies in 310-helices of short alanine peptides were systematically examined by precise DFT calculations with the negative fragmentation approach (NFA), a modified method based on the molecular tailoring approach. The contribution of each H-bond was evaluated in detail from the 310-helical conformation of total energies (whole helical model, WH3-10 model), and the results were compared with the property of H-bond in α-helix from our previous study. The H-bond energies of the WH3-10 model exhibited tendencies different from those exhibited by the α-helix in that they depended on the helical position of the relevant H-bond pair. H-bond pairs adjacent to the terminal H-bond pairs were observed to be strongly destabilized. The analysis of electronic structures indicated that structural characteristics cause the destabilization of the H-bond in 310-helices. We also found that the longer the helix length, the more stable the H-bond in the terminal pairs of the WH3-10 model, suggesting the action of H-bond cooperativity.

| S-EPMC9409261 | biostudies-literature

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.

Project description:Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r2 = 0.50) than the LDA method (r2 ~ 0.47). A single model fit to the entire training set yielded the poorest results (r2 = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.

| S-EPMC5796665 | biostudies-literature

3D-QSAR studies of the chemical modification of hydroxyl groups of biomass (cellulose, hemicelluloses and lignin) using quantum chemical descriptors.

Project description:The models of Quantitative Structure-Activity Relationship (QSAR) are indeed useful for understanding the mechanism of chemical modification of wood by the method of acetylation. This study shows that the electrophile index ω in combination with other descriptors, namely LUMO energy, HOMO, hardness (η) and chemical potential (μ), prove their utility in predicting the chemical modification of wood (gain in mass). The QSAR models are developed through use of Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) methods. The statistical results indicate that the multiple correlation coefficient R 2 = 0.987, R 2 adjusted = 0.981 and RMCE = 0.012. This shows both the favorable estimation stability and the appropriate predictive power. The results also indicate that there are significant correlations between the weight gain and the highest occupied molecular orbital HOMO. They also show that the chemical potential μ which are useful for modeling the wood modification are characterized by large hydrophobic properties and high electrophilic powers and therefore could be applied effectively in estimating the acetylation of wood. Finally, the developed model shows that cellulose and hemicellulose are mostly affected by the chemical modification of hydroxyl groups.

| S-EPMC6716080 | biostudies-literature

Nuclear quantum effects and hydrogen bond fluctuations in water.

Project description:The hydrogen bond (HB) is central to our understanding of the properties of water. However, despite intense theoretical and experimental study, it continues to hold some surprises. Here, we show from an analysis of ab initio simulations that take proper account of nuclear quantum effects that the hydrogen-bonded protons in liquid water experience significant excursions in the direction of the acceptor oxygen atoms. This generates a small but nonnegligible fraction of transient autoprotolysis events that are not seen in simulations with classical nuclei. These events are associated with major rearrangements of the electronic density, as revealed by an analysis of the computed Wannier centers and (1)H chemical shifts. We also show that the quantum fluctuations exhibit significant correlations across neighboring HBs, consistent with an ephemeral shuttling of protons along water wires. We end by suggesting possible implications for our understanding of how perturbations (solvated ions, interfaces, and confinement) might affect the HB network in water.

| S-EPMC3785726 | biostudies-literature

The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds.

Project description:Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirical constants. We selected polychlorinated biphenyls (PCBs) as an object to investigate the intrinsic correlation between 16 quantum chemical descriptors and Hammett constants. The results exhibited extremely high linearity for ? with Qxx/yy/zz, ? and EHOMO based on the meta-position grouping. Polychlorinated dibenzodioxins (PCDDs) and polychlorinated naphthalenes (PCNs) congeners, as two independent compounds, validated the reliability of the relationship. The meta-substituent grouping method between ? and ? was successfully used to predict the rate constant (k) for •OH oxidation of PCBs, as well as the octanol/water partition coefficient (logKOW) and aqueous solubility (-logSW) of PCDDs, and exhibited excellent agreement with experimental measurements. Revealing the intrinsic correlation underlying the empirical constant and quantum chemical descriptors can develop simpler and higher efficient model application in predicting the environmental behavior and chemical properties of compounds.

| S-EPMC6278375 | biostudies-literature

Untangling Hydrogen Bond Networks with Ion Mobility Spectrometry and Quantum Chemical Calculations: A Case Study on H<sup>+</sup>XPGG.

Project description:Ion mobility spectrometry-mass spectrometry and quantum chemical calculations are used to determine the structures and stabilities of singly protonated XaaProGlyGly peptides: H+DPGG, H+NPGG, H+EPGG, and H+QPGG. The IMS distributions are similar, suggesting the peptides adopt closely related structures in the gas phase. Quantum chemical calculations show that all conformers seen in the experimental spectrum correspond to the cis configuration about the Xaa-Pro peptide bond, significantly different from the behavior seen previously for H+GPGG. Density functional theory and quantum theory of atoms in molecules (QTAIM) investigations uncover a silent drama as a minor conformer not observed in the H+DPGG spectrum becomes the preferred conformer in H+QPGG, with both conformers being coincident in collision cross section. Investigation of the highly coupled hydrogen bond network, replete with CH···O interactions and bifurcated hydrogen bonds, reveals the cause of this effect as well as the absence of trans conformers from the spectra. A series of generalized observations are provided to aid in enzyme and ligand design using these coupled hydrogen bond motifs.

| S-EPMC6935874 | biostudies-literature

Clarifying the quantum mechanical origin of the covalent chemical bond.

Project description:Lowering of the electron kinetic energy (KE) upon initial encounter of radical fragments has long been cited as the primary origin of the covalent chemical bond based on Ruedenberg's pioneering analysis of H[Formula: see text] and H2 and presumed generalization to other bonds. This work reports KE changes during the initial encounter corresponding to bond formation for a range of different bonds; the results demand a re-evaluation of the role of the KE. Bonds between heavier elements, such as H3C-CH3, F-F, H3C-OH, H3C-SiH3, and F-SiF3 behave in the opposite way to H[Formula: see text] and H2, with KE often increasing on bringing radical fragments together (though the total energy change is substantially stabilizing). The origin of this difference is Pauli repulsion between the electrons forming the bond and core electrons. These results highlight the fundamental role of constructive quantum interference (or resonance) as the origin of chemical bonding. Differences between the interfering states distinguish one type of bond from another.

| S-EPMC7524788 | biostudies-literature

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors.

Project description:BackgroundIn the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software.ResultsVariable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers.ConclusionsA reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

| S-EPMC4176756 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data