Robust molecular representations for modelling and design derived from atomic partial charges

2016 ◽  
Vol 52 (4) ◽  
pp. 681-684 ◽  
Author(s):  
A. R. Finkelmann ◽  
A. H. Göller ◽  
G. Schneider
Keyword(s):  
Ab Initio ◽  
Molecular Modelling ◽  
Partial Charge ◽  
Partial Charges

Ab initio partial charge schemes are identified for molecular modelling purposes, and potential pitfalls of their application are discussed.

scholarly journals STUDY OF PARAMETERS OF REACTIVITY MOLECULAR IN ADDITION REACTIONS OF HYDROHALIC ACIDS WITH ETHYLENE

2011 ◽  
Vol 08 (15) ◽  
pp. 54-64
Author(s):  
Francisco José Santos LIMA ◽  
Ademir Oliveira da SILVA ◽  
Yuri Lima de BRITO ◽  
Fernanda Louise Cardoso de SOUSA
Keyword(s):  
Free Energy ◽  
Thermodynamic Parameters ◽  
Electrostatic Potential ◽  
Molecular Modelling ◽  
Quantitative Data ◽  
Addition Reactions ◽  
Partial Charge ◽  
Kinetic Behavior ◽  
Atomic Properties ◽  
Molecular Reactivity

The molecular reactivity parameters (PRM´s) have been used to quantify atomic properties that influence in the interactions among the molecules. It is possible throughout the analysis of its magnitudes and stereochemistry arrangement of the system, to foresee and/or to explain certain experimental events. The aim of this work was to establish parallel among the thermodynamic parameters (enthalpy, entropy and free energy), and the results of the simulations of theoretical molecular modelling through the geometric qualitative and semi-quantitative data (densities of coulomb of electrostatic potential and partial charge), generated by a commercial software (WebLab ViewerPro) for the obtaining of PRM´s, in the hydrohalic acids reactions with the ethylene. It can be notice that there is clearly dependence of the reactivity parameters with the thermodynamic and kinetic behavior of these reactions.

scholarly journals Proteomic analysis and molecular modelling characterize the iron-regulatory protein haemojuvelin/repulsive guidance molecule c

2013 ◽  
Vol 452 (1) ◽  
pp. 87-95 ◽  
Author(s):  
Mahta Nili ◽  
Larry David ◽  
Johannes Elferich ◽  
Ujwal Shinde ◽  
Peter Rotwein
Keyword(s):  
Ab Initio ◽  
Molecular Modelling ◽  
Electron Transfer Dissociation ◽  
Structural Information ◽  
Regulatory Protein ◽  
Structural Features ◽  
Single Chain ◽  
Disulfide Linkages ◽  
Repulsive Guidance Molecule ◽  
Guidance Molecule

HJV (haemojuvelin) plays a key role in iron metabolism in mammals by regulating expression of the liver-derived hormone hepcidin, which controls systemic iron uptake and release. Mutations in HJV cause juvenile haemochromatosis, a rapidly progressing iron overload disorder in humans. HJV, also known as RGMc (repulsive guidance molecule c), is a member of the three-protein RGM family. RGMs are GPI (glycosylphosphatidylinositol)-linked glycoproteins that share ~50% amino acid identity and several structural motifs, including the presence of 14 cysteine residues in analogous locations. Unlike RGMa and RGMb, HJV/RGMc is composed of both single-chain and two-chain isoforms. To date there is no structural information for any member of the RGM family. In the present study we have mapped the disulfide bonds in mouse HJV/RGMc using a proteomics strategy combining sequential MS steps composed of ETD (electron transfer dissociation) and CID (collision-induced dissociation), in which ETD induces cleavage of disulfide linkages, and CID establishes disulfide bond assignments between liberated peptides. The results of the present study identified an HJV/RGMc molecular species containing four disulfide linkages. We predict using ab initio modelling that this molecule is a single-chain HJV/RGMc isoform. Our observations outline a general approach using tandem MS and ab initio molecular modelling to define unknown structural features in proteins.

Alchemical Hydration Free Energy Calculations Using Molecular Dynamics with Explicit Polarization and Induced Polarity Decoupling: An On-the-Fly Approach

2020 ◽  
Author(s):  
Braden Kelly ◽  
William Smith
Keyword(s):  
Free Energy ◽  
Gas Phase ◽  
Free Energy Calculations ◽  
Partial Charge ◽  
Average Absolute Deviation ◽  
Absolute Deviation ◽  
Energy Calculations ◽  
Lennard Jones ◽  
Partial Charges ◽  
The Cost

<div>We present a methodology using fixed charge force-fields for alchemical solvation free energy calculations which accounts for the change in polarity that the solute experiences as it transfers from the gas-phase to the condensed phase. We update partial charges using QM/MM snapshots, decoupling the electric field appropriately when updating the partial charges. We also show how to account for the cost of self-polarization. We test our methodology on 30 molecules ranging from small polar to large drug-like molecules. We use Minimum Basis Iterative Stockholder (MBIS), Restrained Electrostatic Potential (RESP) and AM1-BCC partial charge methodologies. Using our method with MP2/cc-pVTZ and MBIS partial charges yields an Average Absolute Deviation (AAD) of 6.3 kJ·mol−1 in comparison with the AM1-BCC result of 8.6 kJ·mol−1. AM1-BCC is within experimental uncertainty on 10% of the data compared to 30% with our method. We conjecture that results can be further improved by using Lennard-Jones and torsional parameters refitted to MBIS and RESP partial charge methods that use high levels of theory.</div>

Electronegativity-Based Computation of 29Si Chemical Shifts

1992 ◽  
Vol 271 ◽  
Author(s):  
Henry. M ◽  
Gerardin. C ◽  
Taulelle. F
Keyword(s):  
Chemical Shifts ◽  
Sol Gel ◽  
Partial Charge ◽  
Detailed Structure ◽  
Mean Excitation Energy ◽  
Charge Model ◽  
Partial Charges ◽  
Nuclear Shielding ◽  
Partial Charge Model ◽  
The Mean

ABSTRACTThe Partial Charge Model has been modified to take into account the detailed structure of any molecular sol-gel precursors or inorganic solid networks. Starting from these structure-dependent partial charges, the classical theory of nuclear shielding is applied to compute the electronic cloud compacity <r-3>p, the population unbalance Pu and also the mean excitation energy ΔE. With these three parameters it is possible to explain the chemical shifts variations, spanning from +40 down to -140 ppm, of more than 50 precursors. Depending on the ligands, the well-known upside-down U-curves for series SiXnY4-n (n=0‥4) can be ascribed either to the population unbalance term Pu or to a competition between the two other terms <r-3>p and ΔE.

Hydrogen bonding at C=Se acceptors in selenoureas, selenoamides and selones

2016 ◽  
Vol 72 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dikima Bibelayi ◽  
Albert S. Lundemba ◽  
Frank H. Allen ◽  
Peter T. A. Galek ◽  
Juliette Pradon ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Hydrogen Bonding ◽  
Hydrogen Bonds ◽  
Ab Initio ◽  
Crystal Engineering ◽  
Cambridge Structural Database ◽  
Bond Lengths ◽  
Partial Charges ◽  
Structural Database

In recent years there has been considerable interest in chalcogen and hydrogen bonding involving Se atoms, but a general understanding of their nature and behaviour has yet to emerge. In the present work, the hydrogen-bonding ability and nature of Se atoms in selenourea derivatives, selenoamides and selones has been explored using analysis of the Cambridge Structural Database andab initiocalculations. In the CSD there are 70 C=Se structures forming hydrogen bonds, all of them selenourea derivatives or selenoamides. Analysis of intramolecular geometries andab initiopartial charges show that this bonding stems from resonance-induced Cδ+=Seδ−dipoles, much like hydrogen bonding to C=S acceptors. C=Se acceptors are in many respects similar to C=S acceptors, with similar vdW-normalized hydrogen-bond lengths and calculated interaction strengths. The similarity between the C=S and C=Se acceptors for hydrogen bonding should inform and guide the use of C=Se in crystal engineering.

An effective partial charge model for bulk and surface properties of cubic ZrO2, Y2O3 and yttrium-stabilised zirconia

2019 ◽  
Vol 21 (46) ◽  
pp. 25635-25648
Author(s):  
Thomas S. Hofer ◽  
Franziska M. Kilchert ◽  
Bagas A. Tanjung
Keyword(s):  
Surface Properties ◽  
Partial Charge ◽  
Interaction Potentials ◽  
Charge Model ◽  
Partial Charges ◽  
Partial Charge Model

Novel interaction potentials using effective partial charges are derived, leading to a superior description of bulk and surface properties.

Fast and accurate prediction of partial charges using Atom-Path-Descriptor-based machine learning

2020 ◽  
Vol 36 (18) ◽  
pp. 4721-4728
Author(s):  
Jike Wang ◽  
Dongsheng Cao ◽  
Cunchen Tang ◽  
Xi Chen ◽  
Huiyong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
High Accuracy ◽  
Free Energy Calculations ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Partial Charge ◽  
Charge Assignment ◽  
Partial Charges ◽  
Extreme Gradient Boosting

Abstract Motivation Partial atomic charges are usually used to calculate the electrostatic component of energy in many molecular modeling applications, such as molecular docking, molecular dynamics simulations, free energy calculations and so forth. High-level quantum mechanics calculations may provide the most accurate way to estimate the partial charges for small molecules, but they are too time-consuming to be used to process a large number of molecules for high throughput virtual screening. Results We proposed a new molecule descriptor named Atom-Path-Descriptor (APD) and developed a set of APD-based machine learning (ML) models to predict the partial charges for small molecules with high accuracy. In the APD algorithm, the 3D structures of molecules were assigned with atom centers and atom-pair path-based atom layers to characterize the local chemical environments of atoms. Then, based on the APDs, two representative ensemble ML algorithms, i.e. random forest (RF) and extreme gradient boosting (XGBoost), were employed to develop the regression models for partial charge assignment. The results illustrate that the RF models based on APDs give better predictions for all the atom types than those based on traditional molecular fingerprints reported in the previous study. More encouragingly, the models trained by XGBoost can improve the predictions of partial charges further, and they can achieve the average root-mean-square error 0.0116 e on the external test set, which is much lower than that (0.0195 e) reported in the previous study, suggesting that the proposed algorithm is quite promising to be used in partial charge assignment with high accuracy. Availability and implementation The software framework described in this paper is freely available at https://github.com/jkwang93/Atom-Path-Descriptor-based-machine-learning Supplementary information Supplementary data are available at Bioinformatics online.

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