Flexible matching of test ligands to a 3D pharmacophore using a molecular superposition force field: Comparison of predicted and experimental conformations of inhibitors of three enzymes

1995 ◽  
Vol 9 (3) ◽  
pp. 237-250 ◽  
Author(s):  
Colin McMartin ◽  
Regine S. Bohacek
Keyword(s):  
Force Field ◽  
Molecular Superposition ◽  
3D Pharmacophore ◽  
Flexible Matching

Millimetre-wave and infrared spectroscopy of Br13 CN: anharmonic force field of cyanogen bromide from spectroscopic data and ab initio calculations

1997 ◽  
Vol 90 (3) ◽  
pp. 495-497
Author(s):  
CLAUDIO ESPOSTI ◽  
FILIPPO TAMASSIA ◽  
CRISTINA PUZZARINI ◽  
RICCARDO TARRONI ◽  
ZDENEK ZELINGER
Keyword(s):  
Infrared Spectroscopy ◽  
Force Field ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Spectroscopic Data ◽  
Cyanogen Bromide ◽  
Millimetre Wave ◽  
Anharmonic Force

Molecular force field evaluation by empirical constraints

1976 ◽  
Vol 73 ◽  
pp. 1051-1057
Author(s):  
Sadao Isotani ◽  
Alain J.-P. Alix
Keyword(s):  
Force Field ◽  
Field Evaluation ◽  
Molecular Force ◽  
Molecular Force Field

Optimization of a New Reactive Force Field for Silver - Based Materials

2020 ◽  
Author(s):  
Clément Dulong ◽  
Bruno Madebène ◽  
Susanna Monti ◽  
Johannes Richardi
Keyword(s):  
Force Field ◽  
Self Assembled Monolayers ◽  
The Novel ◽  
Reactive Force ◽  
Reactive Force Field ◽  
Energetic Properties ◽  
Extended Training ◽  
Geometrical Features ◽  
Assembled Monolayers ◽  
Gold Thiolate

<div><div><div><p>A new reactive force field based on the ReaxFF formalism is effectively parametrized against an extended training set of quantum chemistry data (containing more than 120 different structures) to describe accurately silver- and silver-thiolate systems. The results obtained with this novel representation demonstrate that the novel ReaxFF paradigm is a powerful methodology to reproduce more appropriately average geometric and energetic properties of metal clusters and slabs when compared to the earlier ReaxFF parametrizations dealing with silver and gold. ReaxFF cannot describe adequately specific geometrical features such as the observed shorter distances between the under-coordinated atoms at the cluster edges. Geometric and energetic properties of thiolates adsorbed on a silver Ag20 pyramid are correctly represented by the new ReaxFF and compared with results for gold. The simulation of self-assembled monolayers of thiolates on a silver (111) surface does not indicate the formation of staples in contrast to the results for gold-thiolate systems.</p></div></div></div>

Driving Torsion Scans with Wavefront Propagation

2020 ◽  
Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang
Keyword(s):  
Potential Energy ◽  
Force Field ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Initial Guess ◽  
Field Development ◽  
Force Field Development ◽  
Wavefront Propagation ◽  
Open Source Software Package

<div>The parameterization of torsional / dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields.</div><div>Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms.</div><div>To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values.</div><div>However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses.</div><div>In this paper we propose a systematic and versatile workflow called \textit{TorsionDrive} to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development.</div><div>The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described.</div><div>The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.</div>

Statistical Force-Field for Structural Modeling Using Chemical Cross-Linking/mass Spectrometry Distance Constraints

2018 ◽  
Author(s):  
Allan J. R. Ferrari ◽  
Fabio C. Gozzo ◽  
Leandro Martinez
Keyword(s):  
Mass Spectrometry ◽  
Amino Acid ◽  
Force Field ◽  
Protein Structures ◽  
Protein Structure Determination ◽  
Structural Modeling ◽  
Cross Linking ◽  
Amino Acid Residues ◽  
Distance Constraints ◽  
Chemical Cross Linking

<div><p>Chemical cross-linking/Mass Spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues, which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms. Here, a force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. The force-field can be easily incorporated into current modeling methods and software. In this work, the force-field was implemented within the Rosetta ab initio relax protocol. We show a significant improvement in the quality of the models obtained relative to current strategies for constraint representation. This force-field contributes to the long-desired goal of obtaining the tertiary structures of proteins using XLMS data. Force-field parameters and usage instructions are freely available at http://m3g.iqm.unicamp.br/topolink/xlff <br></p></div><p></p><p></p>

Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

2018 ◽  
Author(s):  
Maximiliano Riquelme ◽  
Alejandro Lara ◽  
David L. Mobley ◽  
Toon Vestraelen ◽  
Adelio R Matamala ◽  
...  
Keyword(s):  
Force Field ◽  
Functional Groups ◽  
Electrostatic Interactions ◽  
Organic Molecules ◽  
Force Fields ◽  
Chemical Elements ◽  
Atomic Charges ◽  
Free Energies ◽  
Molecular Systems ◽  
Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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