Driving Torsion Scans with Wavefront Propagation

Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang

<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>

2020 ◽  
Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang

<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>


2020 ◽  
Vol 5 (Spring 2020) ◽  
Author(s):  
Trevor Heinzmann

Molecular dynamics (MD) simulation is a computational chemistry technique used to observe how a molecular system behaves as time passes. MD is based on solving Newton’s equations of motion. This requires the use of force fields to describe the potential energy function of each different molecule type in molecular system. In order to develop a force field, charges, bonds, angles, and dihedrals must be parameterized to fit quantum mechanics (QM) data. By basing the force field on QM data, MD simulations have higher accuracy while still using the low computational cost of molecular mechanics. This project focuses on developing well-fit force fields for β-lactam class antibiotics for future MD simulations. Full antibiotics are too large of a molecule to parameterize from scratch, so instead we broke them down into fragments. Smaller molecule fragments allow less terms to be optimized which greatly simplifies force field development. By the transferable nature of parameters in CHARMM force fields, the fragment parameters can be transferred to connecting molecules. Due to this, we can build up larger organic molecule force fields piece by piece.In this work, we developed CHARMM force fields for cephalothin, cefotaxime, ceftazidime, and aztreonam.


2003 ◽  
Vol 24 (1) ◽  
pp. 111-128 ◽  
Author(s):  
K. N. Kirschner ◽  
A. H. Lewin ◽  
J. P. Bowen

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