Reduced variable molecular dynamics

2016

The main objective of this review chapter is to give the reader a practical toolbox for applications in quantitative biology and computational drug discovery. The computational technique of molecular dynamics is discussed, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. Additionally, computational methods aimed at characterizing and identifying ligand binding pockets on protein surfaces are discussed. Practical information about available databases and software of use in drug design and discovery is provided.

Pharmacology & Therapeutics, 1993

An overview is presented of computer modeling and simulation methods that play an increasing role in drug design: quantum chemical methods, molecular mechanics, molecular dynamics and Brownian dynamics. The application of molecular dynamics for the prediction of thermodynamic properties like free energy differences and binding constants is discussed. The Brownian dynamics method is presented in connection with the calculation of effective electrostatic forces using the Poisson-Boltzmann equation, which allows one to sample ligand-binding geometries and to predict the kinetics of diffusion-limited enzyme reactions. New techniques that have recently been extensively developed, such as the global energy minimization and quantum-classical dynamics methods, are also introduced. The molecular modeling methods are illustrated with selected examples.

In Silico Pharmacology, 2014

Molecular dynamics (MD) simulation is an emerging in silico technique with potential applications in diverse areas of pharmacology. Over the past three decades MD has evolved as an area of importance for understanding the atomic basis of complex phenomena such as molecular recognition, protein folding, and the transport of ions and small molecules across membranes. The application of MD simulations in isolation and in conjunction with experimental approaches have provided an increased understanding of protein structure-function relationships and demonstrated promise in drug discovery.

Journal of chemical information and modeling, 2018

Conventional de novo drug design is time consuming, laborious, and resource intensive. In recent years, emerging in-silico approaches have been proven to be critical to accelerate the process of bringing drugs to market. Molecular dynamics (MD) simulations of single molecule and molecular complexes have been commonly applied to achieve accurate binding modes and binding energies of drug-receptor interactions. A derivative of MD, namely steered molecular dynamics (SMD), has been demonstrated as a promising tool for rational drug design. In this paper, we review various studies over the last 20 years using SMD simulations, thus paving the way to determine the relationship between protein structure and function. In addition, the paper highlights the use of SMD simulation for in silico drug design. We also aim to establish an understanding on the key interactions which play a crucial role in stabilization of peptide-ligand interfaces, the binding and unbinding mechanism of the ligand-pr...

Journal of Computational Chemistry, 1997

In this article we present a new LINear Constraint Solver (LINCS) for molecular simulations with bond constraints. The algorithm is inherently stable, as the constraints themselves are reset instead of derivatives of the constraints, thereby eliminating drift. Although the derivation of the algorithm is presented in terms of matrices, no matrix matrix multiplications are needed and only the nonzero matrix elements have to be stored, making the method useful for very large molecules. At the same accuracy, the LINCS algorithm is 3 to 4 times faster than the SHAKE algorithm. Parallelization of the algorithm is straightforward.

Journal of …, 1995

In molecular dynamics simulations, the fastest components of the potential eld impose severe restrictions on the stability and hence the speed of computational methods. One possibility for treating this problem is to replace the fastest components with algebraic length constraints. In this paper, the resulting systems of mixed di erential and algebraic equations are studied. Commonly used discretization schemes for constrained Hamiltonian systems are discussed. The form of the nonlinear equations is examined in detail and used to give convergence results for the traditional nonlinear solution technique SHAKE iteration and for a modi cation based on Successive OverRelaxation (SOR). A simple adaptive algorithm for nding the optimal relaxation parameter is presented. Alternative direct methods using sparse matrix techniques are discussed. Numerical results are given for the new techniques, implemented in the molecular modeling software package CHARMM, showing as much as twofold improvement over SHAKE iteration. matrix methods 1. Introduction. In molecular dynamics, the length of timestep for numerically integrating the equations of motion is dictated by the contributions to the force vector which maintain pairs of atoms near some equilibrium distance. The imposition of algebraic constraints that x these lengths removes the associated rapid vibrational modes, enabling the use of longer timesteps without substantially altering important physical characteristics of the motion 1]. Although we treat only length constraints in the present work, constrained techniques are also of interest for conformational search and conformational free energy simulations 2]. In 3] the SHAKE iteration was described for solving the nonlinear equations at each timestep of a constrained version of the Verlet discretization, and a similar scheme was proposed in 4] for use with the RATTLE discretization.

Journal of Computational Chemistry, 2013

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein-ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled proteinligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine-based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein-ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis-Menten mechanism with a competitive inhibitor. V

2021

This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.

Molecules

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

MedChemComm, 2018

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be e...