Molecular dynamics simulation of protein

Biophysical Journal, 2003

Continuum solvent models have been employed in past years for understanding processes such as protein folding or biomolecular association. In the last decade, several attempts have been made to merge atomic detail molecular dynamics simulations with solvent continuum models. Among continuum models, the Poisson-Boltzmann solvent accessible surface area model is one of the oldest and most fundamental. Notwithstanding its wide usage for simulation of biomolecular electrostatic potential, the Poisson-Boltzmann equation has been very seldom used to obtain solvation forces for molecular dynamics simulation. We propose here a fast and reliable methodology to implement continuum forces in standard molecular mechanics and dynamics algorithms. Results for a totally unrestrained 1 ns molecular dynamics simulation of a small protein are quantitatively similar to results obtained by explicit solvent molecular dynamics simulations.

Advances in protein chemistry and structural biology, 2014

Molecular dynamics simulations are used to describe the patterns, strength, and properties of protein behavior, drug-receptor interactions, the solvation of molecules, the Advances in Protein Chemistry and Structural Biology, Volume 94 # 2014 Elsevier Inc.

Wiley Interdisciplinary Reviews: Computational Molecular Science, 2012

ABSTRACT Nuclear magnetic resonance spectroscopy (NMR) and molecular dynamics (MD) simulations are powerful techniques for the structural characterization of macromolecules. NMR is unique in its ability to provide experimental information at atomic level on the structure as well as on the amplitude and rate of structural fluctuations. MD provides physically sound models and potential mechanisms that connect conformations in time. Nevertheless, none of these techniques allow yet obtaining experimentally validated movies of protein motions at atomic resolution. Instead, it is their complementarity and synergy which offer a unique opportunity toward this end. Here, we overview recent examples that illustrate how much these two techniques benefit from each other, both passively and actively, for the characterization of the structural heterogeneity in proteins. © 2012 John Wiley & Sons, Ltd.

Proteins-structure Function and Bioinformatics, 2001

The latest version of the classical molecular interaction potential (CMIP) has the ability to predict the position of crystallographic waters in several proteins with great accuracy. This article analyzes the ability of the CMIP functional to improve the setup procedure of the molecular system in molecular dynamics (MD) simulations of proteins. To this end, the CMIP strategy is used to include both water molecules and counterions in different protein systems. The structural details of the configurations sampled from trajectories obtained using the CMIP setup procedure are compared with those obtained from trajectories derived from a standard equilibration process. The results show that standard MD simulations can lead to artifactual results, which are avoided using the CMIP setup procedure. Because the CMIP is easy to implement at a low computational cost, it can be very useful in obtaining reliable MD trajectories. Proteins 2001;45:428–437. © 2001 Wiley-Liss, Inc.

[Abstract] Molecular dynamics (MD) simulations have become one of the most important tools in understanding the behavior of bio-molecules on nanosecond to microsecond time scales. In this protocol, we provide a general approach and standard setup protocol for MD simulations by using the Gromacs MD suite.

Advances and Applications in Bioinformatics and Chemistry, 2015

Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.

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.

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field’s history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right. 1.

Nuclear magnetic resonance (NMR) spin relaxation is the most informative approach to experimentally probe the internal dynamics of proteins on the picosecond to nanosecond timescale. At the same time, molecular dynamics (MD) simulations of biological macromolecules are steadily improving through better physical models, enhanced sampling algorithms, and increasing computational power, and they provide exquisite information about flexibility and its role in protein stability and molecular interactions. Many examples have shown that MD is now adept in probing protein backbone motion, but improvements are still required towards a quantitative description of the dynamics of side chains, for example probed by the dynamics of methyl groups. Thus far, the comparison of computation with experiment for side chains has primarily focused on the relaxation of 13C and 2H nuclei induced by auto-correlated variation of spin interactions. However, the cross-correlation of 13C-1H dipolar interactions...

Annual review of biophysics and biomolecular structure, 2001

Computer modeling has been developed and widely applied in studying molecules of biological interest. The force field is the cornerstone of computer simulations, and many force fields have been developed and successfully applied in these simulations. Two interesting areas are (a) studying enzyme catalytic mechanisms using a combination of quantum mechanics and molecular mechanics, and (b) studying macromolecular dynamics and interactions using molecular dynamics (MD) and free energy (FE) calculation methods. Enzyme catalysis involves forming and breaking of covalent bonds and requires the use of quantum mechanics. Noncovalent interactions appear ubiquitously in biology, but here we confine ourselves to review only noncovalent interactions between protein and protein, protein and ligand, and protein and nucleic acids.