SBMOpenMM: A Builder of Structure-Based Models for OpenMM

Journal of Physical Chemistry & Biophysics, 2014

MD simulation has become an essential tool for understanding the physical basis of the structure of proteins and their biological functions. During the current decade we witnessed significant progress in MD simulation of proteins with advancement in atomistic simulation algorithms, force fields, computational methods and facilities, comprehensive analysis and experimental validation, as well as integration in wide area bioinformatics and structural/systems biology frameworks. In this review, we present the methodology on protein simulations and recent advancements in the field. MD simulation provides a platform to study protein-protein, protein-ligand and protein-nucleic acid interactions. MD simulation is also done with NMR relaxation timescale in order to get residual dipolar coupling and order parameter of protein molecules.

We present the new release of the ORAC engine (Procacci et al., Comput Chem 1997, 18, 1834, a FORTRAN suite to simulate complex biosystems at the atomistic level. The previous release of the ORAC code included multiple time steps integration, smooth particle mesh Ewald method, constant pressure and constant temperature simulations. The present release has been supplemented with the most advanced techniques for enhanced sampling in atomistic systems including replica exchange with solute tempering, metadynamics and steered molecular dynamics. All these computational technologies have been implemented for parallel architectures using the standard MPI communication protocol.

Journal of Computational Chemistry, 2011

MDAnalysis is an object-oriented library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. It is written in the Python language with some performance-critical code in C. It uses the powerful NumPy package to expose trajectory data as fast and efficient NumPy arrays. It has been tested on systems of millions of particles. Many common file formats of simulation packages including CHARMM, Gromacs, Amber, and NAMD and the Protein Data Bank format can be read and written. Atoms can be selected with a syntax similar to CHARMM's powerful selection commands. MDAnalysis enables both novice and experienced programmers to rapidly write their own analytical tools and access data stored in trajectories in an easily accessible manner that facilitates interactive explorative analysis.

Computer Physics Communications, 1995

A package of computer programs for molecular dynamics simulations-MOIL-is described. A flexible data structure enables the study of macromolecules with potentials consistent with the AMBER/OPLS force field. The supplied parameter set has proteins in mind. In addition to 'wide spread' applications such as energy, energy minimization, normal modes, dynamics and free energy calculations code is also provided to pursue less common applications. This includes reaction path calculations (in condensed phases), uses of the mean field approach for enhanced sampling (LES-locally enhanced sampling) and calculations of curve crossing using the Landau-Zener model. A brief review of the overall program is provided. A few modules are discussed in considerable detail.

Bioinformatics, 2007

Wordom is a versatile program for manipulation of molecular dynamics trajectories and efficient analysis of simulations. Original tools in Wordom include a procedure to evaluate significance of sampling for principal component analysis as well as modules for clustering multiple conformations and evaluation of order parameters for folding and aggregation. The program was developed with special emphasis on user-friendliness, effortless addition of new modules and efficient handling of large sets of trajectories. Availability: The Wordom program is distributed with full source code (in the C language) and documentation for usage and further development as a platform-independent package under a GPL license from

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.

Chemical Reviews, 2006

time-dependent (i.e., kinetic) phenomena. This enables an understanding to be developed of various dynamic aspects of biomolecular structure, recognition, and function. However, when used alone, MD is of limited utility. An MD trajectory (i.e., the progress of simulated structure with respect to time) generally provides data only at the level of atomic positions, velocities, and single-point energies. To obtain the macroscopic properties in which one is usually interested requires the application of statistical mechanics, which connects microscopic simulations and macroscopic observables. Statistical mechanics provides a rigorous framework of mathematical expressions that relate the distributions and motions of atoms and molecules to macroscopic observables such as pressure, heat capacity, and free energies. 17,18 Extraction of these macroscopic observables is therefore possible from the microscopic data, and one can predict, for instance, changes in the binding free energy of a particular drug candidate or the mechanisms and energetic consequences of conformational change in a particular protein. Specific aspects of biomolecular structure, kinetics, and thermodynamics that may be investigated via MD include, for example, macromolecular stability, 19 conformational and allosteric properties, 20 the role of dynamics in enzyme activity, 21,22 molecular recognition and the properties of complexes, 21,23 ion and small molecule transport, 24,25 protein association, 26 protein folding, 27,16 and protein hydration. 28 MD, therefore, provides the opportunity to perform a variety of studies including molecular design (drug design 29 and protein design 30) and structure determination and refinement (Xray,31 NMR, 32 and modeling 33). 3. Molecular Dynamics Methods and Theory Given the structure of a biomolecular system, that is, the relative coordinates of the constituent atoms, there are various computational methods that can be used to investigate and study the dynamics of that system. In the present section, a number of such methods are described and discussed. The majority of important dynamics methodologies are highly dependent upon the availability of a suitable potential-energy function to describe the energy landscape of the system with respect to the aforementioned atomic coordinates. This critical aspect is, therefore, introduced first. 3.1. Potential Functions and the Energy Landscape Choice of an appropriate energy function for describing the intermolecular and intramolecular interactions is critical to a successful (i.e., valid yet tractable) molecular dynamics simulation. In conventional MD simulations, the energy function for nonbonded interactions tends to be a simple pairwise additive function (for computational reasons) of nuclear coordinates only. This use of a single nuclear coordinate to represent atoms is justified in terms of the Born-Oppenheimer approximation. 34 For bonded groups of atoms, that is those that form covalent bonds, bond angles, or dihedral angles, simple two-body, three-body, and four-body terms are used, as described below.

Molecules

Advances in coarse-grained molecular dynamics (CGMD) simulations have extended the use of computational studies on biological macromolecules and their complexes, as well as the interactions of membrane protein and lipid complexes at a reduced level of representation, allowing longer and larger molecular dynamics simulations. Here, we present a computational platform dedicated to the preparation, running, and analysis of CGMD simulations. The platform is built on a completely revisited version of our Martini coarsE gRained MembrAne proteIn Dynamics (MERMAID) web server, and it integrates this with other three dedicated services. In its current version, the platform expands the existing implementation of the Martini force field for membrane proteins to also allow the simulation of soluble proteins using the Martini and the SIRAH force fields. Moreover, it offers an automated protocol for carrying out the backmapping of the coarse-grained description of the system into an atomistic one.

MD simulation has become an essential tool for understanding the physical basis of the structure of proteins and their biological functions. During the current decade we witnessed significant progress in MD simulation of proteins with advancement in atomistic simulation algorithms, force fields, computational methods and facilities, comprehensive analysis and experimental validation, as well as integration in wide area bioinformatics and structural/systems biology frameworks. In this review, we present the methodology on protein simulations and recent advancements in the field. MD simulation provides a platform to study protein-protein, protein-ligand and protein-nucleic acid interactions. MD simulation is also done with NMR relaxation timescale in order to get residual dipolar coupling and order parameter of protein molecules.

Bioinformatics, 2013

Motivation: Molecular simulation has historically been a lowthroughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources.