Quality assurance for biomolecular simulations

Contemporary structural biology has an increased emphasis on high- throughput methods. Biomolecular simulations can add value to structural biology via the provision of dynamic information. However, at present there are no agreed measures for the quality of biomolecular simulation data. In this Letter, we suggest suitable measures for the quality assurance of molecular dynamics simulations of biomolecules. These measures are designed to be simple, fast, and general. Reporting of these measures in simulation papers should become an expected practice, analogous to the reporting of comparable quality measures in protein crystallography. We wish to solicit views and suggestions from the simulation community on methods to obtain reliability measures from molecular- dynamics trajectories. In a database which provides access to previously obtained simulations-for example BioSimGrid (http://www.biosimgrid.org/)-the user needs to be confident that the simulation trajectory is suitable for further investigation. This can be provided by the simulation quality measures which a user would examine prior to more extensive analyses

Three hydrolases and a transferase: comparative analysis of active-site dynamics via the BioSimGrid database

Comparative molecular dynamics (MD) simulations enable us to explore the conformational dynamics of the active sites of distantly related enzymes. We have used the BioSimGrid (http://www.biosimgrid.org) database to facilitate such a comparison. Simulations of four enzymes were analyzed. These included three hydrolases and a transferase, namely acetylcholinesterase, outer-membrane phospholipase A, outer-membrane protease T, and PagP (an outer membrane enzyme which transfers a palmitate chain from a phospholipid to lipid A). A set of 17 simulations were analyzed corresponding to a total of ~0.1 μs simulation time. A simple metric for active site integrity was used to demonstrate the existence of clusters of dynamic conformational behaviour of the active sites. Small (i.e. within a cluster) fluctuations appear to be related to the function of an enzymatically active site. Larger fluctuations (i.e. between clusters) correlate with transitions between catalytically active and inactive states. Overall, these results demonstrate the potential of a comparative MD approach to analysis of enzyme function. This approach could be extended to a wider range of enzymes using current high throughput MD simulation and database methods

BioSimGrid: grid-enabled biomolecular simulation data storage and analysis

In computational biomolecular research, large amounts of simulation data are generated to capture the motion of proteins. These massive simulation data can be analysed in a number of ways to reveal the biochemical properties of the proteins.However, the legacy way of storing these data (usually in the laboratory where the simulations have been run) often hinders a wider sharing and easier cross-comparison of simulation results. The data is commonly encoded in a way specific to the simulation package that produced the data and can only be analysed with tools developed specifically for that simulation package. The BioSimGrid platform seeks to provide a solution to these challenges by exploiting the potential of the Grid in facilitating data sharing. By using BioSimGrid either in a scripting or web environment, users can deposit their data and reuse it for analysis. BioSimGrid tools manage the multiple storage locations transparently to the users and provide a set of retrieval and analysis tools to process the data in a convenient and efficient manner. This paper details the usage and implementation of BioSimGrid using a combination of commercial databases, the Storage Resource Broker and Python scripts, gluing the building blocks together. It introduces a case study of how BioSimGrid can be used for better storage, retrieval and analysis of biomolecular simulation data

BioSimGrid Biomolecular Simulation Database

BioSimGrid is a distributed biomolecular simulation database. It is a general-purpose database for trajectories from molecular dynamics simulations. Though initially designed as a distributed data grid, BioSimGrid allows for installation as a stand-alone instance. This can later be integrated into a wider, networked system. This presentation of BioSimGrid follows a scenario in biological research to demonstrate how to install the system, and how to deposit, query, and analyze trajectories in this system, with real Python code examples for each step. What then follow are explanations of the underlying concepts in the implementation of BioSimGrid: relational database, distributed computing, and the input/output (deposit and analysis) modules. Finishing the presentation is a discussion of the emerging trends and concerns in the further development of BioSimGrid and similar biological databases. This discussion touches on quality-assurance issues and the use of BioSimGrid as a back-end for other speciality databases. The experience of developing BioSimGrid compels the conclusion: In the development and maintenance of biomolecular simulation databases, it is essential that sustainability be asserted as a key principle.</jats:p

BioSimGrid Biomolecular Simulation Database

BioSimGrid is a distributed biomolecular simulation database. It is a general-purpose database for trajectories from molecular dynamics simulations. Though initially designed as a distributed data grid, BioSimGrid allows for installation as a stand-alone instance. This can later be integrated into a wider, networked system. This presentation of BioSimGrid follows a scenario in biological research to demonstrate how to install the system, and how to deposit, query, and analyze trajectories in this system, with real Python code examples for each step. What then follow are explanations of the underlying concepts in the implementation of BioSimGrid: relational database, distributed computing, and the input/output (deposit and analysis) modules. Finishing the presentation is a discussion of the emerging trends and concerns in the further development of BioSimGrid and similar biological databases. This discussion touches on quality-assurance issues and the use of BioSimGrid as a back-end for other speciality databases. The experience of developing BioSimGrid compels the conclusion: In the development and maintenance of biomolecular simulation databases, it is essential that sustainability be asserted as a key principle.</jats:p

Molecular Dynamics Simulation of the M2 Helices within the Nicotinic Acetylcholine Receptor Transmembrane Domain: Structure and Collective Motions

AbstractMultiple nanosecond duration molecular dynamics simulations were performed on the transmembrane region of the Torpedo nicotinic acetylcholine receptor embedded within a bilayer mimetic octane slab. The M2 helices and M2-M3 loop regions were free to move, whereas the outer (M1, M3, M4) helix bundle was backbone restrained. The M2 helices largely retain their hydrogen-bonding pattern throughout the simulation, with some distortions in the helical end and loop regions. All of the M2 helices exhibit bending motions, with the hinge point in the vicinity of the central hydrophobic gate region (corresponding to residues αL251 and αV255). The bending motions of the M2 helices lead to a degree of dynamic narrowing of the pore in the region of the proposed hydrophobic gate. Calculations of Born energy profiles for various structures along the simulation trajectory suggest that the conformations of the M2 bundle sampled correspond to a closed conformation of the channel. Principal components analyses of each of the M2 helices, and of the five-helix M2 bundle, reveal concerted motions that may be relevant to channel function. Normal mode analyses using the anisotropic network model reveal collective motions similar to those identified by principal components analyses