Abstract
Purchase PDF (1346 K)
E-mail Article
Add to my Quick Links
Cited By in Scopus (7)
Purchase PDF (74 K)
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.
doi:10.1016/j.future.2005.10.005 
Copyright © 2005 Elsevier Ltd All rights reserved.
BioSimGrid: Grid-enabled biomolecular simulation data storage and analysis
References and further reading may be available for this article. To view references and further reading you must purchase this article.
Muan Hong Nga, 
, 
, Steven Johnstona, Bing Wuc, Stuart E. Murdockb, Kaihsu Taid, Hans Fangohra, Simon J. Coxa, Jonathan W. Essexb, Mark S.P. Sansomd and Paul Jeffreysc
Received 15 August 2005;
Abstract
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 for processing 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.
Keywords: Biomolecular simulation; Database; Grid computing; Storage resource broker; Python
