Protein structure determination by exhaustive search of Protein Data Bank derived databases.

Parallel sequence and structure alignment tools have become ubiquitous and invaluable at all levels in the study of biological systems. We demonstrate the application and utility of this same parallel search paradigm to the process of protein structure determination, benefitting from the large and growing corpus of known structures. Such searches were previously computationally intractable. Through the method of Wide Search Molecular Replacement, developed here, they can be completed in a few hours with the aide of national-scale federated cyberinfrastructure. By dramatically expanding the range of models considered for structure determination, we show that small (less than 12% structural coverage) and low sequence identity (less than 20% identity) template structures can be identified through multidimensional template scoring metrics and used for structure determination. Many new macromolecular complexes can benefit significantly from such a technique due to the lack of known homologous protein folds or sequences. We demonstrate the effectiveness of the method by determining the structure of a full-length p97 homologue from Trichoplusia ni. Example cases with the MHC/T-cell receptor complex and the EmoB protein provide systematic estimates of minimum sequence identity, structure coverage, and structural similarity required for this method to succeed. We describe how this structure-search approach and other novel computationally intensive workflows are made tractable through integration with the US national computational cyberinfrastructure, allowing, for example, rapid processing of the entire Structural Classification of Proteins protein fragment database.

A grid-enabled web service for low-resolution crystal structure refinement.

Deformable elastic network (DEN) restraints have proved to be a powerful tool for refining structures from low-resolution X-ray crystallographic data sets. Unfortunately, optimal refinement using DEN restraints requires extensive calculations and is often hindered by a lack of access to sufficient computational resources. The DEN web service presented here intends to provide structural biologists with access to resources for running computationally intensive DEN refinements in parallel on the Open Science Grid, the US cyberinfrastructure. Access to the grid is provided through a simple and intuitive web interface integrated into the SBGrid Science Portal. Using this portal, refinements combined with full parameter optimization that would take many thousands of hours on standard computational resources can now be completed in several hours. An example of the successful application of DEN restraints to the human Notch1 transcriptional complex using the grid resource, and summaries of all submitted refinements, are presented as justification.

Adapting federated cyberinfrastructure for shared data collection facilities in structural biology.

Early stage experimental data in structural biology is generally unmaintained and inaccessible to the public. It is increasingly believed that this data, which forms the basis for each macromolecular structure discovered by this field, must be archived and, in due course, published. Furthermore, the widespread use of shared scientific facilities such as synchrotron beamlines complicates the issue of data storage, access and movement, as does the increase of remote users. This work describes a prototype system that adapts existing federated cyberinfrastructure technology and techniques to significantly improve the operational environment for users and administrators of synchrotron data collection facilities used in structural biology. This is achieved through software from the Virtual Data Toolkit and Globus, bringing together federated users and facilities from the Stanford Synchrotron Radiation Lightsource, the Advanced Photon Source, the Open Science Grid, the SBGrid Consortium and Harvard Medical School. The performance and experience with the prototype provide a model for data management at shared scientific facilities.