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This article is part of the supplement: Selected articles from the Computational Structural Bioinformatics Workshop 2009

Open Access Research

Tracing conformational changes in proteins

Nurit Haspel15, Mark Moll1, Matthew L Baker2, Wah Chiu23 and Lydia E Kavraki134*

Author Affiliations

1 Department of Computer Science, Rice University, Houston, TX 77005, USA

2 National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA

3 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA

4 Department of Bioengineering, Rice University, Houston, TX 77005, USA

5 Currently with the Department of Computer Science, the University of Massachusetts Boston, Boston MA 02125, USA

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BMC Structural Biology 2010, 10(Suppl 1):S1  doi:10.1186/1472-6807-10-S1-S1

Published: 17 May 2010

Abstract

Background

Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions.

Results

In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on four well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions.

Conclusions

The proposed method effectively captures large-scale conformational changes and produces pathways that are consistent with experimental data and other computational studies. The method represents an important first step towards a larger scale modeling of more complex biological systems.