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Open Access Research article

Comparative modelling by restraint-based conformational sampling

Nicholas Furnham1*, Paul IW de Bakker12, Swanand Gore1, David F Burke13 and Tom L Blundell1

Author Affiliations

1 Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK

2 Harvard Medical School-Partners Healthcare Center for Genetics and Genomics, Brigham and Women's Hospital, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

3 Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK

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BMC Structural Biology 2008, 8:7  doi:10.1186/1472-6807-8-7

Published: 31 January 2008

Abstract

Background

Although comparative modelling is routinely used to produce three-dimensional models of proteins, very few automated approaches are formulated in a way that allows inclusion of restraints derived from experimental data as well as those from the structures of homologues. Furthermore, proteins are usually described as a single conformer, rather than an ensemble that represents the heterogeneity and inaccuracy of experimentally determined protein structures. Here we address these issues by exploring the application of the restraint-based conformational space search engine, RAPPER, which has previously been developed for rebuilding experimentally defined protein structures and for fitting models to electron density derived from X-ray diffraction analyses.

Results

A new application of RAPPER for comparative modelling uses positional restraints and knowledge-based sampling to generate models with accuracies comparable to other leading modelling tools. Knowledge-based predictions are based on geometrical features of the homologous templates and rules concerning main-chain and side-chain conformations. By directly changing the restraints derived from available templates we estimate the accuracy limits of the method in comparative modelling.

Conclusion

The application of RAPPER to comparative modelling provides an effective means of exploring the conformational space available to a target sequence. Enhanced methods for generating positional restraints can greatly improve structure prediction. Generation of an ensemble of solutions that are consistent with both target sequence and knowledge derived from the template structures provides a more appropriate representation of a structural prediction than a single model. By formulating homologous structural information as sets of restraints we can begin to consider how comparative models might be used to inform conformer generation from sparse experimental data.