A generative, probabilistic model of local protein structure

doi:10.1073/pnas.0801715105

A generative, probabilistic model of local protein structure

*Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark;
^†Department of Statistics, University of Leeds, Leeds, West Yorkshire LS2 9JT, United Kingdom; and
^‡DTU Elektro, Technical University of Denmark, 2800 Lyngby, Denmark

Edited by David Baker, University of Washington, Seattle, WA, and approved March 14, 2008 (received for review February 27, 2008)

Abstract

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence–structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.

Footnotes

^§To whom correspondence should be addressed. E-mail: thamelry{at}binf.ku.dk
Author contributions: W.B. and T.H. designed research; W.B. performed research; K.V.M. and C.C.T. contributed new reagents/analytic tools; and W.B., J.F.-B., A.K., and T.H. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0801715105/DCSupplemental.
Freely available online through the PNAS open access option.