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Reconstructing protein structure from solvent exposure using tabu search

Martin Paluszewski1 email, Thomas Hamelryck2 email and Pawel Winter1 email

1Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark

2Bioinformatics Center, Institute of Molecular Biology, University of Copenhagen, Universitetsparken 15 building 10, 2100 Copenhagen, Denmark

author email corresponding author email

Algorithms for Molecular Biology 2006, 1:20doi:10.1186/1748-7188-1-20

Published: 27 October 2006

Abstract

Background

A new, promising solvent exposure measure, called half-sphere-exposure (HSE), has recently been proposed. Here, we study the reconstruction of a protein's Cα trace solely from structure-derived HSE information. This problem is of relevance for de novo structure prediction using predicted HSE measure. For comparison, we also consider the well-established contact number (CN) measure. We define energy functions based on the HSE- or CN-vectors and minimize them using two conformational search heuristics: Monte Carlo simulation (MCS) and tabu search (TS). While MCS has been the dominant conformational search heuristic in literature, TS has been applied only a few times. To discretize the conformational space, we use lattice models with various complexity.

Results

The proposed TS heuristic with a novel tabu definition generally performs better than MCS for this problem. Our experiments show that, at least for small proteins (up to 35 amino acids), it is possible to reconstruct the protein backbone solely from the HSE or CN information. In general, the HSE measure leads to better models than the CN measure, as judged by the RMSD and the angle correlation with the native structure. The angle correlation, a measure of structural similarity, evaluates whether equivalent residues in two structures have the same general orientation. Our results indicate that the HSE measure is potentially very useful to represent solvent exposure in protein structure prediction, design and simulation.


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