Search Strategies in Structural Bioinformatics

Title: Search Strategies in Structural Bioinformatics

Volume: 9 Issue: 3

Author(s): Mark T. Oakley, Daniel Barthel, Yuri Bykov, Jonathan M. Garibaldi, Edmund K. Burke, Natalio Krasnogor and Jonathan D. Hirst

Affiliation:

Abstract: Optimisation problems pervade structural bioinformatics. In this review, we describe recent work addressing a selection of bioinformatics challenges. We begin with a discussion of research into protein structure comparison, and highlight the utility of Kolmogorov complexity as a measure of structural similarity. We then turn to research into de novo protein structure prediction, in which structures are generated from first principles. In this endeavour, there is a compromise between the detail of the model and the extent to which the conformational space of the protein can be sampled. We discuss some developments in this area, including off-lattice structure prediction using the great deluge algorithm. One strategy to reduce the size of the search space is to restrict the protein chain to sites on a regular lattice. In this context, we highlight the use of memetic algorithms, which combine genetic algorithms with local optimisation, to the study of simple protein models on the two-dimensional square lattice and the face-centred cubic lattice.

Cite this article as:

Oakley T. Mark, Barthel Daniel, Bykov Yuri, Garibaldi M. Jonathan, Burke K. Edmund, Krasnogor Natalio and Hirst D. Jonathan, Search Strategies in Structural Bioinformatics, Current Protein & Peptide Science 2008; 9 (3) . https://dx.doi.org/10.2174/138920308784534032