A global optimization algorithm for protein surface alignment

BMC Bioinformatics

Methodology Article

Concettina Guerra¹  Giampaolo Liuzzi²  Paola Bertolazzi² 

[1] Dipartimento di Ingegneria Informatica, Universitá di Padova, Via Gradenigo, 6a, 35100, Padova, Italy;College of Computing, Georgia Institute of Technology, Atlantic Drive, 801, 30332-0280, Atlanta, (GA), USA;Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti", Consiglio Nazionale delle Ricerche, Viale Manzoni, 30, 00185, Rome, Italy;

关键词: Iterative Close Point;    Dissimilarity Measure;    Continuous Optimization;    Global Optimization Algorithm;    Iterative Close Point;   

DOI  :  10.1186/1471-2105-11-488

 received in 2009-10-07, accepted in 2010-09-29,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundA relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved.ResultsIn this paper we propose a new method for local structural alignment of protein surfaces based on continuous global optimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach.ConclusionsOur method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites.

【 授权许可】

Unknown   
© Bertolazzi et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.