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Open Access Methodology article

A global optimization algorithm for protein surface alignment

Paola Bertolazzi1, Concettina Guerra23* and Giampaolo Liuzzi1

Author Affiliations

1 Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti", Consiglio Nazionale delle Ricerche, Viale Manzoni, 30, 00185 Rome, Italy

2 Dipartimento di Ingegneria Informatica, Universit√° di Padova, Via Gradenigo, 6a, 35100 Padova, Italy

3 College of Computing, Georgia Institute of Technology, Atlantic Drive, 801, 30332-0280 Atlanta (GA), USA

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BMC Bioinformatics 2010, 11:488  doi:10.1186/1471-2105-11-488

Published: 29 September 2010



A 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.


In 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.


Our 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.