Abstract
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.
Highlights
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
A well-known algorithm for shape alignment is the Iterative Closest Point Algorithm [17]. This algorithm stems from the idea that, once a mapping y Î Ψ is fixed, it is possible to compute the isometric transformation a Î Θ that minimizes the function f( y, a) (a closed-form expression for a( y ) has been given in [33] where we refer the interested reader for the relevant details)
Summary
A well-known algorithm for shape alignment is the Iterative Closest Point Algorithm [17]. We present more detailed results on a set of 19 binding sites of proteins in complex with the ligand ATP with the goal of judging the quality of the alignments. As noted in [37], ligand ATP has great variation in its conformation when binding different proteins: it can be in an extended conformation or in a compact one, resulting in different sizes and shapes of the binding regions This is reflected in our experiments, as can be seen from the distance matrix where blue or green areas are present. A similar large-scale experiment is not available for the related problems of aligning protein surfaces and binding sites, despite the growing number of methods and web servers available. Single expression: SAS = (RMSD × 100)/(num aligned atoms) We run both programs on the set of 19 proteins used in [42] for a related different problem, that is binding site recognition within a cavity. We do not report the execution times of MolLoc since they are not available from the web server interface
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