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A global optimization algorithm for protein surface alignment.

Bertolazzi P, Guerra C, Liuzzi G - BMC Bioinformatics (2010)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

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

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

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Example of a computed superimposition. Comparison of CO with MolLoc. Pairwise comparisons of the binding site of protein 1atp with other 18 proteins all binding ATP (columns 2). The results of CO (columns 3-5) and MolLoc (columns 6-8). For a definition of SAS see the text. The comparisons are ranked based on the number of corresponding atoms in CO (column 3).
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Figure 3: Example of a computed superimposition. Comparison of CO with MolLoc. Pairwise comparisons of the binding site of protein 1atp with other 18 proteins all binding ATP (columns 2). The results of CO (columns 3-5) and MolLoc (columns 6-8). For a definition of SAS see the text. The comparisons are ranked based on the number of corresponding atoms in CO (column 3).

Mentions: Figure 3 shows an example superimposition of the binding sites of ligand ATP of proteins 1atp and 1hck after the computed rototraslation is applied.


A global optimization algorithm for protein surface alignment.

Bertolazzi P, Guerra C, Liuzzi G - BMC Bioinformatics (2010)

Example of a computed superimposition. Comparison of CO with MolLoc. Pairwise comparisons of the binding site of protein 1atp with other 18 proteins all binding ATP (columns 2). The results of CO (columns 3-5) and MolLoc (columns 6-8). For a definition of SAS see the text. The comparisons are ranked based on the number of corresponding atoms in CO (column 3).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957401&req=5

Figure 3: Example of a computed superimposition. Comparison of CO with MolLoc. Pairwise comparisons of the binding site of protein 1atp with other 18 proteins all binding ATP (columns 2). The results of CO (columns 3-5) and MolLoc (columns 6-8). For a definition of SAS see the text. The comparisons are ranked based on the number of corresponding atoms in CO (column 3).
Mentions: Figure 3 shows an example superimposition of the binding sites of ligand ATP of proteins 1atp and 1hck after the computed rototraslation is applied.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

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

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus