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A global optimization algorithm for protein surface alignment.

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

Bottom Line: 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.The reported computational experience and comparison show viability of the proposed approach.

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: 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.The reported computational experience and comparison show viability of the proposed approach.

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