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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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Related in: MedlinePlus

Distance matrix. The matrix shows the results of all-to-all comparisons. The 9 ligands are indicated along the rows and columns and the proteins binding each ligand are grouped together. The grid of horizontal and vertical black lines separates different groups of proteins. The matrix is color-coded from 0 (blue) to 1 (red), with red corresponding to high number of aligned atoms and therefore high similarity in the shape of the binding sites and blue to the lowest degree of similarity.
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Figure 1: Distance matrix. The matrix shows the results of all-to-all comparisons. The 9 ligands are indicated along the rows and columns and the proteins binding each ligand are grouped together. The grid of horizontal and vertical black lines separates different groups of proteins. The matrix is color-coded from 0 (blue) to 1 (red), with red corresponding to high number of aligned atoms and therefore high similarity in the shape of the binding sites and blue to the lowest degree of similarity.

Mentions: The results of all-to-all comparisons are illustrated by means of the distance matrix of Figure 1. An entry of the matrix corresponds to a protein pair and contains a value related to the number of aligned atoms of the binding sites of the pair. Namely, in the matrix we report


A global optimization algorithm for protein surface alignment.

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

Distance matrix. The matrix shows the results of all-to-all comparisons. The 9 ligands are indicated along the rows and columns and the proteins binding each ligand are grouped together. The grid of horizontal and vertical black lines separates different groups of proteins. The matrix is color-coded from 0 (blue) to 1 (red), with red corresponding to high number of aligned atoms and therefore high similarity in the shape of the binding sites and blue to the lowest degree of similarity.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Distance matrix. The matrix shows the results of all-to-all comparisons. The 9 ligands are indicated along the rows and columns and the proteins binding each ligand are grouped together. The grid of horizontal and vertical black lines separates different groups of proteins. The matrix is color-coded from 0 (blue) to 1 (red), with red corresponding to high number of aligned atoms and therefore high similarity in the shape of the binding sites and blue to the lowest degree of similarity.
Mentions: The results of all-to-all comparisons are illustrated by means of the distance matrix of Figure 1. An entry of the matrix corresponds to a protein pair and contains a value related to the number of aligned atoms of the binding sites of the pair. Namely, in the matrix we report

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