Abstract
Quantification of local similarity between protein 3D structures is a promising tool in computer-aided drug design and prediction of biological function. Over the last ten years, several computational methods were proposed, mostly based on geometrical comparisons. This review summarizes the recent literature and gives an overview of available programs. A particular interest is given to the underlying methodologies. Our analysis points out strengths and weaknesses of the various approaches. If all described methods work relatively well when two binding sites obviously resemble each other, scoring potential solutions remains a difficult issue, especially if the similarity is low. The other challenging question is the protein flexibility, which is indeed difficult to evaluate from a static representation. Last, most of recently developed techniques are fast and can be applied to large amounts of data. Examples were carefully chosen to illustrate the wide applicability domain of the most popular methods: detection of common structural motifs, identification of secondary targets for a drug-like compound, comparison of binding sites across a functional family, comparison of homology models, database screening.
Keywords: Local similarity, three-dimensional comparison, protein structure, binding site, molecular shape, drug design, function assignment
Current Computer-Aided Drug Design
Title: How to Measure the Similarity Between Protein Ligand-Binding Sites?
Volume: 4 Issue: 3
Author(s): Esther Kellenberger, Claire Schalon and Didier Rognan
Affiliation:
Keywords: Local similarity, three-dimensional comparison, protein structure, binding site, molecular shape, drug design, function assignment
Abstract: Quantification of local similarity between protein 3D structures is a promising tool in computer-aided drug design and prediction of biological function. Over the last ten years, several computational methods were proposed, mostly based on geometrical comparisons. This review summarizes the recent literature and gives an overview of available programs. A particular interest is given to the underlying methodologies. Our analysis points out strengths and weaknesses of the various approaches. If all described methods work relatively well when two binding sites obviously resemble each other, scoring potential solutions remains a difficult issue, especially if the similarity is low. The other challenging question is the protein flexibility, which is indeed difficult to evaluate from a static representation. Last, most of recently developed techniques are fast and can be applied to large amounts of data. Examples were carefully chosen to illustrate the wide applicability domain of the most popular methods: detection of common structural motifs, identification of secondary targets for a drug-like compound, comparison of binding sites across a functional family, comparison of homology models, database screening.
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Cite this article as:
Kellenberger Esther, Schalon Claire and Rognan Didier, How to Measure the Similarity Between Protein Ligand-Binding Sites?, Current Computer-Aided Drug Design 2008; 4 (3) . https://dx.doi.org/10.2174/157340908785747401
DOI https://dx.doi.org/10.2174/157340908785747401 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
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