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Analysis of HSP90-related folds with MED-SuMo classification approach

Authors Olivia Doppelt-Azeroual, Fabrice Moriaud, François Delfaud, Alexandre G de Brevern

Published 26 February 2009 Volume 2009:3 Pages 59—72

DOI https://doi.org/10.2147/DDDT.S4706

Review by Single-blind

Peer reviewer comments 3

Olivia Doppelt-Azeroual1,2, Fabrice Moriaud1, François Delfaud1, Alexandre G de Brevern2

1MEDIT SA, Palaiseau, France; 2INSERM UMR-S 726, Equipe de Bioinformatique Génomique and Moléculaire, (EBGM), DSIMB, Institut National de Transfusion Sanguine (INTS), Université Paris Diderot, Paris, France

Abstract: Three-dimensional structural information is critical for understanding functional protein properties and the precise mechanisms of protein functions implicated in physiological and pathological processes. Comparison and detection of protein binding sites are key steps for annotating structures with functional predictions and are extremely valuable steps in a drug design process. In this research area, MED-SuMo is a powerful technology to detect and characterize similar local regions on protein surfaces. Each amino acid residue’s potential chemical interactions are represented by specific surface chemical features (SCFs). The MED-SuMo heuristic is based on the representation of binding sites by a graph structure suitable for exploration by an efficient comparison algorithm. We use this approach to analyze one particular SCOP superfamily which includes HSP90 chaperone, MutL/DNA topoisomerase, histidine kinases, and α-ketoacid dehydrogenase kinase C (BCK). They share a common fold and a common region for ATP-binding. To analyze both similar and differing features of this fold, we use a novel classification method, the MED-SuMo multi approach (MED-SMA). We highlight common and distinct features of these proteins. The different clusters created by MED-SMA yield interesting observations. For instance, one cluster gathers three types of proteins (HSP90, topoisomerase VI, and BCK) which all bind the drug radicicol.

Keywords: functional classification, surface similarity, protein surface chemical feature, radicicol binding

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