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Computational approaches play an increasingly important role for the analysis and prediction of selectivity profiles. As most of the successfully administered small molecule drugs bind in depressions on the surface of proteins, physicochemical properties of the pocket-exposed amino acids play a central role in ligand recognition during the binding event. Cavbase is an approach to describe binding sites in terms of the exposed physicochemical properties and to compare them independent of their sequence and fold homology. Classification of proteins by means of their binding-site properties is a promising approach to obtain information necessary for selectivity modeling. For this purpose, the workflow clusterScore has been developed to explore the important parameters of a clustering procedure, which will allow an accurate classification of proteins. It has been successfully applied on two diverse and challenging data sets. The predicted number of clusters, as suggested by clusterScore and the subsequent clustering of proteins are in agreement with the EC and Merops classifications. Furthermore, putative cross-reactivity mapped between calpain-1 and cysteine cathepsins on structural level has so far only been described based on ligand data. In a benchmark study using ligand topology, binding site, and sequence information of eleven serine proteases, the emerging clusters indicate a pronounced correlation between the cavity and ligand data. These results emphasize the importance of binding-site information which should be considered for ligand design during lead optimization cycles. The program clusterScore is freely available and can be downloaded from our Web site .


Serghei Glinca, Gerhard Klebe. Cavities Tell More than Sequences: Exploring Functional Relationships of Proteases via Binding Pockets. Journal of chemical information and modeling. 2013 Aug 26;53(8):2082-92

PMID: 23834203

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