Cavities Tell More than Sequences: Exploring Functional Relationships of Proteases via Binding Pockets

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 www.agklebe.de.