Structural and Dynamic Features of F-recruitment Site Driven Substrate Phosphorylation by ERK2

The F-recruitment site (FRS) of active ERK2 binds F-site (Phe-x-Phe-Pro) sequences found downstream of the Ser/Thr phospho-acceptor on cellular substrates. Here we apply NMR methods to analyze the interaction between active ERK2 (ppERK2), and a 13-residue F-site-bearing peptide substrate derived from its cellular target, the transcription factor Elk-1. Our results provide detailed insight into previously elusive structural and dynamic features of FRS/F-site interactions and FRS-driven substrate phosphorylation. We show that substrate F-site engagement significantly quenches slow dynamics involving the ppERK2 activation-loop and the FRS. We also demonstrate that the F-site phenylalanines make critical contacts with ppERK2, in contrast to the proline whose cis-trans isomerization has no significant effect on F-site recognition by the kinase FRS. Our results support a mechanism where phosphorylation of the disordered N-terminal phospho-acceptor is facilitated by its increased productive encounters with the ppERK2 active site due to docking of the proximal F-site at the kinase FRS

CACTUS - Clustering Categorical Data Using Summaries

Clustering is an important data mining problem. Most of the earlier work on clustering focussed on numeric attributes which have a natural ordering on their attribute values. Recently, clustering data with categorical attributes, whose attribute values do not have a natural ordering, has received some attention. However, previous algorithms do not give a formal description of the clusters they discover and some of them assume that the user post-processes the output of the algorithm to identify the final clusters. In this paper, we introduce a novel formalization of a cluster for categorical attributes by generalizing a definition of a cluster for numerical attributes. We then describe a very fast summarizationbased algorithm called CACTUS that discovers exactly such clusters in the data. CACTUS has two important characteristics. First, the algorithm requires only two scans of the dataset, and hence is very fast and scalable. Our experiments on a variety of datasets show that CACTUS outperforms previous work by a factor of 3 to 10. Second, CACTUS can find clusters in subsets of all attributes and can thus perform a subspace clustering of the data. This feature is important if clusters do not span all attributes, a likely scenario if the number of attributes is very large. In a thorough experimental evaluation, we study the performance of CACTUS on real and synthetic datasets.