HENA, heterogeneous network-based data set for Alzheimer's disease.

Alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer's disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer's disease research.

Discovery of specific inhibitors of human USP7/HAUSP deubiquitinating enzyme.

The human USP7 deubiquitinating enzyme was shown to regulate many proteins involved in the cell cycle, as well as tumor suppressors and oncogenes. Thus, USP7 offers a promising, strategic target for cancer therapy. Using biochemical assays and activity-based protein profiling in living systems, we identified small-molecule antagonists of USP7 and demonstrated USP7 inhibitor occupancy and selectivity in cancer cell lines. These compounds bind USP7 in the active site through a covalent mechanism. In cancer cells, these active-site-targeting inhibitors were shown to regulate the level of several USP7 substrates and thus recapitulated the USP7 knockdown phenotype that leads to G1 arrest in colon cancer cells. The data presented in this report provide proof of principle that USP7 inhibitors may be a valuable therapeutic for cancer. In addition, the discovery of such molecules offers interesting tools for studying deubiquitination.

Ubiquitin-specific proteases as cancer drug targets.

Ubiquitin-specific proteases are deubiquitinating enzymes involved in the removal of ubiquitin from specific protein substrates resulting in protein salvage from proteasome degradation, regulation of protein localization or activation. DNA alteration and overexpression in different cancer types, as well as involvement in many cancer-associated pathways, make ubiquitin-specific proteases attractive for the cancer drug discovery purposes. Their proteolytic function associated to available structural biology data reinforce their potential for pharmacological interference. Here, we review this class of enzymes as cancer drug targets in terms of validation and druggability.

Small-molecule inhibitor of USP7/HAUSP ubiquitin protease stabilizes and activates p53 in cells.

Deregulation of the ubiquitin/proteasome system has been implicated in the pathogenesis of many human diseases, including cancer. Ubiquitin-specific proteases (USP) are cysteine proteases involved in the deubiquitination of protein substrates. Functional connections between USP7 and essential viral proteins and oncogenic pathways, such as the p53/Mdm2 and phosphatidylinositol 3-kinase/protein kinase B networks, strongly suggest that the targeting of USP7 with small-molecule inhibitors may be useful for the treatment of cancers and viral diseases. Using high-throughput screening, we have discovered HBX 41,108, a small-molecule compound that inhibits USP7 deubiquitinating activity with an IC(50) in the submicromolar range. Kinetics data indicate an uncompetitive reversible inhibition mechanism. HBX 41,108 was shown to affect USP7-mediated p53 deubiquitination in vitro and in cells. As RNA interference-mediated USP7 silencing in cancer cells, HBX 41,108 treatment stabilized p53, activated the transcription of a p53 target gene without inducing genotoxic stress, and inhibited cancer cell growth. Finally, HBX 41,108 induced p53-dependent apoptosis as shown in p53 wild-type and null isogenic cancer cell lines. We thus report the identification of the first lead-like inhibitor against USP7, providing a structural basis for the development of new anticancer drugs.

From protein-protein complexes to interactomics.

Protein-protein interactions (or PPIs) are key elements for the normal functioning of a living cell. A large description of the protein interactomics field is given in this review where different aspects will be discussed. We first give an introduction of the different large scale experimental approaches from yeast two-hybrid to mass spectrometry used to discover PPIs and build protein interaction maps. Single PPI validation techniques such as co-immunoprecipitation or fluorescence methods are then presented as they are more and more integrated in global PPI discovery strategy. Data from different experimental sets are compared and an assessment of the different large scale technologies is presented. Bioinformatics tools can also predict with a good accuracy PPIs in silico, PPIs databases are now numerous and topological analysis has led to interesting insights into the nature of network connection. Finally, PPI, as an association of two proteins, has been structurally characterized for many protein complexes and is largely discussed throughout existing examples. The results obtained so far already provide the biologist with a large set of structured data from which knowledge on pathways and associated protein function can be extracted.

Protein interaction mapping: a Drosophila case study.

The Drosophila (fruit fly) model system has been instrumental in our current understanding of human biology, development, and diseases. Here, we used a high-throughput yeast two-hybrid (Y2H)-based technology to screen 102 bait proteins from Drosophila melanogaster, most of them orthologous to human cancer-related and/or signaling proteins, against high-complexity fly cDNA libraries. More than 2300 protein-protein interactions (PPI) were identified, of which 710 are of high confidence. The computation of a reliability score for each protein-protein interaction and the systematic identification of the interacting domain combined with a prediction of structural/functional motifs allow the elaboration of known complexes and the identification of new ones. The full data set can be visualized using a graphical Web interface, the PIMRider (http://pim.hybrigenics.com), and is also accessible in the PSI standard Molecular Interaction data format. Our fly Protein Interaction Map (PIM) is surprisingly different from the one recently proposed by Giot et al. with little overlap between the two data sets. Analysis of the differences in data sets and methods suggests alternative strategies to enhance the accuracy and comprehensiveness of the post-genomic generation of broad-scale protein interaction maps.