We investigate the structural properties of hubs that enable them to interact with several partners in protein-protein interaction networks. We find that hubs have more observed and predicted disordered residues with fewer loops/coils, and more charged residues on the surface as compared to non-hubs. Smaller hubs have fewer disordered residues and more charged residues on the surface than larger hubs. We conclude that the global flexibility provided by disordered domains, and high surface charge are complementary factors that play a significant role in the binding ability of hubs.

SUMMARY We define how chronic cigarette smoke-induced time-dependent epigenetic alterations can sensitize human bronchial epithelial cells for transformation by a single oncogene. The smoke-induced chromatin changes include initial repressive polycomb marking of genes, later manifesting abnormal DNA methylation by 10 months. At this time, cells exhibit epithelial to mesenchymal changes, anchorage-independent growth and upregulated RAS/MAPK signaling with silencing of hyper-methylated genes, which normally inhibit these pathways and are associated with smoking related NSCLC. These cells, in the absence of any driver gene mutations, now transform by introducing a single KRAS mutation and form adeno-squamous lung carcinomas in mice. Thus, epigenetic abnormalities may prime for changing oncogene senescence to addiction for a single key oncogene involved in lung cancer initiation.

Hubs are proteins with a large number of interactions in a protein-protein interaction network. They are the principal agents in the interaction network and affect its function and stability. Their specific recognition of many different protein partners is of great interest from the structural viewpoint. Over the last few years, the structural properties of hubs have been extensively studied. We review the currently known features that are particular to hubs, possibly affecting their binding ability. Specifically, we look at the levels of intrinsic disorder, surface charge and domain distribution in hubs, as compared to non-hubs, along with differences in their functional domains.

The proteasome is the degradation machine at the center of the ubiquitin-proteasome system and controls the concentrations of many proteins in eukaryotes. It is highly processive so that substrates are degraded completely into small peptides, avoiding the formation of potentially toxic fragments. Nonetheless, some proteins are incompletely degraded, indicating the existence of factors that influence proteasomal processivity. We have quantified proteasomal processivity and determined the underlying rates of substrate degradation and release. We find that processivity increases with species complexity over a 5-fold range between yeast and mammalian proteasome, and the effect is due to slower but more persistent degradation by proteasomes from more complex organisms. A sequence stretch that has been implicated in causing incomplete degradation, the glycine-rich region of the NFκB subunit p105, reduces the proteasome’s ability to unfold its substrate, and polyglutamine repeats such as found in Huntington’s disease reduce the processivity of the proteasome in a length-dependent manner.

HitPredict is a consolidated resource of experimentally identified, physical protein–protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein–protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein–protein interactions from several species for the study of gene groups.Database URL: http://hintdb.hgc.jp/htp