NetworKIN: a resource for exploring cellular phosphorylation networks.

Protein kinases control cellular responses by phosphorylating specific substrates. Recent proteome-wide mapping of protein phosphorylation sites by mass spectrometry has discovered thousands of in vivo sites. Systematically assigning all 518 human kinases to all these sites is a challenging problem. The NetworKIN database (http://networkin.info) integrates consensus substrate motifs with context modelling for improved prediction of cellular kinase-substrate relations. Based on the latest human phosphoproteome from the Phospho.ELM and PhosphoSite databases, the resource offers insight into phosphorylation-modulated interaction networks. Here, we describe how NetworKIN can be used for both global and targeted molecular studies. Via the web interface users can query the database of precomputed kinase-substrate relations or obtain predictions on novel phosphoproteins. The database currently contains a predicted phosphorylation network with 20,224 site-specific interactions involving 3978 phosphoproteins and 73 human kinases from 20 families.

Integrative analysis of kinase networks in TRAIL-induced apoptosis provides a source of potential targets for combination therapy.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is an endogenous secreted peptide and, in preclinical studies, preferentially induces apoptosis in tumor cells rather than in normal cells. The acquisition of resistance in cells exposed to TRAIL or its mimics limits their clinical efficacy. Because kinases are intimately involved in the regulation of apoptosis, we systematically characterized kinases involved in TRAIL signaling. Using RNA interference (RNAi) loss-of-function and cDNA overexpression screens, we identified 169 protein kinases that influenced the dynamics of TRAIL-induced apoptosis in the colon adenocarcinoma cell line DLD-1. We classified the kinases as sensitizers or resistors or modulators, depending on the effect that knockdown and overexpression had on TRAIL-induced apoptosis. Two of these kinases that were classified as resistors were PX domain-containing serine/threonine kinase (PXK) and AP2-associated kinase 1 (AAK1), which promote receptor endocytosis and may enable cells to resist TRAIL-induced apoptosis by enhancing endocytosis of the TRAIL receptors. We assembled protein interaction maps using mass spectrometry-based protein interaction analysis and quantitative phosphoproteomics. With these protein interaction maps, we modeled information flow through the networks and identified apoptosis-modifying kinases that are highly connected to regulated substrates downstream of TRAIL. The results of this analysis provide a resource of potential targets for the development of TRAIL combination therapies to selectively kill cancer cells.

CoreFlow: a computational platform for integration, analysis and modeling of complex biological data.

A major challenge in mass spectrometry and other large-scale applications is how to handle, integrate, and model the data that is produced. Given the speed at which technology advances and the need to keep pace with biological experiments, we designed a computational platform, CoreFlow, which provides programmers with a framework to manage data in real-time. It allows users to upload data into a relational database (MySQL), and to create custom scripts in high-level languages such as R, Python, or Perl for processing, correcting and modeling this data. CoreFlow organizes these scripts into project-specific pipelines, tracks interdependencies between related tasks, and enables the generation of summary reports as well as publication-quality images. As a result, the gap between experimental and computational components of a typical large-scale biology project is reduced, decreasing the time between data generation, analysis and manuscript writing. CoreFlow is being released to the scientific community as an open-sourced software package complete with proteomics-specific examples, which include corrections for incomplete isotopic labeling of peptides (SILAC) or arginine-to-proline conversion, and modeling of multiple/selected reaction monitoring (MRM/SRM) results. BIOLOGICAL SIGNIFICANCE: CoreFlow was purposely designed as an environment for programmers to rapidly perform data analysis. These analyses are assembled into project-specific workflows that are readily shared with biologists to guide the next stages of experimentation. Its simple yet powerful interface provides a structure where scripts can be written and tested virtually simultaneously to shorten the life cycle of code development for a particular task. The scripts are exposed at every step so that a user can quickly see the relationships between the data, the assumptions that have been made, and the manipulations that have been performed. Since the scripts use commonly available programming languages, they can easily be transferred to and from other computational environments for debugging or faster processing. This focus on 'on the fly' analysis sets CoreFlow apart from other workflow applications that require wrapping of scripts into particular formats and development of specific user interfaces. Importantly, current and future releases of data analysis scripts in CoreFlow format will be of widespread benefit to the proteomics community, not only for uptake and use in individual labs, but to enable full scrutiny of all analysis steps, thus increasing experimental reproducibility and decreasing errors. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Eukaryotic protein domains as functional units of cellular evolution.

Modular protein domains are functional units that can be modified through the acquisition of new intrinsic activities or by the formation of novel domain combinations, thereby contributing to the evolution of proteins with new biological properties. Here, we assign proteins to groups with related domain compositions and functional properties, termed "domain clubs," which we use to compare multiple eukaryotic proteomes. This analysis shows that different domain types can take distinct evolutionary trajectories, which correlate with the conservation, gain, expansion, or decay of particular biological processes. Evolutionary jumps are associated with a domain that coordinately acquires a new intrinsic function and enters new domain clubs, thereby providing the modified domain with access to a new cellular microenvironment. We also coordinately analyzed the covalent and noncovalent interactions of different domain types to assess the molecular compartment occupied by each domain. This reveals that specific subsets of domains demarcate particular cellular processes, such as growth factor signaling, chromatin remodeling, apoptotic and inflammatory responses, or vesicular trafficking. We suggest that domains, and the proteins in which they reside, are selected during evolution through reciprocal interactions with protein domains in their local microenvironment. Based on this scheme, we propose a mechanism by which Tudor domains may have evolved to support different modes of epigenetic regulation and suggest a role for the germline group of mammalian Tudor domains in Piwi-regulated RNA biology.

Linear motif atlas for phosphorylation-dependent signaling.

Systematic and quantitative analysis of protein phosphorylation is revealing dynamic regulatory networks underlying cellular responses to environmental cues. However, matching these sites to the kinases that phosphorylate them and the phosphorylation-dependent binding domains that may subsequently bind to them remains a challenge. NetPhorest is an atlas of consensus sequence motifs that covers 179 kinases and 104 phosphorylation-dependent binding domains [Src homology 2 (SH2), phosphotyrosine binding (PTB), BRCA1 C-terminal (BRCT), WW, and 14-3-3]. The atlas reveals new aspects of signaling systems, including the observation that tyrosine kinases mutated in cancer have lower specificity than their non-oncogenic relatives. The resource is maintained by an automated pipeline, which uses phylogenetic trees to structure the currently available in vivo and in vitro data to derive probabilistic sequence models of linear motifs. The atlas is available as a community resource (http://netphorest.info).