MOTIVATION: Phosphoproteomic experiments are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies. RESULTS: We used curated phosphoproteomic experiments and a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence ( in vivo , in vitro or in silico ) supporting these influence the predictions. CONCLUSIONS: Most models performed well with the Z-test and the GSEA performing best as determined by the area under the ROC curve (Mean AUC = 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions. AVAILABILITY AND IMPLEMENTATION: The KSEA implementation is available in https://github.com/ evocellnet/ksea. Additional data is available in http://phosfate.com. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

The African clawed frog Xenopus laevis is an important model organism for studies in developmental and cell biology, including cell-signaling. However, our knowledge of X. laevis protein post-translational modifications remains scarce. Here, we used a mass spectrometry-based approach to survey the phosphoproteome of this species, compiling a list of 2636 phosphosites. We used structural information and phosphoproteomic data for 13 other species in order to predict functionally important phospho-regulatory events. We found that the degree of conservation of phosphosites across species is predictive of sites with known molecular function. In addition, we predicted kinase-protein interactions for a set of cell-cycle kinases across all species. The degree of conservation of kinase-protein interactions was found to be predictive of functionally relevant regulatory interactions. Finally, using comparative protein structure models, we find that phosphosites within structured domains tend to be located at positions with high conformational flexibility. Our analysis suggests that a small class of phosphosites occurs in positions that have the potential to regulate protein conformation.

MOTIVATION: Most of the structures and functions of proteome globular domains are yet unknown. We can use high-resolution structures from different modular domains in combination with automatic protein design algorithms to predict genome-wide potential interactions of a protein. ADAN database and related web tools are online resources for the predictive analysis of ligand-domain complexes. ADAN database is a collection of different modular protein domains (SH2, SH3, PDZ, WW, etc.). It contains 3505 entries with extensive structural and functional information available, manually integrated, curated and annotated with cross-references to other databases, biochemical and thermodynamical data, simplified coordinate files, sequence files and alignments. Prediadan, a subset of ADAN database, offers position-specific scoring matrices for protein-protein interactions, calculated by FoldX, and predictions of optimum ligands and putative binding partners. Users can also scan a query sequence against selected matrices, or improve a ligand-domain interaction. AVAILABILITY: ADAN is accessible at http://adan-embl.ibmc.umh.es/ or http://adan.crg.es/.

Microblogging platforms and other tools for videos, podcasts, and virtual environments provide an untapped potential for science conferences. Our experiment using FriendFeed to cover ISMB 2008 was educational and surprisingly successful. We found that it enhanced our note-taking skills, allowed us to compile notes from parallel sessions, attracted wider interest from non-attendees, and, in addition to the “live” aspect, generated a permanent archive of the meeting. ISMB/ECCB 2009 will be held in Stockholm. We look forward to the new developments in Web usage by scientists that are sure to emerge between now and then. We also anticipate new and exciting ways to report from Stockholm as it happens; perhaps the ISMB/ECCB 2009 Web site will look something like this: http://www.bork.embl.de/̃jensen/ismb2008/keynotes.php.html?

Current experiments likely cover only a fraction of all protein-protein interactions. Here, we developed a method to predict SH2-mediated protein-protein interactions using the structure of SH2-phosphopeptide complexes and the FoldX algorithm. We show that our approach performs similarly to experimentally derived consensus sequences and substitution matrices at predicting known in vitro and in vivo targets of SH2 domains. We use our method to provide a set of high-confidence interactions for human SH2 domains with known structure filtered on secondary structure and phosphorylation state. We validated the predictions using literature-derived SH2 interactions and a probabilistic score obtained from a naive Bayes integration of information on coexpression, conservation of the interaction in other species, shared interaction partners, and functions. We show how our predictions lead to a new hypothesis for the role of SH2 domains in signaling.

Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5) interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3) interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

The set of four equations in the Materials and Methods section had three extra minus signs, which were incorrect. The following are the correct equations.

Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus), or a long time ago (Neurospora crassa and Schizosaccharomyces pombe), contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.