Reuse of structural domain–domain interactions in protein networks

<p>Abstract</p> <p>Background</p> <p>Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as <it>i</it>Pfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the <it>i</it>Pfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.</p> <p>Results</p> <p>We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between <it>E. coli</it>, <it>S. cerevisiae </it>and <it>H. sapiens</it>. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor.</p> <p>Conclusion</p> <p>Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures.</p

D2P2: database of disordered protein predictions

We present the Database of Disordered Protein Prediction (D2P2), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D2P2 will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life. © The Author(s) 2012. Published by Oxford University Press

Investigating the effect of target of rapamycin kinase inhibition on the Chlamydomonas reinhardtii phosphoproteome: from known homologs to new targets

Recuperado de: https://www.biorxiv.org/content/10.1101/310102v1Target of rapamycin (TOR) kinase is a conserved regulator of cell growth whose activity is modulated in response to nutrients, energy and stress. Key proteins involved in the pathway are conserved in the model photosynthetic microalga Chlamydomonas reinhardtii, but the substrates of TOR kinase and downstream signaling network have not been elucidated. Our study provides a new resource for investigating the phosphorylation networks governed by the TOR kinase pathway in Chlamydomonas. We used quantitative phosphoproteomics to investigate the effects of inhibiting Chlamydomonas TOR kinase on dynamic protein phosphorylation. Wild-type and AZD-insensitive Chlamydomonas strains were treated with TOR-specific chemical inhibitors (rapamycin, AZD8055 and Torin1), after which differentially affected phosphosites were identified. Our quantitative phosphoproteomic dataset comprised 2547 unique phosphosites from 1432 different proteins. Inhibition of TOR kinase caused significant quantitative changes in phosphorylation at 258 phosphosites, from 219 unique phosphopeptides. Our results include Chlamydomonas homologs of TOR signaling-related proteins, including a site on RPS6 with a decrease in phosphorylation. Additionally, phosphosites on proteins involved in translation and carotenoid biosynthesis were identified. Follow-up experiments guided by these phosphoproteomic findings in lycopene beta/epsilon cyclase showed that carotenoid levels are affected by TORC1 inhibition and carotenoid production is under TOR control in algae.National Science Foundation CAREER MCB-155252

Identification of the catalytic motif of the microbial ribosome inactivating cytotoxin colicin E3

Colicin E3 is a cytotoxic ribonuclease that specifically cleaves 16S rRNA at the ribosomal A-site to abolish protein synthesis in sensitive Escherichia coli cells. We have performed extensive mutagenesis of the 96-residue colicin E3 cytotoxic domain (E3 rRNase), assayed mutant colicins for in vivo cytotoxicity, and tested the corresponding E3 rRNase domains for their ability to inactivate ribosome function in vitro. From 21 alanine mutants, we identified five positions where mutation resulted in a colicin with no measurable cytotoxicity (Y52, D55, H58, E62, and Y64) and four positions (R40, R42, E60, and R90) where mutation caused a significant reduction in cytotoxicity. Mutations that were found to have large in vivo and in vitro effects were tested for structural integrity through circular dichroism and fluorescence spectroscopy using purified rRNase domains. Our data indicate that H58 and E62 likely act as the acid–base pair during catalysis with other residues likely involved in transition state stabilization. Both the Y52 and Y64 mutants were found to be highly destabilized and this is the likely origin of the loss of their cytotoxicity. The identification of important active site residues and sequence alignments of known rRNase homologs has allowed us to identify other proteins containing the putative rRNase active site motif. Proteins that contained this active site motif included three hemagglutinin-type adhesins and we speculate that these have evolved to deliver a cytotoxic rRNase into eukaryotic cells during pathogenesis

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Bradyrhizobium diazoefficiens USDA 110–glycine max interactome provides candidate proteins associated with symbiosis

Although the legume−rhizobium symbiosis is a most-important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog- and domain-based approaches, we constructed an interspecies protein interactome containing 5115 protein−protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was further validated by the expression pattern analysis in nodules, gene ontology term semantic similarity, co-expression analysis, and luciferase complementation image assay. In the G. max−B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top 10 highly interacting proteins (hubs) for each species. Kyoto Encyclopedia of Genes and Genomes pathway analysis for each hub showed that a pair of 14-3-3 proteins (SGF14g and SGF14k) and 5 heat shock proteins in G. max are possibly involved in symbiosis, and 10 hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related soluble N-ethylmaleimide sensitive factor attachment protein receptor proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome provide a valuable basis for understanding the molecular mechanism of nodulation in soybean