Suchergebnisse

BACKGROUND: Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. RESULTS: We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. CONCLUSIONS: In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal . Each of the pages contains links to the corresponding documentation and tutorial. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P). We also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208). PK and RDP are recipients of "La Caixa" and "Severo Ochoa" studentships, respectively

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis. RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity. AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html. CONTACT: gian.tartaglia@crg.es/nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013), through the European Research Council,under grant agreement RIBOMYLOME 309545, and from the Spanish Ministry of Economy and Competitiveness (SAF2011-26211). We also acknowledge support from the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208)

From transcription, to transport, storage, and translation, RNA depends on association with different RNA-binding proteins (RBPs). Methods based on next-generation sequencing and protein mass-spectrometry have started to unveil genome-wide interactions of RBPs but many aspects still remain out of sight. How many of the binding sites identified in high-throughput screenings are functional? A number of computational methods have been developed to analyze experimental data and to obtain insights into the specificity of protein–RNA interactions. How can theoretical models be exploited to identify RBPs? In addition to oligomeric complexes, protein and RNA molecules can associate into granular assemblies whose physical properties are still poorly understood. What protein features promote granule formation and what effects do these assemblies have on cell function? Here, we describe the newest in silico, in vitro, and in vivo advances in the field of protein–RNA interactions. We also present the challenges that experimental and computational approaches will have to face in future studies. WIREs RNA 2016, 7:793–810. doi:10.1002/wrna.1378 ; The research leading to this work has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P). We also acknowledge support from AGAUR (2014 SGR 00685) and the Spanish Ministry of Economy and Competitiveness 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208).