Davide Cirillo

catRAPID omics: a web server for large-scale prediction of protein-RNA interactions

Summary: Here we introduce catRAPID omics, a server for large-scale calculations of protein–RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition. Results: We developed a web server to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis (custom libraries can be also generated). The catRAPID omics was benchmarked on the recently published RNA interactomes of Serine/arginine-rich splicing factor 1 (SRSF1), Histone-lysine N-methyltransferase EZH2 (EZH2), TAR DNA-binding protein 43 (TDP43) and RNA-binding protein FUS (FUS) as well as on the protein interactomes of U1/U2 small nucleolar RNAs, X inactive specific transcript (Xist) repeat A region (RepA) and Crumbs homolog 3 (CRB3) 3′-untranslated region RNAs. Our predictions are highly significant (P < 0.05) and will help the experimentalist to identify candidates for further validation. Availability: catRAPID omics can be freely accessed on the Web at http://s.tartaglialab.com/catrapid/omics. Documentation, tutorial and FAQs are available at http://s.tartaglialab.com/page/catrapid_group. Contact: gian.tartaglia@crg.eu

Neurodegenerative diseases: Quantitative predictions of protein–RNA interactions

Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimers, and Parkinsons diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction.

Principles of self-organization in biological pathways: a hypothesis on the autogenous association of alpha-synuclein

Previous evidence indicates that a number of proteins are able to interact with cognate mRNAs. These autogenous associations represent important regulatory mechanisms that control gene expression at the translational level. Using the catRAPID approach to predict the propensity of proteins to bind to RNA, we investigated the occurrence of autogenous associations in the human proteome. Our algorithm correctly identified binding sites in well-known cases such as thymidylate synthase, tumor suppressor P53, synaptotagmin-1, serine/ariginine-rich splicing factor 2, heat shock 70 kDa, ribonucleic particle-specific U1A and ribosomal protein S13. In addition, we found that several other proteins are able to bind to their own mRNAs. A large-scale analysis of biological pathways revealed that aggregation-prone and structurally disordered proteins have the highest propensity to interact with cognate RNAs. These findings are substantiated by experimental evidence on amyloidogenic proteins such as TAR DNA-binding protein 43 and fragile X mental retardation protein. Among the amyloidogenic proteins, we predicted that Parkinson’s disease-related α-synuclein is highly prone to interact with cognate transcripts, which suggests the existence of RNA-dependent factors in its function and dysfunction. Indeed, as aggregation is intrinsically concentration dependent, it is possible that autogenous interactions play a crucial role in controlling protein homeostasis.

Constitutive patterns of gene expression regulated by RNA-binding proteins

BackgroundRNA-binding proteins regulate a number of cellular processes, including synthesis, folding, translocation, assembly and clearance of RNAs. Recent studies have reported that an unexpectedly large number of proteins are able to interact with RNA, but the partners of many RNA-binding proteins are still uncharacterized.ResultsWe combined prediction of ribonucleoprotein interactions, based on catRAPID calculations, with analysis of protein and RNA expression profiles from human tissues. We found strong interaction propensities for both positively and negatively correlated expression patterns. Our integration of in silico and ex vivo data unraveled two major types of protein–RNA interactions, with positively correlated patterns related to cell cycle control and negatively correlated patterns related to survival, growth and differentiation. To facilitate the investigation of protein–RNA interactions and expression networks, we developed the catRAPID express web server.ConclusionsOur analysis sheds light on the role of RNA-binding proteins in regulating proliferation and differentiation processes, and we provide a data exploration tool to aid future experimental studies.

X-inactivation: quantitative predictions of protein interactions in the Xist network

The transcriptional silencing of one of the female X-chromosomes is a finely regulated process that requires accumulation in cis of the long non-coding RNA X-inactive-specific transcript (Xist) followed by a series of epigenetic modifications. Little is known about the molecular machinery regulating initiation and maintenance of chromosomal silencing. Here, we introduce a new version of our algorithm catRAPID to investigate Xist associations with a number of proteins involved in epigenetic regulation, nuclear scaffolding, transcription and splicing processes. Our method correctly identifies binding regions and affinities of protein interactions, providing a powerful theoretical framework for the study of X-chromosome inactivation and other events mediated by ribonucleoprotein associations.

Predictions of protein–RNA interactions

Ribonucleoprotein interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA–protein interactions provide information about the complexity of interaction networks, but require time and considerable efforts. Thus, there is need for reliable computational methods for predicting ribonucleoprotein interactions. In this review, we discuss a number of approaches that have been developed to predict the ability of proteins and RNA molecules to associate.

Characterization of Novel Paternal ncRNAs at the Plagl1 Locus, Including Hymai, Predicted to Interact with Regulators of Active Chromatin

Genomic imprinting is a complex epigenetic mechanism of transcriptional control that utilizes DNA methylation and histone modifications to bring about parent-of-origin specific monoallelic expression in mammals. Genes subject to imprinting are often organised in clusters associated with large non-coding RNAs (ncRNAs), some of which have cis-regulatory functions. Here we have undertaken a detailed allelic expression analysis of an imprinted domain on mouse proximal chromosome 10 comprising the paternally expressed Plagl1 gene. We identified three novel Plagl1 transcripts, only one of which contains protein-coding exons. In addition, we characterised two unspliced ncRNAs, Hymai, the mouse orthologue of HYMAI, and Plagl1it (Plagl1 intronic transcript), a transcript located in intron 5 of Plagl1. Imprinted expression of these novel ncRNAs requires DNMT3L-mediated maternal DNA methylation, which is also indispensable for establishing the correct chromatin profile at the Plagl1 DMR. Significantly, the two ncRNAs are retained in the nucleus, consistent with a potential regulatory function at the imprinted domain. Analysis with catRAPID, a protein-ncRNA association prediction algorithm, suggests that Hymai and Plagl1it RNAs both have potentially high affinity for Trithorax chromatin regulators. The two ncRNAs could therefore help to protect the paternal allele from DNA methylation by attracting Trithorax proteins that mediate H3 lysine-4 methylation. Submitted GenBank nucleotides sequences: Plagl1it: JN595789 Hymai: JN595790

Quantitative predictions of protein interactions with long noncoding RNAs

Long noncoding RNAs (lncRNAs, which comprise 68% of the human transcriptome with average length of 1,000 nt) interact with various RNA-binding proteins (RBPs) to mediate cellular functions1. Identification of lncRNA interactions through experimental methods is challenging and can be complemented by the use of computational models to predict protein partners. Yet it is currently impractical to calculate RBP–lncRNA interactions for large transcripts2.We introduce Global Score as a means to predict protein interactions with large RNAs (http://service.tartaglialab.com/new_submission/globalscore).

SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences

BackgroundThe large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites.ResultsHere, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature.ConclusionsSeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli

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 Supplementary information: Supplementary data are available at Bioinformatics online.