Fabrizio Ferrè

ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins

Multidomain proteins predominate in eukaryotic proteomes. Individual functions assigned to different sequence segments combine to create a complex function for the whole protein. While on-line resources are available for revealing globular domains in sequences, there has hitherto been no comprehensive collection of small functional sites/motifs comparable to the globular domain resources, yet these are as important for the function of multidomain proteins. Short linear peptide motifs are used for cell compartment targeting, protein-protein interaction, regulation by phosphorylation, acetylation, glycosylation and a host of other post-translational modifications. ELM, the Eukaryotic Linear Motif server at http://elm.eu.org/, is a new bioinformatics resource for investigating candidate short non-globular functional motifs in eukaryotic proteins, aiming to fill the void in bioinformatics tools. Sequence comparisons with short motifs are difficult to evaluate because the usual significance assessments are inappropriate. Therefore the server is implemented with several logical filters to eliminate false positives. Current filters are for cell compartment, globular domain clash and taxonomic range. In favourable cases, the filters can reduce the number of retained matches by an order of magnitude or more.

DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification

DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem—to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. Available at: .

SURFACE: a database of protein surface regions for functional annotation

The SURFACE (SUrface Residues and Functions Annotated, Compared and Evaluated, URL http://cbm.bio.uniroma2.it/surface/) database is a repository of annotated and compared protein surface regions. SURFACE contains the results of a large-scale protein annotation and local structural comparison project. A non-redundant set of protein chains is used to build a database of protein surface patches, defined as putative surface functional sites. Each patch is annotated with sequence and structure-derived information about function or interaction abilities. A new procedure for structure comparison is used to perform an all-versus-all patches comparison. Selection of the results obtained with stringent parameters offers a similarity score that can be used to associate different patches and allows reliable annotation by similarity. Annotation exerted through the comparison of regions of protein surface allows the highlighting of similarities that cannot be recognized by other methods of sequence or structure comparison. A graphic representation of the surface patches, functional annotations and the structural superpositions is available through the web interface.

Protein surface similarities: a survey of methods to describe and compare protein surfaces.

Abstract. Many methods have been developed to analyse protein sequences and structures, although less work has been undertaken describing and comparing protein surfaces. Evolution can lead sequences to diverge or structures to change topology; nevertheless, surface determinants that are essential to protein function itself may be mantained. Moreover, different molecules could converge to similar functions by gaining specific surface determinants. In such cases, sequence or structure comparisons are likely to be inadequate in describing or identifying protein functions and evolutionary relationships among proteins. Surface analysis can identify function determinants that are independent of sequence or secondary structure and can therefore be a powerful tool to highlight cases of possible convergent or divergent evolution. This kind of approach can be useful for a better understanding of protein molecular and biochemical mechanisms of catalysis or interaction with a ligand, which are usually surface dependent. Protein surface comparison, when compared to sequence or structure comparison methods, is a hard computational challenge and evaluated methods allowing the comparison of protein surfaces are difficult to find. In this review, we will survey the current knowledge about protein surface similarity and the techniques to detect it.

Revealing protein–lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

Role of CTCF protein in regulating FMR1 locus transcription.

Fragile X syndrome (FXS), the leading cause of inherited intellectual disability, is caused by epigenetic silencing of the FMR1 gene, through expansion and methylation of a CGG triplet repeat (methylated full mutation). An antisense transcript (FMR1-AS1), starting from both promoter and intron 2 of the FMR1 gene, was demonstrated in transcriptionally active alleles, but not in silent FXS alleles. Moreover, a DNA methylation boundary, which is lost in FXS, was recently identified upstream of the FMR1 gene. Several nuclear proteins bind to this region, like the insulator protein CTCF. Here we demonstrate for the first time that rare unmethylated full mutation (UFM) alleles present the same boundary described in wild type (WT) alleles and that CTCF binds to this region, as well as to the FMR1 gene promoter, exon 1 and intron 2 binding sites. Contrariwise, DNA methylation prevents CTCF binding to FXS alleles. Drug-induced CpGs demethylation does not restore this binding. CTCF knock-down experiments clearly established that CTCF does not act as insulator at the active FMR1 locus, despite the presence of a CGG expansion. CTCF depletion induces heterochromatinic histone configuration of the FMR1 locus and results in reduction of FMR1 transcription, which however is not accompanied by spreading of DNA methylation towards the FMR1 promoter. CTCF depletion is also associated with FMR1-AS1 mRNA reduction. Antisense RNA, like sense transcript, is upregulated in UFM and absent in FXS cells and its splicing is correlated to that of the FMR1-mRNA. We conclude that CTCF has a complex role in regulating FMR1 expression, probably through the organization of chromatin loops between sense/antisense transcriptional regulatory regions, as suggested by bioinformatics analysis.

A novel approach to represent and compare RNA secondary structures

Structural information is crucial in ribonucleic acid (RNA) analysis and functional annotation; nevertheless, how to include such structural data is still a debated problem. Dot-bracket notation is the most common and simple representation for RNA secondary structures but its simplicity leads also to ambiguity requiring further processing steps to dissolve. Here we present BEAR (Brand nEw Alphabet for RNA), a new context-aware structural encoding represented by a string of characters. Each character in BEAR encodes for a specific secondary structure element (loop, stem, bulge and internal loop) with specific length. Furthermore, exploiting this informative and yet simple encoding in multiple alignments of related RNAs, we captured how much structural variation is tolerated in RNA families and convert it into transition rates among secondary structure elements. This allowed us to compute a substitution matrix for secondary structure elements called MBR (Matrix of BEAR-encoded RNA secondary structures), of which we tested the ability in aligning RNA secondary structures. We propose BEAR and the MBR as powerful resources for the RNA secondary structure analysis, comparison and classification, motif finding and phylogeny.

Computational methods for analysis and inference of kinase/inhibitor relationships

The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.

RNA-Sequencing for profiling goat milk transcriptome in colostrum and mature milk.

BackgroundIn this work we aimed at sequencing and assembling the goat milk transcriptome corresponding at colostrum and 120 days of lactation. To reconstruct transcripts we used both the genome as reference, and a de novo assembly approach. Additionally, we aimed at identifying the differentially expressed genes (DEGs) between the two lactation stages and at analyzing the expression of genes involved in oligosaccharides metabolism.ResultsA total of 44,635 different transcripts, organized in 33,757 tentative genes, were obtained using the goat genome as reference. A significant sequence similarity match was found for 40,353 transcripts (90%) against the NCBI NT and for 35,701 (80%) against the NR databases. 68% and 69% of the de novo assembled transcripts, in colostrum and 120 days of lactation samples respectively, have a significant match with the merged transcriptome obtained using Cufflinks/Cuffmerge. CSN2, PAEP, CSN1S2, CSN3, LALBA, TPT1, FTH1, M-SAA3, SPP1, GLYCAM1, EEF1A1, CTSD, FASN, RPS29, CSN1S1, KRT19 and CHEK1 were found between the top fifteen highly expressed genes. 418 loci were differentially expressed between lactation stages, among which 207 and 122 were significantly up- and down-regulated in colostrum, respectively. Functional annotation and pathway enrichment analysis showed that in goat colostrum somatic cells predominate biological processes involved in glycolysis, carbohydrate metabolism, defense response, cytokine activity, regulation of cell proliferation and cell death, vasculature development, while in mature milk, biological process associated with positive regulation of lymphocyte activation and anatomical structure morphogenesis are enriched. The analysis of 144 different oligosaccharide metabolism-related genes showed that most of these (64%) were more expressed in colostrum than in mature milk, with eight expressed at very high levels (SLCA3, GMSD, NME2, SLC2A1, B4GALT1, B3GNT2, NANS, HEXB).ConclusionsTo our knowledge, this is the first study comparing goat transcriptome of two lactation stages: colostrum and 120 days. Our findings suggest putative differences of expression between stages and can be envisioned as a base for further research in the topic. Moreover because a higher expression of genes involved in immune defense response, carbohydrate metabolism and related to oligosaccharide metabolism was identified in colostrum we here corroborate the potential of goat milk as a natural source of lactose-derived oligosaccharides and for the development of functional foods.

DBATE: database of alternative transcripts expression.

Abstract The use of high-throughput RNA sequencing technology (RNA-seq) allows whole transcriptome analysis, providing an unbiased and unabridged view of alternative transcript expression. Coupling splicing variant-specific expression with its functional inference is still an open and difficult issue for which we created the DataBase of Alternative Transcripts Expression (DBATE), a web-based repository storing expression values and functional annotation of alternative splicing variants. We processed 13 large RNA-seq panels from human healthy tissues and in disease conditions, reporting expression levels and functional annotations gathered and integrated from different sources for each splicing variant, using a variant-specific annotation transfer pipeline. The possibility to perform complex queries by cross-referencing different functional annotations permits the retrieval of desired subsets of splicing variant expression values that can be visualized in several ways, from simple to more informative. DBATE is intended as a novel tool to help appreciate how, and possibly why, the transcriptome expression is shaped. Database URL: http://bioinformatica.uniroma2.it/DBATE/.