Damiano Piovesan

InterPro in 2017—beyond protein family and domain annotations

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPros predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.

The RING 2.0 web server for high quality residue interaction networks

Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico–chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain–domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π–π stacking and π–cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical re-parameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring.

RepeatsDB: a database of tandem repeat protein structures.

RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services.

FFPred 2.0: improved homology-independent prediction of gene ontology terms for eukaryotic protein sequences.

To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information – either experimentally validated or computationally inferred by similarity – remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy.

INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity

Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga.

Simultaneous quantification of protein order and disorder

Nuclear magnetic resonance spectroscopy is transforming our views of proteins by revealing how their structures and dynamics are closely intertwined to underlie their functions and interactions. Compelling representations of proteins as statistical ensembles are uncovering the presence and biological relevance of conformationally heterogeneous states, thus gradually making it possible to go beyond the dichotomy between order and disorder through more quantitative descriptions that span the continuum between them.

BAR-PLUS: the Bologna Annotation Resource Plus for functional and structural annotation of protein sequences.

We introduce BAR-PLUS (BAR+), a web server for functional and structural annotation of protein sequences. BAR+ is based on a large-scale genome cross comparison and a non-hierarchical clustering procedure characterized by a metric that ensures a reliable transfer of features within clusters. In this version, the method takes advantage of a large-scale pairwise sequence comparison of 13 495 736 protein chains also including 988 complete proteomes. Available sequence annotation is derived from UniProtKB, GO, Pfam and PDB. When PDB templates are present within a cluster (with or without their SCOP classification), profile Hidden Markov Models (HMMs) are computed on the basis of sequence to structure alignment and are cluster-associated (Cluster-HMM). Therefrom, a library of 10 858 HMMs is made available for aligning even distantly related sequences for structural modelling. The server also provides pairwise query sequence–structural target alignments computed from the correspondent Cluster-HMM. BAR+ in its present version allows three main categories of annotation: PDB [with or without SCOP (*)] and GO and/or Pfam; PDB (*) without GO and/or Pfam; GO and/or Pfam without PDB (*) and no annotation. Each category can further comprise clusters where GO and Pfam functional annotations are or are not statistically significant. BAR+ is available at http://bar.biocomp.unibo.it/bar2.0.

MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins

Motivation Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains. Results Here, we have focused on providing a single consensus-based prediction, MobiDB-lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases. Availability and Implementation MobiDB-lite is available as part of the MobiDB database from URL: http://mobidb.bio.unipd.it/. An executable can be downloaded from URL: http://protein.bio.unipd.it/mobidblite/. Contact silvio.tosatto@unipd.it. Supplementary information Supplementary data are available at Bioinformatics online.Motivation: Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains. Results: Here, we have focused on providing a single consensus‐based prediction, MobiDB‐lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB‐lite can be useful in large‐scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases. Availability and Implementation: MobiDB‐lite is available as part of the MobiDB database from URL: http://mobidb.bio.unipd.it/. An executable can be downloaded from URL: http://protein.bio.unipd.it/mobidblite/. Contact: silvio.tosatto@unipd.it Supplementary information: Supplementary data are available at Bioinformatics online.

How to inherit statistically validated annotation within BAR+ protein clusters

BackgroundIn the genomic era a key issue is protein annotation, namely how to endow protein sequences, upon translation from the corresponding genes, with structural and functional features. Routinely this operation is electronically done by deriving and integrating information from previous knowledge. The reference database for protein sequences is UniProtKB divided into two sections, UniProtKB/TrEMBL which is automatically annotated and not reviewed and UniProtKB/Swiss-Prot which is manually annotated and reviewed. The annotation process is essentially based on sequence similarity search. The question therefore arises as to which extent annotation based on transfer by inheritance is valuable and specifically if it is possible to statistically validate inherited features when little homology exists among the target sequence and its template(s).ResultsIn this paper we address the problem of annotating protein sequences in a statistically validated manner considering as a reference annotation resource UniProtKB. The test case is the set of 48,298 proteins recently released by the Critical Assessment of Function Annotations (CAFA) organization. We show that we can transfer after validation, Gene Ontology (GO) terms of the three main categories and Pfam domains to about 68% and 72% of the sequences, respectively. This is possible after alignment of the CAFA sequences towards BAR+, our annotation resource that allows discriminating among statistically validated and not statistically validated annotation. By comparing with a direct UniProtKB annotation, we find that besides validating annotation of some 78% of the CAFA set, we assign new and statistically validated annotation to 14.8% of the sequences and find new structural templates for about 25% of the chains, half of which share less than 30% sequence identity to the corresponding template/s.ConclusionInheritance of annotation by transfer generally requires a careful selection of the identity value among the target and the template in order to transfer structural and/or functional features. Here we prove that even distantly remote homologs can be safely endowed with structural templates and GO and/or Pfam terms provided that annotation is done within clusters collecting cluster-related protein sequences and where a statistical validation of the shared structural and functional features is possible.

RepeatsDB 2.0: improved annotation, classification, search and visualization of repeat protein structures

RepeatsDB 2.0 (URL: http://repeatsdb.bio.unipd.it/) is an update of the database of annotated tandem repeat protein structures. Repeat proteins are a widespread class of non-globular proteins carrying heterogeneous functions involved in several diseases. Here we provide a new version of RepeatsDB with an improved classification schema including high quality annotations for ∼5400 protein structures. RepeatsDB 2.0 features information on start and end positions for the repeat regions and units for all entries. The extensive growth of repeat unit characterization was possible by applying the novel ReUPred annotation method over the entire Protein Data Bank, with data quality is guaranteed by an extensive manual validation for >60% of the entries. The updated web interface includes a new search engine for complex queries and a fully re-designed entry page for a better overview of structural data. It is now possible to compare unit positions, together with secondary structure, fold information and Pfam domains. Moreover, a new classification level has been introduced on top of the existing scheme as an independent layer for sequence similarity relationships at 40%, 60% and 90% identity.