Niall J. Haslam

ELM—the database of eukaryotic linear motifs

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

Understanding eukaryotic linear motifs and their role in cell signaling and regulation.

It is now clear that a detailed picture of cell regulation requires a comprehensive understanding of the abundant short protein motifs through which signaling is channeled. The current body of knowledge has slowly accumulated through piecemeal experimental investigation of individual motifs in signaling. Computational methods contributed little to this process. A new generation of bioinformatics tools will aid the future investigation of motifs in regulatory proteins, and the disordered polypeptide regions in which they frequently reside. Allied to high throughput methods such as phosphoproteomics, signaling networks are becoming amenable to experimental deconstruction. In this review, we summarise the current state of linear motif biology, which uses low affinity interactions to create cooperative, combinatorial and highly dynamic regulatory protein complexes. The discrete deterministic properties implicit to these assemblies suggest that models for cell regulatory networks in systems biology should neither be overly dependent on stochastic nor on smooth deterministic approximations.

ELM: the status of the 2010 eukaryotic linear motif resource.

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a ‘Bar Code’ format, which also displays known instances from homologous proteins through a novel ‘Instance Mapper’ protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.

An analysis of the feasibility of short read sequencing

Several methods for ultra high-throughput DNA sequencing are currently under investigation. Many of these methods yield very short blocks of sequence information (reads). Here we report on an analysis showing the level of genome sequencing possible as a function of read length. It is shown that re-sequencing and de novo sequencing of the majority of a bacterial genome is possible with read lengths of 20–30 nt, and that reads of 50 nt can provide reconstructed contigs (a contiguous fragment of sequence data) of 1000 nt and greater that cover 80% of human chromosome 1.

Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides. We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4–20 amino acids) and one focused on long peptides ( amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure. We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive.

SLiMSearch 2.0: biological context for short linear motifs in proteins

Short, linear motifs (SLiMs) play a critical role in many biological processes. The SLiMSearch 2.0 (Short, Linear Motif Search) web server allows researchers to identify occurrences of a user-defined SLiM in a proteome, using conservation and protein disorder context statistics to rank occurrences. User-friendly output and visualizations of motif context allow the user to quickly gain insight into the validity of a putatively functional motif occurrence. For each motif occurrence, overlapping UniProt features and annotated SLiMs are displayed. Visualization also includes annotated multiple sequence alignments surrounding each occurrence, showing conservation and protein disorder statistics in addition to known and predicted SLiMs, protein domains and known post-translational modifications. In addition, enrichment of Gene Ontology terms and protein interaction partners are provided as indicators of possible motif function. All web server results are available for download. Users can search motifs against the human proteome or a subset thereof defined by Uniprot accession numbers or GO term. The SLiMSearch server is available at: http://bioware.ucd.ie/slimsearch2.html.

Prediction of Short Linear Protein Binding Regions

Short linear motifs in proteins (typically 3-12 residues in length) play key roles in protein-protein interactions by frequently binding specifically to peptide binding domains within interacting proteins. Their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here we present SLiMPred (short linear motif predictor), the first general de novo method designed to computationally predict such regions in protein primary sequences independent of experimentally defined homologs and interactors. The method applies machine learning techniques to predict new motifs based on annotated instances from the Eukaryotic Linear Motif database, as well as structural, biophysical, and biochemical features derived from the protein primary sequence. We have integrated these data sources and benchmarked the predictive accuracy of the method, and found that it performs equivalently to a predictor of protein binding regions in disordered regions, in addition to having predictive power for other classes of motif sites such as polyproline II helix motifs and short linear motifs lying in ordered regions. It will be useful in predicting peptides involved in potential protein associations and will aid in the functional characterization of proteins, especially of proteins lacking experimental information on structures and interactions. We conclude that, despite the diversity of motif sequences and structures, SLiMPred is a valuable tool for prioritizing potential interaction motifs in proteins.

SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs

Short, linear motifs (SLiMs) play a critical role in many biological processes, particularly in protein–protein interactions. The Short, Linear Motif Finder (SLiMFinder) web server is a de novo motif discovery tool that identifies statistically over-represented motifs in a set of protein sequences, accounting for the evolutionary relationships between them. Motifs are returned with an intuitive P-value that greatly reduces the problem of false positives and is accessible to biologists of all disciplines. Input can be uploaded by the user or extracted directly from UniProt. Numerous masking options give the user great control over the contextual information to be included in the analyses. The SLiMFinder server combines these with user-friendly output and visualizations of motif context to allow the user to quickly gain insight into the validity of a putatively functional motif. These visualizations include alignments of motif occurrences, alignments of motifs and their homologues and a visual schematic of the top-ranked motifs. Returned motifs can also be compared with known SLiMs from the literature using CompariMotif. All results are available for download. The SLiMFinder server is available at: http://bioware.ucd.ie/slimfinder.html.

Minimum information about a protein affinity reagent (MIAPAR)

This is a proposal developed within the community as an important first step in formalizing standards in reporting the production and properties of protein binding reagents, such as antibodies, developed and sold for the identification and detection of specific proteins present in biological samples. It defines a checklist of required information, intended for use by producers of affinity reagents, quality-control laboratories, users and databases. We envision that both commercial and freely available affinity reagents, as well as published studies using these reagents, could include a MIAPAR-compliant document describing the products properties with every available binding partner.

A Community Standard Format for the Representation of Protein Affinity Reagents

Protein affinity reagents (PARs), most commonly antibodies, are essential reagents for protein characterization in basic research, biotechnology, and diagnostics as well as the fastest growing class of therapeutics. Large numbers of PARs are available commercially; however, their quality is often uncertain. In addition, currently available PARs cover only a fraction of the human proteome, and their cost is prohibitive for proteome scale applications. This situation has triggered several initiatives involving large scale generation and validation of antibodies, for example the Swedish Human Protein Atlas and the German Antibody Factory. Antibodies targeting specific subproteomes are being pursued by members of Human Proteome Organisation (plasma and liver proteome projects) and the United States National Cancer Institute (cancer-associated antigens). ProteomeBinders, a European consortium, aims to set up a resource of consistently quality-controlled protein-binding reagents for the whole human proteome. An ultimate PAR database resource would allow consumers to visit one on-line warehouse and find all available affinity reagents from different providers together with documentation that facilitates easy comparison of their cost and quality. However, in contrast to, for example, nucleotide databases among which data are synchronized between the major data providers, current PAR producers, quality control centers, and commercial companies all use incompatible formats, hindering data exchange. Here we propose Proteomics Standards Initiative (PSI)-PAR as a global community standard format for the representation and exchange of protein affinity reagent data. The PSI-PAR format is maintained by the Human Proteome Organisation PSI and was developed within the context of ProteomeBinders by building on a mature proteomics standard format, PSI-molecular interaction, which is a widely accepted and established community standard for molecular interaction data. Further information and documentation are available on the PSI-PAR web site.