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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.
Short linear motifs (SLiMs, LMs or MiniMotifs) are regulatory protein modules characterized by their compact interaction interfaces (the affinity and specificity determining residues are usually encoded between 3 and 11 contiguous amino acids (1)) and their enrichment in natively unstructured, or disordered, regions of proteins (2). As a result of limited intermolecular contacts with their interaction partners, SLiMs bind with relatively low affinity (in the low-micromolar range), an advantageous attribute for use as transient, conditional and tunable interactions necessary for many regulatory processes. Due to the limited number of mutations necessary for the genesis of a novel motif, SLiMs are amenable to convergent evolution, functioning as a driver of network evolution by adding novel interaction interfaces, and thereby new functionality, to proteins. This evolutionary plasticity facilitates the rapid proliferation within a proteome, and as a result, motif use is ubiquitous in higher eukaryotes.
SLiMs play an important role for many regulatory processes such as signal transduction, protein trafficking and post-translational modification (3,4). Their importance to the correct functionality of the cell is also reflected by the outcome of motif deregulation. For example, point mutations in SLiMs have been shown to lead severe pathologies such as ‘Noonan-like syndrome’ (5), ‘Liddle’s syndrome’ (6) or ‘Retinitis pigmentosa’ (7). Furthermore, mimicry of linear motifs by viruses to hijack their hosts’ existing cellular machinery plays an important role in many viral life cycles (8). However, despite their obvious importance to eukaryotic cell regulation, our understanding of SLiM biology is relatively limited, and it has been suggested that, to date, we have only discovered a small portion of the human motifs (9).
Several resources are devoted to the annotation and/or detection of SLiMs [Prosite (10), MiniMotifMiner (11) and Scansite (12)]. Here, we report on the 2012 status of the Eukaryotic Linear Motif database.
The ELM initiative (http://elm.eu.org) has focused on gathering, storing and providing information about short linear motifs since 2003. It was established as the first manually annotated collection of SLiM classes and as a tool for discovering linear motif instances in proteins (13). As it was mainly focused on the eukaryotic sequences, it was termed the Eukaryotic Linear Motif resource, usually shortened to ELM. The ELM resource consists of two applications: the ELM database of curated motif classes and instances, and the motif detection pipeline to detect putative SLiM instances in query sequences. In the ELM database, SLiMs are annotated as ‘ELM classes’, divided into four ‘types’: cleavage sites (CLV), ligand binding sites (LIG), sites of post-translational modification (MOD) and subcellular targeting sites (TRG) (Table 1). Currently, the ELM database contains 170 linear motif classes with more than 1800 motif instances linked to more than 1500 literature references (Table 1). Each class is described by a regular expression capturing the key specificity and affinity determining amino acid residues. A regular expression is a computer-readable term for sequence annotation and is used by the ELM motif detection pipeline to scan proteins for putative instances of annotated ELM classes. The search form for sequence input is shown in Figure 1, while the results page showing the putative and annotated instances is illustrated in Figure 2.
The ELM resource is powered by a PostgreSQL relational database for data storage and a PYTHON web framework for data retrieval/visualization. The main tables within the database contain information about ELM classes, ELM instances, sequences, references, taxonomy and links to other databases [the database structure is described in greater detail in (14)].
Since the last release (14), 24 new ELM classes have been added to the ELM database (Table 1) and several more have been updated. One of the newly annotated motif classes is the AGC kinase docking motif (LIG_AGCK_PIF), consisting of three distinct classes. It is present in the non-catalytic C-terminal tail of AGC kinases that constitute a family of serine/threonine kinases consisting of 60 members that regulate critical processes, including cell growth and survival. Deregulation of these enzymes is a causative factor in different diseases such as cancer and diabetes. The motif interacts with the PDK1 Interacting Fragment (PIF) pocket in the kinase domain of AGC kinases. It mediates intramolecular binding to the PIF pocket, serving as a cis-activating module together with other regulatory sequences in the C-tail. Interestingly, in some kinases the motif also acts as a PDK1 docking site that trans-activates PDK1, which itself lacks the regulatory C-tail, by interacting with the PDK1 PIF pocket. PDK1 in turn will phosphorylate and activate the docked kinase. Other novel classes (Table 2) include phosphodegrons, which are important mediators of phosphorylation-dependent protein destruction, and the LYPxL motif, which is involved in endosomal sorting of membrane proteins but is also implicated in retrovirus budding.
Annotated ELM instances serve as representative examples of the respective ELM class. They are also invaluable for the computational analysis and classification of motifs (15). Therefore, special emphasis has been put on the curation of more than 500 novel ELM instances (in 40 different classes) by scanning and annotating more than 400 articles. The number of protein databank (PDB) entries annotated have been increased to 195 (Table 1), meaning that for ~10% of all instances there is a 3D protein structure annotated, giving more detailed information about the biological context of the respective motif.
The ELM website at http://elm.eu.org can be used in two ways: first, as a front-end to explore the ELM database of curated ELM classes and instances, and second, to run the motif detection pipeline to detect putative SLiM instances in query sequences. Both interfaces have been improved with the most notable changes listed below.
To cope with the increasing amount of annotated classes as well as instances, a novel query interface was introduced to assist the user in finding information of interest. The ELM browser (Figure 4) now features a search interface for free text search. In addition, the search results can also be filtered and reordered using buttons (Figure 4, left side) and table headers, respectively, and be downloaded as tab-separated values (TSV).
A candidate page has been introduced to display novel ELM classes that have not yet been annotated in detail or are currently undergoing annotation. We invite researchers to send us their feedback and expert opinion on these classes and to contribute novel motif classes that will be added to the candidate page and ultimately be turned into full ELM classes (Figure 5). Minimum requirements are at least one literature reference as well as a short description. In addition, a draft regular expression or a 3D structure showing the relevant interaction would also be helpful. Currently, the number of possible ELM classes on this candidate list (awaiting further annotation) exceeds the number of completely annotated classes, indicating the great demand for further annotation.
The ELM motif detection pipeline scans protein sequences for matches to the regular expressions of annotated ELM classes (Figure 2). The query output combines these putative instances with information from the database (annotated ELM instances) as well as predictions from different algorithms/filters. The ELM resource employs a structural filter (18) to highlight and mask secondary structure elements, as well as SMART (19) to detect protein domains. Furthermore, an additional disorder prediction algorithm (IUPred) (20) has been included to predict ordered/disordered regions within the protein. IUPred uses a cutoff of 0.5 to classify a sequence region as either structured or disordered, with values above this threshold corresponding to disorder, highlighted in green background and lower values indicating structured regions, displayed in red background in the output graph. Disorder and domain information is combined by background coloring to highlight structured regions within the protein, which allows inspection of SLiMs that reside at domain boundaries and emphasizes motifs in disordered regions.
The conservation of linear motifs can help in assessing the functional relevance of putative instances, with functional instances showing higher overall sequence conservation than non-functional ones (21). Therefore, sequence conservation of the query protein is calculated using a tree-based conservation scoring method (22) and highlighted in the graphical output. Here, lighter shades of blue represent low conservation while dark blue shading corresponds to high-sequence conservation. The actual conservation score can be inspected by moving the mouse over the respective ELM instance (Figure 2).
The functionality of linear motifs can be modulated by modifications such as phosphorylation (23,24). To enable the user to investigate phosphorylation data in the context of putative linear motif instances, phosphorylation annotations from the Phospho.ELM resource (25) have been added to the graphical output (Figure 2, top row). The phosphorylated residues are highlighted in different colors (serine: green, threonine: blue, tyrosine: red); each phosphorylation site is linked to a page showing detailed information about the respective modification site from the manually curated data set of the Phospho.ELM resource.
The importance of the short linear motifs in virus–host interactions makes the ELM resource an important tool for the viral research community. For example, Cruz et al. (26) analyzed a protein phosphatase 1 (PP1) docking motif in ‘protein 7’ of transmissible gastroenteritis virus using the ELM class LIG_PP1. This conserved sequence motif mediates binding to the PP1 catalytic subunit, a key regulator of the cellular antiviral defense mechanisms, and is also found in other viral proteomes, suggesting that it might be a recurring strategy to counteract the hosts’ defense against RNA viruses by dephosphorylating eukaryotic translation initiation factor 2α and ultimately ribonuclease L.
To reflect our increasing awareness of viral motifs (8), special focus has been attributed to the annotation of viral instances in the ELM database: in the latest release, more than 200 novel ELM instances found in 84 different viral taxons have been added. The notion of viruses abusing existing SLiMs in their hosts is demonstrated by viral instances being annotated alongside instances in their hosts’ proteins. For example, the ELM class LIG_PDZ_Class_1 contains 12 instances in human proteins but has recently been expanded with 5 instances from 5 different human pathogenic virus proteins.
The importance of SLiMs is further corroborated by the occurrence of pathologies that are caused by mutations that either mutate existing linear motifs or create novel linear motifs (of undesired function) (27). Examples include ‘Usher's syndrome’ (28), ‘Liddle's Syndrome’ (6) or ‘Golabi-Ito-Hall Syndrome’ (29). The developmental disorder ‘Noonan Syndrome’ can be caused by mutations in Raf-1 that abrogate the interaction with 14-3-3 proteins mediated by corresponding SLiMs and thereby deregulate the Raf-1 kinase activity (30) (the Raf-1 protein sequence features two LIG_14-3-3_1 binding sites that are annotated at 256-261 and 618-623 in the ELM resource). A related disease, ‘Noonan-like Syndrome’, is caused by an S to G mutation at position 2 of the SHOC2 protein, creating a novel myristoylation site (annotated as ELM class MOD_NMyristoyl). This irreversible modification results in aberrant targeting of SHOC2 to the plasma membrane and impaired translocation to the nucleus upon growth factor stimulation (5). More information about the implication of short linear motifs on diseases is collected at http://elm.eu.org/infos/diseases.html.
By providing a high-quality, manually curated data set of linear motif classes with experimentally validated SLiM instances, the ELM database has proven to be invaluable to the community: small-scale (single protein) analyzes benefit from the detailed annotation of each ELM class in attributing novel features to proteins of interest. By using in vitro and in vivo studies, von Nandelstadh et al. (31) could validate a PDZ class III motif, detected by ELM at the carboxy terminus of myotilin and the FATZ (calsarcin/myozenin) families. This evolutionarily conserved carboxy-terminal motif mediates binding to PDZ domains of ZASP/Cypher and other Enigma family members (ALP, CLP-36 and RIL) and disruption of these interactions results in myofibrillar myopathies (32). Additionally, ELM annotations can contribute to high-throughput screenings (33) as well as development of novel algorithms (34–36), methods (37) and databases (38). Furthermore, the highly curated data of the ELM resource are used as a benchmarking data set to evaluate the accuracy of prediction algorithms (21,39,40).
For any such analysis, the user should be aware that many matches to ELM regular expressions are false positives. Before conducting experiments based on ELM results, it is strongly advisable to check if a motif match is conserved, exposed in a cell compartment in which the motif is known to be functional. The ELM resource applies several filters to provide the user with such information that should ideally also be supported by the experimental evidence.
The importance of SLiMs is highlighted by the growing number of instances with relevance to diseases or viruses. Yet, despite their importance and abundance, our understanding of linear motifs is still limited. This is mainly owing to the fact that they are still quite difficult to predict computationally and to investigate experimentally (3,41,42). By better understanding the biology of linear motifs, we hope to increase our insight into diseases and viruses (and vice versa). The ELM resource tries to aid the researcher in the search for putative SLiM instances by providing a feature-rich toolset for sequence analysis. Consequently, with the aforementioned additions and changes, we hope that the ELM resource continues to be a valuable asset to the community.
EMBL international PhD program (to R.J.W.); EMBL Interdisciplinary PostDoc fellowship (EIPOD to N.E.D.); NGFN framework by the Federal Government Department of Education and Science [FKZ01GS0862 (DiGtoP) to M.S. and M.H.]; European Community's Seventh Framework Programme FP7/2009 (SysCilia) (241955 to G.T.) and (SyBoSS) (242129 to K.V.R.); Polish Ministry of Science and Higher Education within Iuventus Plus project (IP2010-0483-70 to M.D.); Biotechnology and Biological Sciences Research Council (BB/F010486/1 to A.C.); Région Alsace and Collège Doctoral Européen (to K.L.); Science Foundation Ireland (08/IN.1/B1864 to G.G.); BBSRC New Investigator Award (BB/I006230/1 to R.J.E.); German Research Foundation (SFB796 Project A2 to H.M.); grants from the Swiss National Science Foundation (to M.O.S.). Funding for open access charge: EMBL.
Conflict of interest statement. None declared.
The authors would like to thank the users of the ELM resource as well as all colleagues, contributors and annotators of the ELM resource.