A structure filter for the Eukaryotic Linear Motif Resource

<p>Abstract</p> <p>Background</p> <p>Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality.</p> <p>Results</p> <p>Current methods assessing motif accessibility do not consider much of the information available, either predicting accessibility from primary sequence or regarding any motif occurring in a globular region as low confidence. We present a method considering accessibility and secondary structural context derived from experimentally solved protein structures to rectify this situation. Putatively functional motif occurrences are mapped onto a representative domain, given that a high quality reference SCOP domain structure is available for the protein itself or a close relative. Candidate motifs can then be scored for solvent-accessibility and secondary structure context. The scores are calibrated on a benchmark set of experimentally verified motif instances compared with a set of random matches. A combined score yields 3-fold enrichment for functional motifs assigned to high confidence classifications and 2.5-fold enrichment for random motifs assigned to low confidence classifications. The structure filter is implemented as a pipeline with both a graphical interface via the ELM resource <url>http://elm.eu.org/</url> and through a Web Service protocol.</p> <p>Conclusion</p> <p>New occurrences of known linear motifs require experimental validation as the bioinformatics tools currently have limited reliability. The ELM structure filter will aid users assessing candidate motifs presenting in globular structural regions. Most importantly, it will help users to decide whether to expend their valuable time and resources on experimental testing of interesting motif candidates.</p

Achieving High Accuracy Prediction of Minimotifs

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease

Reducing False-Positive Prediction of Minimotifs with a Genetic Interaction Filter

Background: Minimotifs are short contiguous peptide sequences in proteins that have known functions. At its simplest level, the minimotif sequence is present in a source protein and has an activity relationship with a target, most of which are proteins. While many scientists routinely investigate new minimotif functions in proteins, the major web-based discovery tools have a high rate of false-positive prediction. Any new approach that reduces false-positives will be of great help to biologists. Methods and Findings: We have built three filters that use genetic interactions to reduce false-positive minimotif predictions. The basic filter identifies those minimotifs where the source/target protein pairs have a known genetic interaction. The HomoloGene genetic interaction filter extends these predictions to predicted genetic interactions of orthologous proteins and the node-based filter identifies those minimotifs where proteins that have a genetic interaction with the source or target have a genetic interaction. Each filter was evaluated with a test data set containing thousands of true and false-positives. Based on sensitivity and selectivity performance metrics, the basic filter had the best discrimination for true positives, whereas the node-based filter had the highest sensitivity. We have implemented these genetic interaction filters on the Minimotif Miner 2.3 website. The genetic interaction filter is particularly useful for improving predictions of posttranslational modifications such as phosphorylation and proteolytic cleavage sites

Computational prediction of Short Linear Motif candidates in the proteome of the Apicomplexan parasite Toxoplasma gondii

Toxoplasma gondii is a unicellular parasite of the Apicomplexan family with the unique ability to infect a wide spectrum of warm-blooded animals, including mammals and birds. Host infection is established by distinct secreted proteins that interact with the cellular machinery and signaling networks of the host cells, hijacking their immune response and subverting cellular processes to their advantage. Short linear motifs (SLiMs) are small functional modules within protein sequences known to mediate protein-protein interaction between parasite and host proteins. By integrating SLiM information with sequences, structural, and experimental data I developed a computational pipeline to identify motif candidates relevant for T. gondii infection. Among these candidates, I identified motifs in microneme, rhoptry, and dense granule proteins that potentially link them to processes like cell attachment, nuclear targeting and cytoskeleton rearrangements. As a proof of concept, the protein-protein interaction of a group of motif candidates related to the innate immune response were tested experimentally in collaboration with the EMBL Protein expression and purification facility. This provided proof of binding and affinity measurements for some of them, and showed that the pipeline is able to identify true binding motifs. Taken together, I developed a computational pipeline that can potentially predict motif candidates relevant for T. gondii infection and provide a resource for further experimental validation and understanding of parasite infection

The eukaryotic linear motif resource - 2018 update.

Short linear motifs (SLiMs) are protein binding modules that play major roles in almost all cellular processes. SLiMs are short, often highly degenerate, difficult to characterize and hard to detect. The eukaryotic linear motif (ELM) resource (elm.eu.org) is dedicated to SLiMs, consisting of a manually curated database of over 275 motif classes and over 3000 motif instances, and a pipeline to discover candidate SLiMs in protein sequences. For 15 years, ELM has been one of the major resources for motif research. In this database update, we present the latest additions to the database including 32 new motif classes, and new features including Uniprot and Reactome integration. Finally, to help provide cellular context, we present some biological insights about SLiMs in the cell cycle, as targets for bacterial pathogenicity and their functionality in the human kinome

Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy

Background: Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions. Methodology/Principal Findings: Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components. Conclusions/Significance: Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is,4.6 times that of random minimotifs. For the molecular function filter this ratio is,2.9. These results, together with the comparison with the published frequency score filter, strongly suggest tha

Prediction of virus-host protein-protein interactions mediated by short linear motifs

Table S4. Supplement information describing the previous files. (Supplement - Prediction of Virus-Host Protein-Protein interactions based on Short Linear Motifs.pdf) available at https://figshare.com/articles/Supplement_-_Prediction_of_Virus-Host_Protein-Protein_interactions_based_on_Short_Linear_Motifs/4667461 . (PDF 166 kb

Alternative translation initiation in rat brain yields K2P2.1 potassium channels permeable to sodium.

K(2P) channels mediate potassium background currents essential to central nervous system function, controlling excitability by stabilizing membrane potential below firing threshold and expediting repolarization. Here, we show that alternative translation initiation (ATI) regulates function of K(2P)2.1 (TREK-1) via an unexpected strategy. Full-length K(2P)2.1 and an isoform lacking the first 56 residues of the intracellular N terminus (K(2P)2.1Delta1-56) are produced differentially in a regional and developmental manner in the rat central nervous system, the latter passing sodium under physiological conditions leading to membrane depolarization. Control of ion selectivity via ATI is proposed to be a natural, epigenetic mechanism for spatial and temporal regulation of neuronal excitability