Computational Prediction of Disordered Protein Motifs Using SLiMSuite.

Short linear motifs (SLiMs) are important mediators of interactions between intrinsically disordered regions of proteins and their interaction partners. Here, we detail instructions for the computational prediction of SLiMs in disordered protein regions, using the main tools of the SLiMSuite package: (1) SLiMProb identifies and calculates enrichment of predefined motifs in a set of proteins; (2) SLiMFinder predicts SLiMs de novo in a set of proteins, accounting for evolutionary relationships; (3) QSLiMFinder increases SLiMFinder sensitivity by focusing SLiM prediction on a specific query protein/region; (4) CompariMotif compares predicted SLiMs to known SLiMs or other SLiM predictions to identify common patterns. For each tool, command-line and online server examples are provided. Detailed notes provide additional advice on different applications of SLiMSuite, including batch running of multiple datasets and conservation masking using alignments of predicted orthologues.

SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions.

Many important cellular processes involve protein-protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

Correction: SLiMEnrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions.

[This corrects the article DOI: 10.7717/peerj.5858.].

SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks.

Short linear motifs (SLiMs) are small protein sequence patterns that mediate a large number of critical protein-protein interactions, involved in processes such as complex formation, signal transduction, localisation and stabilisation. SLiMs show rapid evolutionary dynamics and are frequently the targets of molecular mimicry by pathogens. Identifying enriched sequence patterns due to convergent evolution in non-homologous proteins has proven to be a successful strategy for computational SLiM prediction. Tools of the SLiMSuite package use this strategy, using a statistical model to identify SLiM enrichment based on the evolutionary relationships, amino acid composition and predicted disorder of the input proteins. The quality of input data is critical for successful SLiM prediction. Cytoscape provides a user-friendly, interactive environment to explore interaction networks and select proteins based on common features, such as shared interaction partners. SLiMScape embeds tools of the SLiMSuite package for de novo SLiM discovery (SLiMFinder and QSLiMFinder) and identifying occurrences/enrichment of known SLiMs (SLiMProb) within this interactive framework. SLiMScape makes it easier to (1) generate high quality hypothesis-driven datasets for these tools, and (2) visualise predicted SLiM occurrences within the context of the network. To generate new predictions, users can select nodes from a protein network or provide a set of Uniprot identifiers. SLiMProb also requires additional query motif input. Jobs are then run remotely on the SLiMSuite server ( http://rest.slimsuite.unsw.edu.au) for subsequent retrieval and visualisation. SLiMScape can also be used to retrieve and visualise results from jobs run directly on the server. SLiMScape and SLiMSuite are open source and freely available via GitHub under GNU licenses.

Draft Genome of Australian Environmental Strain WM 09.24 of the Opportunistic Human Pathogen Scedosporium aurantiacum.

We report here the first genome assembly and annotation of the human-pathogenic fungus Scedosporium aurantiacum, with a predicted 10,525 genes, and 11,661 transcripts. The strain WM 09.24 was isolated from the environment at Circular Quay, Sydney, New South Wales, Australia.

Cryptococcus gattii in North American Pacific Northwest: whole-population genome analysis provides insights into species evolution and dispersal.

The emergence of distinct populations of Cryptococcus gattii in the temperate North American Pacific Northwest (PNW) was surprising, as this species was previously thought to be confined to tropical and semitropical regions. Beyond a new habitat niche, the dominant emergent population displayed increased virulence and caused primary pulmonary disease, as opposed to the predominantly neurologic disease seen previously elsewhere. Whole-genome sequencing was performed on 118 C. gattii isolates, including the PNW subtypes and the global diversity of molecular type VGII, to better ascertain the natural source and genomic adaptations leading to the emergence of infection in the PNW. Overall, the VGII population was highly diverse, demonstrating large numbers of mutational and recombinational events; however, the three dominant subtypes from the PNW were of low diversity and were completely clonal. Although strains of VGII were found on at least five continents, all genetic subpopulations were represented or were most closely related to strains from South America. The phylogenetic data are consistent with multiple dispersal events from South America to North America and elsewhere. Numerous gene content differences were identified between the emergent clones and other VGII lineages, including genes potentially related to habitat adaptation, virulence, and pathology. Evidence was also found for possible gene introgression from Cryptococcus neoformans var. grubii that is rarely seen in global C. gattii but that was present in all PNW populations. These findings provide greater understanding of C. gattii evolution in North America and support extensive evolution in, and dispersal from, South America. Importance: Cryptococcus gattii emerged in the temperate North American Pacific Northwest (PNW) in the late 1990s. Beyond a new environmental niche, these emergent populations displayed increased virulence and resulted in a different pattern of clinical disease. In particular, severe pulmonary infections predominated in contrast to presentation with neurologic disease as seen previously elsewhere. We employed population-level whole-genome sequencing and analysis to explore the genetic relationships and gene content of the PNW C. gattii populations. We provide evidence that the PNW strains originated from South America and identified numerous genes potentially related to habitat adaptation, virulence expression, and clinical presentation. Characterization of these genetic features may lead to improved diagnostics and therapies for such fungal infections. The data indicate that there were multiple recent introductions of C. gattii into the PNW. Public health vigilance is warranted for emergence in regions where C. gattii is not thought to be endemic.

Whole CMV proteome pattern recognition analysis after HSCT identifies unique epitope targets associated with the CMV status.

Cytomegalovirus (CMV) infection represents a vital complication after Hematopoietic Stem Cell Transplantation (HSCT). We screened the entire CMV proteome to visualize the humoral target epitope-focus profile in serum after HSCT. IgG profiling from four patient groups (donor and/or recipient +/- for CMV) was performed at 6, 12 and 24 months after HSCT using microarray slides containing 17174 of 15mer-peptides overlapping by 4 aa covering 214 proteins from CMV. Data were analyzed using maSigPro, PAM and the 'exclusive recognition analysis (ERA)' to identify unique CMV epitope responses for each patient group. The 'exclusive recognition analysis' of serum epitope patterns segregated best 12 months after HSCT for the D+/R+ group (versus D-/R-). Epitopes were derived from UL123 (IE1), UL99 (pp28), UL32 (pp150), this changed at 24 months to 2 strongly recognized peptides provided from UL123 and UL100. Strongly (IgG) recognized CMV targets elicited also robust cytokine production in T-cells from patients after HSCT defined by intracellular cytokine staining (IL-2, TNF, IFN and IL-17). High-content peptide microarrays allow epitope profiling of entire viral proteomes; this approach can be useful to map relevant targets for diagnostics and therapy in patients with well defined clinical endpoints. Peptide microarray analysis visualizes the breadth of B-cell immune reconstitution after HSCT and provides a useful tool to gauge immune reconstitution.

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics.

We present a liquid chromatography-mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.

A conserved mammalian protein interaction network.

Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

Patterns of indirect protein interactions suggest a spatial organization to metabolism.

It has long been believed that cells organize their cytoplasm so as to efficiently channel metabolites between sequential enzymes. This metabolic channeling has the potential to yield higher metabolic fluxes as well as better regulatory control over metabolism. One mechanism for achieving such channeling is to ensure that sequential enzymes in a pathway are physically close to each other in the cell. We present evidence that indirect protein interactions between related enzymes represent a global mechanism for achieving metabolic channeling; the intuition being that protein interactions between enzymes and non-enzymatic mediator proteins are a powerful means of physically associating enzymes in a modular fashion. By analyzing the metabolic and protein-protein interactions networks of Escherichia coli, yeast and humans, we are able to show that all three species have many more indirect protein interactions linking enzymes that share metabolites than would be expected by chance. Moreover, these interactions are distributed non-randomly in the metabolic network. Our analyses in yeast and E. coli show that reactions possessing such interactions also show higher flux than do those lacking them. On the basis of these observations, we suggest that an important role of protein interactions with mediator proteins is to contribute to the spatial organization of the cell. This hypothesis is supported by the fact that these mediator proteins are also enriched with annotations related to signal transduction, a system where scaffolding proteins are known to limit cross-talk by controlling spatial localization.

Duplicability of self-interacting human genes.

BACKGROUND: There is increasing interest in the evolution of protein-protein interactions because this should ultimately be informative of the patterns of evolution of new protein functions within the cell. One model proposes that the evolution of new protein-protein interactions and protein complexes proceeds through the duplication of self-interacting genes. This model is supported by data from yeast. We examined the relationship between gene duplication and self-interaction in the human genome. RESULTS: We investigated the patterns of self-interaction and duplication among 34808 interactions encoded by 8881 human genes, and show that self-interacting proteins are encoded by genes with higher duplicability than genes whose proteins lack this type of interaction. We show that this result is robust against the system used to define duplicate genes. Finally we compared the presence of self-interactions amongst proteins whose genes have duplicated either through whole-genome duplication (WGD) or small-scale duplication (SSD), and show that the former tend to have more interactions in general. After controlling for age differences between the two sets of duplicates this result can be explained by the time since the gene duplication. CONCLUSIONS: Genes encoding self-interacting proteins tend to have higher duplicability than proteins lacking self-interactions. Moreover these duplicate genes have more often arisen through whole-genome rather than small-scale duplication. Finally, self-interacting WGD genes tend to have more interaction partners in general in the PIN, which can be explained by their overall greater age. This work adds to our growing knowledge of the importance of contextual factors in gene duplicability.

LogoBar: bar graph visualization of protein logos with gaps.

SUMMARY: LogoBar is a Java application to display protein sequence logos. In our software gaps are accounted for when calculating the information content present at each residue position in a multiple alignment. The resulting logo is displayed as a graph consisting of bars, although traditional letter representation is also possible. Amino acids are displayed from the bottom up with decreasing frequencies i.e. the most abundant residue is placed at the bottom of the logo. The bars can be color-coded according to user specifications. Gaps in the alignment are also displayed, either on top or at the bottom of the logo. Furthermore, residues can either be arranged according to their relative abundance or grouped according to user criteria to emphasize the conserved nature of particular positions. AVAILABILITY: LogoBar and further documentation is available at http://www.biosci.ki.se/groups/tbu/logobar/

Overview and comparison of ortholog databases.

Orthologs are an indispensable bridge to transfer biological knowledge between species, from protein annotations to sophisticated disease models. However, orthology assignment is not trivial. A large number of resources now exist, each with its own idiosyncrasies. The goal of this review is to compare their contents and clarify which database is most suited for a certain task.:

Low frequency of mtDNA point mutations in patients with PEO associated with POLG1 mutations.

Mitochondrial myopathy in progressive external ophthalmoplegia (PEO) has been associated with POLG1 mutations. POLG1 encodes the catalytic alpha subunit of polymerase gamma and is the only polymerase known to be involved in mtDNA replication. It has two functionally different domains, one polymerase domain and one exonuclease domain with proofreading activity. In this study we have investigated whether mtDNA point mutations are involved, directly or indirectly, in the pathogenesis of PEO. Muscle biopsy specimens from patients with POLG1 mutations, affecting either the exonuclease or the polymerase domain, were investigated. Single cytochrome c oxidase (COX)-deficient muscle fibers were dissected and screened for clonally expanded mtDNA point mutations using a sensitive denaturing gradient gel electrophoresis analysis, in which three different regions of mtDNA, including five different tRNA genes, were investigated. To screen for randomly distributed mtDNA point mutations in muscle, two regions of mtDNA including deletion breakpoints were investigated by high-fidelity PCR, followed by cloning and sequencing. Long-range PCR revealed multiple mtDNA deletions in all the patients but not the controls. No point mutations were identified in single COX-deficient muscle fibers. Cloning and sequencing of muscle homogenate identified randomly distributed point mutations at very low frequency in patients and controls (<1:50 000). We conclude that mtDNA point mutations do not appear to be directly or indirectly involved in the pathogenesis of mitochondrial disease in patients with different POLG1 mutations.