The RPSP: Web server for prediction of signal peptides

High-density Peptide Arrays Help to Identify Linear Immunogenic B-cell Epitopes in Individuals Naturally Exposed to Malaria Infection

High-density peptide arrays are an excellent means to profile anti-plasmodial antibody responses. Different protein intrinsic epitopes can be distinguished, and additional insights are gained, when compared with assays involving the full-length protein. Distinct reactivities to specific epitopes within one protein may explain differences in published results, regarding immunity or susceptibility to malaria. We pursued three approaches to find specific epitopes within important plasmodial proteins, (1) twelve leading vaccine candidates were mapped as overlapping 15-mer peptides, (2) a bioinformatical approach served to predict immunogenic malaria epitopes which were subsequently validated in the assay, and (3) randomly selected peptides from the malaria proteome were screened as a control. Several peptide array replicas were prepared, employing particle-based laser printing, and were used to screen 27 serum samples from a malaria-endemic area in Burkina Faso, West Africa. The immunological status of the individuals was classified as "protected" or "unprotected" based on clinical symptoms, parasite density, and age. The vaccine candidate screening approach resulted in significant hits in all twelve proteins and allowed us (1) to verify many known immunogenic structures, (2) to map B-cell epitopes across the entire sequence of each antigen and (3) to uncover novel immunogenic epitopes. Predicting immunogenic regions in the proteome of the human malaria parasite Plasmodium falciparum, via the bioinformatics approach and subsequent array screening, confirmed known immunogenic sequences, such as in the leading malaria vaccine candidate CSP and discovered immunogenic epitopes derived from hypothetical or unknown proteins.

Comparative Analysis of Sequence Clustering Methods for Deduplication of Biological Databases

The massive volumes of data in biological sequence databases provide a remarkable resource for large-scale biological studies. However, the underlying data quality of these resources is a critical concern. A particular challenge is duplication, in which multiple records have similar sequences, creating a high level of redundancy that impacts database storage, curation, and search. Biological database deduplication has two direct applications: for database curation, where detected duplicates are removed to improve curation efficiency, and for database search, where detected duplicate sequences may be flagged but remain available to support analysis.

Clustering methods have been widely applied to biological sequences for database deduplication. Since an exhaustive all-by-all pairwise comparison of sequences cannot scale for a high volume of data, heuristic approaches have been recruited, such as the use of simple similarity thresholds. In this article, we present a comparison between CD-HIT and UCLUST, the two best-known clustering tools for sequence database deduplication. Our contributions include a detailed assessment of the redundancy remaining after deduplication, application of standard clustering evaluation metrics to quantify the cohesion and separation of the clusters generated by each method, and a biological case study that assesses intracluster function annotation consistency to demonstrate the impact of these factors on a practical application of the sequence clustering methods. Our results show that the trade-off between efficiency and accuracy becomes acute when low threshold values are used and when cluster sizes are large. This evaluation leads to practical recommendations for users for more effective uses of the sequence clustering tools for deduplication.

The Streptococcus gordonii Adhesin CshA Protein Binds Host Fibronectin via a Catch-Clamp Mechanism

Adherence of bacteria to biotic or abiotic surfaces is a prerequisite for host colonization and represents an important step in microbial pathogenicity. This attachment is facilitated by bacterial adhesins at the cell surface. Because of their size and often elaborate multidomain architectures, these polypeptides represent challenging targets for detailed structural and functional characterization. The multifunctional fibrillar adhesin CshA, which mediates binding to both host molecules and other microorganisms, is an important determinant of colonization by Streptococcus gordonii, an oral commensal and opportunistic pathogen of animals and humans. CshA binds the high-molecular-weight glycoprotein fibronectin (Fn) via an N-terminal non-repetitive region, and this protein-protein interaction has been proposed to promote S. gordonii colonization at multiple sites within the host. However, the molecular details of how these two proteins interact have yet to be established. Here we present a structural description of the Fn binding N-terminal region of CshA, derived from a combination of X-ray crystallography, small angle X-ray scattering, and complementary biophysical methods. In vitro binding studies support a previously unreported two-state "catch-clamp" mechanism of Fn binding by CshA, in which the disordered N-terminal domain of CshA acts to "catch" Fn, via formation of a rapidly assembled but also readily dissociable pre-complex, enabling its neighboring ligand binding domain to tightly clamp the two polypeptides together. This study presents a new paradigm for target binding by a bacterial adhesin, the identification of which will inform future efforts toward the development of anti-adhesive agents that target S. gordonii and related streptococci.

How protein targeting to primary plastids via the endomembrane system could have evolved? A new hypothesis based on phylogenetic studies

Background

It is commonly assumed that a heterotrophic ancestor of the supergroup Archaeplastida/Plantae engulfed a cyanobacterium that was transformed into a primary plastid; however, it is still unclear how nuclear-encoded proteins initially were imported into the new organelle. Most proteins targeted to primary plastids carry a transit peptide and are transported post-translationally using Toc and Tic translocons. There are, however, several proteins with N-terminal signal peptides that are directed to higher plant plastids in vesicles derived from the endomembrane system (ES). The existence of these proteins inspired a hypothesis that all nuclear-encoded, plastid-targeted proteins initially carried signal peptides and were targeted to the ancestral primary plastid via the host ES.

Results

We present the first phylogenetic analyses of Arabidopsis thaliana α-carbonic anhydrase (CAH1), Oryza sativa nucleotide pyrophosphatase/phosphodiesterase (NPP1), and two O. sativa α-amylases (αAmy3, αAmy7), proteins that are directed to higher plant primary plastids via the ES. We also investigated protein disulfide isomerase (RB60) from the green alga Chlamydomonas reinhardtii because of its peculiar dual post- and co-translational targeting to both the plastid and ES. Our analyses show that these proteins all are of eukaryotic rather than cyanobacterial origin, and that their non-plastid homologs are equipped with signal peptides responsible for co-translational import into the host ES. Our results indicate that vesicular trafficking of proteins to primary plastids evolved long after the cyanobacterial endosymbiosis (possibly only in higher plants) to permit their glycosylation and/or transport to more than one cellular compartment.

Conclusions

The proteins we analyzed are not relics of ES-mediated protein targeting to the ancestral primary plastid. Available data indicate that Toc- and Tic-based translocation dominated protein import into primary plastids from the beginning. Only a handful of host proteins, which already were targeted through the ES, later were adapted to reach the plastid via the vesicular trafficking. They represent a derived class of higher plant plastid-targeted proteins with an unusual evolutionary history.

Reviewers

This article was reviewed by Prof. William Martin, Dr. Philippe Deschamps (nominated by Dr. Purificacion Lopez-Garcia) and Dr Simonetta Gribaldo.

Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.SVMSignal is publicly available at http://bio-cluster.iis.sinica.edu.tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.

Fuzzy clustering of physicochemical and biochemical properties of amino acids

In this article, we categorize presently available experimental and theoretical knowledge of various physicochemical and biochemical features of amino acids, as collected in the AAindex database of known 544 amino acid (AA) indices. Previously reported 402 indices were categorized into six groups using hierarchical clustering technique and 142 were left unclustered. However, due to the increasing diversity of the database these indices are overlapping, therefore crisp clustering method may not provide optimal results. Moreover, in various large-scale bioinformatics analyses of whole proteomes, the proper selection of amino acid indices representing their biological significance is crucial for efficient and error-prone encoding of the short functional sequence motifs. In most cases, researchers perform exhaustive manual selection of the most informative indices. These two facts motivated us to analyse the widely used AA indices. The main goal of this article is twofold. First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited. Second, we prepare three high quality subsets of all available indices. Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results. The processed AAindex1 database, supplementary material and the software are available at http://sysbio.icm.edu.pl/aaindex/.

Analyses of genome architecture and gene expression reveal novel candidate virulence factors in the secretome of Phytophthora infestans

BACKGROUND

Phytophthora infestans is the most devastating pathogen of potato and a model organism for the oomycetes. It exhibits high evolutionary potential and rapidly adapts to host plants. The P. infestans genome experienced a repeat-driven expansion relative to the genomes of Phytophthora sojae and Phytophthora ramorum and shows a discontinuous distribution of gene density. Effector genes, such as members of the RXLR and Crinkler (CRN) families, localize to expanded, repeat-rich and gene-sparse regions of the genome. This distinct genomic environment is thought to contribute to genome plasticity and host adaptation.

RESULTS

We used in silico approaches to predict and describe the repertoire of P. infestans secreted proteins (the secretome). We defined the "plastic secretome" as a subset of the genome that (i) encodes predicted secreted proteins, (ii) is excluded from genome segments orthologous to the P. sojae and P. ramorum genomes and (iii) is encoded by genes residing in gene sparse regions of P. infestans genome. Although including only ~3% of P. infestans genes, the plastic secretome contains ~62% of known effector genes and shows >2 fold enrichment in genes induced in planta. We highlight 19 plastic secretome genes induced in planta but distinct from previously described effectors. This list includes a trypsin-like serine protease, secreted oxidoreductases, small cysteine-rich proteins and repeat containing proteins that we propose to be novel candidate virulence factors.

CONCLUSIONS

This work revealed a remarkably diverse plastic secretome. It illustrates the value of combining genome architecture with comparative genomics to identify novel candidate virulence factors from pathogen genomes.

A predicted physicochemically distinct sub-proteome associated with the intracellular organelle of the anammox bacterium Kuenenia stuttgartiensis

BACKGROUND

Anaerobic ammonium-oxidizing (anammox) bacteria perform a key step in global nitrogen cycling. These bacteria make use of an organelle to oxidize ammonia anaerobically to nitrogen (N2) and so contribute approximately 50% of the nitrogen in the atmosphere. It is currently unknown which proteins constitute the organellar proteome and how anammox bacteria are able to specifically target organellar and cell-envelope proteins to their correct final destinations. Experimental approaches are complicated by the absence of pure cultures and genetic accessibility. However, the genome of the anammox bacterium Candidatus "Kuenenia stuttgartiensis" has recently been sequenced. Here, we make use of these genome data to predict the organellar sub-proteome and address the molecular basis of protein sorting in anammox bacteria.

RESULTS

Two training sets representing organellar (30 proteins) and cell envelope (59 proteins) proteins were constructed based on previous experimental evidence and comparative genomics. Random forest (RF) classifiers trained on these two sets could differentiate between organellar and cell envelope proteins with ~89% accuracy using 400 features consisting of frequencies of two adjacent amino acid combinations. A physicochemically distinct organellar sub-proteome containing 562 proteins was predicted with the best RF classifier. This set included almost all catabolic and respiratory factors encoded in the genome. Apparently, the cytoplasmic membrane performs no catabolic functions. We predict that the Tat-translocation system is located exclusively in the organellar membrane, whereas the Sec-translocation system is located on both the organellar and cytoplasmic membranes. Canonical signal peptides were predicted and validated experimentally, but a specific (N- or C-terminal) signal that could be used for protein targeting to the organelle remained elusive.

CONCLUSIONS

A physicochemically distinct organellar sub-proteome was predicted from the genome of the anammox bacterium K. stuttgartiensis. This result provides strong in silico support for the existing experimental evidence for the existence of an organelle in this bacterium, and is an important step forward in unravelling a geochemically relevant case of cytoplasmic differentiation in bacteria. The predicted dual location of the Sec-translocation system and the apparent absence of a specific N- or C-terminal signal in the organellar proteins suggests that additional chaperones may be necessary that act on an as-yet unknown property of the targeted proteins.

The interactome: predicting the protein-protein interactions in cells

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

XtalPred: a web server for prediction of protein crystallizability

XtalPred is a web server for prediction of protein crystallizability. The prediction is made by comparing several features of the protein with distributions of these features in TargetDB and combining the results into an overall probability of crystallization. XtalPred provides: (1) a detailed comparison of the protein's features to the corresponding distribution from TargetDB; (2) a summary of protein features and predictions that indicate problems that are likely to be encountered during protein crystallization; (3) prediction of ligands; and (4) (optional) lists of close homologs from complete microbial genomes that are more likely to crystallize.

AVAILABILITY

The XtalPred web server is freely available for academic users on http://ffas.burnham.org/XtalPred