Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus

T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood, We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

Estimating the potential refolding yield of recombinant proteins expressed as inclusion bodies

Recombinant protein production in bacteria is efficient except that insoluble inclusion bodies form when some gene sequences are expressed. Such proteins must undergo renaturation, which is an inefficient process due to protein aggregation on dilution from concentrated denaturant. In this study, the protein-protein interactions of eight distinct inclusion-body proteins are quantified, in different solution conditions, by measurement of protein second virial coefficients (SVCs). Protein solubility is shown to decrease as the SVC is reduced (i.e., as protein interactions become more attractive). Plots of SVC versus denaturant concentration demonstrate two clear groupings of proteins: a more aggregative group and a group having higher SVC and better solubility. A correlation of the measured SVC with protein molecular weight and hydropathicity, that is able to predict which group each of the eight proteins falls into, is presented. The inclusion of additives known to inhibit aggregation during renaturation improves solubility and increases the SVC of both protein groups. Furthermore, an estimate of maximum refolding yield (or solubility) using high-performance liquid chromatography was obtained for each protein tested, under different environmental conditions, enabling a relationship between yield and SVC to be demonstrated. Combined, the results enable an approximate estimation of the maximum refolding yield that is attainable for each of the eight proteins examined, under a selected chemical environment. Although the correlations must be tested with a far larger set of protein sequences, this work represents a significant move beyond empirical approaches for optimizing renaturation conditions. The approach moves toward the ideal of predicting maximum refolding yield using simple bioinformatic metrics that can be estimated from the gene sequence. Such a capability could potentially screen, in silico, those sequences suitable for expression in bacteria from those that must be expressed in more complex hosts. (C) 2004 Wiley Periodicals, Inc.

Genes induced by growth hormone in a model of adipogenic differentiation

A substantial number of GH regulated genes have been reported in mature hepatocytes. but genes involved in GH-initiated cell differentiation have not yet been identified. Here we have studied a, ell-characterised model of GH-dependent differentiation, adipogenesis of 3T3-F442A preadipocytes, to identify genes rapidly induced by GH. Using the suppression subtractive hybridisation technique, we have identified eight genes induced within 60 min of GH treatment, and verified these by northern analysis. Six were identifiable as Stat 2. Stat 3, thrombospondin-1. oncostatin M receptor beta chain. a DEAD box RNA helicase. and muscleblind. a developmental transcription factor. Bioinformatic approaches assigned one of the two remaining unknown genes as a novel 436 residue serine,threonine kinase. As each of the identified genes hake important developmental roles. they may be important in initiating GH-induced adipogenesis. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Accurate prediction of scorpion toxin functional properties from primary structures

Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties. (c) 2005 Elsevier Inc. All rights reserved.

A Novel Mammalian Retromer Component, Vps26B

The mammalian retromer protein complex, which consists of three proteins - Vps26, Vps29, and Vps35 - in association with members of the sorting nexin family of proteins, has been implicated in the trafficking of receptors and their ligands within the endosomal/lysosomal system of mammalian cells. A bioinformatic analysis of the mouse genome identified an additional transcribed paralog of the Vps26 retromer protein, which we termed Vps26B. No paralogs were identified for Vps29 and Vps35. Phylogenetic studies indicate that the two paralogs of Vps26 become evident after the evolution of the chordates. We propose that the chordate Vps26-like gene published previously be renamed Vps26A to differentiate it from Vps26B. As for Vps26A, biochemical characterization of Vps26B established that this novel 336 amino acid residue protein is a peripheral membrane protein. Vps26B co-precipitated with Vps35 from transfected cells and the direct interaction between these two proteins was confirmed by yeast 2-hybrid analysis, thereby establishing Vps26B as a subunit of the retromer complex. Within HeLa cells, Vps26B was found in the cytoplasm with low levels at the plasma membrane, while Vps26A was predominantly associated with endosomal membranes. Within A549 cells, both Vps26A and Vps26B co-localized with actin-rich lamellipodia at the cell surface. These structures also co-localized with Vps35. Total internal reflection fluorescence microscopy confirmed the association of Vps26B with the plasma membrane in a stable HEK293 cell line expressing cyan fluorescent protein (CFP)-Vps26B. Based on these observations, we propose that the mammalian retromer complex is located at both endosomes and the plasma membrane in some cell types.

Analysis of the Pasteurella multocida outer membrane sub-proteome and its response to the in vivo environment of the natural host

This study describes the identification of outer membrane proteins (OMPs) of the bacterial pathogen Pasteurella multocida and an analysis of how the expression of these proteins changes during infection of the natural host. We analysed the sarcosine-insoluble membrane fractions, which are highly enriched for OMPs, from bacteria grown under a range of conditions. Initially, the OMP-containing fractions were resolved by 2-DE and the proteins identified by MALDI-TOF MS. In addition, the OMP-containing fractions were separated by 1-D SDS-PAGE and protein identifications were made using nano LC MS/MS. Using these two methods a total of 35 proteins was identified from samples obtained from organisms grown in rich culture medium. Six of the proteins were identified only by 2-DE MALDI-TOF MS, whilst 17 proteins were identified only by 1-D LC MS/MS. We then analysed the OMPs from P. multocida which had been isolated from the bloodstream of infected chickens (a natural host) or grown in iron-depleted medium. Three proteins were found to be significantly up-regulated during growth in vivo and one of these (Pm0803) was also up-regulated during growth in iron-depleted medium. After bioinformatic analysis of the protein matches, it was predicted that over one third of the combined OMPs predicted by the bioinformatics sub-cellular localisation tools PSORTB and Proteome Analyst, had been identified during this study. This is the first comprehensive proteomic analysis of the P. multocida outer membrane and the first proteomic analysis of how a bacterial pathogen modifies its outer membrane proteome during infection.

Definition and spatial annotation of the dynamic secretome during early kidney development

The term secretome has been defined as a set of secreted proteins (Grimmond et al. [2003] Genome Res 13:1350-1359). The term secreted protein encompasses all proteins exported from the cell including growth factors, extracellular proteinases, morphogens, and extracellular matrix molecules. Defining the genes encoding secreted proteins that change in expression during organogenesis, the dynamic secretome, is likely to point to key drivers of morphogenesis. Such secreted proteins are involved in the reciprocal interactions between the ureteric bud (UB) and the metanephric mesenchyme (AM) that occur during organogenesis of the metanephros. Some key metanephric secreted proteins have been identified, but many remain to be determined. In this study, microarray expression profiling of E10.5, E11.5, and E13.5 kidney and consensus bioinformatic analysis were used to define a dynamic secretome of early metanephric development. In situ hybridisation was used to confirm microarray results and clarify spatial expression patterns for these genes. Forty-one secreted factors were dynamically expressed between the E10.5 and E13.5 timeframe profiled, and 25 of these factors had not previously been implicated in kidney development. A text-based anatomical ontology was used to spatially annotate the expression pattern of these genes in cultured metanephric explants.

Identification and analysis of novel genes expressed in the mouse embryonic facial primordia

Craniofacial anomalies are a common feature of human congenital dysmorphology syndromes, suggesting that genes expressed in the developing face are likely to play a wider role in embryonic development. To facilitate the identification of genes involved in embryogenesis, we previously constructed an enriched cDNA library by subtracting adult mouse liver cDNA from that of embryonic day (E)10.5 mouse pharyngeal arch cDNA. From this library, 273 unique clones were sequenced and known proteins binned into functional categories in order to assess enrichment of the library (1). We have now selected 31 novel and poorly characterised genes from this library and present bioinformatic analysis to predict proteins encoded by these genes, and to detect evolutionary conservation. Of these genes 61% (19/31) showed restricted expression in the developing embryo, and a subset of these was chosen for further in silico characterisation as well as experimental determination of subcellular localisation based on transient transfection of predicted full-length coding sequences into mammalian cell lines. Where a human orthologue of these genes was detected, chromosomal localisation was determined relative to known loci for human congenital disease.

Protein kinases associated with the yeast phosphoproteome

Background: Protein phosphorylation is an extremely important mechanism of cellular regulation. A large-scale study of phosphoproteins in a whole-cell lysate of Saccharomyces cerevisiae has previously identified 383 phosphorylation sites in 216 peptide sequences. However, the protein kinases responsible for the phosphorylation of the identified proteins have not previously been assigned. Results: We used Predikin in combination with other bioinformatic tools, to predict which of 116 unique protein kinases in yeast phosphorylates each experimentally determined site in the phosphoproteome. The prediction was based on the match between the phosphorylated 7-residue sequence and the predicted substrate specificity of each kinase, with the highest weight applied to the residues or positions that contribute most to the substrate specificity. We estimated the reliability of the predictions by performing a parallel prediction on phosphopeptides for which the kinase has been experimentally determined. Conclusion: The results reveal that the functions of the protein kinases and their predicted phosphoprotein substrates are often correlated, for example in endocytosis, cytokinesis, transcription, replication, carbohydrate metabolism and stress response. The predictions link phosphoproteins of unknown function with protein kinases with known functions and vice versa, suggesting functions for the uncharacterized proteins. The study indicates that the phosphoproteins and the associated protein kinases represented in our dataset have housekeeping cellular roles; certain kinases are not represented because they may only be activated during specific cellular responses. Our results demonstrate the utility of our previously reported protein kinase substrate prediction approach (Predikin) as a tool for establishing links between kinases and phosphoproteins that can subsequently be tested experimentally.

A DHA14 drug efflux gene from Xanthomonas albilineans confers high-level albicidin antibiotic resistance in Escherichia coli

Aims: Identification of a gene for self-protection from the antibiotic-producing plant pathogen Xanthomonas albilineans, and functional testing by heterologous expression. Methods and Results: Albicidin antibiotics and phytotoxins are potent inhibitors of prokaryote DNA replication. A resistance gene (albF) isolated by shotgun cloning from the X. albilineans albicidin-biosynthesis region encodes a protein with typical features of DHA14 drug efflux pumps. Low-level expression of albF in Escherichia coli increased the MIC of albicidin 3000-fold, without affecting tsx-mediated albicidin uptake into the periplasm or resistance to other tested antibiotics. Bioinformatic analysis indicates more similarity to proteins involved in self-protection in polyketide-antibiotic-producing actinomycetes than to multi-drug resistance pumps in other Gram-negative bacteria. A complex promoter region may co-regulate albF with genes for hydrolases likely to be involved in albicidin activation or self-protection. Conclusions: AlbF is the first apparent single-component antibiotic-specific efflux pump from a Gram-negative antibiotic producer. It shows extraordinary efficiency as measured by resistance level conferred upon heterologous expression. Significance and Impact of the Study: Development of the clinical potential of albicidins as potent bactericidial antibiotics against diverse bacteria has been limited because of low yields in culture. Expression of albF with recently described albicidin-biosynthesis genes may enable large-scale production. Because albicidins are X. albilineans pathogenicity factors, interference with AlbF function is also an opportunity for control of the associated plant disease.