Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning

The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on Filtered Length-adjusted Allergen Peptides (DFLAP). The DFLAP algorithm extracts variable length allergen sequence fragments and employs modern machine learning techniques in the form of a support vector machine. In particular, this new detector shows hitherto unmatched specificity when challenged to the Swiss-Prot repository without appreciable loss of sensitivity. DFLAP is also the first reported detector that successfully discriminates between allergens and non-allergens occurring in protein families known to hold both categories. Allergenicity assessment for specific protein sequences of interest using DFLAP is possible via [email protected]

T cell clones specific for hybrid I-A molecules. Discrimination with monoclonal anti-I-A (k) antibodies

Alloreactive and soluble antigen-reactive, I-A-restricted T cell clones were examined for their ability to recognize hybrid I-A antigens. Several clones that recognized hybrid I-A(b)/I-A(k) molecules on (C57BL/6 x A/J)F(1) [(B6A)F(1)] spleen cells were studied. We were able to distinguish clones that recognized hybrid I-A molecules of the A(b)(a)A(k)(β) type from those that recognized A(k)(a)A(b)(β) molecules. We reached this conclusion by considering data from three independent types of experiments. (a) Monoclonal antibodies were used to inhibit T cell stimulation. Antibodies 10.2.16 and H116.32 distinguished two mutually exclusive “families” of T cell clones. One group of clones was inhibited by 10-2.16 and not H116.32, the other group exhibited reciprocal inhibition. (b) T cell proliferation was assayed using antigen-presenting cells from B6.C-H-2(bml2) (bml2) and [bml2 × B10.A(4R)]F(1) mice. Because the bml2 strain has a mutation that results in an altered A(b)(β) polypeptide chain (A(bm12)(β)), we reasoned that clones that could recognize the [bm12 × B 10.A(4R)]F(1) cells were recognizing A(b)(a)A(k)(β) molecules. Alternatively, clones not recognizing [bml2 × B10.A(4R)]F(1) cells had specificity for A(k)(a)A(b)(β) molecules. (c) I-A molecules immunoprecipitated from radiolabeled (B6A)F(1) splenocyte extracts were analyzed by two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis. These experiments confirmed an earlier report that antibody 10.2.16 recognized determinants on the A(k)(β) chain (12). Antibody H116.32 immunoprecipitated products consistent with recognition of A(k)(a) determinants. Taken together, these three types of results offer conclusive evidence that T cell clones recognizing “hybrid” I-A molecules use either A(b(k)A(k)(β) or A(k)(a)A(b)(β) molecules as recognition or restriction sites. Clones whose proliferation was supported by [bm 12 x B10.A(4R)]F(1) cells and blocked by anti-I-A(k) antibody 10-2.16 recognized A(b)(a)A(k)(β) B molecules. Clones that were blocked by antibody H116.32 and did not recognize [bml2 X B10.A(4R)]F(1) cells use a recognition site(s) on A(b)(a)A(k)(β) molecules. Thus, we can demonstrate both functionally and biochemically that hybrid F(1) I-A molecules of the structure A(k)(a)A(b)(β) and A(b)(a)A(k)(β) both exist on (B6A)F(1) splenocytes and that both configurations are used in immune recognition phenomena

Enforced Bcl-2 Expression Inhibits Antigen-mediated Clonal Elimination of Peripheral B Cells in an Antigen Dose–dependent Manner and Promotes Receptor Editing in Autoreactive, Immature B Cells

The mechanisms that establish immune tolerance in immature and mature B cells appear to be distinct. Membrane-bound autoantigen is thought to induce developmental arrest and receptor editing in immature B cells, whereas mature B cells have shortened lifespans when exposed to the same stimulus. In this study, we used Eμ–bcl-2-22 transgenic (Tg) mice to test the prediction that enforced expression of the Bcl-2 apoptotic inhibitor in B cells would rescue mature, but not immature, B cells from tolerance induction. To monitor tolerance to the natural membrane autoantigen H-2Kb, we bred 3–83μδ (anti-Kk,b) Ig Tg mice to H-2b mice or to mice expressing transgene-driven Kb in the periphery. In 3–83μδ/bcl-2 Tg mice, deletion of autoreactive B cells induced by peripheral Kb antigen expression in the liver (MT-Kb Tg) or epithelia (KerIV-Kb Tg), was partly or completely inhibited, respectively. Furthermore, Bcl-2 protected peritoneal B-2 B cells from deletion mediated by acute antigen exposure, but this protection could be overcome by higher antigen dose. In contrast to its ability to block peripheral self-tolerance, Bcl-2 overexpression failed to inhibit central tolerance induced by bone marrow antigen expression, but instead, enhanced the receptor editing process. These studies indicate that apoptosis plays distinct roles in central and peripheral B cell tolerance

EVALLER: a web server for in silico assessment of potential protein allergenicity

Bioinformatics testing approaches for protein allergenicity, involving amino acid sequence comparisons, have evolved appreciably over the last several years to increased sophistication and performance. EVALLER, the web server presented in this article is based on our recently published ‘Detection based on Filtered Length-adjusted Allergen Peptides’ (DFLAP) algorithm, which affords in silico determination of potential protein allergenicity of high sensitivity and excellent specificity. To strengthen bioinformatics risk assessment in allergology EVALLER provides a comprehensive outline of its judgment on a query protein's potential allergenicity. Each such textual output incorporates a scoring figure, a confidence numeral of the assignment and information on high- or low-scoring matches to identified allergen-related motifs, including their respective location in accordingly derived allergens. The interface, built on a modified Perl Open Source package, enables dynamic and color-coded graphic representation of key parts of the output. Moreover, pertinent details can be examined in great detail through zoomed views. The server can be accessed at http://bioinformatics.bmc.uu.se/evaller.html

Automated QuantMap for rapid quantitative molecular network topology analysis

ABSTRACT Summary: The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment. Availability: http:

Discovery and characterisation of dietary patterns in two Nordic countries. Using non-supervised and supervised multivariate statistical techniques to analyse dietary survey data

This Nordic study encompasses multivariate data analysis (MDA) of preschool Danish as well as pre- and elementary school Swedish consumers. Contrary to other counterparts the study incorporates two separate MDA varieties - Pattern discovery (PD) and predictive modelling (PM). PD, i.e. hierarchical cluster analysis (HCA) and factor analysis (using PCA), helped identifying distinct consumer aggregations and relationships across food groups, respectively, whereas PM enabled the disclosure of deeply entrenched associations. 17 clusters - here defined as dietary prototypes - were identified by means of HCA in the entire bi-national data set. These prototypes underwent further processing, which disclosed several intriguing consumption data relationships: Striking disparity between consumption patterns of Danish and Swedish preschool children was unveiled and further dissected by PM. Two prudent and mutually similar dietary prototypes appeared among each of two Swedish elementary school children data subsets. Dietary prototypes rich in sweetened soft beverages appeared among Danish and Swedish children alike. The results suggest prototype-specific risk assessment and study design