ALLERDB database and integrated bioinformatic tools for assessment of allergenicity and allergic cross-reactivity

Unix interfaces, Kleisli, bucandin structure, etc. -- the heroic beginning of bioinformatics in Singapore

Remarkably, Singapore as one of today's hotspots for bioinformatics and computational biology research appeared de novo out of pioneering efforts of engaged local individuals in the early 90-s that, supported with increasing public funds from 1996 on, morphed into the present vibrant research community. This article brings to mind the pioneers, their first successes and early institutional developments.

Evaluating the in vivo Th2 priming potential among common allergens

Exposure to allergens, both man-made and from our environment is increasingly associated with the development of significant human health issues such as allergy and asthma. Allergen induced production of the cytokine interleukin (IL-)4 by Th2 cells is central to the pathogenesis of allergic disease (Gavett et al., 1994). The development of the G4 mouse, that expresses green fluorescent protein (GFP) as a surrogate for IL-4 protein expression has made it possible to directly track the immune cells that produce IL-4. By combining a reliable intradermal immunisation technique with the transgenic G4 mouse we have been able to develop a novel & unique in vivo primary Th2 immune response model (PTh2). When allergens relevant to human disease are evaluated using the PTh2 assay a dose dependent hierarchy of allergenicity is revealed with environmental allergens (cockroach, house dust mite) the most potent and food allergens being the least. In addition, the PTh2 assay is extremely sensitive to the immunoregulatory effects of Mycobacterial extracts and immunosuppressive drugs on primary Th2 cell development. Taken together, this assay provides a standardised method for the identification of the structural and functional properties of proteins relevant to allergenicity, and is a powerful screening tool for novel lead compounds that are effective at inhibiting the primary Th2 response in allergic diseases.

An Allergen Portrait Gallery: Representative Structures and an Overview of IgE Binding Surfaces

Recent progress in the biochemical classification and structural determination of allergens and allergen-antibody complexes has enhanced our understanding of the molecular determinants of allergenicity. Databases of allergens and their epitopes have facilitated the clustering of allergens according to their sequences and, more recently, their structures. Groups of similar sequences are identified for allergenic proteins from diverse sources, and all allergens are classified into a limited number of protein structural families. A gallery of experimental structures selected from the protein classes with the largest number of allergens demonstrate the structural diversity of the allergen universe. Further comparison of these structures and identification of areas that are different from innocuous proteins within the same protein family can be used to identify features specific to known allergens. Experimental and computational results related to the determination of IgE binding surfaces and methods to define allergen-specific motifs are highlighted.

AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins

Allergy is a major health problem in industrialized countries. The number of transgenic food crops is growing rapidly creating the need for allergenicity assessment before they are introduced into human food chain. While existing bioinformatic methods have achieved good accuracies for highly conserved sequences, the discrimination of allergens and non-allergens from allergen-like non-allergen sequences remains difficult. We describe AllerHunter, a web-based computational system for the assessment of potential allergenicity and allergic cross-reactivity in proteins. It combines an iterative pairwise sequence similarity encoding scheme with SVM as the discriminating engine. The pairwise vectorization framework allows the system to model essential features in allergens that are involved in cross-reactivity, but not limited to distinct sets of physicochemical properties. The system was rigorously trained and tested using 1,356 known allergen and 13,449 putative non-allergen sequences. Extensive testing was performed for validation of the prediction models. The system is effective for distinguishing allergens and non-allergens from allergen-like non-allergen sequences. Testing results showed that AllerHunter, with a sensitivity of 83.4% and specificity of 96.4% (accuracy = 95.3%, area under the receiver operating characteristic curve AROC = 0.928+/-0.004 and Matthew's correlation coefficient MCC = 0.738), performs significantly better than a number of existing methods using an independent dataset of 1443 protein sequences. AllerHunter is available at (http://tiger.dbs.nus.edu.sg/AllerHunter).

A database for allergenic proteins and tools for allergenicity prediction

The AllergenPro database has developed a web-based system that will provide information about allergen in microbes, animals and plants. The database has three major parts and functions:(i) database list; (ii) allergen search; and (iii) allergenicity prediction. The database contains 2,434 allergens related information readily available in the database such as on allergens in rice microbes (712 records), animals (617 records) and plants (1,105 records). Furthermore, this database provides bioinformatics tools for allergenicity prediction. Users can search for specific allergens by various methods and can run tools for allergenicity prediction using three different methods.