AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

A novel process for the production of castor oil by direct solvent extraction

This research was undertaken to: develop a process for the direct solvent extraction of castor oil seeds. A literature survey confirmed the desirability of establishing such a process with emphasis on the decortication, size, reduction, detoxification-deallergenization, and solvent·extraction operations. A novel process was developed for the dehulling of castor seeds which consists of pressurizing the beans and then suddenly releasing the pressure to vaccum. The degree of dehulling varied according to the pressure applied and the size of the beans. Some of the batches were difficult-to-hull, and this phenomenon was investigated using the scanning electron microscope and by thickness and compressive strength measurements. The other variables studied to lesser degrees included residence time, moisture, content, and temperature.The method was successfully extended to cocoa beans, and (with modifications) to peanuts. The possibility of continuous operation was looked into, and a mechanism was suggested to explain the method works. The work on toxins and allergens included an extensive literature survey on the properties of these substances and the methods developed for their deactivation Part of the work involved setting up an assay method for measuring their concentration in the beans and cake, but technical difficulties prevented the completion of this aspect of the project. An appraisal of the existing deactivation methods was made in the course of searching for new ones. A new method of reducing the size of oilseeds was introduced in this research; it involved freezing the beans in cardice and milling them in a coffee grinder, the method was found to be a quick, efficient, and reliable. An application of the freezing technique was successful in dehulling soybeans and de-skinning peanut kernels. The literature on the solvent extraction, of oilseeds, especially castor, was reviewed: The survey covered processes, equipment, solvents, and mechanism of leaching. three solvents were experimentally investigated: cyclohexane, ethanol, and acetone. Extraction with liquid ammonia and liquid butane was not effective under the conditions studied. Based on the results of the research a process has been suggested for the direct solvent extraction of castor seeds, the various sections of the process have analysed, and the factors affecting the economics of the process were discussed.

The psychosocial impact of food allergy and food hypersensitivity in children, adolescents and their families:a review

Food allergy affects 6% of children but there is no cure, and strict avoidance of index allergens along with immediate access to rescue medication is the current best management. With specialist care, morbidity from food allergy in children is generally low, and mortality is very rare. However, there is strong evidence that food allergy and food hypersensitivity has an impact on psychological distress and on the quality of life (QoL) of children and adolescents, as well as their families. Until recently, the measurement of QoL in allergic children has proved difficult because of the lack of investigative tools available. New instruments for assessing QoL in food allergic children have recently been developed and validated, which should provide further insights into the problems these children encounter and will enable us to measure the effects of interventions in patients. This review examines the published impact of food allergy on affected children, adolescents and their families. It considers influences such as gender, age, disease severity, co-existing allergies and external influences, and examines how these may impact on allergy-related QoL and psychological distress including anxiety and depression. Implications of the impact are considered alongside avenues for future research.