TNO Allergenicity risk prediction model
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Prediction of Allergenicity of Novel and Modified Proteins. TNO has developed a n in silico model to predict allergenicity of proteins based on Random Forest modelling. This tool can be applied in combination with extensive knowledge on allergen risk assessment and management in support of your (novel) food dossier as well as in early selection of product leads in NPD.
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This document discusses approaches to preventing allergic diseases in children. It mentions that allergic diseases have increased rapidly in recent decades likely due to environmental and lifestyle changes. Early life exposure to allergens and microbes may influence the development of allergic immune responses. The document discusses genetic and environmental risk factors for atopic dermatitis and reviews evidence on the role of various allergens like food, aeroallergens and bacteria. It proposes that primary prevention aims to prevent sensitization to allergens, secondary prevention prevents disease progression after sensitization and tertiary prevention reduces symptoms after disease onset through allergen avoidance and treatment.
Src jbbr-21-125 Dr. ihsan edan abdulkareem alsaimary PROFESSOR IN MEDICAL M...
Dr. ihsan edan abdulkareem alsaimary
PROFESSOR IN MEDICAL MICROBIOLOGY AND MOLECULAR IMMUNOLOGY
ihsanalsaimary@gmail.com
mobile : 009647801410838
university of basrah - college of medicine - basrah -IRAQ
SOJ Biochem 2016
This document describes the development of a multiplex milk allergen ELISA using oligonucleotide-tethered principal component proteins. It demonstrates the ability to tether known allergenic milk proteins, including both purified proteins and crude extracts, to create an immunoassay for diagnosing cow's milk allergy. Patient sera were screened for specific IgE to provide an allergen profile and identify principal allergenic components. Prior studies developing microarray platforms for allergen testing are reviewed, showing their utility in component-resolved diagnosis. The document focuses on adapting this approach to a microplate-based multiplex platform using oligonucleotide tethering of proteins.
Component resolved diagnosis in food allergy
Component resolved diagnosis in food allergyChulalongkorn Allergy and Clinical Immunology Research Group
Component Resolved Diagnosis in Food allergy
Presented by Sadudee Boonmee, MD.
December20, 2012Development of nano-scale technologies for food safety protection
Nanotechnology holds promise for developing new food safety treatments and diagnostics. The document discusses several ways nanotechnology could improve food safety, such as using antimicrobial encapsulation to inhibit foodborne pathogens. Research at Texas A&M is investigating the use of liposomes encapsulating nisin to inhibit Listeria monocytogenes in milk and exploring applying micellar organic acids and essential oils to fresh produce for pathogen inhibition. Regulatory concerns regarding the use of engineered nanomaterials in foods are also discussed.
Ph02 edible vaccines
This document provides an overview of edible vaccines. It discusses how edible vaccines are produced by introducing genes encoding vaccine antigens into edible plants using transformation methods. The document outlines various plant species used for edible vaccines like tomatoes, rice, maize, potatoes, and tobacco. It discusses factors affecting the efficacy of edible vaccines and provides examples of edible vaccine research for diseases like malaria, measles, hepatitis B, norovirus, and Alzheimer's disease. The conclusion states that edible vaccines could improve vaccination programs in developing countries by reducing costs and need for cold storage.
Gfba Gene Essay
The document describes an experiment using paper chromatography and spectrophotometry to identify amino acids and determine protein concentration in solutions. Paper chromatography was used to separate and identify known amino acids in a solution by their Rf values. These Rf values were then used to identify the amino acids in an unknown solution. Spectrophotometry was used to determine the concentration of protein in another unknown solution by measuring absorbance and applying the Beer-Lambert law. The unknown solution was found to contain histidine and valine based on Rf value comparisons, and its protein concentration was calculated.
Similar to TNO Allergenicity risk prediction model (20)
Efectos potenciales a la salud por transgenicos y pesticidas
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ABPI Conference 2016 - Dr Mene Pangalos on 'Driving innovation in the R&D Eco...
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Monoclonal antibody, history, development and progress
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Src jbbr-21-125 Dr. ihsan edan abdulkareem alsaimary PROFESSOR IN MEDICAL M...
Src jbbr-21-125 Dr. ihsan edan abdulkareem alsaimary PROFESSOR IN MEDICAL M...
Development of nano-scale technologies for food safety protection
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TNO Allergenicity risk prediction model
- 1. PREDICTING ALLERGENICITY RISK IN SILICO RISK PREDICTION MODEL Dr. Marjolein Meijerink Dr. Bas Kremer
- 2. KEY CHALLENGES FOOD & NUTRITION Allergenicity prediction tool How to feed 9 B people by 2050? With foods grown in a ecologically sustainable way? Novel foods, new proteins and sources, such as insects, novel vegetables etc How to develop food proteins with improved functionality? Processing and any other technologies, GMO Lots of innovations, leading to new proteins or protein-containing products in development HOW ABOUT HEALTH RISKS?
- 3. HEALTH RISKS Allergenicity prediction tool Toxicological AllergenicNutritional Microbiological Clear guidance and available tests Limited guidance and tests
- 4. ALLERGENICITY RISK Cross-reactivity vs de novo sensitization Allergenicity prediction tool Cross reactivity Is the novel (processed) protein able to elicit an allergic reaction in a food allergic population (cross reactivity, IgE binding)? De novo sensitization Is the novel (processed) protein able to induce a new allergy (sensitization, IgE production)?
- 5. ALLERGENICITY RISK ASSESSMENT Allergenicity prediction tool Verhoeckx et al., Reg. Toxicol. Pharmacol. (79) 2016 No history New TNO method BIG CHALLENGE for Novel/modified Proteins
- 6. Machine learning as a tool for predicting the allergenicity of new food proteins: Random Forest model developed to distinguish allergenic from “non-allergenic proteins” Over half a million proteins from protein databases. Proteins assigned as allergenic when present in the allergen database COMPARE (n = 1680) Protein characteristics from databases (SwissProt, UniProt) and mathematical programs (e.g. ProtParam, PSIpred) Model predicts with > 85% accuracy if a protein is an allergen or not. BIG DATA SOLUTION Westerhout et al., Reg. Toxicol. Pharmacol. 107 (2019)
- 7. VALIDATED RF MODEL Westerhout et al., Reg. Toxicol. Pharmacol. 107 (2019)
- 8. ALL PROTEINS WERE CORRECTLY PREDICTED! Name Species Sequence similarity with known allergens Predicted allergen with Random Forest prediction model Allergenic Larval cuticle protein A2B Tenebrio molitor N Y Y Larval cuticle protein A1A Tenebrio molitor N Y Y Larval cuticle protein A3A Tenebrio molitor N Y Y Alpha-amylase Tenebrio molitor Y Y Y Tropomyosin-1, isoforms 9A/A/B Drosophila melanogaster Y Y Y Arginine kinase Drosophila melanogaster Y Y Y Cytochrome b Tenebrio molitor N N N Elongation of very long chain fatty acids protein Tenebrio molitor N N N TEST CASE: INSECT PROTEINS Westerhout et al., Reg. Toxicol. Pharmacol. 107 (2019)
- 9. PROTEIN ALLERGENICITY PREDICTION TOOL + Input OutputPrediction tool Yes No
- 10. KEY APPLICATIONS Screening novel or modified proteins for potential allergenicity risks Support in paragraph on allergenicity of novel food proteins or products in EFSA/FDA safety dossiers Selection for further investigation (e.g. IgE binding) Risk-based candidate selection tool for novel or modified proteins in early development programs Allergenicity prediction tool
- 11. INTERESTED TO DISCUSS YOUR NEEDS? PLEASE CONTACT: Bas Kremer Business developer Healthy Living E: bas.kremer@tno.nl P: +31 (0)88 866 6968