DTU Health Tech
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If you need help with the bioinformatics programs, see the "Getting Help" section below the program.
The NetAllergen-1.0 server predicts protein allergenicity integrating MHC class II presentation propensity.
For publication of results, please cite:
NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction
Yuchen Li, Peter Wad Sackett, Morten Nielsen, Carolina Barra
Published: 16 October 2023, Bioinformatics Advances, vbad151, https://doi.org/10.1093/bioadv/vbad151
NetAllergen-1.0 is a predictive model based on the random forest algorithm. It incorporates novel MHC class II presentation propensity features to improve the allergenicity prediction.
NetAllergen-1.0 predicts allergenicity from protein sequences. The program only accepts amino acid sequences in fasta format. The user could paste sequences into the blank window or upload a fasta file by clicking "Browse...".
Motivation
Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In-silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes.
Results
To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited.
Here, you will find the data set used for training and evaluation.
NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction
Yuchen Li, Peter Wad Sackett, Morten Nielsen, Carolina Barra
Published: 16 October 2023, Bioinformatics Advances, vbad151, https://doi.org/10.1093/bioadv/vbad151
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If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).
If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.
Correspondence:
Technical Support: