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Application of Biochip Microfluidic Technology to Detect Serum Allergen-specific Immunoglobulin E sIgE

Application of Biochip Microfluidic Technology to Detect Serum Allergen-specific Immunoglobulin E sIgE

Published on: April 21, 2019

Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein

Kento Goto¹, Norimasa Tamehiro², Takumi Yoshida¹

¹Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan.

The Journal of Biological Chemistry

|April 22, 2023

Summary

This summary is machine-generated.

Researchers developed a data-driven machine learning method to identify novel allergen-specific patterns (ASPs) in proteins. This approach accurately predicts allergenicity, aiding the safety assessment of novel synthetic foods and functional proteins.

Keywords:

allergen amino acid computational biology immunoglobulin E major histocompatibility complex mathematical modeling protein motif statistics

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Removal and Replacement of Endogenous Ligands from Lipid-Bound Proteins and Allergens

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Published on: February 24, 2021

Area of Science:

Food science and technology
Bioinformatics
Molecular biology

Background:

Genome editing and synthetic biology enable novel food and protein production.
Accurate evaluation of food and protein toxicity and allergenicity is crucial.
Known allergen-specific patterns (ASPs) are limited, necessitating new discovery methods.

Purpose of the Study:

To introduce a data-driven, machine learning approach for discovering novel allergen-specific patterns (ASPs) in amino acid sequences.
To enable an exhaustive search for amino acid subsequences with significantly higher frequencies in allergenic proteins.
To improve the prediction of protein allergenicity for novel food and protein safety assessments.

Main Methods:

Developed a data-driven approach utilizing machine learning to identify allergen-specific patterns (ASPs).
Conducted an exhaustive search for amino acid subsequences overrepresented in allergenic proteins.
Created and applied the method to a database of 21,154 proteins with known allergenicity data.

Main Results:

Identified novel allergen-specific patterns (ASPs) consistent with existing biological knowledge.
Achieved higher allergenicity prediction performance compared to existing methods.
Demonstrated the utility of the detected ASPs in evaluating synthetic food and protein safety.

Conclusions:

The proposed data-driven machine learning method effectively discovers novel allergen-specific patterns (ASPs).
This approach enhances the accuracy of allergenicity prediction for proteins.
The method shows promise for evaluating the safety of novel synthetic foods and functional proteins.