About this Research Topic
Proteins in food and consumer products can pose a risk for allergenicity or toxicity. Existing methods for detection and analysis of protein allergenicity and toxicity potential rely heavily on biological assays, including in vitro techniques (e.g., tissue culture) and in vivo (e.g., mouse bioassay), as well as proteomic methods including enzyme-linked immune sorbent assay (ELISA), radioimmunoassay (RIA), and mass spectrometry-based methods. These methods can be expensive, time-consuming, limited to assessing single proteins and often require expert operation.
Evaluating the allergenicity and toxicity potential of food components such as peptides or proteins with the help of bioinformatic tools has recently gained increasing interest for foods and consumer products. Computational and machine learning methods can predict the allergenicity and toxicity of multiple proteins much faster and are much simpler to perform than experimental techniques. However, the accurate prediction and evaluation of the allergenicity and toxicity potential of novel proteins is still a challenge.
The aim of this Research Topic is to cover the publication of new methods, databases web servers, and substantially updated databases and web servers in the field of bioinformatics approaches for the prediction and assessment of protein allergenicity and toxicity potential. This topic will provide an insight into the recent advances and developments of computational methods as well as useful and user-friendly tools, databases and web servers for predicting protein allergenicity and toxicity. Areas to be covered in this Research Topic may include, but are not limited to:
• Methods/databases/web servers for prediction and assessment of protein allergenicity potential
• Methods/databases/web servers for prediction and assessment of protein toxicity potential
Evaluating the allergenicity and toxicity potential of food components such as peptides or proteins with the help of bioinformatic tools has recently gained increasing interest for foods and consumer products. Computational and machine learning methods can predict the allergenicity and toxicity of multiple proteins much faster and are much simpler to perform than experimental techniques. However, the accurate prediction and evaluation of the allergenicity and toxicity potential of novel proteins is still a challenge.
The aim of this Research Topic is to cover the publication of new methods, databases web servers, and substantially updated databases and web servers in the field of bioinformatics approaches for the prediction and assessment of protein allergenicity and toxicity potential. This topic will provide an insight into the recent advances and developments of computational methods as well as useful and user-friendly tools, databases and web servers for predicting protein allergenicity and toxicity. Areas to be covered in this Research Topic may include, but are not limited to:
• Methods/databases/web servers for prediction and assessment of protein allergenicity potential
• Methods/databases/web servers for prediction and assessment of protein toxicity potential
Keywords: protein allergenicity, protein toxicity potential, Bioinformatics
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