Predicting Immunodominant Epitopes in Allergens Using Machine Learning

By Annabel Romero, PhD,

Allergic diseases are the most widespread type of immune disorder in the world. They’re commonly caused by hypersensitivity of the immune system to proteins in typically harmless substances in the environment, such as pollen and certain foods. These allergic reactions occur when serum immune system responses bind to allergens, using protein binding sites known as epitopes.

Epitopes are protein-derived entities consisting of either a linear chain or a three-dimensional structure composed of amino acids. Understanding the epitope landscape for a particular allergen, while very valuable for developing new treatments, is incredibly difficult in practice. Many patient samples are needed to determine the most common epitopes across a population since the immune response can be spread across the entire surface of the allergen and varies by patient.

This white paper demonstrates a novel drug discovery pipeline developed by SFL Scientific which solves this problem. Our pipeline combines recent advances in machine learning and bioinformatics algorithms to predict an allergen protein’s structure and identify its most likely immunodominant epitopes, facilitating the development of new, effective treatments.

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