AI models to predict immunogenicity of peptides for T-cell immunity.

What is it about?

An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life-threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native peptides to elicit a T-cell response, however, we currently lack accurate immunogenicity inference methods. In this work, we benchmarked various machine learning (ML) and artificial intelligence (AI) models on immunogenic peptide collections. We further accurately simulated immunogenic peptides with physicochemical properties and immunogenicity predictions similar to that of real antigens. We showed our models work well on dengue virus, cancer neoantigen and SARS-CoV-2 (COVID) immunogenic peptide collections.

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Photo by Fusion Medical Animation on Unsplash

Photo by Fusion Medical Animation on Unsplash