Development of ML-based epitope prediction algorithms for T-cell MHC and protective B-cell antigens

KEYWORDS: Public datasets | Epitope prediction | Antibodies | Machine Learning & AI | Immunology


African swine fever (ASF) is a highly contagious and deadly viral disease that affects both domestic and wild pigs. It poses a serious problem to livestock health, pig farmers’ livelihoods, food security, as well as biodiversity. Currently, there is no effective vaccine against ASF.
The goal of our customer was to develop the vaccine as efficiently as possible using public data and state-of-the-art computational algorithms.


We started with datasets and features preparation, sourcing from public databases, such as IEDB. Using the data, we developed an algorithm and trained a deep neural network model on a genome scale. With this approach, we were able to identify epitopes inducing neutralizing antibodies and determine the optimal T-cell strings for broad porcine MHC coverage.


Our customer was able to focus the vaccine development efforts on the identified candidates and therefore save time and reduce costs.

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