Haihan Song, MD, Ph.D
Immunotherapy; cancers; innate immunity; adaptive immunity; cell therapy.
One of the fields in immunology for which many accurate algorithms exist is predicting the immunopeptidome. Determining whether a peptide can be presented by the MHC molecules is of utmost importance and can be a step forward towards personalized vaccine development. The most accurate way to determine immunopeptidome is mass spectrometry (MS). However, it is an expensive and time-consuming technique. ML not only can determine the immunopeptidome, but it can also be trained by the current data on human immunopeptidome and predict whether a given peptide can be presented by MHC-I molecules. Data acquired from in vitro studies provide information on peptide-MHC affinity. These data can be used to generate models that can predict presentability of neoantigens discovered through whole exome sequencing of tumors by MHC molecules. Predicting epitopes for MHC-II molecules is even further challenging due to different peptide length. Our DICAT AI-based algorithms have shown promising efficacy in predicting MHC-II epitopes based on their amino acid sequence and designing vaccines targeting MHC-II immunopeptidome. However, since these models are based on in vitro data, they may not predict in vivo interactions with the same level of accuracy.
The department is able to go through databases, retrieve desired information and recruit this information to predict outcome and design therapeutic interventions for a given individual.
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