Deep generative selection models of T and B cell receptor repertoires with soNNia
Giulio Isacchini, Aleksandra M. Walczak, Thierry Mora, Armita Nourmohammad
Abstract
Significance The adaptive immune system relies on many types of B and T cells, whose functions are reflected in the distinct molecular features of their receptor sequences. Here, we introduce an inference framework, soNNia, which integrates interpretable knowledge-based models of immune receptor generation with flexible and powerful deep learning approaches to characterize sequence determinants of receptor function. Using soNNia, we characterize sequence-specific selection associated with receptors harvested from different cell types and tissues. We quantify synergetic interactions between the molecular features of the paired chains making up the receptor. Lastly, we develop a selection-based classifier to identify T cells specific to distinct pathogenic epitopes. Our approach provides a molecular understanding for how sequence determines the specific functionality of immune receptors.