On generative models of T-cell receptor sequences

Abstract

T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free Variational Auto-Encoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable, at a lower computational cost.

References in corpus (8)

• An Introduction to Variational Autoencoders
• Machine learning and the physical sciences
• Statistical inference of the generation probability of T-cell receptors from sequence repertoires
• IGoR: a tool for high-throughput immune repertoire analysis
• Inverse Statistical Physics of Protein Sequences: A Key Issues Review
• OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs
• Inferring interaction partners from protein sequences
• Quantifying selection in immune receptor repertoires

Cited by in corpus (1)

• Deep generative selection models of T and B cell receptor repertoires with soNNia