Deep generative models for T cell receptor protein sequences
By K. Davidsen, Branden J. Olson, William S. DeWitt, Jean Feng, Elias Harkins, Philip Bradley, Frederick Albert Matsen IV. 2019
68 citations recorded by the source corpus.
Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.
Subfields: Immunology, Molecular Biology
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In Immunology
In Molecular Biology
- Xingjie Pan
- Tanja Kortemme
- David Baker
- Jeffrey J. Gray
- I. Samish
- Colin A. Smith
- Jens Meiler
- Noah Ollikainen
Citations in the corpus
- Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “A number of generative models were developed for sequence design, including generative adversarial networks (104), variational autoencoders (105, 106), and graph-based (107, 108) models.”