TCR-TRANSLATE: CONDITIONAL GENERATION OF REAL ANTIGEN- SPECIFIC T-CELL RECEPTOR SEQUENCES

Dhuvarakesh Karthikeyan, Colin Raffel, Benjamin Garrett Vincent, Alex Rubinsteyn

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: Yes

Keywords: seq2seq, immunology, generative protein design, LLMs

TL;DR: Exploring the use of low-resourced machine translation for autoregressive generation of antigen-specific T-cell receptor sequences.

Abstract:

The paradoxical nature of T-cell receptor (TCR) specificity, which requires both
precise recognition and adequate coverage of antigenic peptide-MHCs (pMHCs),
poses a fundamental challenge in immunology. Efforts at modeling this complex
many-to-many mapping have focused on the detection of reactive TCR-pMHC
pairs as a binary classification task, with little success on unseen epitopes. Here,
we present TCR-TRANSLATE, a framework that adapts low-resource machine
translation techniques including semi-synthetic data augmentation and multi-task
objectives to generate target-conditioned CDR3β sequences for unseen input
pMHCs. We examine twelve model variants derived from the BART and T5
model architectures on a target-rich validation set of well-studied antigens, find-
ing an optimal model, TCRT5, that samples known and native-like CDR3β se-
quences for unseen epitopes. Our findings highlight both the potential and lim-
itations of sequence-to-sequence modeling in rapidly generating antigen-specific
TCR repertoires, emphasizing the need for experimental validation to bridge the
gaps between predictions, metrics, and functional capacity.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Dhuvarakesh_Karthikeyan1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 87