It is shown that single-sequence PLMs can perform in-context peptide learning without gradient updates, task-specific retraining, or architectural modification, and MPEP conditioning is established as a lightweight strategy for low-data peptide classification.
Abstract
Protein language models (PLMs) are trained primarily on individual protein sequences, yet many peptide-discovery problems require inference from only a small number of labeled examples. Here, we show that single-sequence PLMs can perform in-context peptide learning without gradient updates, task-specific retraining, or architectural modification. We introduce multi-peptide example prompts (MPEPs), in which demonstration peptides are concatenated with glycine spacers and used as context for scoring query peptides by their prompted probability. We evaluate this approach across a synthetic pattern-completion task, secondary-structure classification, and MHC-II binder prediction using both encoder-only ESM-2 models and decoder-only ProGen2 models. Across tasks, performance improves with the number of peptide examples and with model scale, indicating that PLMs can extract shared sequence-level properties from prompted examples. We further introduce a difference score that contrasts positive-example and negative-example MPEPs, reducing compositional biases in raw PLM probabilities and substantially improving classification. On MHC-II binder prediction, MPEP-based classification with larger ESM-2 models matches or exceeds low-data classifiers trained on frozen ESM-2 embeddings, while requiring no training. These results reveal an unexpected in-context inference capability in single-sequence PLMs and establish MPEP conditioning as a lightweight strategy for low-data peptide classification.
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