Coarse-tuning to improve a protein foundation model for a domain of proteins: the example of outer membrane beta barrels

Coarse-tuning a protein foundation model for Outer Membrane Beta Barrels (OMBBs) yields better transfer-learning performance than the original model, using fewer than 100,000 unlabeled sequences.

• Why it matters: Domain-specific protein environments can limit how well general embeddings transfer, and labeled data are often scarce for specialized protein families.
• What they did: The authors further updated a foundation model with a defined but unlabeled OMBB dataset, then evaluated transfer learning and analyzed coarse-tuned embeddings and attention via contact map predictions.
• The result: Coarse-tuned models outperform the original across multiple transfer learning tasks and their embedding/attention behavior is more aligned with OMBB contact patterns, implying effective specialization with <100,000 sequences.

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