MCRA: Multi-Context Residue Adaptation for Protein Language Model Transfer

Protein language models encode complementary information across layers and sequence contexts, but frozen-backbone transfer often relies on the final layer or a uniform adapter. We introduce MCRA (Multi-Context Residue Adaptation), a residue-wise multi-scale framework that learns task-specific layer fusion, routes residues among context experts, and gates the adapted features with the frozen final-layer representation. On 13 protein prediction benchmarks, we compare MCRA with eight frozen protein models equipped with independently trained six-layer Transformer adapters, using the same data splits and evaluation metrics. MCRA obtains the highest or tied-highest score on 11 tasks. Component comparisons examine layer fusion and context adaptation, while perturbation analyses describe task-dependent changes in sequence-averaged routing. Together, these results support coordinating task-level depth selection with residue-level context adaptation for frozen protein model transfer.