Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution
Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide rich representations for this task, but task-agnostic zero-shot scores can be misaligned with a target assay, while supervised search in high-dimensional embedding spaces can make surrogate modeling and uncertainty estimation sample-inefficient. We propose the Linear Fitness Subspace (LFS) hypothesis: within mutation-induced residue-level representation changes, a compact, assay-specific set of directions makes fitness variation linearly accessible from few labeled variants. This is a local, supervision-recoverable statement rather than a cla...
| Authors | SiYuan Ma, Canran Xiao, Zikai Xiao et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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