Flexible Kernels for Protein Property Prediction
Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge. Accordingly, we introduce a class of sequence kernels that exploit evolutionary substitution matrices as well as local linearity and demonstrate that the resulting Gaussian processes provide data-efficient models of protein property landscapes, frequently outperforming alternatives that rely on foundation model embeddings. Furthermore--by learning what are in effect structure-aware substitution matrices--we show that our kernels can readily incorporate structural information from foundation models. ...
| Authors | Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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