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arXiv · 2606.11057

Flexible Kernels for Protein Property Prediction

Martin Jankowiak·Yerdos Ordabayev·Rudraksh Tuwani·Henry N. Ward·Hunter Nisonoff·James M. McFarland·Gevorg Grigoryan
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Abstract

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. We demonstrate that these structure-conditioned kernels are well suited to multi-task learning across multiple protein property landscapes and can decisively outperform local supervised learning methods.

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Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry N. Ward, Hunter Nisonoff, James M. McFarland, Gevorg Grigoryan. 2026-06-09. Flexible Kernels for Protein Property Prediction. https://arxiv.org/abs/2606.11057

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