Flexible Kernels for Protein Property Prediction

• URL: http://arxiv.org/abs/2606.11057v1
• Date: Tue, 09 Jun 2026 16:20:36 GMT
• Title: Flexible Kernels for Protein Property Prediction
• Abstract summary: We introduce a class of sequence kernels that exploit evolutionary substitution matrices as well as local linearity.<n>We show that the resulting Gaussian processes provide data-efficient models of protein property landscapes.<n>We demonstrate that these structure-conditioned kernels are well suited to multi-task learning across multiple protein property landscapes.
• Score: 2.975516010948129
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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