arXiv Machine Learning By Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry N. Ward, Hunter Nisonoff, James M. McFarland, Gevorg Grigoryan

Flexible Kernels for Protein Property Prediction

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arXiv:2606. 11057v1 Announce Type: new 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.

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arXiv Machine Learning
Aug 27

Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction

The paper introduces a method that uses orthogonal projection to remove the influence of known biochemical features from protein language model (PLM) embeddings, allowing the authors to assess how much these features contribute to protein fitness predictions. By applying this technique to high‑order and interaction effects, they demonstrate that eliminating these interpretable features reduces downstream classifier performance, indicating that PLM embeddings encode patterns correlated with biochemical properties. The authors also show that these biochemical features explain a substantial portion of the variance in the classifier’s predictions, suggesting that PLM embeddings capture biologically relevant information.

By Paulo Yanez Sarmiento, Pia Francesca Rissom, Manuel Pfeuffer, Marco Simnacher, Jordan F. Safer, Sumaiya Iqbal, Henrike O. Heyne, Nadja Klein, Bernhard Y. Renard