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bioRxiv · 10.1101/2025.01.27.635131

Combining Directed Evolution with Machine Learning Enables Accurate Genotype-to-Phenotype Predictions

Howard, A. J.·Rim, E. Y.·Garrett, O. D.·Shim, Y.·Notwell, J. H.·Ronald, P.
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Abstract

Linking sequence variation to phenotypic effects is critical for efficient exploitation of large genomic datasets. Here we present a novel approach combining directed evolution with protein language modeling to characterize naturally-evolved variants of a rice immune receptor. Using high-throughput directed evolution, we engineered the rice immune receptor Pik-1 to bind and recognize the fungal proteins Avr-PikC and Avr-PikF, which evade detection by currently characterized Pik-1 alleles. A protein language model was fine-tuned on this data to correlate sequence variation with ligand binding behavior. This modeling was then used to characterize Pik-1 variants found in the 3,000 Rice Genomes Project dataset. Two variants scored highly for binding against Avr-PikC, and in vitro analyses confirmed their improved ligand binding over the wild-type Pik-1 receptor. Overall, this machine learning approach identified promising sources of disease resistance in rice and shows potential utility for exploring the phenotypic variation of other proteins of interest.

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Howard, A. J., Rim, E. Y., Garrett, O. D., Shim, Y., Notwell, J. H., Ronald, P.. 2025-01-29. Combining Directed Evolution with Machine Learning Enables Accurate Genotype-to-Phenotype Predictions. https://doi.org/10.1101/2025.01.27.635131

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