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

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 <

SUBMITTER: Howard AJ 

PROVIDER: S-EPMC11838293 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature