Machine Learning for Studying Plant Protein Function and Evolution

Machine learning (ML) has transformed plant protein research by elucidating the complex relationships between structure, function, and evolution across diverse species. Advanced architectures including convolutional neural networks for spatial pattern recognition, recurrent models for sequential dependencies, and transformers for long-range interactions have bridged the gap between raw sequence data and three-dimensional structural predictions. While AlphaFold2 and similar frameworks demonstrate remarkable accuracy in structure determination, they encounter limitations with dynamic features characteristic of plant proteins, necessitating hybrid approaches that incorporate biophysical constraints. Transfer learning and multiview frameworks effectively address data scarcity by integrating multiomics datasets, while graph neural networks map protein–metabolite interaction networks to reveal functional modules within stress-response pathways. Explainable AI methodologies provide critical interpretability, connecting computational predictions with established biological principles and guiding hypothesis generation toward the identification of conserved motifs associated with adaptive traits. Despite significant progress, challenges persist in dataset fragmentation, polyploid genome complexity, and generalization across understudied species, underscoring the need for FAIR data practices and equitable model deployment. The integration of ML-generated hypotheses with targeted experimental validation promises to accelerate the development of climate-resilient proteins and crops, addressing global challenges in food security and biodiversity conservation during a period of unprecedented environmental change.