From Possibility to Precision in Macromolecular Ensemble Prediction

Abstract: Proteins and other macromolecules do not exist in a single state but as dynamic ensembles of interconverting conformations, which are essential for functions such as catalysis, allosteric regulation, and molecular recognition. While AI-based structure predictors like AlphaFold have revolutionized static structure prediction, they are not yet capable of capturing conformational heterogeneity. Progress towards the next generation of AI models capable of ensemble prediction is currently limited by the lack of accurate, high-resolution ground truth ensembles at the scale required for training and validation. No single experimental technique can fully resolve the atomistic complexity of conformational landscapes, and fundamental challenges remain in defining, representing, comparing, and validating structural ensembles. Here, we outline the infrastructure and methodological advances needed to overcome these barriers. We highlight emerging strategies for integrating heterogeneous experimental data into unified ensemble encoding representations and how to leverage these new methodologies to build benchmarks and establish ensemble-specific validation protocols. Finally, we discuss how ensemble predictions will be an interactive cycle of experimental and computational innovation. Establishing this ecosystem will allow structural biology to move beyond static snapshots toward a dynamic understanding of molecular behavior that captures the full complexity of biological systems.