The notion that a single, static structure defines a protein is outdated for experimental and structural prediction. Macromolecules adopt an ensemble of conformations, and modeling those structural distributions accurately is now possible. By more correctly encapsulating the underlying experimental data, we can enable both benchmarks for prediction and a new class of "ensemble-function" studies. Moreover, accurately modeling compositional heterogeneity will reveal how ligands interact with the receptors, increasing the potential for an "AlphaFold" type of breakthrough in ligand design. Inevitably, all models are wrong, but we can at least aspire to make more useful models that take advantage of the expressive mmCIF format to better model the heterogeneity in the underlying experimental data.