RosettaFold , OpenFold , ESMFold , RaptorX , and other advanced techniques have reached the stage where single structure prediction of small proteins is robust and reliable, and a current frontier is multiple-state modeling . Establishing consistent ontologies and formats for representing such multiple-state models is crucial for supporting and advancing this important area of structural biology. This perspective addresses the significance and handling of multiple-conformation models of biomolecules. We begin with some key definitions. Conformers refer to atomic structures capable of interconversion without making or breaking covalent bonds. Conformational "collections" are defined here as sets of these conformers. Note that this is a looser definition than the concept of "thermodynamic statistical ensemble" in statistical mechanics, which describes the Boltzmann distribution of conformations contributing to ensemble-averaged measurable parameters. Conformational "states" are distinct conformational models (or collections of conformational models) that, in principle, can be distinguished experimentally. Structural models can be categorized as "single-state" or "multiple-state" based on the nature of the experimental data or the theoretical inference. Multiple-state models may constitute a pair of conformers, pairs of conformational collections, or collections of many conformers of, for example, disordered polymer chains. The distinction between states, and the enumeration of the number of states, is determined by the interpretation used in modeling the data. Terms used for such multiple-state models in the literature include alternative conformations, multi-conformer models, switched folds, metamorphic states, chameleonic states, conformational ensembles, and conformational excited states. As various methods for interpreting experimental data in terms of multiple-state structural models are evolving, there is a current pressing need for standardized representations of such models and their corresponding data in structural databases and across the structural biology community.