Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions; instead, they exist as highly dynamic ensembles of interconverting conformations. Capturing these heterogeneous ensembles with sufficient accuracy is essential for uncovering the fundamental links between sequence characteristics, conformational preferences, and biological function. However, this remains a formidable challenge: experimental techniques typically provide observables that represent averages over a vast number of conformations, while computational approaches rely critically on the accuracy of parameters and the adequacy of conformational sampling. In this review, we provide a comprehensive framework for integrating experimental data and molecular simulations to construct realistic conformational ensembles of IDPs. We discuss strategies for interpreting ensemble-averaged experimental observables, developing physically grounded and transferable computational models, and refining simulated ensembles using experimental restraints. Together, these approaches offer practical guidelines for combining multiple sources of data, assessing ensemble convergence, and validating model predictions, thereby providing a robust route toward generating reliable and predictive ensembles that illuminate the intricate sequence–ensemble–function relationships underpinning disordered protein science.
Saha et al. (Thu,) studied this question.