<div><p>The functions of RNA-dependent RNA polymerases (RdRps) in RNA viruses are demonstrably modulated by native substrates of dynamic and interconvertible conformational ensembles. Many of these are populated by essential flexible or intrinsically disordered regions (IDRs) that lack a stable three-dimensional (3D) structure and that make up nearly 16% of the conserved RdRp domains across Riboviria lineages. Typical structural models of RdRps are conversely generally agnostic of multiple conformations and their fluctuations, whether derived from protein structure predictors or from experimentally resolved structures from crystal states or dynamic conformer sets. In this review, we highlight how biophysics-inspired prediction tools combined with advanced deep learning algorithms, such as AlphaFold2 (AF2), can help efficiently infer the conformational heterogeneity and dynamics of RdRps. We discuss the use of AF2 for protein structure prediction, together with its limitations and impacts on RNA virus protein characterization, and specifically address its low-confidence prediction scores, which largely capture IDRs. Key examples illustrate how biophysical-encoded preferences of generic sequence-ensemble relationships can help estimate the global RdRp structural diversity and RNA virus discovery. The quantitative perception we present also highlights the challenging magnitude of the emergent sequence-to-conformations relationships of proteins and illustrates more robust and accurate annotations of novel or divergent RdRps. Finally, the coarse-grained IDR-based structural depiction of RdRp conformations offers concrete perspectives on an integrative framework to Frontiers in Virology frontiersin.org 01</p></div>Read less <