A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins

The roles of structural dynamics in the cellular functions of RNAs

RNAs fold into 3D structures that range from simple helical elements to complex tertiary structures and quaternary ribonucleoprotein assemblies. The functions of many regulatory RNAs depend on how their 3D structure changes in response to a diverse array of cellular conditions. In this Review, we examine how the structural characterization of RNA as dynamic ensembles of conformations, which form with different probabilities and at different timescales, is improving our understanding of RNA function in cells. We discuss the mechanisms of gene regulation by microRNAs, riboswitches, ribozymes, post-transcriptional RNA modifications and RNA-binding proteins, and how the cellular environment and processes such as liquid-liquid phase separation may affect RNA folding and activity. The emerging RNA-ensemble-function paradigm is changing our perspective and understanding of RNA regulation, from in vitro to in vivo and from descriptive to predictive.

Demonstration of protein cooperativity mediated by RNA structure using the human protein PUM2

Posttranslational gene regulation requires a complex network of RNA-protein interactions. Cooperativity, which tunes response sensitivities, originates from protein-protein interactions in many systems. For RNA-binding proteins, cooperativity can also be mediated through RNA structure. RNA structural cooperativity (RSC) arises when binding of one protein induces a redistribution of RNA conformational states that enhance access (positive cooperativity) or block access (negative cooperativity) to additional binding sites. As RSC does not require direct protein-protein interactions, it allows cooperativity to be tuned for individual RNAs, via alterations in sequence that alter structural stability. Given the potential importance of this mechanism of control and our desire to quantitatively dissect features that underlie physiological regulation, we developed a statistical mechanical framework for RSC and tested this model by performing equilibrium binding measurements of the human PUF family protein PUM2. Using 68 RNAs that contain two to five PUM2-binding sites and RNA structures of varying stabilities, we observed a range of structure-dependent cooperative behaviors. To test our ability to account for this cooperativity with known physical constants, we used PUM2 affinity and nearest-neighbor RNA secondary structure predictions. Our model gave qualitative agreement for our disparate set of 68 RNAs across two temperatures, but quantitative deviations arise from overestimation of RNA structural stability. Our results demonstrate cooperativity mediated by RNA structure and underscore the power of quantitative stepwise experimental evaluation of mechanisms and computational tools.

Blind tests of RNA-protein binding affinity prediction

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.