Blind tests of RNA–protein binding affinity prediction

By K. Kappel, Inga Jarmoskaite, P. Vaidyanathan, W.J. Greenleaf, Daniel Herschlag, Rhiju Das. 2019

26 citations recorded by the source corpus.

Significance RNA and protein molecules interact to perform translation, splicing, and other fundamental processes. These interactions are defined by their strength and specificity, but it remains infeasible to experimentally measure these properties for all biologically important RNA–protein complexes. Development of computational strategies for calculating RNA–protein energetics has been hindered by unique complexities of RNA–protein binding, particularly the propensity of RNA to adopt multiple conformations in the unbound state. We describe a method, Rosetta-Vienna RNP-ΔΔG, combining 3D structure modeling with RNA secondary structure-based energetic calculations to predict RNA–protein relative binding affinities. For several diverse systems and in rigorous blind tests, the accuracy of Rosetta-Vienna RNP-ΔΔG compared with experimental measurements is significantly better than that of prior approaches. 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.

Subfields: Molecular Biology