Blind tests of RNA–protein binding affinity prediction

Abstract

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 highthroughput 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.

Long non-coding RNA ZFAS1 promotes colorectal cancer tumorigenesis and development through DDX21-POLR1B regulatory axis

“…These results were supported by bioinformatics prediction of 2D or 3D structure, and the functional experiments including co-localization of ISH and IF, RNA pull-down assays. Although this regulation pattern of RNA-protein interaction had been widely investigated [ 46 – 48 ], however, no study was reported between ZFAS1 and DDX21 in regard to cancers. In osteosarcoma, ZFAS1 was observed directly interacting with ZEB2 protein and then regulated the stability of ZEB2, thereby leading to increased cell growth and metastasis [ 49 ].…”

On the Upper Bounds of the Real-Valued Predictions

“…Some examples of applications in bioinformatics (more information can be found in the reference therein) include the prediction of residue solvent accessibility, 1 protein folding kinetics, 2 protein stability changes on residue mutations, 3,4 protein affinity changes on residue mutations, 5,6 and binding affinity between RNA and protein molecules. 7 Given that all prediction methods exploit data that may contain a broad range of experimental variability, an estimate of the theoretical upper bound for the prediction is crucial for the understanding and interpretation of the results.…”

A Mathematical Analysis of HDV Genotypes: From Molecules to Cells

“…The outcome of RNA-protein interaction is likely to provide some key insights into these open questions. More generally, the link between the cellular level that is addressed by viral kinetics modeling and the molecular level of examining primary and secondary structures of the RNA molecule might be more thoroughly understood by the prediction of RNA-protein interactions, a computational sub-field that has been developed in recent years and is actively being pursued these days, e.g., [65][66][67].…”