Blind tests of RNA nearest neighbor energy prediction
The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance amongst their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNAs nearest neighbor (NN) free energy parameters, but these methods have not been tested in predictive trials or on non-standard nucleotides. Here, we present the first such tests through a RECCES-Rosetta (Reweighting of Energy-function Collection with Conformational Ensemble Sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values systematic uncertainties. RECCES-Rosetta recovers the ten NN parameters for Watson-Crick stacked base pairs and thirty-two single-nucleotide dangling-end parameters with unprecedented accuracies - root-mean-square deviations (RMSD) of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For set-aside test sets, RECCES-Rosetta gives an RMSD of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and an RMSD of 0.99 kcal/mol on seven stacked pairs involving non-standard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol RMSD accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.\n\nSignificanceUnderstanding RNA machines and how their behavior can be modulated by chemical modification is increasingly recognized as an important biological and bioengineering problem, with continuing discoveries of riboswitches, mRNA regulons, CRISPR-guided editing complexes, and RNA enzymes. Computational strategies to understanding RNA energetics are being proposed, but have not yet faced rigorous tests. We describe a modeling strategy called RECCES-Rosetta that models the full ensemble of motions of RNA in single-stranded form and in helices, including non-standard nucleotides such as 2,6-diaminopurine, a variant of adenosine. When compared to experiments, including blind tests, the energetic accuracies of RECCES-Rosetta calculations are at levels close to experimental error, suggesting that computation can now be used to predict and design basic RNA energetics.