期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：30
页码：8430-8435
DOI：10.1073/pnas.1523335113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance among their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNA’s nearest-neighbor (NN) free-energy parameters, but these methods have not been tested in predictive trials or on nonstandard nucleotides. Here, we present, to our knowledge, 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 10 NN parameters for Watson–Crick stacked base pairs and 32 single-nucleotide dangling-end parameters with unprecedented accuracies: rmsd of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For set-aside test sets, RECCES–Rosetta gives rmsd values of 0.32 kcal/mol on eight stacked pairs involving G–U wobble pairs and 0.99 kcal/mol on seven stacked pairs involving nonstandard 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.
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