Blind tests of RNA nearest neighbor energy prediction

BioRxiv : the Preprint Server for Biology
Fang-Chieh ChouRhiju Das


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 RNA's 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 predi...Continue Reading

Related Concepts

Asphondylia rosetta
Energy Transfer
Suaeda occidentalis
Blinded Clinical Study
Base Pairing

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Proceedings of the National Academy of Sciences of the United States of America
Fang-Chieh ChouRhiju Das
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Shimpei NishidaMichiaki Hamada
BioRxiv : the Preprint Server for Biology
Joseph David YesselmanRhiju Das
© 2020 Meta ULC. All rights reserved