Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are...

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Bibliographic Details
Published in:bioRxiv
Main Authors: Becker, Winston R, Jarmoskaite, Inga, Kappel, Kalli, Vaidyanathan, Pavanapuresan P, Denny, Sarah K, Das, Rhiju, Greenleaf, William J, Herschlag, Daniel
Format: Paper
Language:English
Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 08.03.2019
Cold Spring Harbor Laboratory
Edition:1.1
Subjects:
Algorithms
Biophysics
Biotechnology
Computer applications
Nearest-neighbor
Protein structure
Ribonucleic acid
RNA
Secondary structure
RNA folding
thermodynamics
nearest-neighbor rules
high-throughput biophysics
RNA secondary structure
ISSN:2692-8205, 2692-8205
Online Access:Get full text
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Summary:Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2692-8205
2692-8205
DOI:10.1101/571588