Rapid and accurate determination of atomistic RNA dynamic ensemble models using NMR and structure prediction

Honglue Shi,David A Case,Daniel Herschlag,Hala Abou Assi,Hashim M Al-Hashimi,Joseph D Yesselman,Atul Rangadurai,Rohit Roy

doi:10.1038/s41467-020-19371-y

Abstract

Biomolecules form dynamic ensembles of many inter-converting conformations which are key for understanding how they fold and function. However, determining ensembles is challenging because the information required to specify atomic structures for thousands of conformations far exceeds that of experimental measurements. We addressed this data gap and dramatically simplified and accelerated RNA ensemble determination by using structure prediction tools that leverage the growing database of RNA structures to generate a conformation library. Refinement of this library with NMR residual dipolar couplings provided an atomistic ensemble model for HIV-1 TAR, and the model accuracy was independently supported by comparisons to quantum-mechanical calculations of NMR chemical shifts, comparison to a crystal structure of a substate, and through designed ensemble redistribution via atomic mutagenesis. Applications to TAR bulge variants and more complex tertiary RNAs support the generality of this approach and the potential to make the determination of atomic-resolution RNA ensembles routine.

Highlights

Biomolecules form dynamic ensembles of many inter-converting conformations which are key for understanding how they fold and function
Fragment Assembly of RNA with Full-Atom Refinement (FARFAR)-library better predicts transactivation response element (TAR) residual dipolar coupling (RDC) compared to MDgenerated library
We tested our approach on the transactivation response element (TAR) (Fig. 2a) from HIV-122,23, which has served as a model system for bulge motifs

Summary

Introduction

Biomolecules form dynamic ensembles of many inter-converting conformations which are key for understanding how they fold and function. Determining ensembles is challenging because the information required to specify atomic structures for thousands of conformations far exceeds that of experimental measurements We addressed this data gap and dramatically simplified and accelerated RNA ensemble determination by using structure prediction tools that leverage the growing database of RNA structures to generate a conformation library. To deeply understand RNAs at a level that makes it possible to rationally manipulate their behavior in drug discovery and synthetic biology, we need the ability to determine their dynamic ensembles at atomic resolution This presents a challenge to current biophysical techniques: the information required to specify the position of all atoms in thousands of conformations in an ensemble far exceeds the information content of experimental measurements. We used quantum-mechanical calculations of NMR chemical shifts[21] and cross-validation analysis[12] to test the accuracy of the generated ensembles (Fig. 1)

Methods

Results

Conclusion