Significance

RNA and protein molecules interact to perform translation, splicing, and other fundamental processes. These interactions are defined by their strength and specificity, but it remains infeasible to experimentally measure these properties for all biologically important RNA–protein complexes. Development of computational strategies for calculating RNA–protein energetics has been hindered by unique complexities of RNA–protein binding, particularly the propensity of RNA to adopt multiple conformations in the unbound state. We describe a method, Rosetta-Vienna RNP-ΔΔG, combining 3D structure modeling with RNA secondary structure-based energetic calculations to predict RNA–protein relative binding affinities. For several diverse systems and in rigorous blind tests, the accuracy of Rosetta-Vienna RNP-ΔΔG compared with experimental measurements is significantly better than that of prior approaches.

Abstract

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA–protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA–protein binding. Here, we bring together several advances to complete a calculation framework for RNA–protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein–RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2–RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA–protein binding that can be tested by prospective high-throughput measurements on new systems.

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Acknowledgments

We thank Sarah Denny and members of the R.D. laboratory for useful discussions. Calculations were performed on the Stanford BioX3 cluster, supported by NIH Shared Instrumentation Grant 1S10RR02664701, and the Stanford Sherlock cluster. This work was supported by an NSF Graduate Research Fellowship (K.K.), a Stanford Graduate Fellowship (K.K.), NIH Grants R01GM100953 (to R.D.), R01GM111990 (to W.J.G.), and P01 GM066275 (to R.D., W.J.G.,and D.H.), and a RosettaCommons Grant (to R.D.). W.J.G. is a Chan Zuckerberg Biohub Investigator.

Supporting Information

Appendix (PDF)
Dataset_S01 (TXT)
Dataset_S02 (TXT)
Dataset_S03 (TXT)
Dataset_S04 (TXT)
Dataset_S05 (TXT)
Dataset_S06 (TXT)

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 116 | No. 17
April 23, 2019
PubMed: 30962376

Classifications

Submission history

Published online: April 8, 2019
Published in issue: April 23, 2019

Keywords

  1. RNA–protein complex
  2. conformational change
  3. binding affinity
  4. blind prediction
  5. energetic prediction

Acknowledgments

We thank Sarah Denny and members of the R.D. laboratory for useful discussions. Calculations were performed on the Stanford BioX3 cluster, supported by NIH Shared Instrumentation Grant 1S10RR02664701, and the Stanford Sherlock cluster. This work was supported by an NSF Graduate Research Fellowship (K.K.), a Stanford Graduate Fellowship (K.K.), NIH Grants R01GM100953 (to R.D.), R01GM111990 (to W.J.G.), and P01 GM066275 (to R.D., W.J.G.,and D.H.), and a RosettaCommons Grant (to R.D.). W.J.G. is a Chan Zuckerberg Biohub Investigator.

Notes

This article is a PNAS Direct Submission.

Authors

Affiliations

Biophysics Program, Stanford University, Stanford, CA 94305;
Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305;
Pavanapuresan P. Vaidyanathan
Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305;
Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305;
Department of Applied Physics, Stanford University, Stanford, CA 94305;
Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305;
Biophysics Program, Stanford University, Stanford, CA 94305;
Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305;
Department of Physics, Stanford University, Stanford, CA 94305

Notes

1
To whom correspondence should be addressed. Email: [email protected].
Author contributions: K.K. and R.D. designed research; K.K., I.J., and P.P.V. performed research; K.K., I.J., and P.P.V. contributed new reagents/analytic tools; K.K., I.J., P.P.V., W.J.G., D.H., and R.D. analyzed data; and K.K., I.J., P.P.V., W.J.G., D.H., and R.D. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

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    Blind tests of RNA–protein binding affinity prediction
    Proceedings of the National Academy of Sciences
    • Vol. 116
    • No. 17
    • pp. 8083-8633

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