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Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking.

Kalli Kappel1, Rhiju Das2

  • 1Biophysics Program, Stanford University, Stanford, CA 94305, USA.

Structure (London, England : 1993)
|November 13, 2018
PubMed
Summary

A new computational method, RNP-denovo, models RNA-protein complex structures by simultaneously folding and docking RNA. This approach accurately predicts structures from low-resolution data, advancing our understanding of essential cellular processes.

Keywords:
RNARNA binding proteinRNA-protein complexproteinribonucleoproteinspliceosomestructure modelingtelomerase

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • RNA-protein complexes are vital for cellular functions like translation and gene regulation.
  • Determining the high-resolution structures of these complexes is challenging due to RNA's conformational flexibility.
  • Existing computational methods struggle to integrate low-resolution data and model RNA's conformational changes upon complex formation.

Purpose of the Study:

  • To develop a computational method, RNP-denovo, for de novo modeling of RNA-protein complexes.
  • To enable simultaneous folding and docking of RNA to protein surfaces.
  • To accurately determine structures from low-resolution experimental data.

Main Methods:

  • RNP-denovo, a Rosetta-based computational method, was developed.
  • The method simultaneously models RNA folding and its docking to a protein.
  • It integrates sparse experimental data such as FRET and crosslinking.

Main Results:

  • RNP-denovo successfully sampled native-like structures for diverse RNA-protein complexes with near-nucleotide resolution.
  • The method significantly improved model accuracy in past blind modeling challenges (spliceosome, telomerase, methyltransferase-ribosomal RNA).
  • Performance was validated using previously published low-resolution data.

Conclusions:

  • RNP-denovo provides a robust computational approach for modeling RNA-protein complexes.
  • The method effectively integrates low-resolution data to predict global structures.
  • This opens new avenues for studying the structure-function relationships of these critical molecular machines.