trRosettaRNA

Introduction

trRosettaRNA is a deep learning-based algorithm designed for the automated prediction of RNA 3D structures. Utilizing a transformer network, it predicts both 2D inter-nucleotide geometries and the corresponding 3D structure. If the initial 3D structure is not physically plausible, Rosetta energy minimization is employed to refine the model, incorporating restraints from the predicted 2D inter-nucleotide geometries. Blind tests, including CASP15 and RNA-Puzzles, demonstrate that the automated predictions made by trRosettaRNA are competitive with those produced by leading human teams. The algorithm has been successfully applied to generate confident structural models for 263 Rfam families that lack known structures. Read more about trRosettaRNA...

The output results include (click here for an example):

Submit
  • Provide the RNA data (mandatory)
    •
    Input a RNA sequence (Click for an example input) or a multiple sequence alignment (MSA) (Click for an example input) below.
    Or upload the RNA sequence/MSA file:

    Input type: (Click for explanation)



  • Other information (optional)
    • Email: (Optional, where the results will be sent to)

    Target name: (Optional, your given name to this target)


    Keep my results private (check this box if you want to keep your job private. A key will be assigned for you to access the results)


    News

  • 11/01/2024, The trRosettaRNA server has been improved with an new end-to-end network and enhanced visualization of results.
  • 02/03/2024, The standalone packages have been enhanced to support custom secondary structure files as input.
  • 10/13/2023, The trRosettaRNA paper was accepted for publication by Nature Communications.
  • 10/25/2022, The trRosettaRNA server was established.
    ( >> read more ... ).

    Reference

  • Wang et al, trRosettaRNA: automated prediction of RNA 3D structure with transformer network, Nature Communications, 14: 7266 (2023). (PDF)