trRosetta

Introduction

trRosetta is an algorithm for fast and accurate protein structure prediction. It builds the protein structure based on direct energy minimizations with a restrained Rosetta. The restraints include inter-residue distance and orientation distributions, predicted by a deep neural network. Homologous templates are included in the network prediction to improve the accuracy further. In benchmark tests on CASP13 and CAMEO derived sets, trRosetta outperforms all previously described methods. Read more about trRosetta...

The major results returned include (click here for an example):

Submit

  • Provide the protein data (mandatory)
  • Input a protein sequence (Click for an example input) or a multiple sequence alignment (MSA) below.
    Or upload the protein 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)


    Do not use templates (check this box if you DO NOT want to use any PDB templates; the library was updated on Mar 13, 2022. Check here for more information)

    Run trRosettaX-Single (check this box for single-sequence folding, e.g., no homologous sequences and templates will be used.)

    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

  • 11/07/2022, The paper for trRosettaX-Single was accepted for publication by Nature Computational Science. The source codes can be downloaded here.
  • 08/26/2022, The trRosetta server has predicted the structure models for >100,000 proteins.
  • 07/26/2022, Predicted local accuracy was included for each model. The results page was redesigned to show local accuracy.
  • 05/03/2022, The network was updated with an improved accuracy by ~10%.
  • 01/06/2022, The maximum length of protein sequence was increased from 1000 to 1500.
  • 12/23/2021, Single-sequence folding with trRosettaX-Single was included. It works well for orphand proteins and designed proteins.
    ( >> read more ... ).

    References

  • Du et al, The trRosetta server for fast and accurate protein structure prediction, Nature Protocols, 16: 5634-5651 (2021). (PDF)
  • Su et al, Improved protein structure prediction using a new multi-scale network and homologous templates, Advanced Science, 8, 2102592 (2021). (PDF)
  • Yang et al, Improved protein structure prediction using predicted interresidue orientations, PNAS, 117: 1496-1503 (2020). (PDF)