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):


  • 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 squence/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 Oct 13, 2021. Check here for more information)

    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.)


  • 09/16/2021, A new paper to summarize the latest development of trRosetta was accepted to Advanced Science. All training codes, training data, pre-trained models and inference codes can be downloaded here.
  • 08/31/2021, A new paper with detailed guidelines for using the trRosetta server and the standalone package was accepted to Nature Protocols.
  • 06/24/2021, A new version of the trRosetta standalone package was released: download here.
  • 03/24/2021, A new option was added to support input of a multiple sequence alignment (MSA).
  • 03/04/2021, trRosetta predicts protein structures for every protein family in the Pfam database.
    ( >> read more ... ).


  • Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, J Yang, The trRosetta server for fast and accurate protein structure prediction, Nature Protocols, in press (2021).
  • J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker, Improved protein structure prediction using predicted interresidue orientations, PNAS, 117: 1496-1503 (2020).