trRosetta

The trRosetta modeling results are generally summarized in a webpage, the link of which is sent to the users after the modeling is completed (see an example of the trRosetta output). This page includes a detailed explanation on the data listed on the trRosetta output page.

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About trRosetta

The input to trRosetta is the amino acid sequence of the query protein. shown in Figure 1, the trRosetta works as follows.
(1) When the sequence of query protein is submitted, a deep residual nerual network is applied to predict the inter-residue distance and orientation distribtuions.
(2) The predicted distance and orientation distribtuions are converted into smooth restraints, which are used to guide the Rosetta to build 3D structure models based direct energy mimization.


Figure 1. The flowchart of the trRosetta algorithm.

The predicted inter-residue information


Figure 2. The predicted inter-residue information.

The predicted structure models


Figure 3. The predicted models.

How to cite trRosetta?

Please cite the following article when you use the trRosetta server:
  • 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)
  • Need more help?

    If you have more questions or comments about the server, please email yangjynankai.edu.cn.