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The trRosetta server for fast and accurate protein structure prediction

Nature Protocols volume 16, pages 5634–5651 (2021)Cite this article

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Abstract

The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and accurate protein structure prediction, powered by deep learning and Rosetta. With the input of a protein’s amino acid sequence, a deep neural network is first used to predict the inter-residue geometries, including distance and orientations. The predicted geometries are then transformed as restraints to guide the structure prediction on the basis of direct energy minimization, which is implemented under the framework of Rosetta. The trRosetta server distinguishes itself from other similar structure prediction servers in terms of rapid and accurate de novo structure prediction. As an illustration, trRosetta was applied to two Pfam families with unknown structures, for which the predicted de novo models were estimated to have high accuracy. Nevertheless, to take advantage of homology modeling, homologous templates are used as additional inputs to the network automatically. In general, it takes ~1 h to predict the final structure for a typical protein with ~300 amino acids, using a maximum of 10 CPU cores in parallel in our cluster system. To enable large-scale structure modeling, a downloadable package of trRosetta with open-source codes is available as well. A detailed guidance for using the package is also available in this protocol. The server and the package are available at https://yanglab.nankai.edu.cn/trRosetta/ and https://yanglab.nankai.edu.cn/trRosetta/download/, respectively.

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Fig. 1: Overview of the trRosetta protocol.
Fig. 2: Performance of trRosetta-based methods.
Fig. 3: The comparison between trRosetta results for experiments that use or do not use templates.
Fig. 4: The confidence score estimation, running time and example results.
Fig. 5: The trRosetta homepage for job submission.
Fig. 6: The result page for an example target (PDB ID: 6X61).

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Data availability

The example input and output files can be downloaded from https://yanglab.nankai.edu.cn/trRosetta.

Code availability

The trRosetta server and the standalone package are freely available at https://yanglab.nankai.edu.cn/trRosetta.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC 11871290 and 61873185), Fok Ying-Tong Education Foundation (161003) and KLMDASR.

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Author notes

  1. These authors contributed equally: Zongyang Du, Hong Su.

Authors and Affiliations

  1. School of Mathematical Sciences, Nankai University, Tianjin, China

    Zongyang Du, Hong Su, Wenkai Wang, Lisha Ye & Hong Wei

  2. Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

    Zhenling Peng & Jianyi Yang

  3. Department of Biochemistry, University of Washington, Seattle, WA, USA

    Ivan Anishchenko & David Baker

  4. Institute for Protein Design, University of Washington, Seattle, WA, USA

    Ivan Anishchenko & David Baker

  5. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

    David Baker

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Contributions

J.Y. conceived and supervised the project. Z.D., H.S., W.W., L.Y., H.W., Z.P. and J.Y. designed and performed the experiments. Z.D., J.Y., I.A. and D.B. wrote the manuscript. All authors revised the manuscript.

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Correspondence to Jianyi Yang.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Julia Leman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key reference using this protocol

Yang, J. et al. Proc. Natl Acad. Sci. USA 117, 1496–1503 (2020): https://www.pnas.org/content/117/3/1496

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Supplementary Information

Supplementary Tables 1–4 and Supplementary Figs. 1 and 2.

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Du, Z., Su, H., Wang, W. et al. The trRosetta server for fast and accurate protein structure prediction. Nat Protoc 16, 5634–5651 (2021). https://doi.org/10.1038/s41596-021-00628-9

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