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I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

Nature Protocols volume 17, pages 2326–2353 (2022)Cite this article

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Abstract

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone.

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Fig. 1: Overview of the I-TASSER-MTD protocol for multi-domain protein structure and function prediction.
Fig. 2: Comparison between I-TASSER-MTD and other methods.
Fig. 3: Example of the I-TASSER-MTD results page (Sections 2 and 3).
Fig. 4: Example of the I-TASSER-MTD results page (Sections 4–6).
Fig. 5: Example of the I-TASSER-MTD results page (Sections 7 and 8).
Fig. 6: Example of the I-TASSER-MTD results page (Sections 9–12).

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Data availability

The raw data and example files are available at https://zhanggroup.org/I-TASSER-MTD/ or from the corresponding author upon reasonable request.

Code availability

The I-TASSER-MTD standalone package is freely available for academic use at https://zhanggroup.org/I-TASSER-MTD/.

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Acknowledgements

This work is supported in part by the National Institute of General Medical Sciences (GM136422 and S10OD026825 to Y.Z.), the National Institute of Allergy and Infectious Diseases (AI134678 to Y.Z.), the National Science Foundation (IIS1901191 and DBI2030790 to Y.Z.), the National Nature Science Foundation of China (62173304 and 61773346 to G.Z.), the ‘New Generation Artificial Intelligence’ major project of Science and Technology Innovation 2030 of the Ministry of Science and Technology of China (2021ZD0150100 to G.Z.) and the Key Project of Zhejiang Provincial Natural Science Foundation of China (LZ20F030002 to G.Z.). This work used the Extreme Science and Engineering Discovery Environment (XSEDE)102, which is supported by the National Science Foundation (ACI1548562).

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Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Xiaogen Zhou, Wei Zheng, Yang Li, Robin Pearce, Chengxin Zhang, Eric W. Bell & Yang Zhang

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Xiaogen Zhou & Guijun Zhang

  3. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA

    Yang Zhang

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  1. Xiaogen Zhou
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Y.Z. conceived and designed the project. X.Z. developed the pipeline and performed the test. W.Z. developed the method for domain boundaries prediction. Y.L. developed the method for contacts and distances prediction. C.Z. developed the method for protein function prediction. Y.Z., W.Z., Y.L., C.Z. and R.P. developed the method for individual domain modeling. X.Z. developed the method for multi-domain protein structure assembly. X.Z. and E.B. tested the server. G.Z. helped supervise the research. X.Z. and Y.Z. wrote the manuscript, and all authors read and approved the final manuscript.

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Correspondence to Yang Zhang.

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Nature Protocols thanks Ruben Sánchez-García, Beat R. Vogeli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Zhou, X. et al. Proc. Natl Acad. Sci. USA 116, 15930–15938 (2019): https://doi.org/10.1073/pnas.1905068116

Zhang, C. et al. Nucleic Acids Res. 45, W291–299 (2017): https://doi.org/10.1093/nar/gkx366

Zheng, W. et al. Cell Rep. Methods 1, 100014 (2021): https://doi.org/10.1016/j.crmeth.2021.100014

Hermes, C. et al. Nat. Commun. 12, 144 (2021): https://doi.org/10.1038/s41467-020-20418-3

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Supplementary Information

Supplementary Figs. 1–19, Tables 1 and 2, Notes 1–6, Equations 1–25 and References.

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Zhou, X., Zheng, W., Li, Y. et al. I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction. Nat Protoc 17, 2326–2353 (2022). https://doi.org/10.1038/s41596-022-00728-0

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