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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The raw data and example files are available at https://zhanggroup.org/I-TASSER-MTD/ or from the corresponding author upon reasonable request.
The I-TASSER-MTD standalone package is freely available for academic use at https://zhanggroup.org/I-TASSER-MTD/.
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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).
The authors declare no competing interests.
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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 (2022). https://doi.org/10.1038/s41596-022-00728-0