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Protein–protein contact prediction by geometric triangle-aware protein language models

Nature Machine Intelligence volume 5, pages 1275–1284 (2023)Cite this article

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Abstract

Information regarding the residue–residue distance between interacting proteins is important for modelling the structures of protein complexes, as well as being valuable for understanding the molecular mechanism of protein–protein interactions. With the advent of deep learning, many methods have been developed to accurately predict the intra-protein residue–residue contacts of monomers. However, it is still challenging to accurately predict inter-protein residue–residue contacts for protein complexes, especially hetero-protein complexes. Here we develop a protein language model-based deep learning method to predict the inter-protein residue–residue contacts of protein complexes—named DeepInter—by introducing a triangle-aware mechanism of triangle update and triangle self-attention into the deep neural network. We extensively validate DeepInter on diverse test sets of 300 homodimeric, 28 CASP-CAPRI homodimeric and 99 heterodimeric complexes and compare it with state-of-the-art methods including CDPred, DeepHomo2.0, GLINTER and DeepHomo. The results demonstrate the accuracy and robustness of DeepInter.

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Fig. 1: The framework of DeepInter.
Fig. 2: Comparison of DeepInter with other methods on the Homodimer300 test set with experimental structures as input.
Fig. 3: Comparison of DeepInter with other methods on the Heterodimer99 test set with the input of experimental structures.
Fig. 4: Ablation experiments with DeepInter on the Homodimer300 test set with the input of experimental structures.
Fig. 5: Examples of predicted contact maps.

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Data availability

The data that support the findings of this study are available from the corresponding author upon request. A full list of the protein complexes used in this study is also provided in Supplementary Data 10. The protein structures used in this study are all available in the PDB, and the sequence database of Uniref30_2020_03 used in this study is available at https://www.uniprot.org/help/uniref/. Source data are provided with this paper.

Code availability

The DeepInter package is freely available at http://huanglab.phys.hust.edu.cn/DeepInter/ and https://doi.org/10.5281/zenodo.8304327 ref. 61.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grants nos. 32161133002 and 62072199) and a startup grant of Huazhong University of Science and Technology.

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Authors and Affiliations

  1. School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Peicong Lin, Huanyu Tao, Hao Li & Sheng-You Huang

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Contributions

S.-Y.H. conceived and supervised the project. P.L. and S.-Y.H. designed and performed the experiments. P.L., H.T. and H.L. analysed the data. P.L., H.T., H.L. and S.-Y.H. wrote the paper. All authors reviewed and approved the final version of the paper.

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Correspondence to Sheng-You Huang.

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Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Jacob Huth, in collaboration with the Nature Machine Intelligence team.

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Supplementary information

Supplementary Information

Impact of MSA depth and contact density, Impact of sequence cropping size, Impact of conformational changes, Impact of intrinsically disordered proteins, Impact of structural similarity, Impact of intra-protein distance usage, Supplementary Tables 1–4, Fig. 1, algorithm and reference.

Supplementary Data

Supplementary data 1–10.

Source data

Source Data

Source data Table 1–3, Source data Fig. 2–4, Statistical source data.

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Lin, P., Tao, H., Li, H. et al. Protein–protein contact prediction by geometric triangle-aware protein language models. Nat Mach Intell 5, 1275–1284 (2023). https://doi.org/10.1038/s42256-023-00741-2

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