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Nanobody–antigen interaction prediction with ensemble deep learning and prompt-based protein language models

Nature Machine Intelligence volume 6, pages 1594–1604 (2024)Cite this article

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Abstract

Nanobodies can provide specific binding to divergent antigens, leading to many promising therapeutic and detection applications in recent years. Traditional technologies of nanobody discovery based on alpaca immunization and phage display are very time-consuming and labour-intensive. Despite recent progress in the study of nanobodies, developing fast and accurate computational tools for nanobody–antigen interaction (NAI) prediction is urgently desirable. Here we propose an ensemble deep learning-based framework named DeepNano-seq to predict general protein–protein interaction (PPI) containing NAI from pure sequence information. Quantitative comparison results show that DeepNano-seq possesses the best cross-species generalization ability among existing PPI algorithms. Nevertheless, several of the most effective PPI methods, including DeepNano-seq, demonstrate suboptimal performance for NAI prediction due to the distinction between NAI and PPI at both the pattern and data levels. Therefore, we organize NAI data from the public database for dedicated NAI modelling. Furthermore, we enhance the prediction pipeline of DeepNano-seq by directing the model’s attention to the antigen-binding sites through a prompt-based approach to present the final DeepNano. The comprehensive evaluation demonstrates that DeepNano performs superiorly in NAI prediction and virtual screening of nanobodies. Overall, DeepNano-seq and DeepNano can offer powerful tools for nanobody discovery.

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Fig. 1: Overview of DeepNano-seq and DeepNano.
Fig. 2: Comparison of the proposed DeepNano-seq method with existing PPI algorithms.
Fig. 3: The construction of dedicated NAI predictive models.
Fig. 4: Analysis of nanobody–antigen-binding interfaces and related experiments of DeepNano-site and DeepNano.
Fig. 5: Results of virtual screening experiments.
Fig. 6: Prediction performance of DeepNano-seq and DeepNano at four ESM-2 model scales.

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Data availability

Datasets used in this study and detailed data description documentation have been deposited in our GitHub repository: https://github.com/ddd9898/DeepNano/tree/main/data (ref. 49). The raw data for several graphs in the main text can be found in Supplementary Information. Original nanobody–antigen-binding pairs exploited for training were obtained from the SAbDab-nano database at https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab/nanobodies/. The large amount of natural nanobodies adopted in the case study were downloaded from the INDI database at https://research.naturalantibody.com/nanobodies. Sequences of GST and HSA were obtained from the UniProt database at https://www.uniprot.org/. A description of the data for all PPIs and NAIs used in this study is presented in Supplementary Table 5. In addition, the analysis of differences between PPIs and NAIs21,25,50 is presented in Supplementary Note 5.

Code availability

The code repository of this study, which includes the trained models and the evaluation pipeline, is freely available at https://github.com/ddd9898/DeepNano (ref. 49). The source code based on standard Python packages (including Pymol, Biopython and numpy) to organize nanobody–antigen-binding data from SAbDab-nano can be found at https://github.com/ddd9898/DeepNano-data.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (grant no. 62173204 to M.L.), Beijing National Research Center for Information Science and Technology, the Fundamental Research Funds for the Central Universities (grant no. buctrc202337 to M.L.) and the Engineering Research Center of Intelligent Technology and Equipment for Saving Energy and Increasing Benefit, Ministry of Education.

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Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, China

    Juntao Deng, Miao Gu, Pengyan Zhang, Mingyu Dong, Tao Liu, Yabin Zhang & Min Liu

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  1. Juntao Deng
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Contributions

J.D. and M.L. conceived the original ideas for this study, designed and performed the experiments, and cowrote the manuscript. M.G. and P.Z. contributed to the model design and revision of the manuscript. M.D., T.L. and Y.Z. participated in the experimental design and algorithm programming and helped to prepare figures. M.L. was responsible for the project and guided the work.

Corresponding author

Correspondence to Min Liu.

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Nature Machine Intelligence thanks Sudeep Sarma and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Notes 1–6, Figs. 1–10 and Tables 1–13.

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Deng, J., Gu, M., Zhang, P. et al. Nanobody–antigen interaction prediction with ensemble deep learning and prompt-based protein language models. Nat Mach Intell 6, 1594–1604 (2024). https://doi.org/10.1038/s42256-024-00940-5

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