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DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

Author

Listed:
  • Fenglei Li

    (ShanghaiTech University
    ShanghaiTech University)

  • Qiaoyu Hu

    (ShanghaiTech University)

  • Xianglei Zhang

    (ShanghaiTech University)

  • Renhong Sun

    (Gluetacs Therapeutics (Shanghai) Co., Ltd.)

  • Zhuanghua Liu

    (ShanghaiTech University)

  • Sanan Wu

    (ShanghaiTech University)

  • Siyuan Tian

    (ShanghaiTech University
    ShanghaiTech University)

  • Xinyue Ma

    (ShanghaiTech University
    ShanghaiTech University)

  • Zhizhuo Dai

    (ShanghaiTech University)

  • Xiaobao Yang

    (Gluetacs Therapeutics (Shanghai) Co., Ltd.)

  • Shenghua Gao

    (ShanghaiTech University)

  • Fang Bai

    (ShanghaiTech University
    ShanghaiTech University
    ShanghaiTech University
    Shanghai Clinical Research and Trial Center)

Abstract

The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC50 and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/ ) and at github ( https://github.com/fenglei104/DeepPROTACs ).

Suggested Citation

  • Fenglei Li & Qiaoyu Hu & Xianglei Zhang & Renhong Sun & Zhuanghua Liu & Sanan Wu & Siyuan Tian & Xinyue Ma & Zhizhuo Dai & Xiaobao Yang & Shenghua Gao & Fang Bai, 2022. "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34807-3
    DOI: 10.1038/s41467-022-34807-3
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    2. Kheewoong Baek & David T. Krist & J. Rajan Prabu & Spencer Hill & Maren Klügel & Lisa-Marie Neumaier & Susanne Gronau & Gary Kleiger & Brenda A. Schulman, 2020. "NEDD8 nucleates a multivalent cullin–RING–UBE2D ubiquitin ligation assembly," Nature, Nature, vol. 578(7795), pages 461-466, February.
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    Cited by:

    1. Daniele Antermite & Stig D. Friis & Johan R. Johansson & Okky Dwichandra Putra & Lutz Ackermann & Magnus J. Johansson, 2023. "Late-stage synthesis of heterobifunctional molecules for PROTAC applications via ruthenium-catalysed C‒H amidation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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