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An end-to-end framework for the prediction of protein structure and fitness from single sequence

Author

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  • Yinghui Chen

    (Tsinghua University
    Tsinghua University)

  • Yunxin Xu

    (Tsinghua University
    Tsinghua University)

  • Di Liu

    (Tsinghua University
    Tsinghua University)

  • Yaoguang Xing

    (Tsinghua University
    Tsinghua University)

  • Haipeng Gong

    (Tsinghua University
    Tsinghua University)

Abstract

Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction accuracy, showing promise for many downstream prediction tasks. Here, we propose SPIRED, a single-sequence-based structure prediction model that exhibits comparable performance to the state-of-the-art methods but with approximately 5-fold acceleration in inference and at least one order of magnitude reduction in training consumption. By integrating SPIRED with downstream neural networks, we compose an end-to-end framework named SPIRED-Fitness for the rapid prediction of both protein structure and fitness from single sequence with satisfactory accuracy. Moreover, SPIRED-Stab, the derivative of SPIRED-Fitness, achieves state-of-the-art performance in predicting the mutational effects on protein stability.

Suggested Citation

  • Yinghui Chen & Yunxin Xu & Di Liu & Yaoguang Xing & Haipeng Gong, 2024. "An end-to-end framework for the prediction of protein structure and fitness from single sequence," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51776-x
    DOI: 10.1038/s41467-024-51776-x
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    References listed on IDEAS

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    2. Yunan Luo & Guangde Jiang & Tianhao Yu & Yang Liu & Lam Vo & Hantian Ding & Yufeng Su & Wesley Wei Qian & Huimin Zhao & Jian Peng, 2021. "ECNet is an evolutionary context-integrated deep learning framework for protein engineering," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
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