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Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning

Author

Listed:
  • Ziyi Zhou

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Liang Zhang

    (Shanghai Jiao Tong University)

  • Yuanxi Yu

    (Shanghai Jiao Tong University)

  • Banghao Wu

    (Shanghai Jiao Tong University)

  • Mingchen Li

    (Shanghai Artificial Intelligence Laboratory
    East China University of Science and Technology)

  • Liang Hong

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Artificial Intelligence Laboratory
    Shanghai Jiao Tong University)

  • Pan Tan

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Artificial Intelligence Laboratory)

Abstract

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP’s superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Suggested Citation

  • Ziyi Zhou & Liang Zhang & Yuanxi Yu & Banghao Wu & Mingchen Li & Liang Hong & Pan Tan, 2024. "Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49798-6
    DOI: 10.1038/s41467-024-49798-6
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    References listed on IDEAS

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    1. 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.
    2. Jonathan Frazer & Pascal Notin & Mafalda Dias & Aidan Gomez & Joseph K. Min & Kelly Brock & Yarin Gal & Debora S. Marks, 2021. "Disease variant prediction with deep generative models of evolutionary data," Nature, Nature, vol. 599(7883), pages 91-95, November.
    3. U. T. Bornscheuer & G. W. Huisman & R. J. Kazlauskas & S. Lutz & J. C. Moore & K. Robins, 2012. "Engineering the third wave of biocatalysis," Nature, Nature, vol. 485(7397), pages 185-194, May.
    4. Benedetta Bolognesi & Andre J. Faure & Mireia Seuma & Jörn M. Schmiedel & Gian Gaetano Tartaglia & Ben Lehner, 2019. "The mutational landscape of a prion-like domain," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    5. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    6. Xinqiang Ding & Zhengting Zou & Charles L. Brooks III, 2019. "Deciphering protein evolution and fitness landscapes with latent space models," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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