IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49798-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49798-6
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-49798-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kerr Ding & Michael Chin & Yunlong Zhao & Wei Huang & Binh Khanh Mai & Huanan Wang & Peng Liu & Yang Yang & Yunan Luo, 2024. "Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Guoling Li & Xue Dong & Jiamin Luo & Tanglong Yuan & Tong Li & Guoli Zhao & Hainan Zhang & Jingxing Zhou & Zhenhai Zeng & Shuna Cui & Haoqiang Wang & Yin Wang & Yuyang Yu & Yuan Yuan & Erwei Zuo & Chu, 2024. "Engineering TadA ortholog-derived cytosine base editor without motif preference and adenosine activity limitation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Yaan J. Jang & Qi-Qi Qin & Si-Yu Huang & Arun T. John Peter & Xue-Ming Ding & Benoît Kornmann, 2024. "Accurate prediction of protein function using statistics-informed graph networks," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Daniel J. Diaz & Chengyue Gong & Jeffrey Ouyang-Zhang & James M. Loy & Jordan Wells & David Yang & Andrew D. Ellington & Alexandros G. Dimakis & Adam R. Klivans, 2024. "Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Kian Hong Kock & Patrick K. Kimes & Stephen S. Gisselbrecht & Sachi Inukai & Sabrina K. Phanor & James T. Anderson & Gayatri Ramakrishnan & Colin H. Lipper & Dongyuan Song & Jesse V. Kurland & Julia M, 2024. "DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. 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.
    7. Yinglu Cui & Yanchun Chen & Jinyuan Sun & Tong Zhu & Hua Pang & Chunli Li & Wen-Chao Geng & Bian Wu, 2024. "Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Cheyenne Ziegler & Jonathan Martin & Claude Sinner & Faruck Morcos, 2023. "Latent generative landscapes as maps of functional diversity in protein sequence space," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    9. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative AI," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, National Bureau of Economic Research, Inc.
    10. Anthony C. Bishop & Glorisé Torres-Montalvo & Sravya Kotaru & Kyle Mimun & A. Joshua Wand, 2023. "Robust automated backbone triple resonance NMR assignments of proteins using Bayesian-based simulated annealing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    11. Deyun Qiu & Jinxin V. Pei & James E. O. Rosling & Vandana Thathy & Dongdi Li & Yi Xue & John D. Tanner & Jocelyn Sietsma Penington & Yi Tong Vincent Aw & Jessica Yi Han Aw & Guoyue Xu & Abhai K. Tripa, 2022. "A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    12. Shuo-Shuo Liu & Tian-Xia Jiang & Fan Bu & Ji-Lan Zhao & Guang-Fei Wang & Guo-Heng Yang & Jie-Yan Kong & Yun-Fan Qie & Pei Wen & Li-Bin Fan & Ning-Ning Li & Ning Gao & Xiao-Bo Qiu, 2024. "Molecular mechanisms underlying the BIRC6-mediated regulation of apoptosis and autophagy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    13. Dick Schijven & Sourena Soheili-Nezhad & Simon E. Fisher & Clyde Francks, 2024. "Exome-wide analysis implicates rare protein-altering variants in human handedness," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    14. Xiaoke Yang & Mingqi Zhu & Xue Lu & Yuxin Wang & Junyu Xiao, 2024. "Architecture and activation of human muscle phosphorylase kinase," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. Zheng Shen & Daxiao Sun & Adriana Savastano & Sára Joana Varga & Maria-Sol Cima-Omori & Stefan Becker & Alf Honigmann & Markus Zweckstetter, 2023. "Multivalent Tau/PSD-95 interactions arrest in vitro condensates and clusters mimicking the postsynaptic density," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    16. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.
    17. Efren Garcia-Maldonado & Andrew D. Huber & Sergio C. Chai & Stanley Nithianantham & Yongtao Li & Jing Wu & Shyaron Poudel & Darcie J. Miller & Jayaraman Seetharaman & Taosheng Chen, 2024. "Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    18. Kristy Rochon & Brianna L. Bauer & Nathaniel A. Roethler & Yuli Buckley & Chih-Chia Su & Wei Huang & Rajesh Ramachandran & Maria S. K. Stoll & Edward W. Yu & Derek J. Taylor & Jason A. Mears, 2024. "Structural basis for regulated assembly of the mitochondrial fission GTPase Drp1," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    19. Katherine A. Ray & Joshua D. Lutgens & Ramesh Bista & Jie Zhang & Ronak R. Desai & Melissa Hirsch & Takeshi Miyazawa & Antonio Cordova & Adrian T. Keatinge-Clay, 2024. "Assessing and harnessing updated polyketide synthase modules through combinatorial engineering," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    20. Fan Lu & Liang Zhu & Thomas Bromberger & Jun Yang & Qiannan Yang & Jianmin Liu & Edward F. Plow & Markus Moser & Jun Qin, 2022. "Mechanism of integrin activation by talin and its cooperation with kindlin," Nature Communications, Nature, vol. 13(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49798-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.