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Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction

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  • Yue Peng

    (East China University of Science and Technology)

  • Junze Wu

    (East China University of Science and Technology)

  • Yi Sun

    (East China University of Science and Technology)

  • Yuanxing Zhang

    (Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai))

  • Qiyao Wang

    (East China University of Science and Technology
    Shanghai Engineering Research Center of Maricultured Animal Vaccines
    Laboratory of Aquatic Animal Diseases of MOA)

  • Shuai Shao

    (East China University of Science and Technology
    Shanghai Engineering Research Center of Maricultured Animal Vaccines
    Laboratory of Aquatic Animal Diseases of MOA)

Abstract

Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protein language models (PLMs) have shown sound performance by leveraging extensive evolutionary and sequential protein features. However, the accuracy and sensitivity of effector prediction remain challenging. Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. Biologically information is captured in learned contextualized embeddings to yield meaningful representations. With cross-modality biological features, CLEF outperforms state-of-the-art (SOTA) models in predicting type III, type IV, and type VI secreted effectors (T3SEs/T4SEs/T6SEs) in enteric pathogens. All experimentally verified effectors in Enterohemorrhagic Escherichia coli and 41 of 43 experimentally verified T3SEs of Salmonella Typhimurium are recognized. Moreover, 12 predicted T3SEs and 11 predicted T6SEs are validated by extensive experiments in Edwardsiella piscicida. Furthermore, integrating omics data via CLEF framework enhances protein representations to illustrate effector-effector interactions and determine in vivo colonization-essential genes. Collectively, CLEF provides a blueprint to bridge the gap between in silico PLM’s capacity and experimental biological information to fulfill complicated tasks.

Suggested Citation

  • Yue Peng & Junze Wu & Yi Sun & Yuanxing Zhang & Qiyao Wang & Shuai Shao, 2025. "Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56526-1
    DOI: 10.1038/s41467-025-56526-1
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    References listed on IDEAS

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    2. David Burstein & Tal Zusman & Elena Degtyar & Ram Viner & Gil Segal & Tal Pupko, 2009. "Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach," PLOS Pathogens, Public Library of Science, vol. 5(7), pages 1-12, July.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. Josh Abramson & Jonas Adler & Jack Dunger & Richard Evans & Tim Green & Alexander Pritzel & Olaf Ronneberger & Lindsay Willmore & Andrew J. Ballard & Joshua Bambrick & Sebastian W. Bodenstein & David , 2024. "Accurate structure prediction of biomolecular interactions with AlphaFold 3," Nature, Nature, vol. 630(8016), pages 493-500, June.
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