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<p>Liquid–liquid phase separation regulates biological processes through dynamic condensates. Despite its significance, experimentally validated phase-separating proteins in plants remain limited, complicating predictions. We overcome this gap by applying positive-unlabeled learning, a semi-supervised approach optimized for imbalanced datasets. Leveraging 6,559 reported plant phase-separating proteins from eight species, we train a model integrating sequence-structural features, enabling prediction of 174,656 high-confidence candidates across 14 species. Experimental validation confirms liquid–liquid phase separation in 67.9% of the candidate proteins from <i>Arabidopsis</i>, rice, and maize. This positive-unlabeled framework demonstrates robust predictive power while providing open resources to advance plant phase separation research.</p>

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Prediction of plant phase-separating proteins using positive-unlabeled learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

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  • Ran Fu,
  • Yisu Tian,
  • Hui Ren,
  • Anwen Zhao,
  • Yuxuan Lou,
  • Shiya Mao,
  • Jing Yang,
  • Shan Jiang,
  • Xi Huang,
  • Xiangfeng Wang

摘要

Liquid–liquid phase separation regulates biological processes through dynamic condensates. Despite its significance, experimentally validated phase-separating proteins in plants remain limited, complicating predictions. We overcome this gap by applying positive-unlabeled learning, a semi-supervised approach optimized for imbalanced datasets. Leveraging 6,559 reported plant phase-separating proteins from eight species, we train a model integrating sequence-structural features, enabling prediction of 174,656 high-confidence candidates across 14 species. Experimental validation confirms liquid–liquid phase separation in 67.9% of the candidate proteins from Arabidopsis, rice, and maize. This positive-unlabeled framework demonstrates robust predictive power while providing open resources to advance plant phase separation research.