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<p>Isoforms from the same gene can significantly rewire protein interaction networks, but proteome-wide computational evaluation of these effects remains challenging. In this work, we present DeepISO, a deep learning framework for predicting isoform-specific interactions. DeepISO integrates two graph convolutional neural networks and a random forest model via a logistic regression model. To the best of our knowledge, this is the first approach to jointly leverage AlphaFold-predicted structures and ESM2 language model embeddings for this task. Compared with state-of-the-art PPI prediction tools, DeepISO demonstrates superior performance.</p>

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DeepISO: deep learning-powered prediction of protein–protein interaction rewiring generated by alternative splicing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Xiaokun Guo,
  • Linyang Jiang,
  • Jiajun Li,
  • Mengdi Yuan,
  • Dianke Li,
  • Wenyu Shi,
  • Ziding Zhang,
  • Stefan Wuchty

摘要

Isoforms from the same gene can significantly rewire protein interaction networks, but proteome-wide computational evaluation of these effects remains challenging. In this work, we present DeepISO, a deep learning framework for predicting isoform-specific interactions. DeepISO integrates two graph convolutional neural networks and a random forest model via a logistic regression model. To the best of our knowledge, this is the first approach to jointly leverage AlphaFold-predicted structures and ESM2 language model embeddings for this task. Compared with state-of-the-art PPI prediction tools, DeepISO demonstrates superior performance.