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<p>Most proteins act through interactions with other molecules, yet predicting how single mutations perturb these interactions—defined as ‘protein codes’—remains a central challenge in computational biology. Here we introduce eSIG-Net, the edgetic mutation sequence-based interaction grammar network, a language model that integrates protein sequence embeddings with syntax-aware and evolution-aware mutation encoding and contrastive learning to predict mutation-driven interaction changes. eSIG-Net outperforms state-of-the-art sequence-based and structure-based methods, nominates causal variants and provides mechanistic insights. Together, eSIG-Net is a mutation-centric interaction language model that accurately predicts interaction-specific network rewiring from sequence information alone and generalizes across biological contexts.</p>

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eSIG-Net: an interaction language model that decodes the protein code of single mutations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

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  • Xingxin Pan,
  • Aditya Shrawat,
  • Sidharth Raghavan,
  • Chuanpeng Dong,
  • Yuntao Yang,
  • Zhao Li,
  • W. Jim Zheng,
  • S. Gail Eckhardt,
  • Erxi Wu,
  • Juan I. Fuxman Bass,
  • Daniel F. Jarosz,
  • Sidi Chen,
  • Daniel J. McGrail,
  • Gloria M. Sheynkman,
  • Jason H. Huang,
  • Nidhi Sahni,
  • S. Stephen Yi

摘要

Most proteins act through interactions with other molecules, yet predicting how single mutations perturb these interactions—defined as ‘protein codes’—remains a central challenge in computational biology. Here we introduce eSIG-Net, the edgetic mutation sequence-based interaction grammar network, a language model that integrates protein sequence embeddings with syntax-aware and evolution-aware mutation encoding and contrastive learning to predict mutation-driven interaction changes. eSIG-Net outperforms state-of-the-art sequence-based and structure-based methods, nominates causal variants and provides mechanistic insights. Together, eSIG-Net is a mutation-centric interaction language model that accurately predicts interaction-specific network rewiring from sequence information alone and generalizes across biological contexts.