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<p>Post-translational modifications (PTMs) form a complex combinatorial “code” that orchestrates protein function and cellular signaling. However, deciphering this code by predicting PTM sites and linking sites to their regulatory enzymes remains a fundamental challenge. Here, we present COMPASS-PTM, a mechanism-aware, coarse-to-fine learning framework that unifies residue-level multi-label PTM prediction with enzyme-substrate assignment by jointly modeling PTM patterns and their catalytic regulators. COMPASS-PTM builds upon protein language models, integrating physicochemical descriptors and a crosstalk-aware prompting mechanism to learn biologically coherent patterns of cooperative and antagonistic modifications, while addressing the dual long-tail distribution inherent in PTM data. Across multiple proteome-scale benchmarks, COMPASS-PTM improves over the strongest evaluated baselines, with a 122% relative improvement in F1-score for multi-label site prediction and a 54% gain in zero-shot enzyme assignment. Furthermore, the model demonstrates interpretable generalization, recovering canonical kinase motifs and mechanistically linking missense variants to both local PTM disruptions and global rewiring of enzyme-substrate networks. By coupling statistical learning with explicit biochemical knowledge, COMPASS-PTM unifies site-level and enzyme-level prediction into a single framework that learns the grammar underlying protein regulation and signaling.</p>

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Learning the PTM code through a coarse-to-fine mechanism-aware framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Jingjie Zhang,
  • Hanqun Cao,
  • Zijun Gao,
  • Yu Wang,
  • Shaoning Li,
  • Jun Xu,
  • Cheng Tan,
  • Jun Zhu,
  • Chang-Yu Hsieh,
  • Chunbin Gu,
  • Pheng Ann Heng

摘要

Post-translational modifications (PTMs) form a complex combinatorial “code” that orchestrates protein function and cellular signaling. However, deciphering this code by predicting PTM sites and linking sites to their regulatory enzymes remains a fundamental challenge. Here, we present COMPASS-PTM, a mechanism-aware, coarse-to-fine learning framework that unifies residue-level multi-label PTM prediction with enzyme-substrate assignment by jointly modeling PTM patterns and their catalytic regulators. COMPASS-PTM builds upon protein language models, integrating physicochemical descriptors and a crosstalk-aware prompting mechanism to learn biologically coherent patterns of cooperative and antagonistic modifications, while addressing the dual long-tail distribution inherent in PTM data. Across multiple proteome-scale benchmarks, COMPASS-PTM improves over the strongest evaluated baselines, with a 122% relative improvement in F1-score for multi-label site prediction and a 54% gain in zero-shot enzyme assignment. Furthermore, the model demonstrates interpretable generalization, recovering canonical kinase motifs and mechanistically linking missense variants to both local PTM disruptions and global rewiring of enzyme-substrate networks. By coupling statistical learning with explicit biochemical knowledge, COMPASS-PTM unifies site-level and enzyme-level prediction into a single framework that learns the grammar underlying protein regulation and signaling.