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<p>Protein post-translational modifications (PTMs) orchestrate complex cellular processes, yet current computational methods predominantly tre at them as isolated sequence events. This reductionist approach—coupled with an inability to model 3D steric constraints and long-tail data scarcity—obscures the cooperative nature of PTM networks. Here, we introduce ProteinNexus, a multimodal deep-learning framework designed to bridge the gap between local site prediction and global network comprehension. By dynamically fusing evolutionary sequence data with a physically grounded, conformal equivariant graph neural network, ProteinNexus rigorously captures the 3D microenvironments of modification sites. Coupled with a reinforcement learning-driven specialization strategy, the framework effectively models crosstalk across 10 diverse PTM types, outperforming state-of-the-art methods by 7.5% (Matthews Correlation Coefficient). Beyond superior predictive accuracy, in silico knockouts and evolutionary analyses reveal that ProteinNexus successfully decodes cooperative PTM dynamics, providing a generalizable, structure-aware computational microscope to unravel the regulatory grammar of the proteome.</p>

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An SE(3)-equivariant and dynamic multi-modal engine advancing from PTM site prediction to network understanding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Yiyu Lin,
  • Jiahui Wu,
  • Sen Yang,
  • Yan Wang

摘要

Protein post-translational modifications (PTMs) orchestrate complex cellular processes, yet current computational methods predominantly tre at them as isolated sequence events. This reductionist approach—coupled with an inability to model 3D steric constraints and long-tail data scarcity—obscures the cooperative nature of PTM networks. Here, we introduce ProteinNexus, a multimodal deep-learning framework designed to bridge the gap between local site prediction and global network comprehension. By dynamically fusing evolutionary sequence data with a physically grounded, conformal equivariant graph neural network, ProteinNexus rigorously captures the 3D microenvironments of modification sites. Coupled with a reinforcement learning-driven specialization strategy, the framework effectively models crosstalk across 10 diverse PTM types, outperforming state-of-the-art methods by 7.5% (Matthews Correlation Coefficient). Beyond superior predictive accuracy, in silico knockouts and evolutionary analyses reveal that ProteinNexus successfully decodes cooperative PTM dynamics, providing a generalizable, structure-aware computational microscope to unravel the regulatory grammar of the proteome.