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<p>Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.</p>

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AI proteomics: from protein identification to virtual cells

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Yingying Sun,
  • Jun A,
  • Zhiwei Liu,
  • Rui Sun,
  • Liujia Qian,
  • Samuel H. Payne,
  • Wout Bittremieux,
  • Markus Ralser,
  • Chen Li,
  • Yi Chen,
  • Zhen Dong,
  • Yasset Perez-Riverol,
  • Asif Khan,
  • Chris Sander,
  • Ruedi Aebersold,
  • Juan Antonio Vizcaíno,
  • Jonathan R. Krieger,
  • Jianhua Yao,
  • Wen Han,
  • Linfeng Zhang,
  • Yunping Zhu,
  • Yue Xuan,
  • Benjamin Boyang Sun,
  • Liang Qiao,
  • Henning Hermjakob,
  • Haixu Tang,
  • Huanhuan Gao,
  • Yamin Deng,
  • Qing Zhong,
  • Cheng Chang,
  • Nuno Bandeira,
  • Ming Li,
  • Weinan E,
  • Siqi Sun,
  • Yuedong Yang,
  • Gilbert S. Omenn,
  • Yue Zhang,
  • Ping Xu,
  • Yan Fu,
  • Xiaowen Liu,
  • Christopher M. Overall,
  • Yu Wang,
  • Eric W. Deutsch,
  • Luonan Chen,
  • Jürgen Cox,
  • Vadim Demichev,
  • Fuchu He,
  • Jiaxing Huang,
  • Huilin Jin,
  • Chao Liu,
  • Nan Li,
  • Zhongzhi Luan,
  • Jiangning Song,
  • Kaicheng Yu,
  • Wanggen Wan,
  • Tai Wang,
  • Kang Zhang,
  • Le Zhang,
  • Peter A. Bell,
  • Matthias Mann,
  • Bing Zhang,
  • Tiannan Guo

摘要

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.