AI proteomics: from protein identification to virtual cells

peer-reviewed · Nature Methods · 2026

peer-reviewed · Nature Methods · 2026. Yingying Sun et al. Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective…
Date 2026-07-28
Type peer-reviewed
Venue Nature Methods
Publisher Springer Science and Business Media LLC
Contribution review
DOI 10.1038/s41592-026-03085-y
Citations (OpenAlex) 2
Venue 2-year citedness 22.29

Abstract

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.

Authors

  1. Yingying Sun · Westlake University
  2. Jun A · Westlake University
  3. Zhiwei Liu · Westlake University
  4. Rui Sun · Westlake University
  5. Liujia Qian · Westlake University
  6. Samuel H. Payne · Brigham Young University
  7. Wout Bittremieux · Biomedical Informatics Research Center Antwerp, Indiana University, University of Antwerp, University of California San Diego, University of Washington
  8. Markus Ralser · Charité – Universitätsmedizin Berlin
  9. Chen Li · Monash University
  10. Yi Chen · Westlake University
  11. Zhen Dong · Westlake University
  12. Yasset Perez-Riverol · Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, Centro de Ingeniería Genética y Biotecnología, European Molecular Biology Laboratory, Institute for Research in Biomedicine, Karolinska Institutet, Parc Científic de Barcelona, Universitat de Barcelona
  13. Asif Khan · Harvard Medical School
  14. Chris Sander · Harvard Medical School
  15. Ruedi Aebersold · ETH Zurich, Institute for Systems Biology
  16. Juan Antonio Vizcaíno · European Molecular Biology Laboratory
  17. Jonathan R. Krieger · Bruker Ltd.
  18. Jianhua Yao · Tencent
  19. Wen Han · AI for Science Institute
  20. Linfeng Zhang · AI for Science Institute
  21. Yunping Zhu · Beijing Institute of Lifeomics, Beijing Institute of Radiation Medicine, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing)
  22. Yue Xuan · Thermo Fisher Scientific GmbH
  23. Benjamin Boyang Sun · Bristol Myers Squibb
  24. Liang Qiao · Fudan University
  25. Henning Hermjakob · European Molecular Biology Laboratory
  26. Haixu Tang · Indiana University, Indiana University Bloomington, University of California San Diego
  27. Huanhuan Gao · Westlake University
  28. Yamin Deng · Westlake University
  29. Qing Zhong · The University of Sydney
  30. Cheng Chang · Beijing Institute of Lifeomics, International Academy of Phronesis Medicine (Guangdong), National Center for Protein Sciences (Beijing), State Key Laboratory of Medical Proteomics
  31. Nuno Bandeira · University of California San Diego, University of California System
  32. Ming Li · Bioinformatics Solutions Inc., Central China Institute of Artificial Intelligence, Peng Cheng Laboratory, University of Waterloo, University of Western Ontario
  33. Weinan E · AI for Science Institute, Peking University
  34. Siqi Sun · Fudan University, Shanghai Artificial Intelligence Laboratory
  35. Yuedong Yang · Sun Yat-sen University
  36. Gilbert S. Omenn · University of Michigan
  37. Yue Zhang · Westlake University
  38. Ping Xu · Beijing Institute of Lifeomics, China Medical University, Hebei University, National Center for Protein Sciences (Beijing), Wuhan University
  39. Yan Fu · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  40. Xiaowen Liu · Indiana University Indianapolis, Indiana University School of Medicine, Indiana University-Purdue University Indianapolis, Tulane University, University of Waterloo
  41. Christopher M. Overall · University of British Columbia
  42. Yu Wang · Peng Cheng Laboratory
  43. Eric W. Deutsch · Institute for Systems Biology
  44. Luonan Chen · Shanghai Jiao Tong University
  45. Jürgen Cox · Centre de Recherches sur la Cognition Animale, Consejo Superior de Investigaciones Científicas, Institut Català de Paleontologia Miquel Crusafont, Institut de Biologia Evolutiva, Max Planck Institute of Biochemistry, Universitat Pompeu Fabra, University of Bergen, Université de Toulouse
  46. Vadim Demichev · Charité – Universitätsmedizin Berlin
  47. Fuchu He · Beijing Institute of Lifeomics, International Academy of Phronesis Medicine (Guangdong), National Center for Protein Sciences (Beijing), State Key Laboratory of Medical Proteomics
  48. Jiaxing Huang · Westlake University
  49. Huilin Jin · Anhui University
  50. Chao Liu · Beihang University, Chinese Academy of Sciences, University of Chinese Academy of Sciences
  51. Nan Li · Westlake University
  52. Zhongzhi Luan · Beihang University
  53. Jiangning Song · Monash University
  54. Kaicheng Yu · Westlake University
  55. Wanggen Wan · Shanghai University
  56. Tai Wang · Bristol Myers Squibb
  57. Kang Zhang · Wenzhou Medical University
  58. Le Zhang · Sichuan University
  59. Peter A. Bell · University of British Columbia
  60. Matthias Mann · European Molecular Biology Laboratory, Max Planck Institute of Biochemistry, Max Planck Institute of Molecular Cell Biology and Genetics, Novo Nordisk Foundation, University of California San Diego, University of Copenhagen, University of Southern Denmark
  61. Bing Zhang · Baylor College of Medicine
  62. Tiannan Guo · Westlake Institute for Advanced Study, Westlake University

Methods and tools

  • AI proteomics perspective: 62-author Nature Methods Perspective mapping where AI is reshaping MS-based proteomics: peptide and protein identification and quantification (de novo sequencing among them), protein-protein interactions and complexes, spatial and perturbation proteomics, multi-omics integration, and ultimately AI virtual cells. Closes with a call for an AI-friendly data ecosystem for the field.

Cites (3)

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