AI proteomics: from protein identification to virtual cells

Yingying Sun; A. Jun; 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

doi:10.1038/s41592-026-03085-y

AI proteomics: from protein identification to virtual cells

Yingying Sun
, A. Jun
, 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^*

^*此作品的通讯作者

计算机学院

Affiliated Hangzhou First People’s Hospital
Westlake University
Westlake Center for Intelligent Proteomics
Westlake Laboratory
Biology Department
Brigham Young University
Department of Computer Science
University of Antwerp
Department of Biochemistry
Charité – Universitätsmedizin Berlin
Department of Biochemistry and Molecular Biology
Monash University
Department of Medicine
European Molecular Biology Laboratory
European Molecular Biology Laboratory
Harvard University
Department of Biology
ETH Zurich - Institute for Particle Physics and Astrophysics (IPA)
Bruker Corporation
Tencent
State Key Laboratory of Medical Proteomics
AI for Science Institute
State Key Laboratory of Medical Proteomics
National Center for Protein Sciences (Beijing)
Thermo Fisher Scientific (Bremen) GmbH
Informatics and Predictive Sciences Research
Bristol-Myers Squibb
Fudan University
Indiana University Bloomington
The University of Sydney
Center for Computational Mass Spectrometry
University of California at San Diego
Peng Cheng Laboratory
Bioinformatics Solutions Inc.
University of Waterloo
AI for Science Institute
Peking University
Research Institute of Intelligent Complex Systems
School of Computer Science and Engineering
Sun Yat-Sen University
Departments of Computational Medicine & Bioinformatics
University of Michigan, Ann Arbor
Department of Materials Science and Engineering
State Key Laboratory of Mathematical Science
CAS - Academy of Mathematics and System Sciences
Tulane University
Department of Oral Biological and Medical Sciences
University of British Columbia
Institute for Systems Biology
School of Mathematical Sciences and School of AI
Shanghai Jiao Tong University
Computational Systems Biochemistry Research Group
Max Planck Institute of Biochemistry
Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory)
School of Big Data and Statistics, Anhui University
School of Biological Science and Medical Engineering & School of Engineering Medicine
Beihang University
School of Communication and Information Engineering
Shanghai University
Eye Hospital and Institute for Advanced Study on Eye Health and Diseases
Wenzhou Medical University
Sichuan University
Department of Proteomics and Signal Transduction
Baylor College of Medicine

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2 引用（Scopus）

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.

源语言	英语
页（从-至）	1697-1709
页数	13
期刊	Nature Methods
卷	23
期	9
DOI	https://doi.org/10.1038/s41592-026-03085-y
出版状态	已出版 - 9月 2026

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10.1038/s41592-026-03085-y

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Sun, Y., Jun, A., Liu, Z., Sun, R., Qian, L., Payne, S. H., Bittremieux, W., Ralser, M., Li, C., Chen, Y., Dong, Z., Perez-Riverol, Y., Khan, A., Sander, C., Aebersold, R., Vizcaíno, J. A., Krieger, J. R., Yao, J., Han, W., ... Guo, T. (2026). AI proteomics: from protein identification to virtual cells. Nature Methods, 23(9), 1697-1709. https://doi.org/10.1038/s41592-026-03085-y

@article{56193a771e094320bfc4c2db08da9989,

title = "AI proteomics: from protein identification to virtual cells",

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.",

author = "Yingying Sun and A. Jun and Zhiwei Liu and Rui Sun and Liujia Qian and Payne, \{Samuel H.\} and Wout Bittremieux and Markus Ralser and Chen Li and Yi Chen and Zhen Dong and Yasset Perez-Riverol and Asif Khan and Chris Sander and Ruedi Aebersold and Vizca{\'i}no, \{Juan Antonio\} and Krieger, \{Jonathan R.\} and Jianhua Yao and Wen Han and Linfeng Zhang and Yunping Zhu and Yue Xuan and Sun, \{Benjamin Boyang\} and Liang Qiao and Henning Hermjakob and Haixu Tang and Huanhuan Gao and Yamin Deng and Qing Zhong and Cheng Chang and Nuno Bandeira and Ming Li and Weinan E and Siqi Sun and Yuedong Yang and Omenn, \{Gilbert S.\} and Yue Zhang and Ping Xu and Yan Fu and Xiaowen Liu and Overall, \{Christopher M.\} and Yu Wang and Deutsch, \{Eric W.\} and Luonan Chen and J{\"u}rgen Cox and Vadim Demichev and Fuchu He and Jiaxing Huang and Huilin Jin and Chao Liu and Nan Li and Zhongzhi Luan and Jiangning Song and Kaicheng Yu and Wanggen Wan and Tai Wang and Kang Zhang and Le Zhang and Bell, \{Peter A.\} and Matthias Mann and Bing Zhang and Tiannan Guo",

note = "Publisher Copyright: {\textcopyright} Springer Nature America, Inc. 2026.",

year = "2026",

month = sep,

doi = "10.1038/s41592-026-03085-y",

language = "英语",

volume = "23",

pages = "1697--1709",

journal = "Nature Methods",

issn = "1548-7091",

publisher = "Nature Research",

number = "9",

}

Sun, Y, Jun, A, Liu, Z, Sun, R, Qian, L, Payne, SH, Bittremieux, W, Ralser, M, Li, C, Chen, Y, Dong, Z, Perez-Riverol, Y, Khan, A, Sander, C, Aebersold, R, Vizcaíno, JA, Krieger, JR, Yao, J, Han, W, Zhang, L, Zhu, Y, Xuan, Y, Sun, BB, Qiao, L, Hermjakob, H, Tang, H, Gao, H, Deng, Y, Zhong, Q, Chang, C, Bandeira, N, Li, M, E, W, Sun, S, Yang, Y, Omenn, GS, Zhang, Y, Xu, P, Fu, Y, Liu, X, Overall, CM, Wang, Y, Deutsch, EW, Chen, L, Cox, J, Demichev, V, He, F, Huang, J, Jin, H, Liu, C, Li, N, Luan, Z, Song, J, Yu, K, Wan, W, Wang, T, Zhang, K, Zhang, L, Bell, PA, Mann, M, Zhang, B & Guo, T 2026, 'AI proteomics: from protein identification to virtual cells', Nature Methods, 卷 23, 号码 9, 页码 1697-1709. https://doi.org/10.1038/s41592-026-03085-y

TY - JOUR

T1 - AI proteomics

T2 - from protein identification to virtual cells

AU - Sun, Yingying

AU - Jun, A.

AU - Liu, Zhiwei

AU - Sun, Rui

AU - Qian, Liujia

AU - Payne, Samuel H.

AU - Bittremieux, Wout

AU - Ralser, Markus

AU - Li, Chen

AU - Chen, Yi

AU - Dong, Zhen

AU - Perez-Riverol, Yasset

AU - Khan, Asif

AU - Sander, Chris

AU - Aebersold, Ruedi

AU - Vizcaíno, Juan Antonio

AU - Krieger, Jonathan R.

AU - Yao, Jianhua

AU - Han, Wen

AU - Zhang, Linfeng

AU - Zhu, Yunping

AU - Xuan, Yue

AU - Sun, Benjamin Boyang

AU - Qiao, Liang

AU - Hermjakob, Henning

AU - Tang, Haixu

AU - Gao, Huanhuan

AU - Deng, Yamin

AU - Zhong, Qing

AU - Chang, Cheng

AU - Bandeira, Nuno

AU - Li, Ming

AU - E, Weinan

AU - Sun, Siqi

AU - Yang, Yuedong

AU - Omenn, Gilbert S.

AU - Zhang, Yue

AU - Xu, Ping

AU - Fu, Yan

AU - Liu, Xiaowen

AU - Overall, Christopher M.

AU - Wang, Yu

AU - Deutsch, Eric W.

AU - Chen, Luonan

AU - Cox, Jürgen

AU - Demichev, Vadim

AU - He, Fuchu

AU - Huang, Jiaxing

AU - Jin, Huilin

AU - Liu, Chao

AU - Li, Nan

AU - Luan, Zhongzhi

AU - Song, Jiangning

AU - Yu, Kaicheng

AU - Wan, Wanggen

AU - Wang, Tai

AU - Zhang, Kang

AU - Zhang, Le

AU - Bell, Peter A.

AU - Mann, Matthias

AU - Zhang, Bing

AU - Guo, Tiannan

PY - 2026/9

Y1 - 2026/9

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105046116262

U2 - 10.1038/s41592-026-03085-y

DO - 10.1038/s41592-026-03085-y

M3 - 文章

AN - SCOPUS:105046116262

SN - 1548-7091

VL - 23

SP - 1697

EP - 1709

JO - Nature Methods

JF - Nature Methods

IS - 9

ER -

AI proteomics: from protein identification to virtual cells

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