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Mass-spectrometric exploration of proteome structure and function

Author

Listed:
  • Ruedi Aebersold

    (Institute of Molecular Systems Biology, ETH Zürich
    Faculty of Science, University of Zürich)

  • Matthias Mann

    (Max Planck Institute of Biochemistry
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen)

Abstract

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.

Suggested Citation

  • Ruedi Aebersold & Matthias Mann, 2016. "Mass-spectrometric exploration of proteome structure and function," Nature, Nature, vol. 537(7620), pages 347-355, September.
    • Handle: RePEc:nat:nature:v:537:y:2016:i:7620:d:10.1038_nature19949
    DOI: 10.1038/nature19949
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    Cited by:

    1. Yulin Sun & Zhengguang Guo & Xiaoyan Liu & Lijun Yang & Zongpan Jing & Meng Cai & Zhaoxu Zheng & Chen Shao & Yefan Zhang & Haidan Sun & Li Wang & Minjie Wang & Jun Li & Lusong Tian & Yue Han & Shuangm, 2022. "Noninvasive urinary protein signatures associated with colorectal cancer diagnosis and metastasis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Claudia Ctortecka & Natalie M. Clark & Brian W. Boyle & Anjali Seth & D. R. Mani & Namrata D. Udeshi & Steven A. Carr, 2024. "Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Martin Mehnert & Rodolfo Ciuffa & Fabian Frommelt & Federico Uliana & Audrey Drogen & Kilian Ruminski & Matthias Gstaiger & Ruedi Aebersold, 2020. "Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes," Nature Communications, Nature, vol. 11(1), pages 1-18, December.
    5. Melih Yilmaz & William E. Fondrie & Wout Bittremieux & Carlo F. Melendez & Rowan Nelson & Varun Ananth & Sewoong Oh & William Stafford Noble, 2024. "Sequence-to-sequence translation from mass spectra to peptides with a transformer model," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Erik Hartman & Aaron M. Scott & Christofer Karlsson & Tirthankar Mohanty & Suvi T. Vaara & Adam Linder & Lars Malmström & Johan Malmström, 2023. "Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Hao Hu & Wei Hu & An-Di Guo & Linhui Zhai & Song Ma & Hui-Jun Nie & Bin-Shan Zhou & Tianxian Liu & Xinglong Jia & Xing Liu & Xuebiao Yao & Minjia Tan & Xiao-Hua Chen, 2024. "Spatiotemporal and direct capturing global substrates of lysine-modifying enzymes in living cells," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Nicholas Drachman & Mathilde Lepoitevin & Hannah Szapary & Benjamin Wiener & William Maulbetsch & Derek Stein, 2024. "Nanopore ion sources deliver individual ions of amino acids and peptides directly into high vacuum," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    10. Fushi Wang & Chunxiao Zhao & Pinlong Zhao & Fanfan Chen & Dan Qiao & Jiandong Feng, 2023. "MoS2 nanopore identifies single amino acids with sub-1 Dalton resolution," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    11. Simon Davis & Connor Scott & Janina Oetjen & Philip D. Charles & Benedikt M. Kessler & Olaf Ansorge & Roman Fischer, 2023. "Deep topographic proteomics of a human brain tumour," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    12. Wenping Zhou & Wenxue Li & Shisheng Wang & Barbora Salovska & Zhenyi Hu & Bo Tao & Yi Di & Ujwal Punyamurtula & Benjamin E. Turk & William C. Sessa & Yansheng Liu, 2023. "An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    13. Brad A. Palanski & Nielson Weng & Lichao Zhang & Andrew J. Hilmer & Lalla A. Fall & Kavya Swaminathan & Bana Jabri & Carolina Sousa & Nielsen Q. Fernandez-Becker & Chaitan Khosla & Joshua E. Elias, 2022. "An efficient urine peptidomics workflow identifies chemically defined dietary gluten peptides from patients with celiac disease," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    14. Lu Li & Cuiji Sun & Yaoting Sun & Zhen Dong & Runxin Wu & Xiaoting Sun & Hanbin Zhang & Wenhao Jiang & Yan Zhou & Xufeng Cen & Shang Cai & Hongguang Xia & Yi Zhu & Tiannan Guo & Kiryl D. Piatkevich, 2022. "Spatially resolved proteomics via tissue expansion," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    15. Jongmin Woo & Sarah M. Williams & Lye Meng Markillie & Song Feng & Chia-Feng Tsai & Victor Aguilera-Vazquez & Ryan L. Sontag & Ronald J. Moore & Dehong Hu & Hardeep S. Mehta & Joshua Cantlon-Bruce & T, 2021. "High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    16. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    17. Yuwan Chen & Wen Zhou & Yufei Xia & Weijie Zhang & Qun Zhao & Xinwei Li & Hang Gao & Zhen Liang & Guanghui Ma & Kaiguang Yang & Lihua Zhang & Yukui Zhang, 2023. "Targeted cross-linker delivery for the in situ mapping of protein conformations and interactions in mitochondria," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    18. Kevin L. Yang & Fengchao Yu & Guo Ci Teo & Kai Li & Vadim Demichev & Markus Ralser & Alexey I. Nesvizhskii, 2023. "MSBooster: improving peptide identification rates using deep learning-based features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    19. Daniela Klaproth-Andrade & Johannes Hingerl & Yanik Bruns & Nicholas H. Smith & Jakob Träuble & Mathias Wilhelm & Julien Gagneur, 2024. "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. Sofani Tafesse Gebreyesus & Asad Ali Siyal & Reta Birhanu Kitata & Eric Sheng-Wen Chen & Bayarmaa Enkhbayar & Takashi Angata & Kuo-I Lin & Yu-Ju Chen & Hsiung-Lin Tu, 2022. "Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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