IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34904-3.html
   My bibliography  Save this article

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

Author

Listed:
  • Wen-Feng Zeng

    (Max Planck Institute of Biochemistry)

  • Xie-Xuan Zhou

    (Max Planck Institute of Biochemistry)

  • Sander Willems

    (Max Planck Institute of Biochemistry)

  • Constantin Ammar

    (Max Planck Institute of Biochemistry)

  • Maria Wahle

    (Max Planck Institute of Biochemistry)

  • Isabell Bludau

    (Max Planck Institute of Biochemistry)

  • Eugenia Voytik

    (Max Planck Institute of Biochemistry)

  • Maximillian T. Strauss

    (University of Copenhagen)

  • Matthias Mann

    (Max Planck Institute of Biochemistry
    University of Copenhagen)

Abstract

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34904-3
    DOI: 10.1038/s41467-022-34904-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34904-3
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-34904-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. Ruedi Aebersold & Matthias Mann, 2016. "Mass-spectrometric exploration of proteome structure and function," Nature, Nature, vol. 537(7620), pages 347-355, September.
    3. Michal Bassani-Sternberg & Eva Bräunlein & Richard Klar & Thomas Engleitner & Pavel Sinitcyn & Stefan Audehm & Melanie Straub & Julia Weber & Julia Slotta-Huspenina & Katja Specht & Marc E. Martignoni, 2016. "Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry," Nature Communications, Nature, vol. 7(1), pages 1-16, December.
    4. Bo Wen & Kai Li & Yun Zhang & Bing Zhang, 2020. "Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    5. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    6. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    7. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
    8. Florian Meier & Niklas D. Köhler & Andreas-David Brunner & Jean-Marc H. Wanka & Eugenia Voytik & Maximilian T. Strauss & Fabian J. Theis & Matthias Mann, 2021. "Deep learning the collisional cross sections of the peptide universe from a million experimental values," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    9. Lei Xin & Rui Qiao & Xin Chen & Hieu Tran & Shengying Pan & Sahar Rabinoviz & Haibo Bian & Xianliang He & Brenton Morse & Baozhen Shan & Ming Li, 2022. "A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maximilian T. Strauss & Isabell Bludau & Wen-Feng Zeng & Eugenia Voytik & Constantin Ammar & Julia P. Schessner & Rajesh Ilango & Michelle Gill & Florian Meier & Sander Willems & Matthias Mann, 2024. "AlphaPept: a modern and open framework for MS-based proteomics," Nature Communications, Nature, vol. 15(1), pages 1-16, 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. Charlotte Adams & Wassim Gabriel & Kris Laukens & Mario Picciani & Mathias Wilhelm & Wout Bittremieux & Kurt Boonen, 2024. "Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. 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.
    3. Lei Xin & Rui Qiao & Xin Chen & Hieu Tran & Shengying Pan & Sahar Rabinoviz & Haibo Bian & Xianliang He & Brenton Morse & Baozhen Shan & Ming Li, 2022. "A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. David Gomez-Zepeda & Danielle Arnold-Schild & Julian Beyrle & Arthur Declercq & Ralf Gabriels & Elena Kumm & Annica Preikschat & Mateusz Krzysztof Łącki & Aurélie Hirschler & Jeewan Babu Rijal & Chris, 2024. "Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. 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.
    6. 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.
    7. Charlotte Adams & Wassim Gabriel & Kris Laukens & Mario Picciani & Mathias Wilhelm & Wout Bittremieux & Kurt Boonen, 2024. "Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. 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.
    9. 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.
    10. Weiping Sun & Qianqiu Zhang & Xiyue Zhang & Ngoc Hieu Tran & M. Ziaur Rahman & Zheng Chen & Chao Peng & Jun Ma & Ming Li & Lei Xin & Baozhen Shan, 2023. "Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    11. Hanqing Liao & Carolina Barra & Zhicheng Zhou & Xu Peng & Isaac Woodhouse & Arun Tailor & Robert Parker & Alexia Carré & Persephone Borrow & Michael J. Hogan & Wayne Paes & Laurence C. Eisenlohr & Rob, 2024. "MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. Celina Tretter & Niklas Andrade Krätzig & Matteo Pecoraro & Sebastian Lange & Philipp Seifert & Clara Frankenberg & Johannes Untch & Gabriela Zuleger & Mathias Wilhelm & Daniel P. Zolg & Florian S. Dr, 2023. "Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    13. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative AI," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, National Bureau of Economic Research, Inc.
    14. Deyun Qiu & Jinxin V. Pei & James E. O. Rosling & Vandana Thathy & Dongdi Li & Yi Xue & John D. Tanner & Jocelyn Sietsma Penington & Yi Tong Vincent Aw & Jessica Yi Han Aw & Guoyue Xu & Abhai K. Tripa, 2022. "A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    15. Shuo-Shuo Liu & Tian-Xia Jiang & Fan Bu & Ji-Lan Zhao & Guang-Fei Wang & Guo-Heng Yang & Jie-Yan Kong & Yun-Fan Qie & Pei Wen & Li-Bin Fan & Ning-Ning Li & Ning Gao & Xiao-Bo Qiu, 2024. "Molecular mechanisms underlying the BIRC6-mediated regulation of apoptosis and autophagy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    16. Xiaoke Yang & Mingqi Zhu & Xue Lu & Yuxin Wang & Junyu Xiao, 2024. "Architecture and activation of human muscle phosphorylase kinase," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    17. Efren Garcia-Maldonado & Andrew D. Huber & Sergio C. Chai & Stanley Nithianantham & Yongtao Li & Jing Wu & Shyaron Poudel & Darcie J. Miller & Jayaraman Seetharaman & Taosheng Chen, 2024. "Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    18. Kristy Rochon & Brianna L. Bauer & Nathaniel A. Roethler & Yuli Buckley & Chih-Chia Su & Wei Huang & Rajesh Ramachandran & Maria S. K. Stoll & Edward W. Yu & Derek J. Taylor & Jason A. Mears, 2024. "Structural basis for regulated assembly of the mitochondrial fission GTPase Drp1," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    19. Katherine A. Ray & Joshua D. Lutgens & Ramesh Bista & Jie Zhang & Ronak R. Desai & Melissa Hirsch & Takeshi Miyazawa & Antonio Cordova & Adrian T. Keatinge-Clay, 2024. "Assessing and harnessing updated polyketide synthase modules through combinatorial engineering," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    20. Fan Lu & Liang Zhu & Thomas Bromberger & Jun Yang & Qiannan Yang & Jianmin Liu & Edward F. Plow & Markus Moser & Jun Qin, 2022. "Mechanism of integrin activation by talin and its cooperation with kindlin," Nature Communications, Nature, vol. 13(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34904-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.