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

Author

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  • 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
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    References listed on IDEAS

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    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.

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