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

Zeng, W.-F., Zhou, X.-X., Willems, S., Ammar, C., Wahle, M., Bludau, I., et al. (2022). AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nature Communications, 13(1): 7238. doi:10.1038/s41467-022-34904-3.

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 Creators:
Zeng, Wen-Feng1, Author           
Zhou, Xie-Xuan1, Author
Willems, Sander1, Author           
Ammar, Constantin1, Author           
Wahle, Maria1, Author
Bludau, Isabell1, Author           
Voytik, Eugenia1, Author           
Strauss, Maximillian T.2, Author
Mann, Matthias1, Author
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              
2external, ou_persistent22              

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Free keywords: RETENTION TIME; IDENTIFICATIONScience & Technology - Other Topics;
 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).

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Language(s): eng - English
 Dates: 2022-11-24
 Publication Status: Published online
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 13 (1) Sequence Number: 7238 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723