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Prediction of peptide mass spectral libraries with machine learning

Nature Biotechnology volume 41, pages 33–43 (2023)Cite this article

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Abstract

The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.

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Fig. 1: Fragmentation spectra in shotgun proteomics.
Fig. 2: DDA and DIA.
Fig. 3: Machine learning.
Fig. 4: Machine learning strategies for ion series intensity prediction.
Fig. 5: DDA applications.
Fig. 6: DIA applications.

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Acknowledgements

I thank G. Borner, B. Frohn, T. Geiger, J. L. Restrepo-López, F. Traube and S. Yilmaz for critical reading of the manuscript; C. De Nart for assistance with Fig. 1 and P. Sinitcyn for the re-analysis of data in Fig. 6a. This project was partially funded by the German Ministry for Science and Education (BMBF) funding action MSCoreSys, reference number FKZ 031L0214D.

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  1. Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany

    Jürgen Cox

  2. Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway

    Jürgen Cox

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Cox, J. Prediction of peptide mass spectral libraries with machine learning. Nat Biotechnol 41, 33–43 (2023). https://doi.org/10.1038/s41587-022-01424-w

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