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De novo peptide sequencing by deep learning.

Ngoc Hieu Tran1, Xianglilan Zhang1,2, Lei Xin3

  • 1David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Proceedings of the National Academy of Sciences of the United States of America
|July 20, 2017
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DeepNovo, a deep learning model, significantly improves de novo peptide sequencing accuracy using tandem mass spectrometry. This advanced proteomics tool achieves higher accuracy for amino acids and peptides, enabling complete antibody sequence reconstruction.

Keywords:
MSde novo sequencingdeep learning

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • De novo peptide sequencing from tandem mass spectrometry (MS/MS) is crucial for protein characterization, particularly for novel sequences like monoclonal antibodies (mAbs).
  • Accurate peptide sequencing is essential for understanding protein function and developing targeted therapeutics.

Purpose of the Study:

  • To introduce DeepNovo, a novel deep neural network model for de novo peptide sequencing.
  • To enhance the accuracy and efficiency of peptide sequencing from tandem MS data.

Main Methods:

  • Developed DeepNovo, integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze tandem mass spectra, fragment ions, and peptide sequence patterns.
  • Combined deep learning networks with local dynamic programming to address the complex optimization challenges in de novo sequencing.
  • Trained and evaluated DeepNovo on diverse species data.

Main Results:

  • DeepNovo significantly outperformed existing state-of-the-art methods, demonstrating 7.7–22.9% higher accuracy at the amino acid level and 38.1–64.0% higher accuracy at the peptide level.
  • Successfully reconstructed complete mouse antibody light and heavy chain sequences with 97.5–100% coverage and 97.2–99.5% accuracy, without relying on sequence databases.
  • Demonstrated DeepNovo's retrainable nature for adaptability to various data sources, offering an end-to-end solution.

Conclusions:

  • DeepNovo represents a significant advancement in de novo peptide sequencing, leveraging deep learning and dynamic programming.
  • The model offers a powerful, accurate, and versatile tool for proteomics research, particularly for antibody sequencing and discovery.
  • This work highlights the potential of deep learning in solving complex optimization problems within biological data analysis.