De novo peptide sequencing by deep learning

peer-reviewed · Proceedings of the National Academy of Sciences · 2017

peer-reviewed · Proceedings of the National Academy of Sciences · 2017. Ngoc Hieu Tran et al. De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization…
Date 2017-07-18
Type peer-reviewed
Venue Proceedings of the National Academy of Sciences
Publisher National Academy of Sciences
Contribution algorithm
DOI 10.1073/pnas.1705691114
Citations (OpenAlex) 451
Venue 2-year citedness 8.56

Abstract

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.

Authors

  1. Ngoc Hieu Tran · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc., University of Waterloo
  2. Xianglilan Zhang · Beijing Institute of Microbiology and Epidemiology, University of Waterloo
  3. Lei Xin · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc., Western University
  4. Baozhen Shan · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc., Western University
  5. Ming Li · Bioinformatics Solutions Inc., Central China Institute of Artificial Intelligence, Peng Cheng Laboratory, University of Waterloo, University of Western Ontario

Methods and tools

  • DeepNovo: The model that started the deep-learning wave (CNN+LSTM)

Data deposited

  • Seven-species benchmark (original (DeepNovo, 2017)) · no public address

Cites (18)

Cited by (152)

Seen in the charts

Back to the full map

Back to top