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Molecular & Cellular Proteomics

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Technological Innovation and Resources

Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework

Korrawe Karunratanakul, View ORCID ProfileHsin-Yao Tang, David W. Speicher, Ekapol Chuangsuwanich and View ORCID ProfileSira Sriswasdi  Correspondence email
Molecular & Cellular Proteomics October 7, 2019, mcp.TIR119.001656; https://doi.org/10.1074/mcp.TIR119.001656
Korrawe Karunratanakul
Chulalongkorn University, Thailand
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Hsin-Yao Tang
The Wistar Institute
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  • ORCID record for Hsin-Yao Tang
David W. Speicher
The Wistar Institute, United States
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Ekapol Chuangsuwanich
Chulalongkorn University, Thailand
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Sira Sriswasdi
Faculty of Medicine, Chulalongkorn University, Thailand
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  • ORCID record for Sira Sriswasdi
  • For correspondence: sira.sr@chula.ac.th
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Abstract

Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptidomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides, and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies, including tumor neoantigen discovery, antibody sequencing, and proteome characterization of non-model organisms.

  • Software
  • Deep learning
  • Bioinformatics searching
  • De novo sequencing
  • Mass Spectrometry
  • Peptides*
  • Phosphoproteome

Footnotes

  • Author contributions: K.K. and S.S. performed research; K.K., H.-Y.T., and S.S. analyzed data; K.K., E.C., and S.S. wrote the paper; H.-Y.T. and D.W.S. contributed new reagents/analytic tools; E.C. and S.S. designed research; H.-Y.T. and D.W.S. contributed mass spectrometry data.

  • Received July 2, 2019.
  • Revision received September 9, 2019.
  • Accepted October 4, 2019.
  • Published under license by The American Society for Biochemistry and Molecular Biology, Inc.
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Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework
Korrawe Karunratanakul, Hsin-Yao Tang, David W. Speicher, Ekapol Chuangsuwanich, Sira Sriswasdi
Molecular & Cellular Proteomics October 7, 2019, mcp.TIR119.001656; DOI: 10.1074/mcp.TIR119.001656

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Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework
Korrawe Karunratanakul, Hsin-Yao Tang, David W. Speicher, Ekapol Chuangsuwanich, Sira Sriswasdi
Molecular & Cellular Proteomics October 7, 2019, mcp.TIR119.001656; DOI: 10.1074/mcp.TIR119.001656
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