Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review

peer-reviewed · Analytica Chimica Acta · 2023

peer-reviewed · Analytica Chimica Acta · 2023. Cheuk Chi A. Ng et al. Peptide sequencing is of great significance to fundamental and applied research in the fields such as…
Date 2023-08-01
Type peer-reviewed
Venue Analytica Chimica Acta
Publisher Elsevier BV
Contribution review
DOI 10.1016/j.aca.2023.341330
Citations (OpenAlex) 45
Venue 2-year citedness 5.41

Abstract

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

Authors

  1. Cheuk Chi A. Ng · The Hong Kong Polytechnic University, The Hong Kong Polytechnic University Shenzhen Research Institute
  2. Yin Zhou · The Hong Kong Polytechnic University, The Hong Kong Polytechnic University Shenzhen Research Institute
  3. Zhong-Ping Yao · The Hong Kong Polytechnic University, The Hong Kong Polytechnic University Shenzhen Research Institute

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