Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

peer-reviewed · Protein Science · 2020

peer-reviewed · Protein Science · 2020. Isabelle O’Bryon et al. Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed…
Date 2020-09-01
Type peer-reviewed
Venue Protein Science
Publisher Wiley
Contribution review
DOI 10.1002/pro.3919
Citations (OpenAlex) 40
Venue 2-year citedness 3.27

Abstract

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

Authors

  1. Isabelle O’Bryon · Pacific Northwest National Laboratory
  2. Sarah C. Jenson · Pacific Northwest National Laboratory
  3. Eric D. Merkley · Pacific Northwest National Laboratory

Methods and tools

  • Flying under the radar review: O’Bryon, Jenson & Merkley 2020 Protein Science review arguing that de novo peptide identification is under-appreciated relative to database-search methods, with concrete forensic / non-model / immunopeptidomic cases where it wins.

Cites (31)

Cited by (14)

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