A Hybrid Method for Peptide Identification Using Integer Linear Optimization, Local Database Search, and Quadrupole Time-of-Flight or OrbiTrap Tandem Mass Spectrometry

peer-reviewed · Journal of Proteome Research · 2008

peer-reviewed · Journal of Proteome Research · 2008. Peter A. DiMaggio et al. A novel hybrid methodology for the automated identification of peptides via de novo integer linear…
Date 2008-04-01
Type peer-reviewed
Venue Journal of Proteome Research
Publisher American Chemical Society (ACS)
Contribution algorithm
DOI 10.1021/pr700577z
Citations (OpenAlex) 20
Venue 2-year citedness 3.83

Abstract

A novel hybrid methodology for the automated identification of peptides via de novo integer linear optimization, local database search, and tandem mass spectrometry is presented in this article. A modified version of the de novo identification algorithm PILOT, is utilized to construct accurate de novo peptide sequences. A modified version of the local database search tool FASTA is used to query these de novo predictions against the nonredundant protein database to resolve any low-confidence amino acids in the candidate sequences. The computational burden associated with performing several alignments is alleviated with the use of distributive computing. Extensive computational studies are presented for this new hybrid methodology, as well as comparisons with MASCOT for a set of 38 quadrupole time-of-flight (QTOF) and 380 OrbiTrap tandem mass spectra. The results for our proposed hybrid method for the OrbiTrap spectra are also compared with a modified version of PepNovo, which was trained for use on high-precision tandem mass spectra, and the tag-based method InsPecT. The de novo sequences of PILOT and PepNovo are also searched against the nonredundant protein database using CIDentify to compare with the alignments achieved by our modifications of FASTA. The comparative studies demonstrate the excellent peptide identification accuracy gained from combining the strengths of our de novo method, which is based on integer linear optimization, and database driven search methods.

Authors

  1. Peter A. DiMaggio · Imperial College London, Princeton University
  2. Christodoulos A. Floudas · Princeton University, The Scripps Research Institute
  3. Bingwen Lu · Pfizer (United States), Pfizer Inc., Princeton University, The Scripps Research Institute, University of Southern California
  4. John R. Yates · Princeton University, The Scripps Research Institute

Methods and tools

  • PILOT: Integer-linear-optimization formulation for de novo peptide identification from tandem mass spectra.

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