De Novo Peptide Identification via Tandem Mass Spectrometry and Integer Linear Optimization

peer-reviewed · Analytical Chemistry · 2007

peer-reviewed · Analytical Chemistry · 2007. Peter A. DiMaggio et al. A novel methodology for the automated de novo identification of peptides via integer linear optimization…
Date 2007-02-01
Type peer-reviewed
Venue Analytical Chemistry
Publisher American Chemical Society (ACS)
Contribution algorithm
DOI 10.1021/ac0618425
Citations (OpenAlex) 55
Venue 2-year citedness 7.10

Abstract

A novel methodology for the automated de novo identification of peptides via integer linear optimization (also referred to as integer linear programming or ILP) and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. A variety of challenging peptide identification problems, accompanied by a comparative study with five state-of-the-art methods, are examined to illustrate the proposed method’s ability to address (a) residue-dependent fragmentation properties that result in missing ion peaks and (b) the variability of resolution in different mass analyzers. A preprocessing algorithm is utilized to identify important m/z values in the tandem mass spectrum. Missing peaks, due to residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to select the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the ILP sequencing stages with the experimental tandem mass spectrum. The novel, proposed de novo method, denoted as PILOT, is compared to existing popular methods such as Lutefisk, PEAKS, PepNovo, EigenMS, and NovoHMM for a set of spectra resulting from QTOF and ion trap instruments.

Authors

  1. Peter A. DiMaggio · Imperial College London, Princeton University
  2. Christodoulos A. Floudas · Princeton University, The Scripps Research Institute

Methods and tools

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

Cites (10)

Cited by (29)

Cited in patents (3)

  • US11573239B2 Methods and systems for de novo peptide sequencing using deep learning ↗ (Bioinformatics Solutions Inc., priority 2017-07-17, active)
  • US11694769B2 Systems and methods for de novo peptide sequencing from data-independent acquisition using deep learning ↗ (Bioinformatics Solutions Inc., priority 2017-07-17, active)
  • US12340874B1 Artificial neural network models for prediction of de novo sequencing of chains of amino acids ↗ (Verily Life Sciences, priority 2017-11-03, active)

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