A mixed-integer optimization framework for de novo peptide identification

peer-reviewed · AIChE Journal · 2007

peer-reviewed · AIChE Journal · 2007. Peter A. DiMaggio et al. A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass…
Date 2007-01-01
Type peer-reviewed
Venue AIChE Journal
Publisher Wiley
Contribution algorithm
DOI 10.1002/aic.11061
Citations (OpenAlex) 9
Venue 2-year citedness 3.54

Abstract

A novel methodology for the de novo identification of peptides by mixed-integer optimization 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. Several problems are examined to illustrate the proposed method’s ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from 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 identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.

Authors

  1. Peter A. DiMaggio · Princeton University
  2. Christodoulos A. Floudas · Princeton University

Methods and tools

Cites (14)

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