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Article

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

Supporting Info

Peter A. DiMaggio, Jr. and Christodoulos A. Floudas*

Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263

Bingwen Lu and John R. Yates, III

Department of Cell Biology, The Scripps Research Institute, SR11, La Jolla, California 92037

J. Proteome Res., 2008, 7 (4), pp 1584–1593

DOI: 10.1021/pr700577z

Publication Date (Web): March 7, 2008

* Author to whom all correspondence should be addressed. Tel.: (609) 258-4595 . E-mail address: floudas@titan.princeton.edu.

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(1, 2) is utilized to construct accurate de novo peptide sequences. A modified version of the local database search tool FASTA(3) 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(4) 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,(5) which was trained for use on high-precision tandem mass spectra, and the tag-based method InsPecT.(6) The de novo sequences of PILOT and PepNovo are also searched against the nonredundant protein database using CIDentify(7) 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.

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