A new approach to protein identification

Nuno Bandeira, Dekel Tsur, Ari Frank, Pavel Pevzner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


Advances in tandem mass-spectrometry (MS/MS) steadily increase the rate of generation of MS/MS spectra and make it more computationally challenging to analyze such huge datasets. As a result, the existing approaches that compare spectra against databases are already facing a bottleneck, particularly when interpreting spectra of post-translationally modified peptides. In this paper we introduce a new idea that allows one to perform MS/MS database search... without ever comparing a spectrum against a database. The idea has two components: experimental and computational. Our experimental idea is counterintuitive: we propose to intentionally introduce chemical damage to the sample. Although it does not appear to make any sense from the experimental perspective, it creates a large number of "spectral pairs" that, as we show below, open up computational avenues that were never explored before. Having a spectrum of a modified peptide paired with a spectrum of an unmodified peptide, allows one to separate the prefix and suffix ladders, to greatly reduce the number of noise peaks, and to generate a small number of peptide reconstructions that are very likely to contain the correct one. The MS/MS database search is thus reduced to extremely fast pattern matching (rather than time-consuming matching of spectra against databases). In addition to speed, our approach provides a new paradigm for identifying post-translational modifications.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 10th Annual International Conference, RECOMB 2006, Proceedings
Number of pages16
StatePublished - 14 Jul 2006
Externally publishedYes
Event10th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2006 - Venice, Italy
Duration: 2 Apr 20065 Apr 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3909 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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