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Predicting molecular formulas of fragment ions with isotope patterns in tandem mass spectra

  • Jingfen Zhang
  • , Wen Gao
  • , Jinjin Cai
  • , Simin He
  • , Rong Zeng
  • , Runsheng Chen

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on isotope patterns, enumerate all possible formulas for an ion, and exclude certain formulas violating chemical constraints. However, these methods cannot be well generalized to the component prediction of fragment ions in tandem mass spectra. In this paper, a new method, FFP (Fragment ion Formula Prediction), is presented to predict elemental component formulas of fragment ions. In the FFP method, the prediction of the best formulas is converted into the minimization of the distance between theoretical and observed isotope patterns. And, then, a novel local search model is proposed to generate a set of candidate formulas efficiently. After the search, FFP applies a new multiconstraint filtering to exclude as many invalid and improbable formulas as possible. FFP is experimentally compared with the previous enumeration methods, and shown to outperform them significantly. The results of this paper can help to improve the reliability of de novo in the identification of peptide sequences. © 2005 IEEE.
Original languageEnglish
Pages (from-to)217-230
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume2
Issue number3
DOIs
Publication statusPublished - Jul 2005
Externally publishedYes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was funded by the National Key Basic Research and Development Program (973) of China under Grant No. 2002CB713807.

Research Keywords

  • Isotope patterns
  • Peptide sequencing
  • Tandem mass spectra

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