Literature DB >> 14641094

Deriving statistical models for predicting peptide tandem MS product ion intensities.

F Schütz1, E A Kapp, R J Simpson, T P Speed.   

Abstract

Improved search algorithms and scoring functions are required before the identification of peptide tandem MS data can be considered to be fully reliable and automatable. The development of models that can accurately predict product ion spectra from a peptide sequence would certainly help achieve this goal, but this firstly requires a better understanding of the process of fragmentation of peptides in the gas-phase. We summarize recent developments in this area and show that the prediction of product ion spectra is feasible and should improve the identification of peptide tandem MS data, especially for peptides that currently give low or insignificant scores with current search algorithms.

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Year:  2003        PMID: 14641094     DOI: 10.1042/bst0311479

Source DB:  PubMed          Journal:  Biochem Soc Trans        ISSN: 0300-5127            Impact factor:   5.407


  10 in total

1.  A case study of de novo sequence analysis of N-sulfonated peptides by MALDI TOF/TOF mass spectrometry.

Authors:  Bart Samyn; Griet Debyser; Kjell Sergeant; Bart Devreese; Jozef Van Beeumen
Journal:  J Am Soc Mass Spectrom       Date:  2004-12       Impact factor: 3.109

2.  HMMatch: peptide identification by spectral matching of tandem mass spectra using hidden Markov models.

Authors:  Xue Wu; Chau-Wen Tseng; Nathan Edwards
Journal:  J Comput Biol       Date:  2007-10       Impact factor: 1.479

3.  Combinatorial approach for large-scale identification of linked peptides from tandem mass spectrometry spectra.

Authors:  Jian Wang; Veronica G Anania; Jeff Knott; John Rush; Jennie R Lill; Philip E Bourne; Nuno Bandeira
Journal:  Mol Cell Proteomics       Date:  2014-02-03       Impact factor: 5.911

4.  Using ion mobility data to improve peptide identification: intrinsic amino acid size parameters.

Authors:  Stephen J Valentine; Michael A Ewing; Jonathan M Dilger; Matthew S Glover; Scott Geromanos; Chris Hughes; David E Clemmer
Journal:  J Proteome Res       Date:  2011-04-01       Impact factor: 4.466

5.  Multifactorial Understanding of Ion Abundance in Tandem Mass Spectrometry Experiments.

Authors:  Zeeshan Fazal; Bruce R Southey; Jonathan V Sweedler; Sandra L Rodriguez-Zas
Journal:  J Proteomics Bioinform       Date:  2013-01-29

6.  Predicting intensity ranks of peptide fragment ions.

Authors:  Ari M Frank
Journal:  J Proteome Res       Date:  2009-05       Impact factor: 4.466

7.  A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data.

Authors:  Cong Zhou; Lucas D Bowler; Jianfeng Feng
Journal:  BMC Bioinformatics       Date:  2008-07-30       Impact factor: 3.169

8.  A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data.

Authors:  Zhuo Zhang; Shiwei Sun; Xiaopeng Zhu; Suhua Chang; Xiaofei Liu; Chungong Yu; Dongbo Bu; Runsheng Chen
Journal:  BMC Bioinformatics       Date:  2006-04-26       Impact factor: 3.169

9.  SAMPI: protein identification with mass spectra alignments.

Authors:  Hans-Michael Kaltenbach; Andreas Wilke; Sebastian Böcker
Journal:  BMC Bioinformatics       Date:  2007-03-26       Impact factor: 3.169

10.  Basophile: accurate fragment charge state prediction improves peptide identification rates.

Authors:  Dong Wang; Surendra Dasari; Matthew C Chambers; Jerry D Holman; Kan Chen; Daniel C Liebler; Daniel J Orton; Samuel O Purvine; Matthew E Monroe; Chang Y Chung; Kristie L Rose; David L Tabb
Journal:  Genomics Proteomics Bioinformatics       Date:  2013-03-08       Impact factor: 7.691

  10 in total

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