Premium
Development and assessment of scoring functions for protein identification using PMF data
Author(s) -
Song Zhao,
Chen Luonan,
Ganapathy Ashwin,
Wan XiuFeng,
Brechenmacher Laurent,
Tao Nengbing,
Emerich David,
Stacey Gary,
Xu Dong
Publication year - 2007
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200600305
Subject(s) - computer science , identification (biology) , software , quantitative proteomics , data mining , matching (statistics) , proteomics , pattern recognition (psychology) , chemistry , artificial intelligence , statistics , mathematics , biology , gene , biochemistry , botany , programming language
PMF is one of the major methods for protein identification using the MS technology. It is faster and cheaper than MS/MS. Although PMF does not differentiate trypsin‐digested peptides of identical mass, which makes it less informative than MS/MS, current computational methods for PMF have the potential to improve its detection accuracy by better use of the information content in PMF spectra. We developed a number of new probability‐based scoring functions for PMF protein identification based on the MOWSE algorithm. We considered a detailed distribution of matching masses in a protein database and peak intensity, as well as the likelihood of peptide matches to be close to each other in a protein sequence. Our computational methods are assessed and compared with other methods using PMF data of 52 gel spots of known protein standards. The comparison shows that our new scoring schemes have higher or comparable accuracies for protein identification in comparison to the existing methods. Our software is freely available upon request. The scoring functions can be easily incorporated into other proteomics software packages.