CHUNGONG YU
These two authors contributed equally to this paper.
Bioinformatics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
YU LIN
These two authors contributed equally to this paper.
Bioinformatics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Graduate School of the Chinese Academy of Science, Beijing 100039, China
SHIWEI SUN
Bioinformatics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Graduate School of the Chinese Academy of Science, Beijing 100039, China
JINJIN CAI
Bioinformatics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Graduate School of the Chinese Academy of Science, Beijing 100039, China
JINGFEN ZHANG
Bioinformatics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Graduate School of the Chinese Academy of Science, Beijing 100039, China
DONGBO BU
To whom the correspondence should be addressed.
Bioinformatics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
ZHUO ZHANG
Institute of Biophysics, Chinese Academy of Sciences, Beijing 100035, China
Graduate School of the Chinese Academy of Science, Beijing 100039, China
RUNSHENG CHEN
To whom the correspondence should be addressed.
Institute of Biophysics, Chinese Academy of Sciences, Beijing 100035, China

Received 2 October 2006
Revised 2 January 2007
Accepted 22 January 2007

In protein identification by tandem mass spectrometry, it is critical to accurately predict the theoretical spectrum for a peptide sequence. To date, the widely-used database searching methods adopted simple statistical models for predicting. For some peptide, these models usually yield a theoretical spectrum with a significant deviation from the experimental one. In this paper, in order to derive an improved predicting model, we utilized a non-linear programming model to quantify the factors impacting peptide fragmentation. Then, an iterative algorithm was proposed to solve this optimization problem. Upon a training set of 1803 spectra, the experimental result showed a good agreement with some known principles about peptide fragmentation, such as the tendency to cleave at the middle of peptide, and Pro's preference of the N-terminal cleavage. Moreover, upon a testing set of 941 spectra, comparison of the predicted spectra against the experimental ones showed that this method can generate reasonable predictions. The results in this paper can offer help to both database searching and de novo methods.

Mass spectrum; mobile proton hypothesis; linear programming; iterative algorithm