A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search

摘要

An accurate scoring function for database search is crucial for peptide identification using tandem mass spectrometry. Although many mathematical models have been proposed to score peptides against tandem mass spectra, we design a unique method (called HMMscore) that combines information on consecutive ions, ie, b_i and b_(i+1), and complementary ions, ie, b_i and y_(n-i) for a peptide of n amino acids, plus information on machine accuracy and mass peak intensity, into a hidden Markov model (HMM). In addition, we develop a way to calculate statistical significance of the HMM scores. We implement the method and test them on experimental data. The results show that the accuracy of our method is high compared to MASCOT, and that the false positive rate of HMMscore is low.