PepHMM: 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, our method (called PepHMM, http://msms.cmb.usc.edu) is unique in that it combines information on machine accuracy, mass peak intensity, and correlation among ions into a hidden Markov model (HMM). In addition, we develop a method to calculate statistical significance of the HMM scores. We implement the method and test them on two sets of experimental data generated by two different types of mass spectrometers and compare the results with MASCOT and SEQUEST under the same condition. One experimental results show that PepHMM has a much higher accuracy (with 6.5percent error rate) than MASCOT (with 17.4percent error rate), and the other experimental results show that PepHMM identifies 43 and 31percent more correct spectra than SEQUEST and MASCOT, respectively.