Charge state determination of peptide tandem mass spectra using support vector machine (SVM)

A single mass spectrometry experiment could produce hundreds of thousands of tandem mass spectra. Several search engines have been developed to interpret tandem mass spectra. All search engines need to determine the masses of peptide ions from mass/charge ratios of ions. Unfortunately, mass spectrometers do not detect the charges of ions. A current strategy is to search candidate peptides multiply times, once for each possible charge state (typically +2 or +3). However, this strategy not only wastes the search time but also increases the risk of false positive peptide identification. This paper aims at discriminating doubly charged spectra from triply charged ones. 28 features are introduced to describe the discriminant characteristics of doubly charged and triply charged spectra. The support vector machine (SVM) technique is used to train the classifier on these 28 features. To verify the proposed method, computational experiments are conducted on two types of datasets: ISB dataset generated from the low-resolution ion-trap instrument and TOV dataset generated from the high-resolution quadrupole-time-of-flight (Q-TOF) instrument. For each type of dataset, the SVM-based classifiers are trained and tested on 20 randomly sampled sub-datasets. The results show that the proposed method reaches averagely 95% and 93% of correct rates to discriminate doubly charged spectra from triply charged ones for the low-resolution ISB dataset and the high-resolution TOV dataset, respectively.