A feature-based deisotoping method for tandem mass spectra

Abstract

For high-resolution tandem mass spectra, the determination of monoisotopic masses of fragment ions plays a key role in the subsequent peptide and protein identification. It can directly influence the subsequent analysis of mass spectra including peptide determination and quantification. However, there are two difficulties during the process of detecting fragment ions: First, in some cases many real fragment ions have very low intensity and they can be removed as noise peaks by accident. Numerous noisy peaks in tandem mass spectra can cause either false negative or false positive fragment ions. Second, due to the existence of heavy isotopes in nature, more than one isotopic peak for each fragment ion is resolved in high-resolution tandem mass spectra. Though isotopic peaks can provide us with useful information, such as compound composition and charge states, they can increase the computational cost if peptide identification is done without removing them. In addition, isotopic peaks can overlap, which could result in wrong interpretation of masses of fragment ions. In bottom-up proteomics, proteins are firstly cleaved into smaller peptides which are then used to be analyzed. Since tandem mass spectra of smaller peptides are easier than that of the intact proteins, bottom-up spectra are most often used in the identification of peptides and proteins. In this paper, to increase the accuracy of the peptide identification and reduce the complexity of tandem mass spectral analysis, we present a new algorithm for deisotoping the bottom-up spectra. Isotopic-cluster graphs are constructed to describe the relationship between all possible isotopic clusters. Based on the relationships in isotopic-cluster graphs each possible isotopic cluster is evaluated with a score function that is built by combining non-intensity and intensity features of fragment ions. The non-intensity features are used to prevent fragment ions with low intensity from being removed. Dynamic programming is adopted to find the paths with the highest score, which are presumably the most reliable isotopic clusters. Experimental results show that the average Mascot scores and F-scores of identified peptides from spectra processed by our deisotoping method are greater than those by widely used YADA and MS-Deconv software.