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Game-Theory-Based Search Engine to Automate the Mass Assignment in Complex Native Electrospray Mass Spectra

By Y.H. Tseng, C. Uetrecht, S.C. Yang, A. Barendregt, A.J.R. Heck and W.P. Peng


Electrospray ionization coupled to native mass spectrometry (MS) has evolved into an important tool in structural biology to decipher the composition of protein complexes. However, the mass analysis of heterogeneous protein assemblies is hampered because of their overlapping charge state distributions, fine structure, and peak broadening. To facilitate the mass analysis, it is of importance to automate preprocessing raw mass spectra, assigning ion series to peaks and deciphering the subunit compositions. So far, the automation of preprocessing raw mass spectra has not been accomplished; Massign was introduced to simplify data analysis and decipher the subunit compositions. In this study, we develop a search engine, AutoMass, to automatically assign ion series to peaks without any additional user input, for example, limited ranges of charge states or ion mass. AutoMass includes an ion intensity-dependent method to check for Gaussian distributions of ion series and an ion intensity-independent method to address highly overlapping and non-Gaussian distributions. The minimax theorem from game theory is adopted to define the boundaries. With AutoMass, the boundaries of ion series in the well-resolved tandem mass spectra of the hepatitis B virus (HBV) capsids and those of the mass spectrum from CRISPR-related cascade protein complex are accurately assigned. Theoretical and experimental HBV ion masses are shown in agreement up to 0.03%. The analysis is finished within a minute on a regular workstation. Moreover, less well-resolved mass spectra, for example, complicated multimer mass spectra and norovirus capsid mass spectra at different levels of desolvation, are analyzed. In sum, this first-ever fully automatic program reveals the boundaries of overlapping ion peak series and can further aid developing high-throughput native MS and top-down proteomics

Year: 2013
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