Increasing peptide identification in tandem mass spectrometry through automatic function switching optimization
- Resource Type
- Authors
- Brian Carrillo; Kossi Lekpor; C.M. Yanofsky; Daniel Boismenu; Alexander W. Bell; Robert E. Kearney
- Source
- Journal of the American Society for Mass Spectrometry. 16:1818-1826
- Subject
- Analytical chemistry
Inverse
Peptide
Tandem mass spectrometry
Mass spectrometry
Peptide Mapping
01 natural sciences
Mass Spectrometry
03 medical and health sciences
Data acquisition
Artificial Intelligence
Sequence Analysis, Protein
Structural Biology
Liquid chromatography–mass spectrometry
Animals
Cells, Cultured
Spectroscopy
030304 developmental biology
chemistry.chemical_classification
0303 health sciences
Tandem
Chemistry
business.industry
Gene Expression Profiling
010401 analytical chemistry
Automation
Rats
0104 chemical sciences
Microsomes, Liver
Peptides
business
Algorithm
Algorithms
- Language
- ISSN
- 1044-0305
Comprehensive proteomic studies that employ MS directed peptide sequencing are limited by optimal peptide separation and MS and tandem MS data acquisition routines. To identify the optimal parameters for data acquisition, we developed a system that models the automatic function switching behavior of a mass spectrometer using an MS-only dataset. Simulations were conducted to characterize the number and the quality of simulated fragmentation as a function of the data acquisition routines and used to construct operating curves defining tandem mass spectra quality and the number of peptides fragmented. Results demonstrated that one could optimize for quality or quantity, with the number of peptides fragmented decreasing as quality increased. The predicted optimal operating curve indicated that significant improvements can be realized by selecting the appropriate data acquisition parameters. The simulation results were confirmed experimentally by testing 10 LC MS/MS data acquisition parameter sets on an LC-Q-TOF-MS. Database matching of the experimental fragmentation returned peptide scores consistent with the predictions of the model. The results of the simulations of mass spectrometer data acquisition routines reveal an inverse relationship between the quality and the quantity of peptide identifications and predict an optimal operating curve that can be used to select an optimal data acquisition parameter for a given (or any) sample.