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Quantitative characterization of galectin-3-C affinity mass spectrometry measurements: Comprehensive data analysis, obstacles, shortcuts and robustness.
Rapid Communications in Mass Spectrometry : RCM 2017 October 31
RATIONALE: Affinity mass spectrometry (AMS) is an emerging tool in the field of the study of protein•carbohydrate complexes. However, experimental obstacles and data analysis are preventing faster integration of AMS methods into the glycoscience field. Here we show how analysis of direct electrospray ionization mass spectrometry (ESI-MS) AMS data can be simplified for screening purposes, even for complex AMS spectra.
METHODS: A direct ESI-MS assay was tested in this study and binding data for the galectin-3C•lactose complex were analyzed using a comprehensive and simplified data analysis approach. In the comprehensive data analysis approach, noise, all protein charge states, alkali ion adducts and signal overlap were taken into account. In a simplified approach, only the intensities of the fully protonated free protein and the protein•carbohydrate complex for the main protein charge state were taken into account.
RESULTS: In our study, for high intensity signals, noise was negligible, sodiated protein and sodiated complex signals cancelled each other out when calculating the Kd value, and signal overlap influenced the Kd value only to a minor extent. Influence of these parameters on low intensity signals was much higher. However, low intensity protein charge states should be avoided in quantitative AMS analyses due to poor ion statistics.
CONCLUSIONS: The results indicate that noise, alkali ion adducts, signal overlap, as well as low intensity protein charge states, can be neglected for preliminary experiments, as well as in screening assays. One comprehensive data analysis performed as a control should be sufficient to validate this hypothesis for other binding systems as well.
METHODS: A direct ESI-MS assay was tested in this study and binding data for the galectin-3C•lactose complex were analyzed using a comprehensive and simplified data analysis approach. In the comprehensive data analysis approach, noise, all protein charge states, alkali ion adducts and signal overlap were taken into account. In a simplified approach, only the intensities of the fully protonated free protein and the protein•carbohydrate complex for the main protein charge state were taken into account.
RESULTS: In our study, for high intensity signals, noise was negligible, sodiated protein and sodiated complex signals cancelled each other out when calculating the Kd value, and signal overlap influenced the Kd value only to a minor extent. Influence of these parameters on low intensity signals was much higher. However, low intensity protein charge states should be avoided in quantitative AMS analyses due to poor ion statistics.
CONCLUSIONS: The results indicate that noise, alkali ion adducts, signal overlap, as well as low intensity protein charge states, can be neglected for preliminary experiments, as well as in screening assays. One comprehensive data analysis performed as a control should be sufficient to validate this hypothesis for other binding systems as well.
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