Using multivariate statistical methods to model the electrospray ionization response of GXG tripeptides based on...

Area of Science:

• Analytical Chemistry
• Mass Spectrometry
• Biochemistry

Background:

• Electrospray ionization mass spectrometry (ESI-MS) is crucial for peptide analysis.
• Accurate quantification in ESI-MS requires understanding peptide response factors.
• Existing methods for determining response factors can be laborious.

Purpose of the Study:

• To develop and validate a novel, efficient method for determining peptide response factors using ESI-MS.
• To investigate the relationship between peptide physicochemical properties and their ionization behavior.
• To establish predictive models for ESI-MS response based on molecular characteristics.

Main Methods:

• A novel flow injection method utilizing band-broadening dispersion in PEEK tubing was employed.
• Response factors for twelve GXG peptides were measured relative to GGG.
• Physicochemical parameters (nonpolar surface area, polar surface area, gas-phase basicity, proton affinity, Log D) were correlated with response factors.
• Multivariate statistical analyses, including support vector regression, were used for predictive modeling.

Main Results:

• The novel flow injection method successfully determined peptide response factors without standard curves.
• Relative response factors showed trends correlated with peptide physicochemical properties.
• Support vector regression models demonstrated high predictive accuracy for ESI-MS response (12-fold cross-validation).
• Solution pH significantly influenced peptide response factors, necessitating pH-specific models.

Conclusions:

• The developed flow injection method offers an efficient alternative for determining peptide response factors in ESI-MS.
• Peptide physicochemical properties are key determinants of ionization efficiency in ESI-MS.
• Predictive models, particularly support vector regression, can accurately forecast ESI-MS response, aiding quantitative analysis.
• Understanding the impact of pH is critical for accurate peptide quantification using ESI-MS.