Using multivariate statistical methods to model the electrospray ionization response of GXG tripeptides based on multiple physicochemical parameters

Abstract

Response factors were determined for twelve GXG peptides (where G stands for glycine and X is any of alanine [A], arginine [R], asparagine [N]) by electrospray ionization mass spectrometry (ESI-MS). The response factors were measured using a novel flow injection method. This new method is based on the Gaussian distribution of analyte concentration resulting from bandbroadening dispersion experienced by the analyte upon passage through an extended volume of PEEK tubing. This method removes the need for preparing a discrete series of standard solutions to assess concentration-dependent response. Relative response factors were calculated for each peptide with reference to GGG. The observed trends in the relative response factors were correlated with several analyte physicochemical parameters, chosen based on current understanding of ion release from charged droplets during the ESI process. These include analyte properties: nonpolar surface area; polar surface area; gas-phase basicity; proton affinity; and Log D. Multivariate statistical analysis using multiple linear regression, decision tree, and support vector regression models were investigated to assess their potential for predicting ESI response based on the analyte properties. The support vector regression model was more versatile and produced the least predictive error following 12-fold cross-validation. The effect of variation in solution pH on the relative response factors is highlighted, as evidenced by the different predictive models obtained for peptide response at two pH values (pH ¼ 6.0 and 9.0). The relationship between physicochemical parameters and associated ionization efficiencies for GXG tripeptides is discussed based on the equilibrium partitioning model.

Electrospray Ionization Efficiency Is Dependent on Different Molecular Descriptors with Respect to Solvent pH and Instrumental Configuration

“…This curious finding may be related to the fact that our dataset was not particularly designed to assess this parameter since our substances were all quite similar in size compared to other studies using analytes much more different to each other when relating ESI responsiveness and molecular size [17–19,21–23]. …”

“…Also, the investigated compounds often were polyfunctional and had a limited range of ionization efficiencies which hampered relating ionization efficiency successfully to the molecular structure [18]. Therefore, a reasonably large, fully characterized set of analytes was suggested to be ideal for the identification of general trends underlying the process [19]. …”

“…As a consequence, multivariate analyses are required broadening the scope of such investigations to many physicochemical properties and many compounds assessed at a time, extracting also the interactions between all assessed parameters [17–19,21–24]. Thus, Mandra et al [23] investigated 84 factors of several pharmaceuticals and found the number of free rotatable bonds positively correlated to ESI-MS response in addition to the confirmation of known parameters such as pKb, logD (at high pH), the molecular volume related to the molecules polarizability, and the polar surface area.…”

Exploring analyte response in an ESI-MS system with different chemometric tools

“…Generally speaking, ESI-MS ionization response of an analyte can be considered as constituted by two components: compound-dependent factors and instrumental factors. Elucidation of ESI mechanisms and development of models to describe ionization efficiency (IE) as a function of compounds and matrix characteristics, and of instrumental factors in such a complex multivariate system has been an issue of research in recent and present years [2][3][4][5][6][7][8][9][10]. However, a fully comprehensive model still appears elusive [7].…”

“…Strategies that enable identification of the main effects as well as interactions among instrumental parameters and analyte physicochemical properties are essential for system understanding and response optimization [6]. In this context, the need of utilizing chemometric tools for system knowledge and for efficient experimental designs and analyses becomes apparent [6,7,11,12].…”