Physicochemical Parameters Affecting the Electrospray Ionization Efficiency of Amino Acids after Acylation

• Published in: Analytical chemistry (Washington) Vol. 89; no. 17; pp. 9159 - 9166
• Main Authors: Hermans, Jos, Ongay, Sara, Markov, Vadym, Bischoff, Rainer
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 05-09-2017
• Subjects: Acylation; Amino acids; Anhydrides; Atomic structure; Biomolecules; Chains; Chemistry; Chromatography; Correlation analysis; Derivatives; Hydrophobicity; Ionization; Liquid chromatography; Mass spectrometry; Mass spectroscopy; Mathematical models; Molecules; Physicochemical properties; Polyethylene glycol; Predictions; Surface tension
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.7b01899

Electrospray ionization (ESI) is widely used in liquid chromatography coupled to mass spectrometry (LC–MS) for the analysis of biomolecules. However, the ESI process is still not completely understood, and it is often a matter of trial and error to enhance ESI efficiency and, hence, the response of a given set of compounds. In this work we performed a systematic study of the ESI response of 14 amino acids that were acylated with organic acid anhydrides of increasing chain length and with poly­(ethylene glycol) (PEG) changing certain physicochemical properties in a predictable manner. By comparing the ESI response of 70 derivatives, we found that there was a strong correlation between the calculated molecular volume and the ESI response, while correlation with hydrophobicity (log P values), pK a, and the inverse calculated surface tension was significantly lower although still present, especially for individual derivatized amino acids with increasing acyl chain lengths. Acylation with PEG containing five ethylene glycol units led to the largest gain in ESI response. This response was maximal independent of the calculated physicochemical properties or the type of amino acid. Since no actual physicochemical data is available for most derivatized compounds, the responses were also used as input for a quantitative structure–property relationship (QSPR) model to find the best physicochemical descriptors relating to the ESI response from molecular structures using the amino acids and their derivatives as a reference set. A topological descriptor related to molecular size (SPAN) was isolated next to a descriptor related to the atomic composition and structural groups (BIC0). The validity of the model was checked with a test set of 43 additional compounds that were unrelated to amino acids. While prediction was generally good (R 2 > 0.9), compounds containing halogen atoms or nitro groups gave a lower predicted ESI response.