Electromembrane extraction of peptides based on charge, hydrophobicity, and size - A large-scale fundamental study of the extraction window.

This study investigated the capability of electromembrane extraction (EME) as a general technique for peptides, by extracting complex pools of peptides comprising in total of 5953 different substances, varying in size from seven to 16 amino acids. Electromembrane extraction was conducted from a sample adjusted to pH 3.0 and utilized a liquid membrane consisting of 2-nitrophenyl octyl ether and carvacrol (1:1 w/w), containing 2% (w/w) di(2-ethylhexyl) phosphate. The acceptor phase was 50 mM phosphoric acid (pH 1.8), the extraction time was 45 min, and 10 V was used. High extraction efficiency, defined as a higher peptide signal in the acceptor than the sample after extraction, was achieved for 3706 different peptides. Extraction efficiencies were predominantly influenced by the hydrophobicity of the peptides and their net charge in the sample. Hydrophobic peptides were extracted with a net charge of +1, while hydrophilic peptides were extracted when the net charge was +2 or higher. A computational model based on machine learning was developed to predict the extractability of peptides based on peptide descriptors, including the grand average of hydropathy index and net charge at pH 3.0 (sample pH). This research shows that EME has general applicability for peptides and represents the first steps toward in silico prediction of extraction efficiency.

Automated Protein Biomarker Analysis: on-line extraction of clinical samples by Molecularly Imprinted Polymers.

Robust biomarker quantification is essential for the accurate diagnosis of diseases and is of great value in cancer management. In this paper, an innovative diagnostic platform is presented which provides automated molecularly imprinted solid-phase extraction (MISPE) followed by liquid chromatography-mass spectrometry (LC-MS) for biomarker determination using ProGastrin Releasing Peptide (ProGRP), a highly sensitive biomarker for Small Cell Lung Cancer, as a model. Molecularly imprinted polymer microspheres were synthesized by precipitation polymerization and analytical optimization of the most promising material led to the development of an automated quantification method for ProGRP. The method enabled analysis of patient serum samples with elevated ProGRP levels. Particularly low sample volumes were permitted using the automated extraction within a method which was time-efficient, thereby demonstrating the potential of such a strategy in a clinical setting.