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

Author:

Rye Torstein Kige1,Lee Chien‐Yun2,Zellner Andreas2,Moen Sara Haglund1,Dowlatshah Samira1,Grønhaug Halvorsen Trine1,Pedersen‐Bjergaard Stig13ORCID,Hansen Frederik André1ORCID

Affiliation:

1. Department of Pharmacy University of Oslo Blindern Norway

2. School of Life Sciences Technical University of Munich Freisng Germany

3. Department of Pharmacy Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark

Abstract

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.

Funder

Norges Forskningsråd

Bundesministerium für Bildung und Forschung

Technische Universität München

Deutsche Forschungsgemeinschaft

Publisher

Wiley

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