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Updated: May 3, 2026

Cell Co-culture Patterning Using Aqueous Two-phase Systems
Published on: March 26, 2013
Modeling the partitioning of amino acids in aqueous two phase systems
Dragana P C de Barros1, Sara R R Campos2, Pedro P Madeira3
1IBB-Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisboa, Portugal; Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal.
Abstract:
A new model to obtain fast prediction of partition coefficients in polymer/polymer aqueous two phase systems (ATPSs) is presented, using amino acids as test systems. In particular, the partitioning behavior of eleven amino acids (glycine, alanine, leucine, phenylalanine, lysine, arginine, histidine, aspartic acid, glutamic acid, glutamine and serine) has been studied in 6 polymer/polymer ATPSs, formed by different pairs of nonionic polymers, including polyethylene glycol (PEG), Dextran, Ucon and Ficoll at 0.15M NaCl in 0.01M sodium phosphate buffer. The partition coefficients of the amino acids in the different ATPSs under study showed linear correlations as described by the Collander equation. Based on continuum electrostatics (CE), a semi-empirical model was developed to study the partitioning behavior in ATPSs. The approach employs a thermodynamic cycle where the electrostatic and nonpolar contributions to the free energy of partition are assumed to be additive. Three systems were chosen for the modeling studies: PEG-Dextran, PEG-Ficoll and Ficoll-Dextran. In general, the model was found to correctly predict the preferred phase for the studied amino acids, and, except for the charged ones, a good quantitative correlation of the calculated and experimental partition free energies was also obtained (e.g., with RMSE values of 150Jmol(-1) for PEG-Ficoll). The model performance could be improved by grouping amino acids according to their electrostatic properties, resulting in very good quantitative partition coefficient predictions (e.g., RMSE values for nonpolar amino acids of 29, 16 and 0.4Jmol(-1) for PEG-Dextran, PEG-Ficoll and Ficoll-Dextran system, respectively). The good performance of the proposed model in predicting partition coefficients of amino acids, the building blocks of proteins, offers a good prospect to its application to protein molecules and complexes.
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