Quantitative model of the phase behavior of recombinant pH-responsive elastin-like polypeptides.

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Quantitative models are required to engineer biomaterials with environmentally responsive properties. With this goal in mind, we developed a model that describes the pH-dependent phase behavior of a class of stimulus responsive elastin-like polypeptides (ELPs) that undergo reversible phase separation in response to their solution environment. Under isothermal conditions, charged ELPs can undergo phase separation when their charge is neutralized. Optimization of this behavior has been challenging because the pH at which they phase separate, pHt, depends on their composition, molecular weight, concentration, and temperature. To address this problem, we developed a quantitative model to describe the phase behavior of charged ELPs that uses the Henderson-Hasselbalch relationship to describe the effect of side-chain ionization on the phase-transition temperature of an ELP. The model was validated with pH-responsive ELPs that contained either acidic (Glu) or basic (His) residues. The phase separation of both ELPs fit this model across a range of pH. These results have important implications for applications of pH-responsive ELPs because they provide a quantitative model for the rational design of pH-responsive polypeptides whose transition can be triggered at a specified pH.





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Mackay, J Andrew, Daniel J Callahan, Kelly N Fitzgerald and Ashutosh Chilkoti (2010). Quantitative model of the phase behavior of recombinant pH-responsive elastin-like polypeptides. Biomacromolecules, 11(11). pp. 2873–2879. 10.1021/bm100571j Retrieved from https://hdl.handle.net/10161/4020.

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Ashutosh Chilkoti

Alan L. Kaganov Distinguished Professor of Biomedical Engineering

Ashutosh Chilkoti is the Alan L. Kaganov Professor of Biomedical Engineering and Chair of the Department of Biomedical Engineering at Duke University.

My research in biomolecular engineering and biointerface science focuses on the development of new molecular tools and technologies that borrow from molecular biology, protein engineering, polymer chemistry and surface science that we then exploit for the development of applications that span the range from bioseparations, plasmonic biosensors, low-cost clinical diagnostics, and drug delivery.

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