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- W2018133635 abstract "Abstract Reversible precipitation can be used as an efficient purification tool for proteins. In addition, identifying conditions under which precipitation or aggregation occurs is of key importance in the bioprocessing and pharmaceutical industry, as this can aid in better formulations and hinder aggregation in chromatography. We have evaluated the precipitation of proteins as determined by light scattering in microplates as a tool for the high‐throughput determination of thermodynamic parameters for protein precipitation, with the potential for screening of formulation additives and relevant bioprocess conditions such as pH. This provides a useful complementary technique to existing microplate‐based protein thermostability measurements. Using hen egg‐white lysozyme and alcohol dehydrogenase as model proteins we have determined the extent of reversible precipitation as a function of ammonium sulfate and sodium chloride concentrations, and also demonstrated global fitting of the data to generate a model where the fraction precipitated can be predicted for any given condition. The global fit provided thermodynamic parameters, including the free energy for protein precipitation, and also allowed an approximate determination of the average size of the structural nucleus that contributes to the free energy of precipitation for each protein. The rapid collection of thermodynamic parameters for protein precipitation, in parallel with protein thermostability measurements, will provide a powerful platform for protein formulation, and also lead to datasets useful for testing theoretical predictions of reversible precipitation based on the molecular modeling of specific protein structure interactions. Biotechnol. Bioeng. 2011;108: 322–332. © 2010 Wiley Periodicals, Inc." @default.
- W2018133635 created "2016-06-24" @default.
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- W2018133635 date "2010-10-21" @default.
- W2018133635 modified "2023-10-16" @default.
- W2018133635 title "Thermodynamic parameters for salt-induced reversible protein precipitation from automated microscale experiments" @default.
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- W2018133635 doi "https://doi.org/10.1002/bit.22957" @default.
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