Protein solubility modeling

Agena, Sabine M.; Pusey, Marc L.; Bogle, David

doi:10.1002/(sici)1097-0290(19990720)64:2<144::aid-bit3>3.0.co;2-p

Cited by 25 publications

(19 citation statements)

References 24 publications

(24 reference statements)

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“…A predictive relation between the B 22 of a solution and the ideal crystallization condition for the protein was proposed by George and Wilson 10. Agena et al11 used the Universal Quasi‐chemical (UNIQUAC) activity coefficient model to predict the solubility from B 22 values. They assumed that the protein solubility is inversely proportional to the protein activity coefficient with an unknown proportionality constant.…”

Section: Introductionmentioning

confidence: 99%

Correlation of second virial coefficient with solubility for proteins in salt solutions

Mehta¹,

White²,

Litster³

2011

Biotechnology Progress

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In this work, osmotic second virial coefficients (B(22)) were determined and correlated with the measured solubilities for the proteins, α-amylase, ovalbumin, and lysozyme. The B(22) values and solubilities were determined in similar solution conditions using two salts, sodium chloride and ammonium sulfate in an acidic pH range. An overall decrease in the solubility of the proteins (salting out) was observed at high concentrations of ammonium sulfate and sodium chloride solutions. However, for α-amylase, salting-in behavior was also observed in low concentration sodium chloride solutions. In ammonium sulfate solutions, the B(22) are small and close to zero below 2.4 M. As the ammonium sulfate concentrations were further increased, B(22) values decreased for all systems studied. The effect of sodium chloride on B(22) varies with concentration, solution pH, and the type of protein studied. Theoretical models show a reasonable fit to the experimental derived data of B(22) and solubility. B(22) is also directly proportional to the logarithm of the solubility values for individual proteins in salt solutions, so the log-linear empirical models developed in this work can also be used to rapidly predict solubility and B(22) values for given protein-salt systems.

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Section: Introductionmentioning

confidence: 99%

Correlation of second virial coefficient with solubility for proteins in salt solutions

Mehta¹,

White²,

Litster³

2011

Biotechnology Progress

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“…Thus, it might be expected that aggregation of ConA to higher mers than tetramers strengthens the intermolecular network between assay components and causes a higher viscosity. This assumption is supported by the fact that the applied concentration of ConA is close to its solubility limit in solutions of electrolytes used in the medium [41]. …”

Section: Resultsmentioning

confidence: 99%

Micro-Electromechanical Affinity Sensor for the Monitoring of Glucose in Bioprocess Media

Theuer

Lehmann

Junne

et al. 2017

IJMS

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An affinity-viscometry-based micro-sensor probe for continuous glucose monitoring was investigated with respect to its suitability for bioprocesses. The sensor operates with glucose and dextran competing as binding partner for concanavalin A, while the viscosity of the assay scales with glucose concentration. Changes in viscosity are determined with a micro-electromechanical system (MEMS) in the measurement cavity of the sensor probe. The study aimed to elucidate the interactions between the assay and a typical phosphate buffered bacterial cultivation medium. It turned out that contact with the medium resulted in a significant long-lasting drift of the assay’s viscosity at zero glucose concentration. Adding glucose to the medium lowers the drift by a factor of eight. The cglc values measured off-line with the glucose sensor for monitoring of a bacterial cultivation were similar to the measurements with an enzymatic assay with a difference of less than ±0.15 g·L−1. We propose that lectin agglomeration, the electro-viscous effect, and constitutional changes of concanavalin A due to exchanges of the incorporated metal ions may account for the observed viscosity increase. The study has demonstrated the potential of the MEMS sensor to determine sensitive viscosity changes within very small sample volumes, which could be of interest for various biotechnological applications.

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“…Compared to solubility models using UNIQUAC [23] (at least 23 experimental protein-solubility data are necessary), the sol-mxDLVO model allows for a prediction of protein solubility with less experimental protein solubility data. Our model thus permits a significant reduction in experimental effort for estimating protein solubility compared to state-of-the-art screening methods and other solubility models.…”

Section: Discussionmentioning

confidence: 99%

Non‐monotonic course of protein solubility in aqueous polymer‐salt solutions can be modeled using the sol‐mxDLVO model

Herhut

Brandenbusch

Sadowski

2015

Biotechnology Journal

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Protein purification is often performed using cost-intensive chromatographic steps. To discover economic alternatives (e.g., crystallization), knowledge on protein solubility as a function of temperature, pH, and additives in solution as well as their concentration is required. State-of-the-art models for predicting protein solubility almost exclusively consider aqueous salt systems, whereas "salting-in" and "salting-out" effects induced by the presence of an additional polymer are not considered. Thus, we developed the sol-mxDLVO model. Using this newly developed model, protein solubility in the presence of one salt and one polymer, especially the non-monotonic course of protein solubility, could be predicted. Systems considered included salts (NaCl, Na-p-Ts, (NH(4))(2) SO(4)) and the polymer polyethylene glycol (MW: 2000 g/mol, 12000 g/mol) and proteins lysozyme from chicken egg white (pH 4 to 5.5) and D-xylose ketol-isomerase (pH 7) at 298.15 K. The results show that by using the sol-mxDLVO model, protein solubility in polymer-salt solutions can be modeled in good agreement with the experimental data for both proteins considered. The sol-mxDLVO model can describe the non-monotonic course of protein solubility as a function of polymer concentration and salt concentration, previously not covered by state-of-the-art models.

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Protein solubility modeling

Cited by 25 publications

References 24 publications

Correlation of second virial coefficient with solubility for proteins in salt solutions

Correlation of second virial coefficient with solubility for proteins in salt solutions

Micro-Electromechanical Affinity Sensor for the Monitoring of Glucose in Bioprocess Media

Non‐monotonic course of protein solubility in aqueous polymer‐salt solutions can be modeled using the sol‐mxDLVO model

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