Correlation of second virial coefficients and solubilities useful in protein crystal growth

Guo, Bin; Kao, Shangming; McDonald, Heather M.; Asanov, Alexander; Combs, Leon L.; Wilson, William W.

doi:10.1016/s0022-0248(98)00842-2

Cited by 235 publications

(323 citation statements)

References 17 publications

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“…1) Neal et al, 1998;Tessier & Lenhoff, 2003;Bonneté & Vivarè s, 2002;Demoruelle et al, 2002;Ruppert et al, 2001). An empirical correlation between B (a dilute solution parameter) and the solubility s (a phase-transition parameter) has also been demonstrated for several proteins (Demoruelle et al, 2002;Gripon et al, 1997;Guo et al, 1999).…”

Section: Reviewmentioning

confidence: 85%

“…The experiments shown here took approximately 20 min to generate a value for B, compared with Comparison of SIC (blue diamonds) and SLS data for lysozyme as a function of NaCl concentration. The data are taken from the following references: blue diamonds, Valente et al (2005); red squares, García, Holman et al (2003); green triangles, Guo et al (1999); purple circles, . several hours for each SLS data point.…”

Section: Reviewmentioning

confidence: 99%

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Applications of the second virial coefficient: protein crystallization and solubility

Wilson

DeLucas

2014

Acta Crystallogr F Struct Biol Commun

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This article begins by highlighting some of the ground-based studies emanating from NASA's Microgravity Protein Crystal Growth (PCG) program. This is followed by a more detailed discussion of the history of and the progress made in one of the NASA-funded PCG investigations involving the use of measured second virial coefficients (Bvalues) as a diagnostic indicator of solution conditions conducive to protein crystallization. A second application of measuredBvalues involves the determination of solution conditions that improve or maximize the solubility of aqueous and membrane proteins. These two important applications have led to several technological improvements that simplify the experimental expertise required, enable the measurement of membrane proteins and improve the diagnostic capability and measurement throughput.

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Section: Reviewmentioning

confidence: 85%

Section: Reviewmentioning

confidence: 99%

Applications of the second virial coefficient: protein crystallization and solubility

Wilson

DeLucas

2014

Acta Crystallogr F Struct Biol Commun

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“…George and Wilson have shown (3) that proteins generally crystallize when their B 22 values are in a "crystallization slot" ranging from approximately −0.2 to −8 (×10 −4 mol ml/g 2 ). This B 22 range, confirmed by several research groups, represents slightly to moderately attractive forces between proteins, a condition that appears to be important for nucleation and subsequent crystal formation (2,(8)(9)(10).…”

Section: Introductionmentioning

confidence: 52%

“…The mathematical relationship between the B 22 value and solubility, derived by Haas et al (1), indicates a marked increase in solubility with increasing B 22 value. This relationship has been validated experimentally for a variety of proteins (1,6,8). Thus, a second application of the second virial coefficient involves its use as a diagnostic for protein solubility.…”

Section: Introductionmentioning

confidence: 96%

High-Throughput Self-Interaction Chromatography: Applications in Protein Formulation Prediction

et al. 2008

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Purpose. Demonstrate the ability of an artificial neural network (ANN), trained on a formulation screen of measured second virial coefficients to predict protein self-interactions for untested formulation conditions. Materials and Methods. Protein self-interactions, quantified by the second virial coefficient, B 22 , were measured by self-interaction chromatography (SIC). The B 22 values of lysozyme were measured for an incomplete factorial distribution of 81 formulation conditions of the screen components. The influence of screen parameters (pH, salt and additives) on B 22 value was modeled by training an ANN using B 22 value measurements. After training, the ANN was asked to predict the B 22 value for the complete factorial of parameters screened (12,636 conditions). Twenty of these predicted values (distributed throughout the range of predictions) were experimentally measured for comparison. Results. The ANN was able to predict lysozyme B 22 values with a significance of p<0.0001 and RMSE of 2.6×10 −4 mol ml/g 2 . Conclusions. The results indicate that an ANN trained on measured B 22 values for a small set of formulation conditions can accurately predict B 22 values for untested formulation conditions. As a measure of protein-protein interactions correlated with solubility, B 22 value predictions based on a small screen may enable rapid determination of high solubility formulations.

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“…The large amounts of sample required make systematic exploration by conventional techniques infeasible, and screening is typically directed toward an incomplete factorial or sparse-matrix approach, a brute-force process requiring large numbers of experiments (15,16). There have been numerous attempts to rationalize this procedure, for example, by using computational approaches to predict phase behavior (17,18) or by trying to correlate measurements of osmotic second virial coefficients (19,20) with crystallization conditions. Practical limitations have thus far prevented these techniques from being generally applicable.…”

mentioning

confidence: 99%

Systematic investigation of protein phase behavior with a microfluidic formulator

Hansen

Sommer

Quake

2004

Proc. Natl. Acad. Sci. U.S.A.

178

179

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We demonstrated a microfluidic device for rapidly generating complex mixtures of 32 stock reagents in a 5-nl reactor. This ''formulation chip'' is fully automated and allows thousands of experiments to be performed in a single day with minimal reagent consumption. It was applied to systematically study the phase behavior of the protein xylanase over a large and complex chemical space. For each chemical formulation that demonstrated a pronounced effect on solubility, the protein phase behavior was completely mapped in the chip, generating a set of empirical phase diagrams. This ab initio phase information was used to devise a rational crystallization screen that resulted in 72-fold improvement in successful crystallization hits compared with conventional sparse matrix screens. This formulations tool allows a physicsbased approach to protein crystallization that may prove useful in structural genomics efforts.T he application of x-ray crystallography to the determination of protein structure with atomic resolution was a triumph of structural biology in the 20th century. Since the first solution of the structure of myoglobin in 1958 (1), Ͼ23,000 different structures have been deposited in the Protein Data Bank, and their role in relating structure to function in biology has been profound. As structure determination efforts continue to move past the most tractable crystallization targets (typically small soluble proteins) and focus instead on more challenging macromolecules, such as large protein complexes and membrane proteins (2), the need to better understand and explore the crystallization process has become urgent. That is because once high-quality crystals are in hand, advances in x-ray sources, computer codes, and related technology have made it relatively straightforward to obtain the structure. Determining the appropriate crystallization conditions has become one of the most significant remaining bottlenecks to structure determination (3).Understanding the phase behavior of proteins is an essential part of the crystallization process. The growth of crystals from a protein solution requires the existence of a nontrivial phase diagram, which allows the protein state to be manipulated between at least two thermodynamic phases: soluble and precipitated. The processes of crystal nucleation and growth arise on the boundary between these two phases and are governed by subtle effects in physical chemistry. There are a variety of schemes that manipulate the kinetics of the crystallization process, and all take advantage of generic features of these phase diagrams (4). However, in practice, the phase behavior of very few proteins has been studied in detail (5-12), and solubility information for a specific protein is rarely available for crystallization and optimization experiments (13,14).Furthermore, it is often an arduous process to find the right combination of chemicals that yields appropriate phase behavior for a given protein. Every protein is different, and even a modest subset of stock precipitating solutions...

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Correlation of second virial coefficients and solubilities useful in protein crystal growth

Cited by 235 publications

References 17 publications

Applications of the second virial coefficient: protein crystallization and solubility

Applications of the second virial coefficient: protein crystallization and solubility

High-Throughput Self-Interaction Chromatography: Applications in Protein Formulation Prediction

Systematic investigation of protein phase behavior with a microfluidic formulator

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