Single-crystal – oriented-crystal – and epitaxy growth processes; – Processes of growth from liquid or supercritical state – Having growth from a solution comprising a solvent which is...
Reexamination Certificate
2001-04-23
2002-04-09
Hiteshew, Felisa (Department: 1765)
Single-crystal, oriented-crystal, and epitaxy growth processes;
Processes of growth from liquid or supercritical state
Having growth from a solution comprising a solvent which is...
C117S069000, C117S937000
Reexamination Certificate
active
06368402
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to microbatch crystallization under oil using a high throughput method. Microbatch crystallization under oil requires very low volumes of both a protein and a crystallization cocktail solution. This is critical for a high throughput application where a large number of experiments are being conducted at the same time. Many proteins can be difficult if not impossible to obtain in large quantities and cocktail solutions are costly to produce. According to the present invention, one plate of 1,536 crystallization experiments is set up using as little as 600 &mgr;l of protein solution. From this volume, 100 &mgr;l of the protein is retrieved at the conclusion of the experiments for use in further studies.
2. Prior Art
A number of investigators have attempted to condense their experiences in the crystal growth laboratory into a list of recipes of reagents that have found success as crystallizing agents. The most used of these is the list compiled by Jancarik, J. and Kim, S.-H. (1991), J. Appl. Cryst. 24, 409-411 which is often referred to as the “sparse matrix sampling” screen. The list is a “heavily biased” selection of conditions out of many variables including sampling pH, additives and precipitating agents. The bias is a reflection of personal experience and literature reference towards pH values, additives and agents that have successfully produced crystals in the past. Commercialization of the sparse matrix screen has led to its popularity; easy and simple to use, it is often the first strategy in the crystal growth lab.
The agents chosen by Jancarik and Kim are designed to maximize the frequency of precipitation outcomes for a broad variety of proteins. They were chosen because in a large percentage of experiments employing them “something happened”. This highlights a fundamental difference between the present invention and the approach taken by Jancarik, Kim and their successors. The latter try to identify sets of chemical agents that maximize the probability of inducing precipitation (preferably crystallization) across the board, while the present invention relies on a set of chemical agents with precise patterns of precipitation, patterns that are as diverse as possible among the proteins that constitute the information repository. The fact that the sparse matrix approach does not always work has led to the design of other lists targeted at proteins. These include those by Cudney, R., Patel, S., Weisgraber, K., Newhouse, Y. and McPherson, A. (1994), Acta Cryst. D50, 414-423 directed to nucleic acids, by Berger, I., Kang, C. H., Sinha, N., Wolters, M., and Rich, A. (1996), Acta Cryst. D52, 465-468 directed to other classes of macromolecules, and by Garavito, M. (1991), in “Crystallization of Membrane Proteins”, H. Michel (Ed.), CRC Press, pp. 89-105.
The sparse matrix approach is based on unbiased attempts to sample the multi-dimensional space of crystallization parameters. At least 23 parameters have been identified as having had an effect on crystallization outcomes. If one were to attempt a simple, exhaustive two-level experimental design for an unknown protein, i.e., two pH values, two temperatures, two kinds of crystallizing agents, etc., it would require 2
23
or over eight million experiments. Hence the need for sampling.
Carter, C. W. (1997), in Methods in Enzymology 276, 74-99 made major advances in the area of crystal growth by applying partial factorial designs, principally incomplete factorial designs. In these designs, relative levels of important chemical factors are sampled to achieve good coverage and good balance in the sampling. However, incomplete factorial designs are no more than (or no less than) scaffolds upon which the crystal grower must build experiments.
In other words, once the crystal grower has defined the multi-dimensional space which should be sampled, the factorial designer chooses from the large number of possible experiments those that should be executed to insure good coverage of the space identified. The crystal grower must decide upon the important variables to be tested, and the limits on those variables within which to sample. The machinery of factorial design offers no guidance on those issues. Other sampling strategies based on orthogonal arrays by Kingston, R. L., Baker, H. M. and Baker, E. N. (1994), Acta Cryst D50, 429-440 and on random samplings by Shieh, H.-Y., Stallings, W. C., Stevens, A. M., and Stegeman, R. A. (1995), Acta Cryst. D51, 305-310 have been described as well.
A fundamentally different approach to strategic planning of crystallization experiments is one in which physical principles believed to augur well for success are exploited. This class includes the work of Riès-Kautt, M. and Ducruix, A. (1997), Methods in Enzymology 276, 23-59 who have investigated solubility determinants for proteins as a function of pH and pI. In particular, Riès-Kautt, Ducruix and co-workers investigated the Hofmeister series developed by Cacace, M. G., Landau, E. M., and Ramsden, J. J. (1997), Quart. Res Biophys. 30, 241-277 and found that protein solubility follows the series or its reverse, depending on the pH of the experiment and the pI of the protein. Also within this approach are recent advances by George, A., Chiang, Y., Guo, B., Arabshaki, A., Cai, Z., and Wilson, W. W. (1997), Methods in Enzymology 276, 100-109 in the use of light scattering as a predictive tool and by George, A. and Wilson, W. W. (1994), Acta Cryst. D50, 361-365 who have shown that a dilute solution property, the second virial coefficient of the osmotic pressure lowering, falls within a narrow range of values (the “crystallization slot”) for solutions conducive to crystallization. Work by Rosenbaum, D., Zamora, P. C., and Zukoski, C. F. (1996), Phys. Rev. Lett. 76, 150-153; Rosenbaum, D. and Zukoski, C. F. (1996), J. Crystal Growth 169, 752-758; Gripon, C., Legrand, L., Rosenman, I., Vidal, O., Robert, M. C., and Boué, F. (1997), J. Crystal Growth 177, 238-247 and 178, 575-584; and Gripon, C., Legrand, L., Rosenman, I., Boué, F., and Regnaut, C. (1998), J. Crystal Growth 183, 258-268 suggest that the second virial coefficient is a fundamentally important determinant of crystallization from aqueous protein solutions.
The final approach to strategic planning tactics is the construction and analysis of the Biological Macromolecule Crystallization Database (BMCD), in which details of macromolecular crystallizations abstracted from the primary literature have been collected. The BMCD was created by Gilliland, G. L., Tung, M., Blakeslee, D. M., and Ladner, J. E. (1994), Acta Cryst. D50, 408-413 and has, over the last decade, grown to include crystallization data on over three thousand crystal entries covering over two thousand distinct macromolecules (Version 3.0). The record structures of the BMCD, while not requiring any particular record to be complete, include entries for the macromolecule, the crystal data, the crystallization conditions, the primary literature references, and a field for comments. These data have been abstracted, where available, from the primary literature and there are entries for every major class of macromolecule (protein, nucleic acid, virus, etc.) that have been studied in the diffraction lab. Each record is a record of success—there are no records describing crystallization experiments that failed to yield crystals. Gilliland has pointed out that the data in the BMCD “have not been verified and the information present in this data set often represents the author's [Gilliland's] interpretation of the literature”.
Gilliland was first to analyze the BMCD to develop crystal growth strategies for macromolecules. He showed that ammonium sulfate and polyethylene glycol were favored crystallizing agents and that vapor diffusion was a favored crystallization method. While both observations were part of the common lore of crystal growth, Gilliland used the BMCD to quantitate their use.
Samuzdi and co-workers delved more deeply into t
Collins Robert J.
DeTitta George T.
Luft Joseph R.
Wolfley Jennifer
Hauptman-Woodward Medical Research Institute, Inc.
Hodgson & Russ LLP
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