Benefits of automated crystallization plate tracking, imaging, and analysis.

We describe the design of a database and software for managing and organizing protein crystallization data. We also outline the considerations behind the design of a fast web interface linking protein production data, crystallization images, and automated image analysis. The database and associated interfaces underpin the Oxford Protein Production Facility (OPPF) crystallization laboratory, collecting, in a routine and automatic manner, up to 100,000 images per day. Over 17 million separate images are currently held in this database. We discuss the substantial scientific benefits automated tracking, imaging, and analysis of crystallizations offers to the structural biologist: analysis of the time course of the trial and easy analysis of trials with related crystallization conditions. Features of this system address requirements common to many crystallographic laboratories that are currently setting up (semi-)automated crystallization imaging systems.

[1]  Julie Wilson,et al.  Towards the automated evaluation of crystallization trials. , 2002, Acta crystallographica. Section D, Biological crystallography.

[2]  David I. Stuart,et al.  A procedure for setting up high-throughput nanolitre crystallization experiments. II. Crystallization results , 2003 .

[3]  Junwen Wang,et al.  Predictive models for protein crystallization. , 2004, Methods.

[4]  Adam Godzik,et al.  Structural genomics of the Thermotoga maritima proteome implemented in a high-throughput structure determination pipeline , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  N. Chayen.  Protein crystallization for genomics: throughput versus output , 2004, Journal of Structural and Functional Genomics.

[6]  Miroslaw Cygler,et al.  The structural genomics experimental pipeline: Insights from global target lists , 2004, Proteins.

[7]  Ming Luo,et al.  The Southeast Collaboratory for Structural Genomics: a high-throughput gene to structure factory. , 2003, Accounts of chemical research.

[8]  David I. Stuart,et al.  A procedure for setting up high-throughput nanolitre crystallization experiments. I. Protocol design and validation , 2003 .

[9]  Bernhard Rupp.  High-throughput crystallography at an affordable cost: the TB Structural Genomics Consortium Crystallization Facility. , 2003, Accounts of chemical research.

[10]  Julie Wilson.  Automated Evaluation of Crystallisation Experiments , 2004 .

[11]  Aled Edwards,et al.  High-throughput protein crystallization. , 2003, Journal of structural biology.

[12]  Rebecca Page,et al.  Crystallization data mining in structural genomics: using positive and negative results to optimize protein crystallization screens. , 2004, Methods.

[13]  Slawomir K. Grzechnik,et al.  Shotgun crystallization strategy for structural genomics: an optimized two-tiered crystallization screen against the Thermotoga maritima proteome. , 2003, Acta crystallographica. Section D, Biological crystallography.

[14]  Arnon Chait,et al.  Efficient protein crystallization. , 2003, Journal of Structural Biology.

[15]  Bernhard Rupp,et al.  Maximum-likelihood crystallization. , 2003, Journal of structural biology.

[16]  A. Deacon,et al.  A scaleable and integrated crystallization pipeline applied to mining the Thermotoga maritima proteome , 2004, Journal of Structural and Functional Genomics.

[17]  Mark Gerstein,et al.  Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis. , 2004, Journal of molecular biology.