Intelligent decision support for protein crystal growth

Authors:
I. Jurisica;P. Rogers;J. I. Glasgow;S. Fortier;J. R. Luft;J. R. Wolfley;M. A. Bianca;D. R. Weeks;G. T. DeTitta
Affiliations:
Ontario Cancer Institute, Princess Margaret Hospital, University Health Network, Division of Cancer Informatics, 610 University Avenue, Room 8-413, Toronto, Ontario M5G 2M9, Canada;Ontario Cancer Institute, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada;Department of Computing and Information Science, Queen's University, Kingston, Ontario K7L 3H2, Canada;Department of Chemistry, Queen's University, Kingston, Ontario K7L 3N6, Canada;Hauptman-Woodward Medical Research Institute (HWI), 73 High Street, Buffalo, New York;Hauptman-Woodward Medical Research Institute (HWI), 73 High Street, Buffalo, New York;Hauptman-Woodward Medical Research Institute (HWI), 73 High Street, Buffalo, New York;Hauptman-Woodward Medical Research Institute (HWI), 73 High Street, Buffalo, New York;Hauptman-Woodward Medical Research Institute (HWI), 73 High Street, Buffalo, New York
Venue:
IBM Systems Journal - Deep computing for the life sciences
Year:
2001

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Abstract

Current structural genomics projects are likely to produce hundreds of proteins a year for structural analysis. The primary goal of our research is to speed up the process of crystal growth for proteins in order to enable the determination of protein structure using single crystal X-ray diffraction. We describe Max, a working prototype that includes a high-throughput crystallization and evaluation setup in the wet laboratory and an intelligent software system in the computer laboratory. A robotic setup for crystal growth is able to prepare and evaluate over 40 thousand crystallization experiments a day. Images of the crystallization outcomes captured with a digital camera are processed by an image-analysis component that uses the two-dimensional Fourier transform to perform automated classification of the experiment outcome. An information repository component, which stores the data obtained from crystallization experiments, was designed with an emphasis on correctness, completeness, and reproducibility. A case-based reasoning component provides support for the design of crystal growth experiments by retrieving previous similar cases, and then adapting these in order to create a solution for the problem at hand. While work on Max is still in progress, we report here on the implementation status of its components, discuss how our work relates to other research, and describe our plans for the future.