Artificial Intelligence Methods for Crystallization

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DESCRIPTION (provided by applicant): It is widely believed that crystallization is the rate-limiting step in most X-ray structure determinations. We have therefore been developing computational tools to facilitate this process, including the XtalGrow suite of programs. Here we propose to improve the power and scope of these tools along two fronts: 1. Initial screening, the (iterative) set of experiments that hopefully, yields one or more preliminary "hits" (crystalline material that is demonstrably protein); and 2. Optimization experiments that begin with an initial hit and end with diffraction-quality crystals. A central concept of this proposal is that this tool building requires a knowledge-based foundation. Therefore, one of the broad goals of the proposal is to develop a framework for the acquisition and encoding of knowledge in computationally tractable forms; specifically, forms that will yield more effective crystallization procedures. We are interested in how the data interact and how that can be used to improve the crystallization process. While available data, both in the literature and from other projects in the laboratory will continue to be used wherever possible, our analysis has also demonstrated the need to be pro-active i.e. to gather selected data required to complete the knowledge base. We propose to do this by: I. Deepening the data representations is several areas including additional protein characteristics, incorporating a hierarchy of chemical additives and acquiring detailed response data. 11. Improving the efficiency of crystallization screens: Initial crystallization screens would be improved by applying inductive reasoning to the refinement of Bayesian belief nets; procedures would also be developed for dealing with the absence of promising results by identifying unexplored regions of the parameter space and using additional measurements, such as dynamic light scattering and cloud point determinations to further refine the Bayesian belief nets and steer experimentation in more promising directions. Optimization screens would be improved by applying Case-Based and Bayesian methods here as well as by further developments of automated image analysis. III. Improving the "user friendliness," integration and automation of the entire system.