LABELIT: Automated Diffraction Analysis for X-ray Crystallography

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DESCRIPTION (provided by applicant): X-ray crystallography is a key method for elucidating macromolecular structure and function. Present-day crystallographic initiatives include increasing our understanding of infectious disease by examining thousands of structures in pathogenic proteomes, facilitating drug discovery by mapping out protein-drug interactions, and probing ever larger complexes including whole viruses and large molecular factories such as the ribosome, chaperonin, and RNA polymerase. Although the acquisition and analysis of X-ray diffraction data is routine in simple cases, it becomes much more challenging in situations where hundreds of protein crystal samples must be studied, or where the size or characteristics of the molecule under investigation preclude the use of existing methods. Our long term goal is to develop better theoretical methods, allowing crystallographers to operate more efficiently in today's high-throughput experimental environment. We recently introduced novel methods in our software package, LABELIT (Lawrence Berkeley Laboratory Indexing Toolbox), which we will further develop and extend. Our specific aims will be to: 1. Develop new technology to handle cases where existing methods fail. In particular we will perform image processing to automatically rank the quality of crystal diffraction patterns without time-consuming visual examination; use maximum-likelihood techniques for refining the model of the diffraction pattern; and fix existing methods that sometimes misidentify the diffraction pattern's symmetry. 2. Disseminate the software to as wide an audience as possible. One important step will be to create a link between our program and another popular data processing program, MOSFLM. We will provide a flexible interface that is easily modified by users, and support all major hardware and computing platforms. 3. Address problems of scale, such as the handling of data from large numbers of crystals, and the real-time tracking of results throughout the data collection process. X-ray crystallography is relevant to public health because it is a premiere technique for examining fundamental atomic processes, such as how cancer and inherited diseases work, how viruses infect the cell, how certain drug molecules act, and why pathogens become resistant to drugs. Our work will create software to allow X-ray crystallographers to perform this difficult experiment rapidly and efficiently.