LABELIT: Automated Diffraction Analysis for X-ray Crystallography

描述（申请人提供）：x射线晶体学是研究大分子结构和功能的关键方法。当今晶体学的创举包括：通过检查致病蛋白质组中的数千种结构，增加我们对传染病的理解；通过绘制蛋白质-药物相互作用的图谱，促进药物的发现；探测更大的复合物，包括整个病毒和大型分子工厂，如核糖体、伴侣蛋白和RNA聚合酶。虽然x射线衍射数据的获取和分析在简单的情况下是常规的，但在必须研究数百个蛋白质晶体样品的情况下，或者在被调查分子的大小或特征排除使用现有方法的情况下，它变得更具挑战性。我们的长期目标是开发更好的理论方法，使晶体学家在当今高通量的实验环境中更有效地工作。我们最近在我们的软件包LABELIT（劳伦斯伯克利实验室索引工具箱）中引入了新的方法，我们将进一步开发和扩展它。我们的具体目标是：1。开发新技术来处理现有方法失败的情况。特别是，我们将执行图像处理以自动排列晶体衍射图案的质量，而无需耗时的视觉检查；使用最大似然技术来改进衍射图样的模型；并修正现有的方法，这些方法有时会错误地识别衍射图案的对称性。2. 将软件传播给尽可能广泛的受众。一个重要的步骤是在我们的程序和另一个流行的数据处理程序MOSFLM之间创建一个链接。我们将提供用户易于修改的灵活界面，并支持所有主要硬件和计算平台。3. 解决规模问题，例如处理来自大量晶体的数据，以及在整个数据收集过程中实时跟踪结果。x射线晶体学与公共卫生相关，因为它是检验基本原子过程的首要技术，例如癌症和遗传性疾病如何工作，病毒如何感染细胞，某些药物分子如何起作用，以及病原体为什么对药物产生耐药性。我们的工作将创建软件，使x射线晶体学家能够快速有效地进行这项困难的实验。

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.