Artificial Intelligence Methods for Crystallization

描述（由申请人提供）：人们普遍认为结晶是大多数x射线结构测定中的限速步骤。因此，我们一直在开发计算工具来促进这一过程，包括XtalGrow程序套件。在这里，我们建议从两个方面改进这些工具的功能和范围：初始筛选，即（迭代的）一组有希望产生一个或多个初步“命中”（明显是蛋白质的结晶物质）的实验；和2。以初始撞击开始并以衍射质量晶体结束的优化实验。这个建议的中心概念是这个工具构建需要一个基于知识的基础。因此，该提案的主要目标之一是开发一个框架，以计算可处理的形式获取和编码知识；具体地说，是能产生更有效结晶过程的形式。我们感兴趣的是这些数据是如何相互作用的，以及如何利用这些数据来改进结晶过程。虽然文献和实验室其他项目的可用数据将继续尽可能地使用，但我们的分析也表明，需要积极主动，即收集完成知识库所需的选定数据。我们建议这样做：1 .深化数据表示是几个方面，包括额外的蛋白质特征，结合化学添加剂的层次结构和获取详细的响应数据。11. 提高结晶筛选的效率：将归纳推理应用于贝叶斯信念网的细化，可以提高初始结晶筛选的效率；还将制定程序，通过确定参数空间的未探索区域和使用额外的测量（如动态光散射和云点确定）来处理缺乏有希望的结果，以进一步完善贝叶斯信念网并将实验引导到更有希望的方向。通过应用基于案例的方法和贝叶斯方法以及自动化图像分析的进一步发展，优化屏幕将得到改进。3。提高整个系统的“用户友好性”、集成度和自动化程度。

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.