LIKELIHOOD BASED PHASING METHODS IN PHASER

为了在结构基因组学倡议中取得成功，至关重要的是这些方法必须同样强大、可靠、 并且尽可能地自动化。在我们的新项目Phaser的工作中，我们一直在应用基于可能性的 用分子置换和分子置换两种方法解决X射线结晶学中的位相问题 试验性分阶段。在自动化的背景下，基于可能性的方法有两个优点。第一, 它们更强大，因此具有更大的收敛半径。其次，可能性是一种自然现象。 衡量满足数据的成功程度的评分函数，这使其成为 自动化决策。我们将增强分子中已有的自动化功能 更换和实验移相模块，使之尽可能只有衍射光 需要数据和序列信息作为输入。我们将努力提高成熟度和力量 通过考虑到到目前为止被忽略的更多的相关性，以便 减少计算需求。我们还将努力使我们的算法更容易被其他人访问 PHENIX中的项目，方法是使用Boost.Python库使其可从Python脚本中使用 支撑菲尼克斯的语言。这将使自动化管道的发展结合 来自PHENIX所有组件的算法。 随着人类基因组的完成，有了一个新的焦点，即寻找方法来开发 它所透露的信息。在许多情况下，知道基因编码的蛋白质的3D结构 在理解它们如何协同工作以及确定这些结构的主要方法方面具有非常宝贵的价值 是X射线结晶学的方法。我们将统计方法应用于三维结构的工作 决心有助于使这些方法更快、更自动化、更强大。

For success in the structural genomics initiatives, it is essential that the methods be as powerful, as reliable, and as automated as possible. In work on our new program, Phaser, we have been applying likelihood-based methods to solving the phase problem of X-ray crystallography by both molecular replacement and experimental phasing. Likelihood-based methods have two advantages in the context of automation. First, they are more powerful and therefore have a greater convergence radius. Second, likelihood is a natural scoring function to measure success in satisfying the data, which makes it an excellent criterion for automated decision-making. We will enhance the automation features that already exist in the molecular replacement and experimental phasing modules of Phaser so that, as much as possible, only the diffraction data and sequence information are needed as input. We will work to increase the sophistication and power of our methods, by taking account of more of the correlations that have been neglected to date in order to reduce the computational demands. We will also work to make our algorithms easily accessible to other Projects within PHENIX, by using the Boost.Python library to make them available from the Python scripting language that underpins PHENIX. This will allow the development of automated pipelines combining algorithms from all the components of PHENIX. With the completion of the human genome, there is a new focus on finding ways to exploit the wealth of information it has revealed. In many cases, knowing the 3D structure of the proteins encoded by the genes is invaluable in understanding how they work together, and the primary method to determine those structures is the method of X-ray crystallography. Our work on applying statistical methods to 3D structure determination is helping to make those methods faster, more automatic and more powerful.