LIKELIHOOD BASED PHASING METHODS IN PHASER

Phaser 中基于似然的定相方法

• 批准号: 7208311
• 负责人: Randy J Read
• 金额: $ 12.05万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7208311
• 关键词: X ray crystallography; bioengineering /biomedical engineering; biomedical automation; computational biology; computer program /software; data management; functional /structural genomics; information systems; macromolecule; mathematical model; model design /development; structural biology

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.

为了结构基因组学计划的成功，方法必须强大、可靠、 并尽可能自动化。在我们的新程序 Phaser 的工作中，我们一直在应用基于可能性的方法 分子置换和X射线晶体学相问题的解决方法 实验阶段。基于可能性的方法在自动化背景下有两个优点。第一的， 它们更强大，因此具有更大的收敛半径。其次，可能性是自然现象 评分函数来衡量满足数据的成功程度，这使其成为一个很好的标准 自动化决策。我们将增强分子中已经存在的自动化功能 Phaser 的替换和实验定相模块，以便尽可能仅衍射 需要数据和序列信息作为输入。我们将努力提高复杂性和力量 我们的方法，通过考虑更多迄今为止被忽略的相关性，以便 减少计算需求。我们还将努力使我们的算法易于被其他人访问 PHENIX 内的项目，通过使用 Boost.Python 库使它们可通过 Python 脚本使用 支持 PHENIX 的语言。这将允许开发结合 来自 PHENIX 所有组件的算法。 随着人类基因组的完成，寻找利用人类基因组财富的方法成为了新的焦点。 它所透露的信息。在许多情况下，了解基因编码的蛋白质的 3D 结构 对于理解它们如何协同工作以及确定这些结构的主要方法非常有价值 是X射线晶体学的方法。我们将统计方法应用于 3D 结构的工作 决心有助于使这些方法更快、更自动化、更强大。