Automated de novo building of protein models into electron microscopy maps

自动将蛋白质模型从头构建到电子显微镜图谱中

• 批准号: BB/P000517/1
• 负责人: Kevin Cowtan
• 金额: $ 33.11万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP000517%2F1
• 关键词: Automated; de; novo; building; protein

Scientists are interested in the atomic structure of biological molecules, in other words what the molecules look like. Knowing in detail what a molecule looks like provides important clues to how it might work. If we can go further and capture molecules in the process of interacting with other biological molecules, or artificial compounds such as drugs, we get a clearer picture of how they work.Most of our knowledge of the structure of biological molecules comes from X-ray crystallography. However over the past decade a new technique, electron microscopy (EM) has become popular. Individual molecules held in a thin film of liquid solvent are frozen and placed in an electron microscope, which captures images of the molecules. Many individual views can be combined to construct a model of the structure of the molecule in 3 dimensions.Until recently these images were of limited resolution - they were 'fuzzy' - and so individual groups of atoms could not be seen. The EM user therefore needed to have some knowledge of the structure of the molecule, or at least parts of it, in advance. These fragments can then be fitted into the EM image to give an indication of the whole structure, and allowed large molecular machines such as the Ribosome to be understood.New electron detectors have allowed EM images to be determined at much higher resolutions, so that small groups of atoms can be distinguished. The resulting images are of similar quality to those from X-ray crystallography. This has allowed the atomic structure of the molecule to be determined without any prior knowledge of the structure in favourable cases. However at the moment the process of interpreting the map in terms of atomic features is often performed manually, at a cost of considerable effort and a potential lack of objectivity in the results.The aim of this project is to take an existing method for automatically building atomic models into images from X-ray crystallography, and modify the software to work effectively with the images from electron microscopy. Not only will this make the process of building an atomic model into an electron microscopy image much less time consuming, it will allow multiple models to be built into different images of the molecule as an assessment of the accuracy and reliability of the results. It will be possible to go back and check existing structures by rebuilding the maps automatically. This will provide a useful check on the quality of existing models determined from EM images.The project involves modifying existing computer software for building atomic models to adapt it to work on a new type of image. The software is already good at interpreting crystallographic images at the kind of resolutions produced by electron microscopy experiments, but works less well with EM images because it has been "trained" to work with crystallography images. Some retraining, and possibly some new methods, will be required.All of the software produced by the project will be distributed freely to academic users through existing software suites for crystallography and electron microscopy. The source code for software will also be distributed so that other developers can learn from it or modify it.

科学家们对生物分子的原子结构感兴趣，换句话说，分子看起来像什么。详细了解一个分子的样子可以为它如何工作提供重要的线索。如果我们能更进一步，在与其他生物分子或药物等人工化合物相互作用的过程中捕获分子，我们就能更清楚地了解它们是如何工作的。我们对生物分子结构的大部分知识都来自X射线晶体学。然而，在过去的十年中，一种新的技术，电子显微镜（EM）已经变得流行。将液体溶剂薄膜中的单个分子冷冻并放置在电子显微镜中，电子显微镜捕获分子的图像。许多单独的视图可以结合起来构建分子结构的三维模型。直到最近，这些图像的分辨率有限-它们是“模糊的”-因此无法看到单个原子组。因此，EM用户需要事先了解分子的结构，或至少部分结构。这些碎片可以与电子显微镜的图像相匹配，从而显示出整个结构，并使人们能够理解核糖体这样的大分子机器。新的电子探测器使电子显微镜的图像具有更高的分辨率，从而可以区分小的原子群。由此产生的图像与X射线晶体学的图像质量相似。这使得在有利的情况下，在没有任何结构的先验知识的情况下确定分子的原子结构。然而，目前，根据原子特征解释图谱的过程通常是手动进行的，这需要付出相当大的努力，而且结果可能缺乏客观性。本项目的目的是采用现有的方法，自动将原子模型构建到X射线晶体学图像中，并修改软件，使其能够有效地与电子显微镜图像一起工作。这不仅将使将原子模型构建到电子显微镜图像中的过程耗时更少，还将允许将多个模型构建到分子的不同图像中，以评估结果的准确性和可靠性。通过自动重建地图，可以返回并检查现有结构。这将为从EM图像确定的现有模型的质量提供有用的检查。该项目涉及修改用于构建原子模型的现有计算机软件，以使其适用于新类型的图像。该软件已经很好地解释了电子显微镜实验产生的那种分辨率的晶体学图像，但与EM图像的效果不太好，因为它已经被“训练”来处理晶体学图像。一些再培训，可能还有一些新的方法，将需要。所有的软件生产的项目将免费分发给学术用户通过现有的软件套件结晶学和电子显微镜。软件的源代码也将被分发，以便其他开发人员可以从中学习或修改。

项目成果

Automating tasks in protein structure determination with the clipper python module.

• DOI: 10.1002/pro.3299
• 发表时间: 2018-01
• 期刊: Protein science : a publication of the Protein Society
• 作者: McNicholas S;Croll T;Burnley T;Palmer CM;Hoh SW;Jenkins HT;Dodson E;Cowtan K;Agirre J
• 通讯作者: Agirre J