Automated de novo building of protein models into electron microscopy maps

科学家们对生物分子的原子结构很感兴趣，换句话说，分子是什么样子的。详细了解一个分子的样子可以为它的工作原理提供重要的线索。如果我们能更进一步，捕捉到与其他生物分子或药物等人工化合物相互作用过程中的分子，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）已经流行起来。单个分子被保存在液体溶剂的薄膜中，然后被冷冻并放置在电子显微镜中，电子显微镜可以捕捉到分子的图像。许多单独的观点可以结合起来，在三维空间中构建分子结构的模型。直到最近，这些图像的分辨率都是有限的——它们是“模糊的”——所以单个的原子群是看不见的。因此，EM用户需要提前了解分子的结构，或者至少是部分结构。然后，这些片段可以被放入电子显微镜图像中，以显示整个结构，并允许像核糖体这样的大型分子机器被理解。新的电子探测器使EM图像能够以更高的分辨率确定，因此可以区分小群原子。所得到的图像与x射线晶体学的图像质量相似。在有利的情况下，这使得分子的原子结构可以在没有任何先验知识的情况下确定。然而，目前根据原子特征解释地图的过程通常是手动执行的，这需要付出相当大的努力，而且结果可能缺乏客观性。该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，并修改软件以有效地处理电子显微镜图像。这不仅将使构建原子模型到电子显微镜图像的过程更节省时间，而且还将允许将多个模型构建到不同的分子图像中，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的结构。这将为从EM图像确定的现有模型的质量提供有用的检查。该项目涉及修改现有的用于构建原子模型的计算机软件，使其适应于处理一种新型图像。该软件已经可以很好地解释电子显微镜实验产生的晶体图像分辨率，但在EM图像上表现不佳，因为它已经被“训练”为处理晶体图像。需要进行一些再培训，可能还需要一些新方法。该项目生产的所有软件将通过现有的晶体学和电子显微镜软件套件免费分发给学术用户。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它。

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.