CCP4 Grant Renewal 2014-2019: Question-driven crystallographic data collection and advanced structure solution

蛋白质、DNA和RNA是构成生物体的细胞的活跃机器，它们统称为大分子。它们执行维持生命的所有功能，从新陈代谢到复制，再到细胞与其环境之间的信息交换。它们是由基因组中DNA序列形式的“蓝图”编码的，该蓝图描述了如何将它们作为构建模块的线性字符串。然而，为了发挥作用，大多数大分子折叠成精确的3D结构，这反过来又主要取决于制造它们的构建块的顺序。对分子三维结构的了解使我们既能理解它的功能，又能设计出干扰它的化学物质。由于分子生物学的进步，包括人类基因组计划在内的许多项目已经确定了许多生物体的完整DNA序列，从中我们现在可以读取许多大分子的线性蓝图。然而，到目前为止，三维结构不能仅从序列的知识来预测。一种“看到”大分子并确定其三维结构的方法，包括首先将被研究的分子结晶，然后用合适的辐射对其成像。大分子太小，无法用普通光线看到，因此需要一种不同的方法。用光学显微镜我们看不到比光的波长还小的物体，大约是百万分之一米：原子比它小1000倍。然而，x射线的波长与原子的大小大致相同。出于这个原因，为了解决大分子结构的原子细节，我们用x射线而不是可见光对它们成像。对已结晶的大分子结构进行成像的过程被称为x射线晶体学。x射线晶体学就像用显微镜放大可见光看不见的小物体。不幸的是，x射线晶体学是复杂的，因为与显微镜不同，x射线没有透镜系统，因此需要额外的信息和复杂的计算来重建最终的图像。这些信息可能来自已知的蛋白质结构，使用分子替代（MR）方法，或来自其他来源，包括电子显微镜（EM）。一旦知道了结构，就更容易确定大分子是如何对活细胞机制做出贡献的。药物研究将此作为设计药物的基础，根据需要打开或关闭这些分子。药物被设计成与目标分子相互作用，以阻止或促进它们在体内进行的化学过程。其他应用包括蛋白质工程和碳水化合物工程。该项目的目的是改进从x射线晶体学实验中提取3D结构所需的关键计算工具。它将为协作计算项目（CCP4于1979年首次建立）提供持续的支持，该项目已成为该任务的主要软件来源之一。该项目将有助于高效和有效地利用同步加速器，使x射线在大多数晶体学实验中使用。它将提供更强大的工具，允许用户在与未知结构的匹配非常差的情况下利用已知蛋白质结构的信息。它还将使电子显微镜信息的使用自动化，即使晶体结构在生长蛋白质晶体的过程中被扭曲。最后，它将允许结构被解决，即使在质量差和非常小的晶体获得。

Proteins, DNA and RNA are the active machines of the cells which make up living organisms, and are collectively known as macromolecules. They carry out all of the functions that sustain life, from metabolism through replication to the exchange of information between a cell and its environment. They are coded for by a 'blueprint' in the form of the DNA sequence in the genome, which describes how to make them as linear strings of building blocks. In order to function, however, most macromolecules fold into a precise 3D structure, which in turn depends primarily on the sequence of building blocks from which they are made. Knowledge of the molecule's 3D structure allows us both to understand its function, and to design chemicals to interfere with it.Due to advances in molecular biology, a number of projects, including the Human Genome Project, have led to the determination of the complete DNA sequences of many organisms, from which we can now read the linear blueprints for many macromolecules. As yet, however, the 3D structure cannot be predicted from knowledge of the sequence alone. One way to "see" macromolecules, and so to determine their 3D structure, involves initially crystallising the molecule under investigation, and subsequently imaging it with suitable radiation. Macromolecules are too small to see with normal light, and so a different approach is required. With an optical microscope we cannot see objects which are smaller than the wavelength of light, roughly 1 millionth of a metre: Atoms are about 1000 times smaller than this. However X-rays have a wavelength about the same as the size of the atoms. For this reason, in order to resolve the atomic detail of macromolecular structure, we image them with X-rays rather than with visible light. The process of imaging the structures of macromolecules that have been crystallised is known as X-ray crystallography. X-ray crystallography is like using a microscope to magnify objects that are too small to be seen with visible light. Unfortunately X-ray crystallography is complicated because, unlike a microscope, there is no lens system for X-rays and so additional information and complex computation are required to reconstruct the final image. This information may come from known protein structures using the Molecular Replacement (MR) method, or from other sources including Electron Microscopy (EM).Once the structure is known, it is easier to pinpoint how macromolecules contribute to the living cellular machinery. Pharmaceutical research uses this as the basis for designing drugs to turn the molecules on or off when required. Drugs are designed to interact with the target molecule to either block or promote the chemical processes which they perform within the body. Other applications include protein engineering and carbohydrate engineering.The aim of this project is to improve the key computational tools needed to extract a 3D structure from X-ray crystallography experiments. It will provide continuing support to a Collaborative Computing Project (CCP4 first established in 1979), which has become one of the leading sources of software for this task. The project will help efficient and effective use to be made of the synchrotrons that make the X-rays that are used in most crystallographic experiments. It will provide more powerful tools to allow users to exploit information from known protein structures when the match to the unknown structure is very poor. It will also automate the use of information from electron microscopy, even when the crystal structure has been distorted by the process of growing the protein crystal. Finally, it will allow structures to be solved, even when poor quality and very small crystals are obtained.