Validation of NMR protein structures using FIRST and RCI

蛋白质结构对于理解蛋白质的功能和药物设计是必不可少的。为了利用结构，用户了解结构的好坏是至关重要的。这些结构主要是由X射线结晶学和核磁共振产生的。对于晶体结构，有可靠的方法可以知道结构有多好。它们是基于这样一个事实，即可以使用结构来精确地计算输入数据应该是什么样子：因此，与实际衍射数据的比较可以给出可靠的质量定量测量。对于核磁共振，没有这样的测量，这意味着到目前为止，很难知道核磁共振结构有多好。这不仅是结构信息使用者的问题，也是计算结构的科学家的问题，因为他们也无法判断他们的结构有多好。在这个建议中，我们描述了一种计算核磁共振结构质量的方法(即验证)，基于比较两种局部刚性度量，一种来自结构，另一种来自原始输入数据。第一个测量是使用一种基于图论的已建立的识别刚性星团的方法来计算的，该方法称为First，由我们的合作者Sljoka博士开发。第二种方法使用随机线圈指数(RCI)，这是一个基于一个简单的概念的程序，即当蛋白质局部无序时，蛋白质主干原子的核磁共振频率(化学位移)有非常典型的‘随机线圈’位移，因此实验测量的蛋白质位移可以用来量化给定氨基酸残基的无序程度。因此，对这两种局部刚度的测量进行比较，就可以进行逐个残留物的测试，以确定结构的刚度与实验确定的“真实”刚度相比有多好。虽然这不是结构和输入之间的直接比较，但它很可能是人们对核磁共振结构所能获得的最接近的比较，并且是对核磁共振结构确定过程的重大改进。这份提案描述了我们将如何实施比较并检查它是否按预期工作，然后我们将如何将其提供给社区并使用它来检查核磁共振结构，例如通过报告所有现有蛋白质核磁共振结构的质量(目标1)。开发了验证工具后，我们建议将其应用于一些有用的目的。第一个(目标2)是确定一组“好的”和“坏的”核磁共振结构。到目前为止，还没有好的方法来知道结构有多好：通过识别这样的结构，我们希望通过制定质量标准来为结构生物界产生重要的资源，从而刺激对结构质量的进一步研究。晶体结构通常由平均位置的一组坐标表示(连同代表每个坐标中的不确定性的‘B因子’)，而核磁共振结构总是表示为一个(通常是20个)结构的集合。这有一个合理的原因，那就是核磁共振结构天生就没有晶体结构定义得那么好。然而，这是令人困惑的，也是没有必要的。我们的目标是应用我们的方法来更紧密地定义一个整体中有多少结构是真正必要的，以及其中一些是否纯粹是错误的。为了帮助这一过程，我们将改进目前从结构计算化学位移的方法，通过修改它们来处理系综。最后，我们将使用我们的方法来研究一类重要的蛋白质结构，称为固有无序蛋白质，以测试目前的方法是否提供了真实构象集合的正确表示。它们代表了大约三分之一的人类蛋白质(包括许多负责信号传递的蛋白质)，所以这是一个重要的话题。

Protein structures are essential for understanding protein function, and for drug design. In order to make use of structures, it is vital for users to know how good the structures are. The structures are generated mainly from X-ray crystallography and NMR. For crystal structures, there are reliable ways of knowing how good the structure is. These are based on the fact that a structure can be used to calculate exactly what the input data should look like: a comparison with the actual diffraction data therefore gives a reliable quantitative measure of quality. For NMR, there is no such measure, meaning that so far it is very difficult to know how good an NMR structure is. This is a problem not only for users of structural information, but also for the scientists who calculate the structures, since they also have no way to judge how good their structures are.In this proposal we describe a method for calculating the quality of NMR structures (ie, validation), based on comparing two measures of local rigidity, one derived from the structures and one from the original input data. The first measure is calculated using an established method for identifying rigid clusters based on graph theory, called FIRST, and developed by our collaborator Dr Sljoka. The second method uses the Random Coil Index (RCI), which is a program based on the simple idea that the NMR frequencies ('chemical shifts') of protein backbone atoms have very characteristic 'random coil' shifts when the protein is locally disordered, and therefore that the experimentally measured shifts in a protein can be used to quantify to what extent a given amino acid residue is disordered. A comparison of these two measures of local rigidity therefore provides a residue-by-residue test of how well the rigidity of the structures compares to the experimentally determined 'true' rigidity. Although this is not a direct comparison between structure and input, it is likely to be as close as one can get for NMR structures, and is a major improvement in the NMR structure determination process. The proposal describes how we will go about implementing the comparison and checking that it works as expected, and then how we will make it available to the community and use it to examine NMR structures, for example by reporting on the quality of all existing protein NMR structures (objective 1).Having developed the validation tool, we then propose to apply it to some useful ends. The first of these (objective 2) is to identify sets of 'good' and 'bad' NMR structures. So far there has been no good way to know how good structures are: by identifying such structures we expect to generate an important resource for the structural biology community by marking out quality criteria and therefore stimulating further research into structure quality.Whereas crystal structures are typically represented by a single set of coordinates at the average position (together with 'B factors' that represent the uncertainty in each coordinate), NMR structures are always represented as an ensemble of (typically 20) structures. There is a valid reason for this, that NMR structures are inherently less well defined than crystal structures. Nevertheless, it is confusing and unnecesary. We aim to apply our method to define more closely how many structures in an ensemble are really necessary, and whether some are simply wrong. In order to assist the process, we will improve current methods for calculating chemical shifts from structures, by modifying them to work on ensembles. Finally, we shall use our methods to look at an important class of protein structures called Intrinsically Disordered Proteins, to test whether current methods provide a correct representation of the true conformational ensemble. These represent roughly one third of human proteins (including many responsible for signalling), so are an important topic.