Validation of NMR protein structures using FIRST and RCI

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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.