Nuclear Magnetic Resonance Spectroscopy of Proteins - Structure Validation

Structure Validation

Is important to note that the ensemble of structures obtained is an "experimental model", i.e. a representation of certain kind of experimental data. To acknowledge this fact is really important because it means that the model could be a good or bad representations of that experimental data. In general the quality of a model will depend on both the quantity and quality of experimental data used to generate it and the correct interpretation of such data.

It is important to remember that every experiment has associated errors. Random errors will affect the reproducibility and precision of the resulting structures. If the errors are systematic, the accuracy of the model will be affected. The precision indicates the degree of reproducibility of the measurement and is often expressed as the variance of the measured data set under the same conditions. The accuracy, however, indicates the degree to which a measurement approaches its "true" value.

Ideally, a model of a protein will be more accurate the more fit the actual molecule that represents and will be more precise as there is less uncertainty about the positions of their atoms. In practice there is no "standard molecule" against which to compare models of proteins, so the accuracy of a model is given by the degree of agreement between the model and a set of experimental data. Historically, the structures determined by NMR have been, generally but not necessarily, of lower quality than those determined by X-ray diffraction. This is due, in part, to the lower amount of information contained in data obtained by NMR. Because of this fact it has become common practice to establish the quality of NMR ensembles, by comparing it against the unique conformation determined by X-ray diffraction, for the same protein. However the X-ray diffraction structure may not exist, and more importantly, since the proteins in solution are flexible molecules, a protein represented by a single structure may lead to underestimate the intrinsic variation of the atomic positions of a protein. A set of conformations, determined by NMR or X-ray crystallography may be a better representation of the experimental data of a protein than a unique conformation.

The utility of a model will be given, at least in part, by the degree of accuracy and precision of the model. An accurate model with relatively poor precision could be useful to study the evolutionary relationships between the structures of a set of proteins, whereas the rational drug design requires both precise and accurate models. A model that is not accurate, regardless of the degree of precision with which it was obtained will not be very useful.

Since protein structures are experimental models that can contain errors, it is very important to be able to detect these errors. The process aimed at the detection of errors is known as validation. There are several methods to validate structures, some are statistical like PROCHECK and WHAT IF while others are based on physical principles as CheShift, or a mixture of statistical and physics principles PSVS