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Figure 6
Venn diagram of different biophysical methods that can contribute to GMMA in SEDPHAT. Hierarchy of parameters: macromolecular binding parameters that are central TO the model for each data type, such as Kd, ΔG and ΔΔG, are indicated in bold red; macromolecular parameters that serve as observables in each technique, such as mass in AUC and SPR, translational frictional coefficient in SV and DLS, spectroscopic changes upon binding in fluorescence or other spectroscopy and rotational diffusion coefficient in fluorescence anisotropy, are indicated in bold black. In ITC, the enthalpic changes (ΔH and ΔΔH) as binding parameters are directly probed calorimetrically. Finally, in most methods there are technical and `nuisance' parameters that are usually unrelated to the molecules under study, such as baseline offsets and sample dimensions (meniscus and/or bottom in SV), but also incompetent fractions, as indicated in grey. Some of these local parameters may be constrained to be the same in a subset of experiments, such as concentration errors. A given model is projected into each of the data spaces, optimizing the local and nuisance parameters, compared with the experimental data, and a global measure for the goodness of fit is calculated, which is then optimized by nonlinear regression.

Journal logoBIOLOGICAL
ISSN: 1399-0047
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