Interpretation of solution scattering data by combining Bayesian inference with molecular dynamics simulations

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Small-angle scattering (SAS), using either X-rays or Neutrons, is a popular method for probing structures and ensembles of proteins in solution. However, the interpretation of the data has remained challenging, mainly due to the low information content and due to systematic errors in experimental signals. Consequently, upon fitting of structural models against SAXS data, the models are frequently overfitted, the uncertainty of the models cannot be quantified, or it remains often unclear if alternative models may equally explain the experimental data. We aim to overcome these problems by unsatisfactory situation by combining molecular dynamics (MD) simulations with rigorous statistical paradigms, namely with Bayesian inference and with the maximum entropy principle. We aim to quantify the ambiguity and the confidence intervals for structural models fitted against SAS data, and we aim to derive statistically founded solution ensembles of disordered proteins. In this funding period, we will in addition combine novel high-precision SAS data with MD simulations to scrutinize the structure of the hydration layer of biomolecules.