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Abstract

Protein NMR spectroscopy is a modern experimental technique for elucidating the three-dimensional structure of biological macromolecules in solution. From the data-analytical point of view, structure determination has always been considered an optimisation problem: much effort has been spent on the development of minimisation strategies; the underlying rationale, however, has not been revised. Conceptual difficulties with this approach arise since experiments only provide incomplete structural information: structure determination is an inference problem and demands for a probabilistic treatment. In order to generate realistic conformations, strong prior assumptions about physical interactions are indispensable. These interactions impose a complex structure on the posterior distribution making simulation of such models particularly difficult. We demonstrate, that posterior sampling is feasible using a combination of multiple Markov Chain Monte Carlo techniques. We apply the methodology to a sparse data set obtained from a perdeuterated sample of the Fyn SH3 domain.