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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.