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A new principle for macromolecular structure determination


Habeck,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Habeck, M., Rieping, W., & Nilges, M. (2004). A new principle for macromolecular structure determination. In 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (pp. 157-166). Melville: American Institute of Physics.

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.