Efficient inference of protein structural ensembles
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It is becoming clear that traditional, single-structure models of proteins are insufficient for understanding their biological function. Here, we outline one method for inferring, from experiments, not only the most common structure a protein adopts (native state), but the entire ensemble of conformations the system can adopt. Such ensemble mod- els are necessary to understand intrinsically disordered proteins, enzyme catalysis, and signaling. We suggest that the most difficult aspect of generating such a model will be finding a small set of configurations to accurately model structural heterogeneity and present one way to overcome this challenge.
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Bayesian Sampling of Structural Ensembles: The Role of Ensemble-Counting Measures
Bayesian sampling of structural ensembles requires an explicit ensemble-counting measure; the flat measure in Lagrange-multiplier space is non-normalizable for finite samples while the Jeffreys measure restores normal...
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