Bayesian methods for protein structure calculation from sparse, heterogenous and lowquality data

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Proteins carry out diverse functions in the living cell by means of a specific three-dimensional structure into which they fold. Methods to determine protein structures include X-ray crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. With decreasing data quality and quantity, structure determination often becomes a matter of pass or fail. Even so the data may still be informative. The aim of this project is to develop computational tools for calculating protein structures from experimental data that traditionally have been considered insufficient for atomic resolution. These tools should operate automatically and require only minimal human intervention. Deficiencies in the data will be compensated for by prior structural knowledge. The remaining uncertainty about the structure needs to represented adequately. This requires the sampling of alternative conformations which are equally compatible with both data and prior knowledge and includes the quantification of their precision and likelihood. Bayesian probability theory provides the optimal mathematical framework to develop these tools. It enables the unbiased analysis of noisy and incomplete data, integrates structural information from diverse sources and furnishes an inference machinery to estimate parameter uncertainties and missing information.