Bayesian methods for integrative structural biology: validation, sampling and modeling with EM data

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Hybrid methods provide insights into the 3D structures of large macromolecular complexes. Although the number of hybrid structures continues to grow, the community still lacks a generally accepted quality measure for structures obtained by integrating data from multiple experimental sources. Statistical methods, in particular those based on Bayesian inference, enable us to make sound statements about the quality of a hybrid structure. The first aim of this project is to develop new quality measures for the validation of hybrid structures by applying concepts and techniques from Bayesian inference. A prerequisite for statistical model assessment is that we sample conformational space exhaustively. Therefore, an important aspect is to also improve conformational sampling techniques.The second aim of this proposal is to enhance Bayesian modeling with cryo-electron microscopy (cryo-EM) data. Cryo-EM has emerged as a powerful method to characterize the structure of large macromolecular assemblies and can reach atomic or near-atomic resolution. To improve structural modeling with cryo-EM maps, we will build on our Inferential Structural Determination (ISD) software, which currently can be used for rigid and flexible fitting into low- to medium-resolution density maps. Our goal is to also support modeling with high-resolution maps such that structure modeling with ISD spans the entire range from high to low resolution. To this end, we will develop new probabilistic models for high-resolution maps, efficient algorithms for enhanced conformational sampling and methods for de novo modeling with cryo-EM maps.