Using in vivo genetic and physical interaction data for structure determination of protein assemblies

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PROJECT SUMMARY Many proteins function by forming macromolecular assemblies. Describing the structures of these assemblies in their cellular environment remains challenging. Traditional structural biology approaches may provide high- resolution atomic structures but usually require purified samples and might describe only a few conformers. We propose using data from in vivo genetic interaction and quantitative cross-linking mass-spectrometry (qXL-MS) experiments to build structural models of protein assemblies, empowering the scientific community to address structural questions that are currently out of reach of traditional structural biology methods. For example, genetic interaction mapping by point-mutant epistatic miniarray profile (pE-MAP) platform and deep mutational scanning (DMS) have emerged as powerful tools to interrogate proteins, at a residue resolution, in the context of their biologically relevant functions. Similarly, in vivo qXL-MS approaches are well-suited to dissect physical interactions between proteins, a full range of structural dynamics, and conformational changes at residue resolution. Notably, in vivo genetic interaction and cross-linking experiments can be performed under varying conditions to determine how protein functional states respond to changes in the cellular environment, a problem difficult to approach by other methods. However, in vivo genetic interaction and cross-linking datasets are usually noisy, sparse, and ambiguous, making structural interpretation challenging. To fully realize the potential of in vivo genetic and physical interaction data, we need new computational methods that maximize the structural information extracted from these datasets. Here, we propose a comprehensive research program to develop tools to build integrative/hybrid structure models of stable and transient protein assemblies. We will focus on (1) developing Bayesian scoring functions that objectively quantify the noise and ambiguity in the in vivo experimental data, therefore increasing the accuracy and precision of the models; (2) building Bayesian hierarchical models to represent the ensembles of protein assemblies, therefore allowing the application to conformational and compositionally heterogeneous systems; and (3) creating validation tools to assess the precision and accuracy of structural models obtained using in vivo data, therefore allowing judicious use of the models. Finally, in close collaboration with experimentalists, we will apply these methods to determine the structures of protein assemblies that have been refractive to traditional structural biology methods, including vaccinia virus protein assemblies, TRIM5α bound to the HIV-1 capsid, and Ddis shuttling factors associated with the proteasome. In conclusion, we will expand the scope of structural biology by increasing the variety of input information used for integrative/hybrid structure modeling and thus allow structural modeling of biological systems that are not amenable to traditional structural biology approaches. Our methods will be implemented in the open-source Integrative Modeling Platform (IMP) software and contribute to the worldwide Protein Data Bank (wwPDB) effort to validate, archive, and disseminate integrative structures.