determination is typically carried out in vitro and requires pure samples of the studied assemblies. Here, we describe an integrative structure determination approach based on in vivo measurements of genetic interactions of point mutants of the assembly proteins. Using the point-mutant epistatic miniarray profiling (pE-MAP) to measure growth phenotypes (Braberg et al. Cell, 2013), we construct phenotypic profiles for point mutations crossed against single gene deletions, other point mutations, and/or chemical or physical perturbations. We quantify the similarity between pE-MAP profiles, allowing us to convert the phenotypic data into upper distance bounds on pairs of mutated residues. We parametrized these distance restraints using a pE-MAP dataset of 305 point mutations in histones H3 and H4 crossed against an array of 1,400 gene deletion alleles. Using integrative structure modeling based on this data, we reconstructed the structure of the H3-H4 dimer with an accuracy better than 3.0 Å RMSD for Ca-atoms. To assess the applicability of the approach, we determined the structures of two additional protein assemblies from pE-Map datasets not used in the parametrization: (1) subunits Rpb1 and Rpb2 of yeast RNA polymerase using a dataset that includes 53 single point mutants and a library of 1,200 gene-deletion alleles, and (2) subunits RpoB and RpoC of a bacterial RNA polymerase using a dataset of 49 point mutations subject to 139 different conditions (e.g., treatments with chemicals and temperature shocks). The resulting model accuracy is comparable to that obtained with chemical cross-linking data. The approach is implemented in our open-source Integrative Modeling Platform software (http://integrativemodeling.org), thus allowing us to model structures based on other types of structural datasets as well as genetic interactions data.
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