  Integrative modeling enables structure determination for large macromolecular
assemblies by combining data from multiple sources of experiment data with
theoretical and computational predictions. Recent advancements in AI-based
structure prediction and electron cryo-microscopy have sparked renewed
enthusiasm for integrative modeling; structures from AI-based methods can be
integrated with in situ maps to characterize large assemblies. This approach
previously allowed us and others to determine the architectures of diverse
macromolecular assemblies, such as nuclear pore complexes, chromatin
remodelers, and cell-cell junctions. Experimental data spanning several scales
was used in these studies, ranging from high-resolution data, such as X-ray
crystallography and Alphafold structures, to low-resolution data, such as
cryo-electron tomography maps and data from co-immunoprecipitation experiments.
Two recurrent modeling challenges emerged across a range of studies. First,
modeling disordered regions, which constituted a significant portion of these
assemblies, necessitated the development of new methods. Second, methods needed
to be developed to utilize the information from cryo-electron tomography, a
timely challenge as structural biology is increasingly moving towards in situ
characterization. Here, we recapitulate recent developments in the modeling of
disordered proteins and the analysis of cryo-electron tomography data and
highlight opportunities for method development in the context of integrative
modeling.
