Recent methods from statistical inference and machine learning to improve integrative modeling of macromolecular assemblies

Integrative modeling of macromolecular assemblies allows for structural characterization of large assemblies that are recalcitrant to direct experimental observation. A Bayesian inference approach facilitates combining data from complementary experiments along with physical principles, statistics of known structures, and prior models, for structure determination. Here, we review recent methods for integrative modeling based on statistical inference and machine learning. These methods improve over the current state-of-the-art by enhancing the data collection, optimizing coarse-grained model representations, making scoring functions more accurate, sampling more efficient, and model analysis more rigorous. We also discuss three new frontiers in integrative modeling: incorporating recent deep learning-based methods, integrative modeling with in situ data, and metamodeling.