Proteins are dynamic molecules that mediate most biological processes through interactions with other proteins and biomolecules. A fundamental understanding of the mechanisms governing protein interactions requires intricate knowledge of the three-dimensional structures of biomolecular complexes. Despite advances in experimental structure determination, we have structural insights into only a small fraction of known complexes. Computational modeling provides an invaluable complementary tool to explore protein interactions in a rapid and high-throughput manner. A principal challenge limiting the accuracy of current computational methods is the ability to predict binding-induced conformational changes during protein–protein association. In this dissertation, I address this challenge by creating new tools to predict atomistic models of flexible protein complexes. First, I develop a heterodimer docking protocol that incorporates flexibility by efficiently simulating conformational selection from hundreds of pre-generated backbone conformations and identifies the near-native models with a novel, coarse-grained score function called Motif Dock Score (MDS). On a benchmark of 88 complexes with different degrees of flexibility, this protocol, RosettaDock 4.0, is the first method to successfully dock approximately 50% of complexes with conformational change of up to 2.2 Å. Next, I present the results of our participation in the community-wide blind experiment, Critical Assessment of PRedicted Interactions (CAPRI) rounds 37–45, where I use various docking methods to predict the structures of protein homomer, heteromer and oligosaccharide complexes. In the process, I identify inadequacies in these methods and propose enhancements. Based on the shortcomings identified in CAPRI, I develop a protocol to predict the structure of symmetric homomers from monomeric inputs with a focus on tightly-packed complexes. This method, Rosetta SymDock2, leverages MDS in the coarse-grained phase and simulates subunit flexibility through induced fit by all-atom flexible-backbone refinement. It outperforms competing algorithms by docking 61% of cyclic complexes and 42% of dihedral complexes in a diverse benchmark of 43 homomers. In the course of developing these algorithms, I also discover that the binding energy wells of homomers are narrower, steeper and deeper than those of heterodimers, thus explaining their increased stability. Finally, I present preliminary results to propose data-driven strategies that can overcome current barriers to accurate modeling
