(15e) Capturing Binding-Induced Conformational Changes in Protein Engineering Calculations

Protein-protein interactions are often targets of protein engineering, and binding is often associated with conformational changes.  Such changes can affect affinities and specificities, and the mechanisms of allosteric proteins or molecular switches are often based on binding-associated conformational changes.  Despite this importance, it is still very difficult to capture protein backbone conformational changes in protein-protein docking and design calculations.  In this study, we use a set of known bound and unbound protein structures to test methods to computationally capture protein backbone conformational change related to binding.  We address the following questions: (1) How well can we sample protein backbone conformations using existing computational models? (2) How much closer must unbound proteins need to be to the bound-state proteins for successful high-resolution protein-protein docking? (3) How do induced-fit and conformational selection mechanisms balance in protein-protein interactions? We test various Rosetta-based sampling techniques and coarse-grained normal mode analysis. Based on principal component analysis of computational protein ensembles, we find that protein motions made by each method show better correlations with interface regions than entire protein folds. Although the magnitude of motions in each protein ensemble are smaller in computational models, the direction of motions overlap with the actual conformational change between unbound and bound-state proteins, suggesting that the principal components can be used to extend motions to biologically relevant magnitudes. We demonstrate how the essential dynamics extracted from each computational model can be used in protein-protein docking simulations. Finally, we present backbone and docking energy landscapes that delineate the scope of induced-fit and conformational selections molecular recognition mechanisms, suggesting strategies for improving protein engineering approaches.