Representing protein native states using weighted conformation ensembles

The important structural and functional roles played by proteins in the proper functioning of cellular processes cannot be overstated. To comprehensively understand their functional behaviors, structural models derived from experimental data have been developed and these models have played a significant role in explaining the functional mechanisms of proteins. The paradigm “structure drives function” had been active for many years until recent evidence suggested that the complex functions of proteins could not be fully explained by a single structure and dynamics played a very important role in deciphering their functions. To incorporate dynamics into structural representations, ensembles of conformations, instead of a single structure, are used frequently in recent literature and are found to be successful in explaining the functions of many proteins. The work described in this thesis focuses on methods used to construct such ensemble representations of proteins. A careful investigation of the issues and challenges in obtaining such ensembles is undertaken. In the first part of the thesis, we focus on representing the native state of a given protein using a weighted ensemble representation, where relative populations (or Boltzmann weights) are assigned for individual members of the ensemble. This representation has the advantage of representing the dynamics using only a few conformational states, thereby minimizing the potential of over-fitting, while capturing the dynamics of the protein that a single average structure misses. Using Ubiquitin as an example, we show that determination of such a weighted ensemble representation is feasible when using RDCs as constraints. Moreover, the conformational states of the weighted ensemble are biologically relevant to the functional behaviors of the protein.