Seeing the Unseen: Incorporating Dynamics for the Understanding and Prediction of Macromolecular Properties
The passing of time is a constant. In the same way that time shapes our everyday lives, it also plays an essential role in the microscopic universe through its translation into macromolecular dynamics. Proteins, in particular, visit a variety of short-lived - or metastable - states, and the conformations and rate of transitions between these states dictate their function in the cell. However, many of these alternative states are not accessible to established biophysical techniques, such that many puzzling results and challenges arise when trying to use these static observations to understand and predict their properties. In this dissertation, we emphasize the importance of accessing and characterizing the intrinsic dynamics of these macromolecules through the application of computational techniques in four distinct projects that allow us to observe molecular motion at the atomic level to widen our understanding of their function, and furthermore enable us to make predictions on properties that would be inaccessible from static models. In conjunction with experimental validation, we study the role of electrostatic interactions in mediating the allosteric activation of Protein Kinase A, an ubiquitous enzyme involved in key signal transduction pathways, as well as develop a methodology that incorporates dynamic descriptors to improve the efficiency of the challenging process of novel protein design. Additionally, through the application of the exciting Markov State Model methodology to access longer timescales than typically available to computational simulations, we help set the stage for the interpretation of a novice experimental technique, diffuse scattering, in terms of atomic motions in crystalline environments, and explore the conformational landscape of the essential tumor suppressor p53 and its cancer-related mutant Y220C, which leads to the identification of a novel cryptic pocket. These test cases present significant advances to our understanding of protein dynamics in fields as varied as protein design and cancer therapeutics, and taken together, stress the necessity of putting time, and dynamics, in the center stage of protein study.