Allostery and Activation in the G Protein-Coupled Receptor, Rhodopsin

Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biochemistry and Biophysics, 2014.G protein-coupled receptors (GPCRs) are a biomedically important class of membrane proteins that are targeted by many small molecule drugs. These proteins act as molecular transducers, allosterically passing signals across the cell membrane. Although this allosteric modulation of signal is vital to their pertinence as drug targets, the details of the signal transduction mechanism are not well-understood. These proteins are highly dynamic, sampling an ensemble of conformational states. Here, we leverage multiple simulation paradigms to better understand the dynamics of GPCRs, with a particular focus on the mammalian dimlight receptor, rhodopsin. Using all-atom simulations, we find that the ligand makes a concerted transition early in the activation process. Coupling our simulations with solid-state NMR datasets (provided by a collaborator) we are able to distinguish between two long-standing hypotheses concerning how ligand transitions are accomplished. We used a separate ensemble of simulations to analyze how that ligand motion impacts activation, finding that the protein is more dynamic in its apo-form and that the presence of ligand can destabilize certain conformational ensembles. We also used simpler models to study activation, a process that is too slow to be observed with all-atom simulations. We first employed an Elastic Network Model. Our work here shows that even when long-timescale dynamics are used to reparametrize such models, they still suffer from having only one energy basin, and are not suitable for predicting the dynamics of conformational change from one minimum energy structure to another. By contrast, using a structure-based model, we are able to efficiently sample activation dynamics. Using quantitative, dataderived methods we identify a network of interacting residues spread throughout the protein and show that activation of rhodopsin proceeds through a path that is distinct from another model GPCR, the 2 adrenergic receptor. By combining these three simulation techniques and experimental NMR, we construct a quantitative picture of the dynamics involved in the allosteric activation of rhodopsin

Elastic Network Models Are Robust to Variations in Formalism

Understanding the functions of biomolecules requires insight not only from structures but from dynamics as well. Often, the most interesting processes occur on time scales too slow for exploration by conventional molecular dynamics (MD) simulations. For this reason, alternative computational methods such as elastic network models (ENMs) have become increasingly popular. These simple, coarse-grained models represent molecules as beads connected by harmonic springs; the system’s motions are solved analytically by normal-mode analysis. In the past few years, many different formalisms for performing ENM calculations have emerged, and several have been optimized using all-atom MD simulations. In contrast to other studies, we have compared the various formalisms in a systematic, quantitative way. In this study, we optimize many ENM functional forms using a uniform data set containing only long (>1 μs) all-atom MD simulations. Our results show that all models once optimized produce spring constants for immediate neighboring residues that are orders of magnitude stiffer than more distal contacts. In addition, the statistical significance of ENM performance varied with model resolution. We also show that fitting long trajectories does not improve ENM performance due to a problem inherent in all network models tested: they underestimate the relative importance of the most concerted motions. Finally, we characterize ENMs’ resilience by tessellating the parameter space to show that broad ranges of parameters produce similar quality predictions. Taken together, our data reveal that the choice of spring function and parameters are not vital to the performance of a network model and that simple parameters can by derived “by hand” when no data are available for fitting, thus illustrating the robustness of these models

Retinal Ligand Mobility Explains Internal Hydration and Reconciles Active Rhodopsin Structures

Rhodopsin, the mammalian dim-light receptor, is one of the best-characterized G-protein-coupled receptors, a pharmaceutically important class of membrane proteins that has garnered a great deal of attention because of the recent availability of structural information. Yet the mechanism of rhodopsin activation is not fully understood. Here, we use microsecond-scale all-atom molecular dynamics simulations, validated by solid-state ²H nuclear magnetic resonance spectroscopy, to understand the transition between the dark and metarhodopsin I (Meta I) states. Our analysis of these simulations reveals striking differences in ligand flexibility between the two states. Retinal is much more dynamic in Meta I, adopting an elongated conformation similar to that seen in the recent activelike crystal structures. Surprisingly, this elongation corresponds to both a dramatic influx of bulk water into the hydrophobic core of the protein and a concerted transition in the highly conserved Trp265^6.48 residue. In addition, enhanced ligand flexibility upon light activation provides an explanation for the different retinal orientations observed in X-ray crystal structures of active rhodopsin