Nicholas Leioatts

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 (2)H 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.

Structural basis of G protein-coupled receptor-Gi protein interaction: formation of the cannabinoid CB2 receptor-Gi protein complex.

Background: CB2 couples with only Gi protein. Results: Cross-linking studies using LC-MS/MS and ESI-MS/MS identified three specific CB2-Gαi cross-link sites. MD showed an orientation change from the β2-AR*/Gs geometry makes all cross-links possible. Conclusion: Second intracellular loop of CB2 interactions are key for Gi complex formation. Significance: Findings should be relevant for other GPCRs that couple to Gi proteins. In this study, we applied a comprehensive G protein-coupled receptor-Gαi protein chemical cross-linking strategy to map the cannabinoid receptor subtype 2 (CB2)- Gαi interface and then used molecular dynamics simulations to explore the dynamics of complex formation. Three cross-link sites were identified using LC-MS/MS and electrospray ionization-MS/MS as follows: 1) a sulfhydryl cross-link between C3.53(134) in TMH3 and the Gαi C-terminal i-3 residue Cys-351; 2) a lysine cross-link between K6.35(245) in TMH6 and the Gαi C-terminal i-5 residue, Lys-349; and 3) a lysine cross-link between K5.64(215) in TMH5 and the Gαi α4β6 loop residue, Lys-317. To investigate the dynamics and nature of the conformational changes involved in CB2·Gi complex formation, we carried out microsecond-time scale molecular dynamics simulations of the CB2 R*·Gαi1β1γ2 complex embedded in a 1-palmitoyl-2-oleoyl-phosphatidylcholine bilayer, using cross-linking information as validation. Our results show that although molecular dynamics simulations started with the G protein orientation in the β2-AR*·Gαsβ1γ2 complex crystal structure, the Gαi1β1γ2 protein reoriented itself within 300 ns. Two major changes occurred as follows. 1) The Gαi1 α5 helix tilt changed due to the outward movement of TMH5 in CB2 R*. 2) A 25° clockwise rotation of Gαi1β1γ2 underneath CB2 R* occurred, with rotation ceasing when Pro-139 (IC-2 loop) anchors in a hydrophobic pocket on Gαi1 (Val-34, Leu-194, Phe-196, Phe-336, Thr-340, Ile-343, and Ile-344). In this complex, all three experimentally identified cross-links can occur. These findings should be relevant for other class A G protein-coupled receptors that couple to Gi proteins.

Lightweight Object Oriented Structure Analysis: Tools for Building Tools to Analyze Molecular Dynamics Simulations

LOOS (Lightweight Object Oriented Structure‐analysis) is a C++ library designed to facilitate making novel tools for analyzing molecular dynamics simulations by abstracting out the repetitive tasks, allowing developers to focus on the scientifically relevant part of the problem. LOOS supports input using the native file formats of most common biomolecular simulation packages, including CHARMM, NAMD, Amber, Tinker, and Gromacs. A dynamic atom selection language based on the C expression syntax is included and is easily accessible to the tool‐writer. In addition, LOOS is bundled with over 140 prebuilt tools, including suites of tools for analyzing simulation convergence, three‐dimensional histograms, and elastic network models. Through modern C++ design, LOOS is both simple to develop with (requiring knowledge of only four core classes and a few utility functions) and is easily extensible. A python interface to the core classes is also provided, further facilitating tool development.

The interplay of structure and dynamics: Insights from a survey of HIV‐1 reverse transcriptase crystal structures

HIV‐1 reverse transcriptase (RT) is a critical drug target for HIV treatment, and understanding the exact mechanisms of its function and inhibition would significantly accelerate the development of new anti‐HIV drugs. It is well known that structure plays a critical role in protein function, but for RT, structural information has proven to be insufficient—despite enormous effort—to explain the mechanism of inhibition and drug resistance of non‐nucleoside RT inhibitors. We hypothesize that the missing link is dynamics, information about the motions of the system. However, many of the techniques that give the best information about dynamics, such as solution nuclear magnetic resonance and molecular dynamics simulations, cannot be easily applied to a protein as large as RT. As an alternative, we combine elastic network modeling with simultaneous hierarchical clustering of structural and dynamic data. We present an extensive survey of the dynamics of RT bound to a variety of ligands and with a number of mutations, revealing a novel mechanism for drug resistance to non‐nucleoside RT inhibitors. Hydrophobic core mutations restore active‐state motion to multiple functionally significant regions of HIV‐1 RT. This model arises out of a combination of structural and dynamic information, rather than exclusively from one or the other. Proteins 2013; 81:1792–1801.

Retinal Conformation Changes Rhodopsin’s Dynamic Ensemble

G protein-coupled receptors are vital membrane proteins that allosterically transduce biomolecular signals across the cell membrane. However, the process by which ligand binding induces protein conformation changes is not well understood biophysically. Rhodopsin, the mammalian dim-light receptor, is a unique test case for understanding these processes because of its switch-like activity; the ligand, retinal, is bound throughout the activation cycle, switching from inverse agonist to agonist after absorbing a photon. By contrast, the ligand-free opsin is outside the activation cycle and may behave differently. We find that retinal influences rhodopsin dynamics using an ensemble of all-atom molecular dynamics simulations that in aggregate contain 100 μs of sampling. Active retinal destabilizes the inactive state of the receptor, whereas the active ensemble was more structurally homogenous. By contrast, simulations of an active-like receptor without retinal present were much more heterogeneous than those containing retinal. These results suggest allosteric processes are more complicated than a ligand inducing protein conformational changes or simply capturing a shifted ensemble as outlined in classic models of allostery.

Structure-Based Simulations Reveal Concerted Dynamics of GPCR Activation

G protein‐coupled receptors (GPCRs) are a vital class of proteins that transduce biological signals across the cell membrane. However, their allosteric activation mechanism is not fully understood; crystal structures of active and inactive receptors have been reported, but the functional pathway between these two states remains elusive. Here, we use structure‐based (Gō‐like) models to simulate activation of two GPCRs, rhodopsin and the β2 adrenergic receptor (β2AR). We used data‐derived reaction coordinates that capture the activation mechanism for both proteins, showing that activation proceeds through quantitatively different paths in the two systems. Both reaction coordinates are determined from the dominant concerted motions in the simulations so the technique is broadly applicable. There were two surprising results. First, the main structural changes in the simulations were distributed throughout the transmembrane bundle, and not localized to the obvious areas of interest, such as the intracellular portion of Helix 6. Second, the activation (and deactivation) paths were distinctly nonmonotonic, populating states that were not simply interpolations between the inactive and active structures. These transitions also suggest a functional explanation for β2ARs basal activity: it can proceed through a more broadly defined path during the observed transitions. Proteins 2014; 82:2538–2551.

Elucidating Elastic Network Model Robustness by Parametrization with Molecular Dynamics

Recently, there has been a surge in elastic network model (ENM) parametrizations using molecular dynamics (MD) simulations. These simple, coarse-grained models represent proteins as beads connected by harmonic springs. The motions of this system are then elucidated by normal mode analysis. The goal of these recent parametrizations is to use MD to optimize predicted motions. In this study, we optimize many ENM functional forms using a uniform dataset containing only long MD simulations. Our results show that, across all models tested, residues neighboring in sequence adopt spring constants 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 to parametrization by tessellating the parameter space. Taken together our data reveals that choice of spring function and parameters are not vital to performance of a network model and that simple parameters can by derived “by hand” when no data is available for fitting, thus illustrating the robustness of the models.