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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 employ 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 non-monotonic, populating states that were not simply interpolations between the inactive and active structures. These transitions also suggest a functional explanation for β2AR’s basal activity: it can proceed through a more broadly defined path during the observed transitions.

The global fold of human cannabinoid type 2 (CB2 ) receptor in the agonist-bound active state in lipid bilayers was investigated by solid-state (13)C- and (15)N magic-angle spinning (MAS) NMR, in combination with chemical-shift prediction from a structural model of the receptor obtained by microsecond-long molecular dynamics (MD) simulations. Uniformly (13)C- and (15)N-labeled CB2 receptor was expressed in milligram quantities by bacterial fermentation, purified, and functionally reconstituted into liposomes. (13)C MAS NMR spectra were recorded without sensitivity enhancement for direct comparison of Cα, Cβ, and C=O bands of superimposed resonances with predictions from protein structures generated by MD. The experimental NMR spectra matched the calculated spectra reasonably well indicating agreement of the global fold of the protein between experiment and simulations. In particular, the (13) C chemical shift distribution of Cα resonances was shown to be very sensitive to both the primary amino acid sequence and the secondary structure of CB2. Thus the shape of the Cα band can be used as an indicator of CB2 global fold. The prediction from MD simulations indicated that upon receptor activation a rather limited number of amino acid residues, mainly located in the extracellular Loop 2 and the second half of intracellular Loop 3, change their chemical shifts significantly (≥ 1.5 ppm for carbons and ≥ 5.0 ppm for nitrogens). Simulated two-dimensional (13) Cα(i)-(13)C=O(i) and (13)C=O(i)-(15)NH(i + 1) dipolar-interaction correlation spectra provide guidance for selective amino acid labeling and signal assignment schemes to study the molecular mechanism of activation of CB2 by solid-state MAS NMR.

HIV-1 reverse transcriptase 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 reverse transcriptase, structural information has proven to be insufficient – despite enormous effort – to explain the mechanism of inhibition and drug resistance of non-nucleoside reverse transcriptase 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 NMR and molecular dynamics simulations, cannot be easily applied to a protein as large as reverse transcriptase. 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 reverse transcriptase bound to a variety of ligands and with a number of mutations, revealing a novel mechanism for drug resistance to non-nucleoside reverse transcriptase 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.

Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all-atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis-testing situations. Historically, ENMs have been validated via comparison to crystallographic B-factors, but this comparison is relatively low-resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM-type models by quantitatively comparing their predictions to microsecond-scale all-atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well-optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all-atom simulations run for several hundred nanoseconds. Proteins 2010. © 2010 Wiley-Liss, Inc.