Use of paramagnetic 19F NMR to monitor domain movement in a glutamate transporter homolog.

In proteins where conformational changes are functionally important, the number of accessible states and their dynamics are often difficult to establish. Here we describe a novel 19F-NMR spectroscopy approach to probe dynamics of large membrane proteins. We labeled a glutamate transporter homolog with a 19F probe via cysteine chemistry and with a Ni2+ ion via chelation by a di-histidine motif. We used distance-dependent enhancement of the longitudinal relaxation of 19F nuclei by the paramagnetic metal to assign the observed resonances. We identified one inward- and two outward-facing states of the transporter, in which the substrate-binding site is near the extracellular and intracellular solutions, respectively. We then resolved the structure of the unanticipated second outward-facing state by cryo-EM. Finally, we showed that the rates of the conformational exchange are accessible from measurements of the metal-enhanced longitudinal relaxation of 19F nuclei.

How structural elements evolving from bacterial to human SLC6 transporters enabled new functional properties.

BACKGROUND: Much of the structure-based mechanistic understandings of the function of SLC6A neurotransmitter transporters emerged from the study of their bacterial LeuT-fold homologs. It has become evident, however, that structural differences such as the long N- and C-termini of the eukaryotic neurotransmitter transporters are involved in an expanded set of functional properties to the eukaryotic transporters. These functional properties are not shared by the bacterial homologs, which lack the structural elements that appeared later in evolution. However, mechanistic insights into some of the measured functional properties of the eukaryotic transporters that have been suggested to involve these structural elements are sparse or merely descriptive. RESULTS: To learn how the structural elements added in evolution enable mechanisms of the eukaryotic transporters in ways not shared with their bacterial LeuT-like homologs, we focused on the human dopamine transporter (hDAT) as a prototype. We present the results of a study employing large-scale molecular dynamics simulations and comparative Markov state model analysis of experimentally determined properties of the wild-type and mutant hDAT constructs. These offer a quantitative outline of mechanisms in which a rich spectrum of interactions of the hDAT N-terminus and C-terminus contribute to the regulation of transporter function (e.g., by phosphorylation) and/or to entirely new phenotypes (e.g., reverse uptake (efflux)) that were added in evolution. CONCLUSIONS: The findings are consistent with the proposal that the size of eukaryotic neurotransmitter transporter termini increased during evolution to enable more functions (e.g., efflux) not shared with the bacterial homologs. The mechanistic explanations for the experimental findings about the modulation of function in DAT, the serotonin transporter, and other eukaryotic transporters reveal separate roles for the distal and proximal segments of the much larger N-terminus in eukaryotic transporters compared to the bacterial ones. The involvement of the proximal and distal segments - such as the role of the proximal segment in sustaining transport in phosphatidylinositol 4,5-bisphosphate-depleted membranes and of the distal segment in modulating efflux - may represent an evolutionary adaptation required for the function of eukaryotic transporters expressed in various cell types of the same organism that differ in the lipid composition and protein complement of their membrane environment.

Thermodynamic Coupling Function Analysis of Allosteric Mechanisms in the Human Dopamine Transporter.

Allostery plays a crucial role in the mechanism of neurotransmitter-sodium symporters, such as the human dopamine transporter. To investigate the molecular mechanism that couples the transport-associated inward release of the Na+ ion from the Na2 site to intracellular gating, we applied a combination of the thermodynamic coupling function (TCF) formalism and Markov state model analysis to a 50-µs data set of molecular dynamics trajectories of the human dopamine transporter, in which multiple spontaneous Na+ release events were observed. Our TCF approach reveals a complex landscape of thermodynamic coupling between Na+ release and inward-opening, and identifies diverse, yet well-defined roles for different Na+-coordinating residues. In particular, we identify a prominent role in the allosteric coupling for the Na+-coordinating residue D421, where mutation has previously been associated with neurological disorders. Our results highlight the power of the TCF analysis to elucidate the molecular mechanism of complex allosteric processes in large biomolecular systems.

Molecular simulations and free-energy calculations suggest conformation-dependent anion binding to a cytoplasmic site as a mechanism for Na+/K+-ATPase ion selectivity.

Na+/K+-ATPase transports Na+ and K+ ions across the cell membrane via an ion-binding site becoming alternatively accessible to the intra- and extracellular milieu by conformational transitions that confer marked changes in ion-binding stoichiometry and selectivity. To probe the mechanism of these changes, we used molecular simulation and free-energy perturbation approaches to identify probable protonation states of Na+- and K+-coordinating residues in E1P and E2P conformations of Na+/K+-ATPase. Analysis of these simulations revealed a molecular mechanism responsible for the change in protonation state: the conformation-dependent binding of an anion (a chloride ion in our simulations) to a previously unrecognized cytoplasmic site in the loop between transmembrane helices 8 and 9, which influences the electrostatic potential of the crucial Na+-coordinating residue Asp926 This mechanistic model is consistent with experimental observations and provides a molecular-level picture of how E1P to E2P enzyme conformational transitions are coupled to changes in ion-binding stoichiometry and selectivity.

Computational screening and selection of cyclic peptide hairpin mimetics by molecular simulation and kinetic network models.

Designing peptidomimetic compounds to have a preorganized structure in solution is highly nontrivial. To show how simulation-based approaches can help speed this process, we performed an extensive simulation study of designed cyclic peptide mimics of a ß-hairpin from bacterial protein LapD involved in a protein-protein interaction (PPI) pertinent to bacterial biofilm formation. We used replica exchange molecular dynamics (REMD) simulation to screen 20 covalently cross-linked designs with varying stereochemistry and selected the most favorable of these for massively parallel simulation on Folding@home in explicit solvent. Markov state models (MSMs) built from the trajectory data reveal how subtle chemical modifications can have a significant effect on conformational populations, leading to the overall stabilization of the target structure. In particular, we identify a key steric interaction between a methyl substituent and a valine side chain that acts to allosterically shift population between native and near-native states, which could be exploited in future designs. Visualization of this mechanism is aided considerably by the tICA method, which identifies degrees of freedom most important in slow conformational transitions. The combination of quantitative detail and human comprehension provided by MSMs suggests such approaches will be increasingly useful for design.

Cholesterol occupies the lipid translocation pathway to block phospholipid scrambling by a G protein-coupled receptor.

Class A (rhodopsin-like) G protein-coupled receptors (GPCRs) are constitutive phospholipid scramblases as evinced after their reconstitution into liposomes. Yet phospholipid scrambling is not detectable in the resting plasma membrane of mammalian cells that is replete with GPCRs. We considered whether cholesterol, a prominent component of the plasma membrane, limits the ability of GPCRs to scramble lipids. Our previous Markov State Model (MSM) analysis of molecular dynamics simulations of membrane-embedded opsin indicated that phospholipid headgroups traverse a dynamically revealed hydrophilic groove between transmembrane helices (TM) 6 and 7 while their tails remain in the bilayer. Here, we present comparative MSM analyses of 150-µs simulations of opsin in cholesterol-free and cholesterol-rich membranes. Our analyses reveal that cholesterol inhibits phospholipid scrambling by occupying the TM6/7 interface and stabilizing the closed groove conformation while itself undergoing flip-flop. This mechanism may explain the inability of GPCRs to scramble lipids at the plasma membrane.

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry.

Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity of the ternary complex using Hamiltonian replica exchange simulations and small-angle X-ray scattering. (3) We assess the ubiquitination of the POI in the context of the full Cullin-RING Ligase, confirming experimental ubiquitinomics results. Differences in degradation efficiency can be explained by the proximity of lysine residues on the POI relative to ubiquitin.

A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.

In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.

Mechanisms of Lipid Scrambling by the G Protein-Coupled Receptor Opsin.

Several class-A G protein-coupled receptor (GPCR) proteins act as constitutive phospholipid scramblases catalyzing the transbilayer translocation of >10,000 phospholipids per second when reconstituted into synthetic vesicles. To address the molecular mechanism by which these proteins facilitate rapid lipid scrambling, we carried out large-scale ensemble atomistic molecular dynamics simulations of the opsin GPCR. We report that, in the process of scrambling, lipid head groups traverse a dynamically revealed hydrophilic pathway in the region between transmembrane helices 6 and 7 of the protein while their hydrophobic tails remain in the bilayer environment. We present quantitative kinetic models of the translocation process based on Markov State Model analysis. As key residues on the lipid translocation pathway are conserved within the class-A GPCR family, our results illuminate unique aspects of GPCR structure and dynamics while providing a rigorous basis for the design of variants of these proteins with defined scramblase activity.

A Markov State-based Quantitative Kinetic Model of Sodium Release from the Dopamine Transporter.

The dopamine transporter (DAT) belongs to the neurotransmitter:sodium symporter (NSS) family of membrane proteins that are responsible for reuptake of neurotransmitters from the synaptic cleft to terminate a neuronal signal and enable subsequent neurotransmitter release from the presynaptic neuron. The release of one sodium ion from the crystallographically determined sodium binding site Na2 had been identified as an initial step in the transport cycle which prepares the transporter for substrate translocation by stabilizing an inward-open conformation. We have constructed Markov State Models (MSMs) from extensive molecular dynamics simulations of human DAT (hDAT) to explore the mechanism of this sodium release. Our results quantify the release process triggered by hydration of the Na2 site that occurs concomitantly with a conformational transition from an outward-facing to an inward-facing state of the transporter. The kinetics of the release process are computed from the MSM, and transition path theory is used to identify the most probable sodium release pathways. An intermediate state is discovered on the sodium release pathway, and the results reveal the importance of various modes of interaction of the N-terminus of hDAT in controlling the pathways of release.

Kinetic network models of tryptophan mutations in ß-hairpins reveal the importance of non-native interactions.

We present an analysis of the most extensive explicit-solvent simulations of ß-hairpins to date (9.4 ms in aggregate), with the aim of probing the effects of tryptophan mutations on folding. From molecular simulations of GB1 hairpin, trpzip4, trpzip5, and trpzip6 performed on Folding@home, Markov State Models (MSMs) were constructed using a unified set of metastable states, enabling objective comparison of folding mechanisms. MSM models display quantitative agreement with experimental structural observables and folding kinetics, and predict multimodal kinetics due to specific non-native kinetic traps, which be identified as on- or off-pathway from the network topology. We quantify kinetic frustration by several means, including the s-ensemble method to evaluate glasslike behavior. Free-energy profiles and transition state movement clearly show stabilization of non-native states as Trp mutations are introduced. Remarkably, we find that "ß-capped" sequences (trpzip4 and trpzip5) are able to overcome this frustration and remain cooperative two-state folders with a large time-scale gap. These results suggest that, while ß-capping motifs are robust, fold stabilization by tryptophan generally may require overcoming significant non-native kinetic traps, perhaps explaining their under-representation in natural proteins.

Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models.

Markov state models (MSMs), which model conformational dynamics as a network of transitions between metastable states, have been increasingly used to model the thermodynamics and kinetics of biomolecules. In considering perturbations to molecular dynamics induced by sequence mutations, chemical modifications, or changes in external conditions, it is important to assess how transition rates change, independent of changes in metastable state definitions. Here, we present a surprisal metric to quantify the difference in metastable state transitions for two closely related MSMs, taking into account the statistical uncertainty in observed transition counts. We show that the surprisal is a relative entropy metric closely related to the Jensen-Shannon divergence between two MSMs, which can be used to identify conformational states most affected by perturbations. As examples, we apply the surprisal metric to a two-dimensional lattice model of a protein hairpin with mutations to hydrophobic residues, all-atom simulations of the Fs peptide α-helix with a salt-bridge mutation, and a comparison of protein G ß-hairpin with its trpzip4 variant. Moreover, we show that surprisal-based adaptive sampling is an efficient strategy to reduce the statistical uncertainty in the Jensen-Shannon divergence, which could be a useful strategy for molecular simulation-based ab initio design.