Enhanced rewarding properties of morphine, but not cocaine, in beta(arrestin)-2 knock-out mice.

The reinforcing and psychomotor effects of morphine involve opiate stimulation of the dopaminergic system via activation of mu-opioid receptors (muOR). Both mu-opioid and dopamine receptors are members of the G-protein-coupled receptor (GPCR) family of proteins. GPCRs are known to undergo desensitization involving phosphorylation of the receptor and the subsequent binding of beta(arrestins), which prevents further receptor-G-protein coupling. Mice lacking beta(arrestin)-2 (beta(arr2)) display enhanced sensitivity to morphine in tests of pain perception attributable to impaired desensitization of muOR. However, whether abrogating muOR desensitization affects the reinforcing and psychomotor properties of morphine has remained unexplored. In the present study, we examined this question by assessing the effects of morphine and cocaine on locomotor activity, behavioral sensitization, conditioned place preference, and striatal dopamine release in beta(arr2) knock-out (beta(arr2)-KO) mice and their wild-type (WT) controls. Cocaine treatment resulted in very similar neurochemical and behavioral responses between the genotypes. However, in the beta(arr2)-KO mice, morphine induced more pronounced increases in striatal extracellular dopamine than in WT mice. Moreover, the rewarding properties of morphine in the conditioned place preference test were greater in the beta(arr2)-KO mice when compared with the WT mice. Thus, beta(arr2) appears to play a more important role in the dopaminergic effects mediated by morphine than those induced by cocaine.

Impaired formation of beta-adrenergic receptor-nucleotide regulatory protein complexes in pseudohypoparathyroidism.

Decreased activity of the guanine nucleotide regulatory protein (N) of the adenylate cyclase system is present in cell membranes of some patients with pseudohypoparathyrodism (PHP-Ia) whereas others have normal activity of N (PHP-Ib). Low N activity in PHP-Ia results in a decrease in hormone (H)-stimulatable adenylate cyclase in various tissues, which might be due to decreased ability to form an agonist-specific high affinity complex composed of H, receptor (R), and N. To test this hypothesis, we compared beta-adrenergic agonist-specific binding properties in erythrocyte membranes from five patients with PHP-Ia (N = 45% of control), five patients with PHP-Ib (N = 97%), and five control subjects. Competition curves that were generated by increasing concentrations of the beta-agonist isoproterenol competing with [125I]pindolol were shallow (slope factors less than 1) and were computer fit to a two-state model with corresponding high and low affinity for the agonist. The agonist competition curves from the PHP-Ia patients were shifted significantly (P less than 0.02) to the right as a result of a significant (P less than 0.01) decrease in the percent of beta-adrenergic receptors in the high affinity state from 64 +/- 22% in PHP-Ib and 56 +/- 5% in controls to 10 +/- 8% in PHP-Ia. The agonist competition curves were computer fit to a "ternary complex" model for the two-step reaction: H + R + N in equilibrium HR + N in equilibrium HRN. The modeling was consistent with a 60% decrease in the functional concentration of N, and was in good agreement with the biochemically determined decrease in erythrocyte N protein activity. These in vitro findings in erythrocytes taken together with the recent observations that in vivo isoproterenol-stimulated adenylate cyclase activity is decreased in patients with PHP (Carlson, H. E., and A. S. Brickman, 1983, J. Clin. Endocrinol. Metab. 56:1323-1326) are consistent with the notion that N is a bifunctional protein interacting with both R and the adenylate cyclase. It may be that in patients with PHP-Ia a single molecular and genetic defect accounts for both decreased HRN formation and decreased adenylate cyclase activity, whereas in PHP-Ib the biochemical lesion(s) appear not to affect HRN complex formation.

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

Development and Application of Covalent-Labeling Strategies for the Large-Scale Thermodynamic Analysis of Protein Folding and Ligand Binding

Thermodynamic stability measurements on proteins and protein-ligand complexes can offer insights not only into the fundamental properties of protein folding reactions and protein functions, but also into the development of protein-directed therapeutic agents to combat disease. Conventional calorimetric or spectroscopic approaches for measuring protein stability typically require large amounts of purified protein. This requirement has precluded their use in proteomic applications. Stability of Proteins from Rates of Oxidation (SPROX) is a recently developed mass spectrometry-based approach for proteome-wide thermodynamic stability analysis. Since the proteomic coverage of SPROX is fundamentally limited by the detection of methionine-containing peptides, the use of tryptophan-containing peptides was investigated in this dissertation. A new SPROX-like protocol was developed that measured protein folding free energies using the denaturant dependence of the rate at which globally protected tryptophan and methionine residues are modified with dimethyl (2-hydroxyl-5-nitrobenzyl) sulfonium bromide and hydrogen peroxide, respectively. This so-called Hybrid protocol was applied to proteins in yeast and MCF-7 cell lysates and achieved a ~50% increase in proteomic coverage compared to probing only methionine-containing peptides. Subsequently, the Hybrid protocol was successfully utilized to identify and quantify both known and novel protein-ligand interactions in cell lysates. The ligands under study included the well-known Hsp90 inhibitor geldanamycin and the less well-understood omeprazole sulfide that inhibits liver-stage malaria. In addition to protein-small molecule interactions, protein-protein interactions involving Puf6 were investigated using the SPROX technique in comparative thermodynamic analyses performed on wild-type and Puf6-deletion yeast strains. A total of 39 proteins were detected as Puf6 targets and 36 of these targets were previously unknown to interact with Puf6. Finally, to facilitate the SPROX/Hybrid data analysis process and minimize human errors, a Bayesian algorithm was developed for transition midpoint assignment. In summary, the work in this dissertation expanded the scope of SPROX and evaluated the use of SPROX/Hybrid protocols for characterizing protein-ligand interactions in complex biological mixtures.

Using Helix-coil Models to Study Protein Unfolded States

An abstract of a thesis devoted to using helix-coil models to study unfolded states.\\

Research on polypeptide unfolded states has received much more attention in the last decade or so than it has in the past. Unfolded states are thought to be implicated in various

misfolding diseases and likely play crucial roles in protein folding equilibria and folding rates. Structural characterization of unfolded states has proven to be

much more difficult than the now well established practice of determining the structures of folded proteins. This is largely because many core assumptions underlying

folded structure determination methods are invalid for unfolded states. This has led to a dearth of knowledge concerning the nature of unfolded state conformational

distributions. While many aspects of unfolded state structure are not well known, there does exist a significant body of work stretching back half a century that

has been focused on structural characterization of marginally stable polypeptide systems. This body of work represents an extensive collection of experimental

data and biophysical models associated with describing helix-coil equilibria in polypeptide systems. Much of the work on unfolded states in the last decade has not been devoted

specifically to the improvement of our understanding of helix-coil equilibria, which arguably is the most well characterized of the various conformational equilibria

that likely contribute to unfolded state conformational distributions. This thesis seeks to provide a deeper investigation of helix-coil equilibria using modern

statistical data analysis and biophysical modeling techniques. The studies contained within seek to provide deeper insights and new perspectives on what we presumably

know very well about protein unfolded states. \\

Chapter 1 gives an overview of recent and historical work on studying protein unfolded states. The study of helix-coil equilibria is placed in the context

of the general field of unfolded state research and the basics of helix-coil models are introduced.\\

Chapter 2 introduces the newest incarnation of a sophisticated helix-coil model. State of the art modern statistical techniques are employed to estimate the energies

of various physical interactions that serve to influence helix-coil equilibria. A new Bayesian model selection approach is utilized to test many long-standing

hypotheses concerning the physical nature of the helix-coil transition. Some assumptions made in previous models are shown to be invalid and the new model

exhibits greatly improved predictive performance relative to its predecessor. \\

Chapter 3 introduces a new statistical model that can be used to interpret amide exchange measurements. As amide exchange can serve as a probe for residue-specific

properties of helix-coil ensembles, the new model provides a novel and robust method to use these types of measurements to characterize helix-coil ensembles experimentally

and test the position-specific predictions of helix-coil models. The statistical model is shown to perform exceedingly better than the most commonly used

method for interpreting amide exchange data. The estimates of the model obtained from amide exchange measurements on an example helical peptide

also show a remarkable consistency with the predictions of the helix-coil model. \\

Chapter 4 involves a study of helix-coil ensembles through the enumeration of helix-coil configurations. Aside from providing new insights into helix-coil ensembles,

this chapter also introduces a new method by which helix-coil models can be extended to calculate new types of observables. Future work on this approach could potentially

allow helix-coil models to move into use domains that were previously inaccessible and reserved for other types of unfolded state models that were introduced in chapter 1.

Electrostatic Contributions to the Thermodynamics of Ribonuclease P Protein Folding

Electrostatic interactions are of fundamental importance in determining the structure and stability of macromolecules. For example, charge-charge interactions modulate the folding and binding of proteins and influence protein solubility. Electrostatic interactions are highly variable and can be both favorable and unfavorable. The ability to quantify these interactions is challenging but vital to understanding the detailed balance and major roles that they have in different proteins and biological processes. Measuring pKa values of ionizable groups provides a sensitive method for experimentally probing the electrostatic properties of a protein.

pKa values report the free energy of site-specific proton binding and provide a direct means of studying protein folding and pH-dependent stability. Using a combination of NMR, circular dichroism, and fluorescence spectroscopy along with singular value decomposition, we investigated the contributions of electrostatic interactions to the thermodynamic stability and folding of the protein subunit of Bacillus subtilis ribonuclease P, P protein. Taken together, the results suggest that unfavorable electrostatics alone do not account for the fact that P protein is intrinsically unfolded in the absence of ligand because the pKa differences observed between the folded and unfolded state are small. Presumably, multiple factors encoded in the P protein sequence account for its IUP property, which may play an important role in its function.

Calcium/Calmodulin-Dependent Protein Kinase II Serves as a Biochemical Integrator of Calcium Signals for the Induction of Synaptic Plasticity

Repetitive Ca2+ transients in dendritic spines induce various forms of synaptic plasticity by transmitting information encoded in their frequency and amplitude. CaMKII plays a critical role in decoding these Ca2+ signals to initiate long-lasting synaptic plasticity. However, the properties of CaMKII that mediate Ca2+ decoding in spines remain elusive. Here, I measured CaMKII activity in spines using fast-framing two-photon fluorescence lifetime imaging. Following each repetitive Ca2+ elevations, CaMKII activity increased in a stepwise manner. This signal integration, at the time scale of seconds, critically depended on Thr286 phosphorylation. In the absence of Thr286 phosphorylation, only by increasing the frequency of repetitive Ca2+ elevations could high peak CaMKII activity or plasticity be induced. In addition, I measured the association between CaMKII and Ca2+/CaM during spine plasticity induction. Unlike CaMKII activity, association of Ca2+/CaM to CaMKII plateaued at the first Ca2+ elevation event. This result indicated that integration of Ca2+ signals was initiated by the binding of Ca2+/CaM and amplified by the subsequent increases in Thr286-phosphorylated form of CaMKII. Together, these findings demonstrate that CaMKII functions as a leaky integrator of repetitive Ca2+ signals during the induction of synaptic plasticity, and that Thr286 phosphorylation is critical for defining the frequencies of such integration.