Plasma membrane calcium pumps in smooth muscle: from fictional molecules to novel inhibitors

Plasma membrane Ca2+ pumps (PMCA pumps) are Ca2+-Mg2+ ATPases that expel Ca2+ from the cytosol to extracellular space and are pivotal to cell survival and function. PMCA pumps are encoded by the genes PMCA1, -2, -3, and -4. Alternative splicing results in a large number of isoforms that differ in their kinetics and activation by calmodulin and protein kinases A and C. Expression by 4 genes and a multifactorial regulation provide redundancy to allow for animal survival despite genetic defects. Heterozygous mice with ablation of any of the PMCA genes survive and only the homozygous mice with PMCA1 ablation are embryolethal. Some PMCA isoforms may also be involved in other cell functions. Biochemical and biophysical studies of PMCA pumps have been limited by their low levels of expression. Delineation of the exact physiological roles of PMCA pumps has been difficult since most cells also express sarco/endoplasmic reticulum Ca2+ pumps and a Na+-Ca2+-exchanger, both of which can lower cytosolic Ca2+. A major limitation in the field has been the lack of specific inhibitors of PMCA pumps. More recently, a class of inhibitors named caloxins have emerged, and these may aid in delineating the roles of PMCA pumps.     

Modulation of human 5-lipoxygenase activity by membrane lipids

Mammalian 5-lipoxygenase (5-LO) catalyzes the conversion of arachidonic acid (AA) to leukotrienes, potent inflammatory mediators. 5-LO is activated by a Ca(2+)-mediated translocation to membranes, and demonstrates the characteristic features of interfacially activated enzymes, yet the mechanism of membrane binding of 5-LO is not well understood. In an attempt to understand the mechanism of lipid-mediated activation of 5-LO, we have studied the effects of a large set of lipids on human recombinant 5-LO activity, as well as mutual structural effects of 5-LO and membranes. In the presence of 0.35 mM phosphatidylcholine (PC) and 0.2 mM Ca(2+), there was substrate inhibition at >100 microM AA. Data analysis at low AA concentrations yielded the following: K(m) approximately 103 microM and k(cat) approximately 56 s(-1). 5-LO activity was supported by PC more than by any other lipid tested except for a cationic lipid, which was more stimulatory than PC. Binding of 5-LO to zwitterionic and acidic membranes was relatively weak; the extent of binding increased 4-8 times in the presence of Ca(2+), whereas binding to cationic membranes was stronger and essentially Ca(2+)-independent. Polarized attenuated total reflection infrared experiments implied that 5-LO binds to membranes at a defined orientation with the symmetry axis of the putative N-terminal beta-barrel tilted approximately 45 degrees from the membrane normal. Furthermore, membrane binding of 5-LO resulted in dehydration of the membrane surface and was paralleled with stabilization of the structures of both 5-LO and the membrane. Our results provide insight into the understanding of the effects of membrane surface properties on 5-LO-membrane interactions and the interfacial activation of 5-LO.     

Does native state topology determine the RNA folding mechanism?

Recent studies in protein folding suggest that native state topology plays a dominant role in determining the folding mechanism, yet an analogous statement has not been made for RNA, most likely due to the strong coupling between the ionic environment and conformational energetics that make RNA folding more complex than protein folding. Applying a distributed computing architecture to sample nearly 5000 complete tRNA folding events using a minimalist, atomistic model, we have characterized the role of native topology in tRNA folding dynamics: the simulated bulk folding behavior predicts well the experimentally observed folding mechanism. In contrast, single-molecule folding events display multiple discrete folding transitions and compose a largely diverse, heterogeneous dynamic ensemble. This both supports an emerging view of heterogeneous folding dynamics at the microscopic level and highlights the need for single-molecule experiments and both single-molecule and bulk simulations in interpreting bulk experimental measurements.     

First LC-MS/MS electrospray ionization validated method for the quantification of perindopril and its metabolite perindoprilat in human plasma and its application to bioequivalence study

A high throughput bioanalytical method based on solid phase extraction and liquid chromatography-tandem mass spectrometry (LC-MS/MS), has been developed for the estimation of perindopril and its metabolite perindoprilat, an angiotensin-converting enzyme inhibitor in human plasma. Ramipril was used as internal standard (IS). The extraction of perindopril, perindoprilat and ramipril from the plasma involved treatment with phosphoric acid followed by solid phase extraction (SPE) using hydrophilic lipophilic balance HLB cartridge. The SPE eluate without drying were analyzed by LC-MS/MS, equipped with turbo ion spray (TIS) source, operating in the negative ion and selective reaction monitoring (SRM) acquisition mode to quantify perindopril and perindoprilat in human plasma. The total chromatographic run time was 1.5 min with retention time for perindopril, perindoprilat and ramipril at 0.33, 0.35 and 0.30 min. The developed method was validated in human plasma matrix, with a sensitivity of 0.5 ng/ml (CV, 7.67%) for perindopril and 0.3 ng/ml (CV, 4.94%) for perindoprilat. This method was extensively validated for its accuracy, precision, recovery, stability studies and matrix effect especially because the pattern of elution of all the analytes appears as flow injection elution. Sample preparation by this method yielded extremely clean extracts with very good and consistent mean recoveries; 78.29% for perindopril, 76.32% for perindoprilat and 77.72% for IS. The response of the LC-MS/MS method for perindopril and perindoprilat was linear over the range 0.5-350.0 ng/ml for perindopril and 0.3-40 ng/ml for perindoprilat with correlation coefficient, r>/=0.9998 and 0.9996, respectively. The method was successfully applied for bioequivalence studies in human subjects samples with 4 mg immediate release (IR) formulations.     

Isoform-specific membrane insertion of secretory phospholipase A2 and functional implications

Despite increasing evidence that the membrane-binding mode of interfacial enzymes including the depth of membrane insertion is crucial for their function, the membrane insertion of phospholipase A(2) (PLA(2)) enzymes has not been studied systematically. Here, we analyze the membrane insertion of human group IB PLA(2) (hIBPLA(2)) and compare it with that of a structurally homologous V3W mutant of human group IIA PLA(2) (V3W-hIIAPLA(2)) and with a structurally divergent group III bee venom PLA(2) (bvPLA(2)). Increasing the anionic charge of membranes results in a blue shift of the fluorescence of Trp(3) of hIBPLA(2), a decrease in quenching by acrylamide, and an increase in enzyme activity, reflecting an enhancement in the membrane binding of PLA(2). Fluorescence quenching by brominated lipids indicates significant penetration of Trp(3) into fluid POPC/POPG membranes but little insertion into the solid DPPC/DPPG membranes. Increased membrane fluidity also supports hIBPLA(2) activity, suggesting that membrane insertion of hIBPLA(2) is controlled by membrane fluidity and is necessary for the full activity of the enzyme. Trp fluorescence quenching of the V3W-hIIAPLA(2) and bvPLA(2) by water- and membrane-soluble quenchers indicates substantial membrane insertion of Trp(3) of V3W-hIIAPLA(2), similar to that found for hIBPLA(2), and no insertion of tryptophans of bvPLA(2). Our results provide evidence that (a) structurally similar group IB and IIA PLA(2)s, but not structurally diverse group III PLA(2), significantly penetrate into membranes; (b) membrane insertion is controlled by membrane fluidity and facilitates activation of IB and IIA PLA(2)s; and (c) structurally distinct PLA(2) isoforms may employ different tactics of substrate accession/product release during lipid hydrolysis.     

Direct association between caspase 3 and alpha5beta1 integrin and its role during anoikis of rat fibroblasts

We have demonstrated a direct association between alpha5beta1 integrin and caspase 3, both pro- and mature enzyme, in various sub-cellular compartments of rat fibroblasts undergoing anoikis. Integrin associated caspase 3 showed high activity in the plasma membranes, whereas in the cytosol and microsomal fraction it showed little or no activity. Our results suggest a possible role for recycled un-ligated alpha5beta1 integrin molecules between cytosol and plasma membrane, in regulation of caspase-3 activity and induction of cell death in adhesion-deprived cells.     

Calculation of rate spectra from noisy time series data

As the resolution of experiments to measure folding kinetics continues to improve, it has become imperative to avoid bias that may come with fitting data to a predetermined mechanistic model. Toward this end, we present a rate spectrum approach to analyze timescales present in kinetic data. Computing rate spectra of noisy time series data via numerical discrete inverse Laplace transform is an ill‐conditioned inverse problem, so a regularization procedure must be used to perform the calculation. Here, we show the results of different regularization procedures applied to noisy multiexponential and stretched exponential time series, as well as data from time‐resolved folding kinetics experiments. In each case, the rate spectrum method recapitulates the relevant distribution of timescales present in the data, with different priors on the rate amplitudes naturally corresponding to common biases toward simple phenomenological models. These results suggest an attractive alternative to the “Occam's razor” philosophy of simply choosing models with the fewest number of relaxation rates. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Effects of Familial Mutations on the Monomer Structure of Aβ42

Amyloid beta (Aβ) peptide plays an important role in Alzheimer’s disease. A number of mutations in the Aβ sequence lead to familial Alzheimer’s disease, congophilic amyloid angiopathy, or hereditary cerebral hemorrhage with amyloid. Using molecular dynamics simulations of ∼200 μs for each system, we characterize and contrast the consequences of four pathogenic mutations (Italian, Dutch, Arctic, and Iowa) for the structural ensemble of the Aβ monomer. The four familial mutations are found to have distinct consequences for the monomer structure.Amyloid beta (Aβ) peptides have long been thought to play a central role in Alzheimer’s disease (AD). Usually 40 or 42 residues in length, Aβ peptides are proteolytic products of the Aβ precursor protein and they aggregate to form the fibrillar plaques in AD patients’ brains. Besides fibrillar plaques, Aβ oligomers are also neurotoxic. The significance and nature of Aβ oligomerization has recently become a focus of intensive research studies and debates (1,2). Notably, numerous pathogenic mutations have been identified in the Aβ precursor protein sequence and in the enzymes involved in Aβ processing (3). These mutations generally lead to early onset of AD or cerebral amyloid angiopathy. Understanding how the pathogenic mutations alter Aβ oligomerization/aggregation is essential to our understanding of the disease mechanism.Four of these pathogenic mutations (Italian E22K, Dutch E22Q, Arctic E22G, and Iowa D23N) cluster in the region of E22 and D23 in the Aβ sequence (distal from proteolytic cleavage sites) and they have higher neurotoxicity compared to wild-type (WT) Aβ (4). These mutations are thought to modify the physicochemistry of the peptide. For example, kinetic studies (4) show that the E22K and E22Q mutations lead to faster peptide aggregation, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (although the E22G mutation shows increased protofibril formation (5)). Recent solid-state NMR studies also suggest that rather than the in-register β-sheet conformation adopted by WT Aβ, the Iowa D23N mutant forms amyloid fibrils with antiparallel β-sheet structure (6).To understand how the mutations modify the peptide oligomerization/aggregation it is critical to characterize the starting point of the process, the monomers. Unfortunately, investigating the early phase of the oligomerization process experimentally is a challenging task due to the high aggregation propensity of Aβ and its intrinsic disorder. Therefore, a number of computational approaches have been adopted to investigate the consequences of mutations for the monomer structure (7–16). However, due to the high computational demands of explicit-solvent molecular dynamics (MD) simulations to simulate full-length Aβ peptides, most of these computational studies are either on Aβ fragments (to decrease the system size) using explicit-solvent simulations (8–12) or on full-length Aβ using implicit-solvent simulations (which are less computationally demanding and enable longer simulation times, but lack explicit water molecules in the simulations to fully describe water-peptide interactions) (13–15). In a very recent report, explicit-solvent simulations were used to study the effects of the E22Q mutation on full-length Aβ; however, rather limited data (<10 μs) were collected (16). Thus, characterizing full-length Aβ monomers remains quite a daunting task even with simulations.To characterize the effects of mutations on full-length Aβ monomer using explicit-solvent MD simulations, we employed distributed computing (17) to simulate the WT Aβ₄₂, Aβ₄₂-E22K, Aβ₄₂-E22Q, Aβ₄₂-E22G, and Aβ₄₂-D23N monomers. MD simulations of >200 μs were performed for each system and AMBER ff99sb (18) and the tip3p water model (19) were used for force field parameters. Peptide configurations in the MD trajectories were clustered with the root mean-square deviation metric to identify representative conformations (i.e., states) and transitions between these states were counted. Markov state model analysis was then performed where the master equations were solved and the equilibrium population of each state deduced (20). Details of the MD simulation procedures and Markov state model analysis can be found in the Supporting Material.Each of the five Aβ monomer systems exhibits great structural diversity and can only be characterized in an ensemble fashion (rather than described by a handful of representative configurations). This is in accord with the notion that full-length Aβ peptides are intrinsically disordered (21,22). Using the Dictionary of Secondary Structure of Proteins program (23) to assign secondary structure, it is clear that the five Aβ monomer systems are found overall not well structured, although small β-hairpins and α-helices are observed. In Fig. 1 we plot the residue-dependent extended β propensity and α-helix propensity, in the top and bottom panels, respectively, for each Aβ monomer system. Although we are reasonably confident of the convergence behavior of the α-helix propensity, we note that the convergence of the extended β-propensity might be more challenging and demand a much longer sampling time than the current aggregate simulation time of ∼200 μs (24).Open in a separate windowFigure 1Ensemble-averaged %population of β-strand (top) and α-helix (bottom) propensity for all five monomer systems. The sequence of the WT Aβ₄₂ is given on the x axis.We observe in Fig. 1 that all five Aβ monomer systems share a rather similar residue-dependent tendency to form an extended β-structure, although minor differences are present. On the other hand, these pathogenic mutations alter the α-helix propensity quite significantly. The E22K and E22Q mutations increase the α-helix propensity in the region of residues 20–23. All four mutations (E22K, E22Q, E22G, and D23N) decrease the α-helix propensity in the region of residues 33–36.Notably, we find that in all five systems only short stretches of α-helices are formed. That is, when a residue is involved in α-helix formation, it participates in forming mostly short helical segments (consisting of only four helical residues). To provide more insight into the changes of α-helix propensity due to the mutations, in Fig. S1 we plot the tendency of forming short α-helices along the sequence for all five systems. Each data point in Fig. S1 represents the propensity to form an α-helix of four residues in length, ending at the specific residue. For example, in the structural ensemble adopted by the WT peptide, ∼5.5% of the conformations have a short α-helix of size four, involving residues 15–18. We see from Fig. S1 that the E22K and E22Q mutations induce the formation of two short helices in residues 19–22 and 20–23. The higher α-helix propensity in this region for the E22K mutant compared to the WT was previously attributed to the elimination of the electrostatic repulsion between E22 and D23 in the WT by the mutation and the longer aliphatic chain of K22 in the mutant compared to E22 in the WT (9,22). This is consistent with the observation that the E22Q mutation also induces helix formation in this region (by eliminating the electrostatic repulsion between E22 and D23 in the WT) but to a lesser extent, possibly due to the shorter aliphatic chain of Q22 compared to K22.In the E22G mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, glycine is known to be a helix breaker (25), leading to diminished α-helix propensity in the region around residue G22 seen in Fig. S1.In the D23N mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, it does not induce (or rather even slightly decreases) helix formation around residue 23. This may be due to the short aliphatic chain of N23 but it is possible that the mutation induces some nonlocal effects on the peptide structure, disfavoring helix formation in this region.It is worth noting that all four mutations (E22K, E22Q, E22G, and D23N) virtually eliminate the α-helix propensity in the region of residues 33–36. This region is rather far away from the mutation sites in sequence but its α-helix propensity is nonetheless affected. The origin of such a nonlocal effect is less straightforward to explain and further analysis will aid untangling this behavior. Nonetheless, the diminished α-helix propensity in the region of residues 33–36 appears to be a consistent feature across all four mutants.The four mutations studied here (E22K, E22Q, E22G, and D23N) have been thought to modify the physicochemistry of the peptide and alter the oligomerization/aggregation process, leading to higher neurotoxicity. In predicting intrinsic aggregation propensities using peptide sequences, all four mutants are suggested to be more aggregation prone (26). On the other hand, kinetic studies show that only the E22K and E22Q mutants aggregate more quickly, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (4). Our simulation results suggest these pathogenic mutations have complicated effects on the monomer structure—all four mutations decrease helix propensity in residues 33–36, whereas only the E22K and E22Q mutations increase helix propensity in residues 20–23. It is interesting to note that α-helix propensity is generally thought to anticorrelate with aggregation propensity; however, recent studies have suggested an important role of α-helical intermediates in amyloid oligomerization (27–29). Our studies suggest that it would be of great value to investigate how the distinct patterns of α-helix propensity in these five systems may propagate to give rise to different oligomerization kinetics or even mechanisms. The pathogenic mutations studied here have complex effects on the oligomerization of the peptide. The characterization of the monomer structural ensembles reported here should aid understanding of such an important and complicated process.     

A-site residues move independently from P-site residues in all-atom molecular dynamics simulations of the 70S bacterial ribosome

The ribosome is a large macromolecular machine, and correlated motion between residues is necessary for coordinating function across multiple protein and RNA chains. We ran two all-atom, explicit solvent molecular dynamics simulations of the bacterial ribosome and calculated correlated motion between residue pairs by using mutual information. Because of the short timescales of our simulation (ns), we expect that dynamics are largely local fluctuations around the crystal structure. We hypothesize that residues that show coupled dynamics are functionally related, even on longer timescales. We validate our model by showing that crystallographic B-factors correlate well with the entropy calculated as part of our mutual information calculations. We reveal that A-site residues move relatively independently from P-site residues, effectively insulating A-site functions from P-site functions during translation.