Study Of The Binding Of Substrate By The Phe557Val Mutant Soybean Lipoxygenase-1

Lipoxygenases are a class of enzymes which consist of non-heme iron dioxygenases that are produced by fungi, plants, and mammals and catalyze the oxygenation of polyunsaturated fatty acid substrates to unsaturated fatty acid hydroperoxide products. The unsaturated fatty acid hydroperoxide products are stereo- and regiospecific. One such lipoxygenase, soybean lipoxygenase-1 (SBLO-1), catalyzes the conversion of linoleate to 13-hydroperoxy-9(Z),11(E)-octadecadienoate (13-HPOD) and a small amount of 9-hydroperoxy-10(E),12(Z)-octadecadienoate (9-HPOD). Although the structure of SBLO-1 is known and it is the most widely studied lipoxygenase, how it binds to substrate is still poorly understood. Two competing binding hypotheses that have been used to understand and explain the binding are the head first binding model and the tail first binding model. The head first binding model predicts linoleate binds with its polar carboxylate group in the binding pocket and the methyl terminus at the surface of the binding pocket. The tail first binding model predicts that linoleate binds with its methyl terminus end in the binding pocket and the polar carboxylate group at the surface of the binding pocket. Both binding models have been used in the explanation of previous work. In previous work the replacement of phenylalanine with valine has been performed to produce the phe557val mutant SBLO-1. The mutant SBLO-1 was then used in the enzymatic oxygenation of linoleate. With this mutant, the amount of 9-HPOD that is formed increases. This result has been interpreted using the head-first binding model in which the smaller valine residue allows linoleate to bind with the polar carboxylate group of linoleate interacting with arginine-707. The work presented in this thesis confirms the regiochemical results of the previous work and further tests the head-first binding model. If head-first binding occurs, the 9-HPOD is expected to have primarily S configuration. Utilizing chiral-phase HPLC, it was found that the 9-HPOD produced by the phe557val mutant SBLO-1 is primarily S, consistent with head-first binding. The head-first binding model was also tested using linoleyl dimethylamine (LDMA), which has been shown to be a good substrate for SBLO-1 at pH 7.0, where LDMA is thought to be positively charged. This model predicts that less of the 9-peroxide should be produced with this substrate. Through the use of gas chromatography/mass spectrometry, it was found that the conversion of LDMA by the phe557val mutant SBLO-1 resulted in the formation of a 46:54 mixture of the 13-peroxide:9-peroxide. The higher amount of 9-peroxide is the opposite of what is expected for the currently proposed model suggesting that the proposed model may not be entirely correct. The results thus far have been consistent with reverse binding but not with the proposed interaction of the polar end of the substrate with arginine-707.

Binding of the cellulose-binding domain of exoglucanase Cex from Cellulomonas fimi to insoluble microcrystalline cellulose is entropically driven.

Isothermal titration microcalorimetry is combined with solution-depletion isotherm data to analyze the thermodynamics of binding of the cellulose-binding domain (CBD) from the beta-1,4-(exo)glucanase Cex of Cellulomonas fimi to insoluble bacterial microcrystalline cellulose. Analysis of isothermal titration microcalorimetry data against two putative binding models indicates that the bacterial microcrystalline cellulose surface presents two independent classes of binding sites, with the predominant high-affinity site being characterized by a Langmuir-type Ka of 6.3 (+/-1.4) x 10(7) M-1 and the low-affinity site by a Ka of 1.1 (+/-0.6) x 10(6) M-1. CBDCex binding to either site is exothermic, but is mainly driven by a large positive change in entropy. This differs from protein binding to soluble carbohydrates, which is usually driven by a relatively large exothermic standard enthalpy change for binding. Differential heat capacity changes are large and negative, indicating that sorbent and protein dehydration effects make a dominant contribution to the driving force for binding.

Inhaled linalool-induced sedation in mice

Linalool is a monoterpene often found as a major component of essential oils obtained from aromatic plant species., many of which are used in traditional medical systems as hypno-sedatives. Psychopharmacological evaluations of linalool (i.p. and i.c.v.) revealed marked sedative and anticonvulsant central effects in various mouse models. Considering this profile and alleged effects of inhaled lavender essential oil, the purpose of this study was to examine the sedative effects of inhaled linalool in mice. Mice were placed in an inhalation chamber during 60 min, in an atmosphere saturated with 1% or 3% linalool. Immediately after inhalation, animals were evaluated regarding locomotion, barbiturate-induced sleeping time, body temperature: and motor coordination (rota-rod test). The 1% and 3% linalool increased (p < 0.01) pentobarbital sleeping time and reduced (p<0.01) body temperature. The 3% linalool decreased (p<0.01) locomotion. Motor coordination was not affected. Hence, linalool inhaled for I h seems to induce sedation without significant impairment in motor abilities, a side effect shared by most psycholeptic drugs. (C) 2008 Elsevier GmbH. All rights reserved.

Incorporation of dithiooxamide as a complexing agent into cellulose for the removal and pre-concentration of Cu(II) and Cd(II) ions from natural water samples

The present study describes the incorporation of a complexing agent, dithiooxamide, into microcrystalline cellulose for use in the pre-concentration of Cu(II) and Cd(II) ions from aqueous samples. The FTIR spectrum of the adsorbent exhibited an absorption band in the region of 800 cm-1, which confirmed the binding of the silylating agent to the matrix. Elemental analysis indicated the amount of 0.150 mmol g-1 of the complexing agent. The adsorption data were fit to the modified Langmuir equation, and the maximum amount of metal species extracted from the solution, Ns, was determined to be 0.058 and 0.072 mmol g-1 for Cu(II) and Cd(II), respectively. The covering fraction φ, which was 0.39 and 0.48 for Cu(II) and Cd(II), respectively, was used to estimate a 1:2 (metal:ligand) ratio in the formed complex, and a binding model was proposed based on this information. The adsorbent was applied in the pre-concentration of natural water samples and exhibited an enrichment factor of approximately 50-fold for the species studied, which enabled its use in the analysis of trace metals in aqueous samples. The system was validated by the analysis of certified standard (1643e), and the adsorbent was stable for more than 20 cycles, thus enabling its safe reutilization. © 2012 Elsevier B.V. All rights reserved.

Synthesis of photo-crosslinking probes and their application for the site-selective chemical modification of the 5-HT3 receptor

The 5-HT3 receptor (5-HT3R) is an important ion channel responsible for the transmission of nerve impulses in the CNS and PNS that is activated by the endogenous agonist serotonin (5-hydroxytryptamine, 5-HT). 5-HT3R is the only serotonin receptor belonging to the Cys-loop superfamily of neurotransmitter receptors. Different structural biology approaches can be applied, such as crystallization and x-ray analysis. Nonetheless, characterizing the exact ligand binding site(s) of these dynamic receptors is still challenging. The use of photo-crosslinking probes is an alternative validated approach allowing identification of regions in the protein that are important for the binding of small molecules. We designed our probes based on the core structure of the 5-HT3R antagonist granisetron, a FDA approved drug used for the treatment of chemotherapy-induced nausea and vomiting. We synthesized a small library of photo-crosslinking probes by conjugating diazirines and benzophenones via various linkers to granisetron. We were able to obtain several compounds with diverse linker lengths and different photo-crosslinking moieties that show nanomolar binding affinity for the orthosteric binding site. Furthermore we established a stable h5-HT3R expressing cell line and a purification protocol to yield the receptor in a high purity. Several experiments showed unambiguously that we are able to photo-crosslink our probes with the receptor site-specifically. The functionalised protein was analysed by Western blot and MS-analysis. This yielded the exact covalent modification site, corroborating current ligand binding models derived from mutagenesis and docking studies.

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-All binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes. (C) 2001 by Elsevier Science Inc.

Predicting metal-binding site residues in low-resolution structural models

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Ticks produce highly selective chemokine binding proteins with antiinflammatory activity

Bloodsucking parasites such as ticks have evolved a wide variety of immunomodulatory proteins that are secreted in their saliva, allowing them to feed for long periods of time without being detected by the host immune system. One possible strategy used by ticks to evade the host immune response is to produce proteins that selectively bind and neutralize the chemokines that normally recruit cells of the innate immune system that protect the host from parasites. We have identified distinct cDNAs encoding novel chemokine binding proteins (CHPBs), which we have termed Evasins, using an expression cloning approach. These CHBPs have unusually stringent chemokine selectivity, differentiating them from broader spectrum viral CHBPs. Evasin-1 binds to CCL3, CCL4, and CCL18; Evasin-3 binds to CXCL8 and CXCL1; and Evasin-4 binds to CCL5 and CCL11. We report the characterization of Evasin-1 and -3, which are unrelated in primary sequence and tertiary structure, and reveal novel folds. Administration of recombinant Evasin-1 and - 3 in animal models of disease demonstrates that they have potent antiinflammatory properties. These novel CHBPs designed by nature are even smaller than the recently described single-domain antibodies (Hollinger, P., and P. J. Hudson. 2005. Nat. Biotechnol. 23: 1126-1136), and may be therapeutically useful as novel antiinflammatory agents in the future.

Differential expression of microRNAs in mouse pain models

Background: MicroRNAs (miRNAs) are short non-coding RNAs that inhibit translation of target genes by binding to their mRNAs. The expression of numerous brain-specific miRNAs with a high degree of temporal and spatial specificity suggests that miRNAs play an important role in gene regulation in health and disease. Here we investigate the time course gene expression profile of miR-1, -16, and -206 in mouse dorsal root ganglion (DRG), and spinal cord dorsal horn under inflammatory and neuropathic pain conditions as well as following acute noxious stimulation. Results: Quantitative real-time polymerase chain reaction analyses showed that the mature form of miR-1, -16 and -206, is expressed in DRG and the dorsal horn of the spinal cord. Moreover, CFA-induced inflammation significantly reduced miRs-1 and -16 expression in DRG whereas miR-206 was downregulated in a time dependent manner. Conversely, in the spinal dorsal horn all three miRNAs monitored were upregulated. After sciatic nerve partial ligation, miR-1 and -206 were downregulated in DRG with no change in the spinal dorsal horn. On the other hand, axotomy increases the relative expression of miR-1, -16, and 206 in a time-dependent fashion while in the dorsal horn there was a significant downregulation of miR-1. Acute noxious stimulation with capsaicin also increased the expression of miR-1 and -16 in DRG cells but, on the other hand, in the spinal dorsal horn only a high dose of capsaicin was able to downregulate miR-206 expression. Conclusions: Our results indicate that miRNAs may participate in the regulatory mechanisms of genes associated with the pathophysiology of chronic pain as well as the nociceptive processing following acute noxious stimulation. We found substantial evidence that miRNAs are differentially regulated in DRG and the dorsal horn of the spinal cord under different pain states. Therefore, miRNA expression in the nociceptive system shows not only temporal and spatial specificity but is also stimulus-dependent.

Changes in Calcium-Binding Protein Expression in Human Cortical Contusion Tissue

Traumatic brain injury (TBI) produces several cellular changes, such as gliosis, axonal and dendritic plasticity, and inhibition-excitation imbalance, as well as cell death, which can initiate epileptogenesis. It has been demonstrated that dysfunction of the inhibitory components of the cerebral cortex after injury may cause status epilepticus in experimental models; we proposed to analyze the response of cortical interneurons and astrocytes after TBI in humans. Twelve contusion samples were evaluated, identifying the expression of glial fibrillary acidic protein (GFAP) and calcium-binding proteins (CaBPs). The study was made in sectors with and without preserved cytoarchitecture evaluated with NeuN immunoreactivity (IR). In sectors with total loss of NeuN-IR the results showed a remarkable loss of CaBP-IR both in neuropil and somata. In sectors with conserved cytoarchitecture less drastic changes in CaBP-IR were detected. These changes include a decrease in the amount of parvalbumin (PV-IR) neurons in layer II, an increase of calbindin (CB-IR) neurons in layers III and V, and an increase in calretinin (CR-IR) neurons in layer II. We also observed glial fibrillary acidic protein immunoreactivity (GFAP-IR) in the white matter, in the gray-white matter transition, and around the sectors with NeuN-IR total loss. These findings may reflect dynamic activity as a consequence of the lesion that is associated with changes in the excitatory circuits of neighboring hyperactivated glutamatergic neurons, possibly due to the primary impact, or secondary events such as hypoxia-ischemia. Temporal evolution of these changes may be the substrate linking severe cortical contusion and the resulting epileptogenic activity observed in some patients.

Metabolite mean transit times in the liver as predicted by various models of hepatic elimination

Predicted area under curve (AUC), mean transit time (MTT) and normalized variance (CV2) data have been compared for parent compound and generated metabolite following an impulse input into the liver, Models studied were the well-stirred (tank) model, tube model, a distributed tube model, dispersion model (Danckwerts and mixed boundary conditions) and tanks-in-series model. It is well known that discrimination between models for a parent solute is greatest when the parent solute is highly extracted by the liver. With the metabolite, greatest model differences for MTT and CV2 occur when parent solute is poorly extracted. In all cases the predictions of the distributed tube, dispersion, and tasks-in-series models are between the predictions of the rank and tube models. The dispersion model with mixed boundary conditions yields identical predictions to those for the distributed tube model (assuming an inverse gaussian distribution of tube transit times). The dispersion model with Danckwerts boundary conditions and the tanks-in series models give similar predictions to the dispersion (mixed boundary conditions) and the distributed tube. The normalized variance for parent compound is dependent upon hepatocyte permeability only within a distinct range of permeability values. This range is similar for each model but the order of magnitude predicted for normalized variance is model dependent. Only for a one-compartment system is the MIT for generated metabolite equal to the sum of MTTs for the parent compound and preformed metabolite administered as parent.

Computational binding assays of antigenic peptides

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1-5%.