Articulating discursive and materialist conceptions of practice in the logics approach to critical policy analysis

In this article it is argued that while Glynos and Howarth’s logics of critical explanation (LCE) offers an important and promising contribution to critical policy analysis, it, along with other approaches that focus on the meaning of social action, faces a growing challenge in the form of a so-called new materialist turn in social and political theory. The article argues that there is much to be gained for the logics approach in paying closer attention to the materiality of practices in terms not only of lending greater clarity to the conception and role of social practices in the logics approach but also in enabling it fully to deliver on its critical ambition. The article explores an alternative materialist approach to the study of social practices, which hails from the post-actor–networktheory tradition and which has ontological affinities with post-structuralism. The article begins with a brief analysis of the new materialist turn in its various guises. It then critically examines the logics approach, and, in particular its conception of practice. It then explores an alternative materialist and ethnographic reading of practice, focusing on medical and care practices. It concludes with an examination of the implications for a more materialist conception of practices for the LCE’s broad deconstructive, psychoanalytic and onto-political ambitions.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

In silico modelling of the interaction of flavonoids with human P-glycoprotein nucleotide-binding domain

A three-dimensional model of human ABCB1 nucleotide-binding domain (NBD) was developed by homology modelling using the high-resolution human TAP1 transporter structure as template. Interactions between NBD and flavonoids were investigated using in silico docking studies. Ring-A of unmodified flavonoid was located within the NBD P-loop with the 5-hydroxyl group involved in hydrogen bonding with Lys1076. Ring-B was stabilised by hydrophobic stacking interactions with Tyr1044. The 3-hydroxyl group and carbonyl oxygen were extensively involved in hydrogen bonding interactions with amino acids within the NBD. Addition of prenyl, benzyl or geranyl moieties to ring-A (position-6) and hydrocarbon substituents (O-n-butyl to O-n-decyl) to ring-B (position-4) resulted in a size-dependent decrease in predicted docking energy which reflected the increased binding affinities reported in vitro.

Corticobasal degeneration and dementia

Corticobasal degeneration is a rare, progressive neurodegenerative disorder which significantly impairs movement. The most common initial symptom is asymmetric limb clumsiness with or without accompanying rigidity or tremor. Subsequently, the disease progresses to affect gait and there is a slow progression to influence ipsilateral arms and legs. Apraxia and dementia are the most common cortical signs. Clinical diagnosis of the disorder is difficult as the symptoms resemble those of related neurodegenerative disorders. Histopathologically, there is widespread neuronal and glial pathology including tau-immunoreactive neuronal cytoplasmic inclusions, neuropil threads, oligodendroglial inclusions, astrocytic plaques, together with abnormally enlarged ‘ballooned’ neurons. Corticobasal degeneration has affinities both with the parkinsonian syndromes including Parkinson’s disease, progressive supranuclear palsy, and multiple system atrophy and with the fronto-temporal dementias. Treatment of corticobasal degeneration involves managing and reducing the effect of symptoms.

Dementia with Lewy bodies

Dementia with Lewy bodies (‘Lewy body dementia' or ‘diffuse Lewy body disease') (DLB) is the second commonest form of dementia after Alzheimer’s disease (AD). Characteristic of DLB are: (1) fluctuating cognitive ability with variations in attention and alertness, (2) recurrent visual hallucinations, and (3) motor features including akinesia, rigidity, and tremor. Various brain regions are affected in DLD including cortical and limbic regions. Histopathologically, alpha-synuclein-immunoreactive Lewy bodies (LB) are observed in the substantia nigra and in the cerebral cortex. DLB has affinities both with the parkinsonian syndromes including Parkinson’s disease (PD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and multiple system atrophy (MSA), and with AD, which can make differential diagnosis difficult. The presence of visual hallucinations may aid differential diagnosis of the parkinsononian syndromes and occipital hypometabolism may be a useful potential method of distinguishing DLB from AD. Treatment of CBD involves managing and reducing the effect of symptoms.

Synthesis of substituted 3-anilino-5-phenyl-1,3-dihydro-2H-1,4-benzodiazepine-2-ones and their evaluation as cholecystokinin-ligands

3-Amino-1,4-benzodiazepines as well as chemically related diverse amines were prepared from oxazepam and subsequently screened on the cholecystokinin receptor in a radiolabel binding assay. Oxazepam 2 was activated via its 3-chloro-1,4-benzodiazepine intermediate 3 and was reacted with a large series of aliphatic and aromatic amines. The substituted 3-anilino-1,4-benzodiazepine structure was identified as lead structure in a diverse series of 3-amino-1,4-benzodiazepines 4-38 and the full SAR (structure-activity relationship) optimisation provided 3-anilinobenzodiazepines 16-38 with CCK 1 receptor selectivity to CCK 2. The compounds 18, 24, 28 and 33 have shown affinities at the CCK 1 receptor of 11, 10, 11 and 9 nM, respectively. These equipotent CCK 1 ligands were fully evaluated in behaviour pharmacological essays. An antidepressant effect was identified in the tail suspension- and the Porsolt swimming-test. The ED 50 values for 24 and 28 were determined in these assays as 0.46 and 0.49 mg/kg. The mixed antagonist 37 showed in addition to the antidepressant effects anxiolytic properties. © 2006 Wiley-VCH Verlag GmbH & Co. KGaA.

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.

Coupling in silico and in vitro analysis of peptide-MHC binding:a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

N-terminal His7-modification of glucagon-like peptide-1(7-36) amide generates dipeptidyl peptidase IV-stable analogues with potent antihyperglycaemic activity

Glucagon-like peptide-1(7-36)amide (GLP-1) possesses several unique and beneficial effects for the potential treatment of type 2 diabetes. However, the rapid inactivation of GLP-1 by dipeptidyl peptidase IV (DPP IV) results in a short half-life in vivo (less than 2 min) hindering therapeutic development. In the present study, a novel His7-modified analogue of GLP-1, N-pyroglutamyl-GLP-1 as well as N-acetyl-GLP-1 were synthesised and tested for DPP IV stability and biological activity. Incubation of GLP-1 with either DPP IV or human plasma resulted in rapid degradation of native GLP-1 to GLP-1(9-36)amide, while N-acetyl-GLP-1 and N-pyroglutamyl-GLP-1 were completely resistant to degradation. N-acetyl-GLP-1 and N-pyroglutamyl-GLP-1 bound to the GLP-1 receptor but had reduced affinities (IC50 values 32.9 and 6.7 nM, respectively) compared with native GLP-1 (IC50-37 nM). Similarly, both analogues stimulated cAMP production with EC50 values of 16.3 and 27 nM respectively compared with GLP-1 (EC50 4.7 nM). However, N-acetyl-GLP-1 and N-pyroglutamyl-GLP-1 exhibited potent insulinotropic activity in vitro at 5.6 mM glucose (P< 0.05 to P< 0.001) similar to native GLP-1. Both analogues (25 nM/kg body weight) lowered plasma glucose and increased plasma insulin levels when administered in conjunction with glucose (18 nM/kg body weight) to adult obese diabetic (ob/ob) mice. N-pyroglutamyl-GLP-1 was substantially better at lowering plasma glucose compared with the native peptide, while N-acetyl-GLP-1 was significantly more potent at stimulating insulin secretion. These studies indicate that N-terminal modification of GLP-1 results in DPP IV-resistant and biologically potent forms of GLP-1. The particularly powerful antihyperglycaemic action of N-pyroglutamyl-GLP-1 shows potential for the treatment of type 2 diabetes. © 2004 Society for Endocrinology.

Novel dipeptidyl peptidase IV resistant analogues of glucagon-like peptide-1(7-36)amide have preserved biological activities in vitro conferring improved glucose-lowering action in vivo

Although the incretin hormone glucagon-like peptide-1 (GLP-1) is a potent stimulator of insulin release, its rapid degradation in vivo by the enzyme dipeptidyl peptidase IV (DPP IV) greatly limits its potential for treatment of type 2 diabetes. Here, we report two novel Ala8-substituted analogues of GLP-1, (Abu8)GLP-1 and (Val8)GLP-1 which were completely resistant to inactivation by DPP IV or human plasma. (Abu8)GLP-1 and (Val8)GLP-1 exhibited moderate affinities (IC50: 4.76 and 81.1 nM, respectively) for the human GLP-1 receptor compared with native GLP-1 (IC50: 0.37 nM). (Abu8)GLP-1 and (Val8)GLP-1 dose-dependently stimulated cAMP in insulin-secreting BRIN BD11 cells with reduced potency compared with native GLP-1 (1.5- and 3.5-fold, respectively). Consistent with other mechanisms of action, the analogues showed similar, or in the case of (Val8)GLP-1 slightly impaired insulin releasing activity in BRIN BD11 cells. Using adult obese (ob/ob) mice, (Abu8 )GLP-1 had similar glucose-lowering potency to native GLP-1 whereas the action of (Val8)GLP-1 was enhanced by 37%. The in vivo insulin-releasing activities were similar. These data indicate that substitution of Ala8 in GLP-1 with Abu or Val confers resistance to DPP IV inactivation and that (Val8)GLP-1 is a particularly potent N-terminally modified GLP-1 analogue of possible use in type 2 diabetes.

MHC class II binding prediction by molecular docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15% of all overlapping peptides, originating from one protein. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Plant biogeography and conservation on a tropical island: Isla del Coco, Costa Rica

Isla del Coco (Cocos Island) is a small volcanic island located in the Pacific 500 km west of Costa Rica. Three collecting trips to Isla del Coco, in addition to herbarium research, were completed in order to assess the floristic diversity of the island. The current flora of Isla del Coco contains 262 plant species of which 37 (19.4%) are endemic. This study reports 58 species as new to the island. Seventy-one species (27.1%) were identified as introduced by humans. In addition, five potentially invasive plant species are identified. Seven vegetation types are identified on the island: bayshore, coastal cliff, riparian, low elevation humid forest, high elevation cloud forest, landslide and islet. ^ The biogeographic affinities of the native and endemic species are with Central America/northern South America and to a lesser extent, the Caribbean. Endemic species in the genus Epidendrum were investigated to determine whether an insular radiation event had produced two species found on Isla del Coco. Phylogenetic analysis of the internal transcribed spacer (ITS) of nuclear ribosomal DNA was not able to disprove that the endemic species in this genus are not sister species. Molecular biogeographic analyses of ITS sequence data determined that the Isla del Coco endemic species in the genera Epidendrum, Pilea and Psychotria are most closely related to Central American/northern South American taxa. No biogeographical links were found between the floras of Isla del Coco and the Galápagos Islands. ^ The native and endemic plant diversity of Isla del Coco is threatened with habitat degradation by introduced pigs and deer, and to a lesser extent, by exotic plant species. The IUCN Red List and RAREplants criteria were used to assess the extinction threat for the 37 endemic plant taxa found on the island. All of the endemic species are considered threatened with extinction at the Critically Endangered (CR) by the IUCN criteria or either CR or Endangered (EN) using RAREplants methodology. ^

Tracking the Effects of Salt Water Encroachment on South Florida Coastal Ecotones Using Mollusks

Many coastal wetland communities of south Florida have been cut off from freshwater sheet flow for decades and are migrating landward due to salt-water encroachment. A paleoecological study using mollusks was conducted to assess the rates and effects of salt-water encroachment due to freshwater diversion and sea level rise on coastal wetland basins in Biscayne National Park. Modem mollusk distributions taken from 226 surface sites were used to determine local habitat affinities which were applied to infer past environments from mollusk distributions found in soil cores. Mollusks species compositions were found to be strongly correlated to habitat and salinity, providing reliable predictions. Wetland soils were cored to bedrock at 36locations. Mollusks were abundant throughout the cores and 15 of the 20 most abundant taxa served as bioindicators of salinity and habitat. Historic accounts coupled with mollusk based inference models indicate (1) increasing salinity levels along the coast and encroaching into the interior with mangroves communities currently migrating westward, (2) replacement of a mixed graminoid-mangrove zone by a dense monoculture of dwarf mangroves, and (3) a confinement of freshwater and freshwater graminoid marsh to landward areas between urban developments and drainage canals.