In silico identification of novel G protein coupled receptors

G-protein coupled receptors (GPCRs) are a superfamily of membrane integral proteins responsible for a large number of physiological functions. Approximately 50% of marketed drugs are targeted toward a GPCR. Despite showing a high degree of structural homology, there is a large variance in sequence within the GPCR superfamily which has lead to difficulties in identifying and classifying potential new GPCR proteins. Here the various computational techniques that can be used to characterize a novel GPCR protein are discussed, including both alignment-based and alignment-free approaches. In addition, the application of homology modeling to building the three-dimensional structures of GPCRs is described.

Role of the dsRNA-dependent protein kinase (PKR) in the attenuation of protein loss from muscle by insulin and insulin-like growth factor-I (IGF-I)

Proteolysis-inducing factor (PIF), a tumour-produced cachectic factor, induced a dose-dependent decrease in protein synthesis in murine myotubes, together with an increase in phosphorylation of eucaryotic initiation factor 2 (eIF2) on the alpha-subunit. Both insulin (1 nM) and insulin-like growth factor I (IGF-I) (13.2 nM) attenuated the depression of protein synthesis by PIF and the increased phosphorylation of eIF2alpha, by inhibiting the activation (autophosphorylation) of the dsRNA-dependent protein kinase (PKR) by induction of protein phosphatase 1. A low-molecular weight inhibitor of PKR also reversed the depression of protein synthesis by PIF to the same extent, as did insulin and IGF-I. Both insulin and IGF-I-stimulated protein synthesis in the presence of PIF, and this was attenuated by Salubrinal, an inhibitor of phospho eIF2alpha phosphatase, suggesting that at least part of this action was due to their ability to inhibit phosphorylation of eIF2alpha. Both insulin and IGF-I also attenuated the induction of protein degradation in myotubes induced by PIF, this effect was also attenuated by Salubrinal. These results suggest an alternative mechanism involving PKR to explain the effect of insulin and IGF-I on protein synthesis and degradation in skeletal muscle in the presence of catabolic factors.

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.

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

The use of erythrocytic and animal models in the study of protein phosphorylation

Phosphorylation processes are common post-transductional mechanisms, by which it is possible to modulate a number of metabolic pathways. Proteins are highly sensitive to phosphorylation, which governs many protein-protein interactions. The enzymatic activity of some protein tyrosine-kinases is under tyrosine-phosphorylation control, as well as several transmembrane anion-fluxes and cation exchanges. In addition, phosphorylation reactions are involved in intra and extra-cellular 'cross-talk' processes. Early studies adopted laboratory animals to study these little known phosphorylation processes. The main difficulty encountered with these animal techniques was obtaining sufficient kinase or phosphatase activity suitable for studying the enzymatic process. Large amounts of biological material from organs, such as the liver and spleen were necessary to conduct such work with protein kinases. Subsequent studies revealed the ubiquity and complexity of phosphorylation processes and techniques evolved from early rat studies to the adaptation of more rewarding in vitro models. These involved human erythrocytes, which are a convenient source both for the enzymes, we investigated and for their substrates. This preliminary work facilitated the development of more advanced phosphorylative models that are based on cell lines. © 2005 Elsevier B.V. All rights reserved.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

Immunoinformatics and the in silico prediction of immunogenicity:an introduction

Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of its principal goals is the effective prediction of immunogenicity, be that at the level of epitope, subunit vaccine, or attenuated pathogen. Immunogenicity is the ability of a pathogen or component thereof to induce a specific immune response when first exposed to surveillance by the immune system, whereas antigenicity is the capacity for recognition by the extant machinery of the adaptive immune response in a recall response. In thisbook, we introduce these subjects and explore the current state of play in immunoinformatics and the in silico prediction of immunogenicity.

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.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

Second messenger pathways in the action of cachectic factors

Cachexia in cancer is characterised by progressive depletion of both adipose tissue stores and skeletal muscle mass. Two catabolic factors produced by cachexia-inducing tumours have the potential for inducing these changes in body composition: (i) proteolysis-inducing factor (PIF) which acts on skeletal muscle to induce both protein degradation and inhibit protein synthesis, (ii) lipid-mobilising factor (LMF), which has been shown to directly induce lipolysis in isolated epididymal murine white adipocytes. Administration of lipid-mobilising factor (LMF) to mice produced a specific reduction in carcass lipid with a tendency to increase non-fat carcass mass. Treatment of murine myoblasts, myotubes and tumour cells with tumour-produced LMF, caused concentration dependent stimulation of protein synthesis, within a 24hr period. It produced an increase in intracellular cyclic AMP levels, which was linearly related to the increase in protein synthesis. The observed effect was attenuated by pretreating cells with the adenylate cyclase inhibitor, MDL12330A and was additive with stimulation produced by forskolin. Both propranolol and a specific 3 adrenergic antagonist SR59230A, significantly reduced the stimulation of protein synthesis induced by LMF. LMF also affected protein degradation in vitro, as demonstrated by a reduction in proteasome activity, a key component of the ubiquitin-dependent proteolytic pathway. These effects were opposite to those produced by PIF which caused both a decrease in the rate of protein synthesis and an elevation on protein breakdown when incubated in vitro.Incubation of LMF with a fat cell line produced alterations in the levels of guanine-nucleotide binding proteins (G proteins). This was also evident in adipocyte plasma membranes isolated from mice bearing the tumour model of cachexia, MAC16 adenocarcinoma and from patients with cancer cachexia. Progression through the cachectic state induced an upregulation of stimulatory G proteins paralleled with a downregulation of inhibitory G proteins. These changes would contribute to the increased lipid mobilisation seen in cancer cachexia.

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.

α-Tocopherol supplementation does not affect monocyte endothelial adhesion or C-reactive protein levels but reduces soluble vascular adhesion molecule-1 in the plasma of healthy subjects

Vascular monocyte retention in the subintima is pivotal to the development of cardiovascular disease and is facilitated by up-regulation of adhesion molecules on monocytes/endothelial cells during oxidative stress. Epidemiological studies have shown that cardiovascular disease risk is inversely proportional to plasma levels of the dietary micronutrients, vitamin C and vitamin E (α-tocopherol). We have tested the hypothesis that α-tocopherol supplementation may alter endothelial/monocyte function and interaction in subjects with normal ascorbate levels (> 50 μM), as ascorbate has been shown to regenerate tocopherol from its oxidised tocopheroxyl radical form in vitro. Healthy male subjects received α-tocopherol supplements (400 IU RRR-α-tocopherol /day for 6 weeks) in a placebo-controlled, double-blind intervention study. There were no significant differences in monocyte CD11b expression, monocyte adhesion to endothelial cells, plasma C-reactive protein or sICAM- 1 concentrations post-supplementation. There was no evidence for nuclear translocation of NF-κB in isolated resting monocytes, nor any effect of α-tocopherol supplementation. However, post-supplementation, sVCAM-1 levels were decreased in all subjects and sE-selectin levels were increased in the vitamin C-replete group only; a weak positive correlation was observed between sE-selectin and α-tocopherol concentration. In conclusion, α-tocopherol supplementation had little effect on cardiovascular disease risk factors in healthy subjects and the effects of tocopherol were not consistently affected by plasma vitamin C concentration. © W. S. Maney & Son Ltd.

Novel peptide antagonists of adrenomedullin and calcitonin gene-related peptide receptors:identification, pharmacological characterization, and interactions with position 74 in receptor activity-modifying protein 1/3

Human adrenomedullin (AM) is a 52-amino acid peptide belonging to the calcitonin peptide family, which also includes calcitonin gene-related peptide (CGRP) and AM2. The two AM receptors, AM(1) and AM(2), are calcitonin receptor-like receptor (CL)/receptor activity-modifying protein (RAMP) (RAMP2 and RAMP3, respectively) heterodimers. CGRP receptors comprise CL/RAMP1. The only human AM receptor antagonist (AM(22-52)) is a truncated form of AM; it has low affinity and is only weakly selective for AM(1) over AM(2) receptors. To develop novel AM receptor antagonists, we explored the importance of different regions of AM in interactions with AM(1), AM(2), and CGRP receptors. AM(22-52) was the framework for generating further AM fragments (AM(26-52) and AM(30-52)), novel AM/alphaCGRP chimeras (C1-C5 and C9), and AM/AM(2) chimeras (C6-C8). cAMP assays were used to screen the antagonists at all receptors to determine their affinity and selectivity. Circular dichroism spectroscopy was used to investigate the secondary structures of AM and its related peptides. The data indicate that the structures of AM, AM2, and alphaCGRP differ from one another. Our chimeric approach enabled the identification of two nonselective high-affinity antagonists of AM(1), AM(2), and CGRP receptors (C2 and C6), one high-affinity antagonist of AM(2) receptors (C7), and a weak antagonist selective for the CGRP receptor (C5). By use of receptor mutagenesis, we also determined that the C-terminal nine amino acids of AM seem to be responsible for its interaction with Glu74 of RAMP3. We provide new information on the structure-activity relationship of AM, alphaCGRP, and AM2 and how AM interacts with CGRP and AM(2) receptors.