Novel monoclonal antibody and peptide binders for Mycobacterium avium subsp. paratuberculosis and their application for magnetic separation

The generation of novel Mycobacterium avium subsp. paratuberculosis (MAP)-specific monoclonal antibodies and phage-display derived peptide binders, along with their application for the magnetic separation (MS) of MAP cells, is described. Our aim was to achieve even greater MAP capture capability than is possible with peptide-mediated magnetic separation (PMS) using a 50:50 mix of biotinylated-aMp3 and biotinylated-aMptD peptide-coated beads. Gamma-irradiated whole MAP cells and ethanol extracted antigens (EEA) from these cells were used to elicit an immune response and as phage-display biopanning targets. A range of novel binders was obtained and coated onto paramagnetic beads, both individually and in various combinations, for MS evaluation. IS900 PCR was employed after MS to provide quick results. Capture sensitivity was assessed using a range of MAP concentrations after which the most promising beads were tested for their specificity for MAP, by performing MS followed by culture using 10 other Mycobacterium species. Magnetic beads coated with the biotinylated EEA402 peptide demonstrated a greater level of MAP capture than the current PMS method, even when low numbers of MAP (<10 cfu/ml) were present; however these beads also captured a range of other mycobacteria and so lacked capture specificity. Magnetic beads coated with monoclonal antibodies 6G11 and 15D10 (used as a 50:50 mix or as dually coated beads) also demonstrated improved MAP capture relative to the current PMS method, but with little cross-reactivity to other Mycobacterium spp. Therefore, two new MS protocols are suggested, the application of which would be dependent upon the required endpoint. Biotinylated EEA402-coated beads could potentially be used with a MAP-specific PCR to ensure detection specificity, while beads coated with 6G11 and 15D10 monoclonal antibodies could be used with culture or the phage amplification assay.

Improved Detection of Mycobacterium bovis in Bovine Tissues Using Immunomagnetic Separation Approaches

Immunomagnetic separation (IMS) represents a simple but effective method of selectively capturing and concentrating Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB), from tissue samples. It is a physical cell separation technique that does not impact cell viability, unlike traditional chemical decontamination prior to culture. IMS is performed with paramagnetic beads coated with M. bovis-specific antibody and peptide binders. Once captured by IMS, M. bovis cells can be detected by either PCR or cultural detection methods. Increased detection rates of M. bovis, particularly from non-visibly lesioned lymph node tissues from bTB reactor animals, have recently been reported when IMS-based methods were employed.

Analysis of the antigen combining site: Correlations between length and sequence composition of the hypervariable loops and the nature of the antigen

It has long been suggested that the overall shape of the antigen combining site (ACS) of antibodies is correlated with the nature of the antigen. For example, deep pockets are characteristic of antibodies that bind haptens, grooves indicate peptide binders, while antibodies that bind to proteins have relatively flat combining sites. In. 1996, MacCallum, Martin and Thornton used a fractal shape descriptor and showed a strong correlation of the shape of the binding region with the general nature of the antigen. However, the shape of the ACS is determined primarily by the lengths of the six complementarity-determining regions (CDRs). Here, we make a direct correlation between the lengths of the CDRs and the nature of the antigen. In addition, we show significant differences in the residue composition of the CDRs of antibodies that bind to different antigen classes. As well as helping us to understand the process of antigen recognition, autoimmune disease and cross-reactivity these results are of direct application in the design of antibody phage libraries and modification of affinity. (C) 2003 Elsevier Science Ltd. All rights reserved.

Phage Display-Derived Binders Able to Distinguish Listeria monocytogenes from Other Listeria Species

The objective of this study was to produce phage display-derived binders with the ability to distinguish Listeria monocytogenes from other Listeria spp., which may have potential utility to enhance detection of Listeria monocytogenes. To obtain binders with the desired binding specificity a series of surface and solution phage-display biopannings were performed. Initially, three rounds of surface biopanning against gamma-irradiated L. monocytogenes serovar 4b cells were performed followed by an additional surface biopanning round against L. monocytogenes 4b which included prior subtraction biopanning against gamma-irradiated L. innocua cells. In an attempt to further enhance binder specificity for L. monocytogenes 4b two rounds of solution biopanning were performed, both rounds included initial subtraction solution biopanning against L. innocua. Subsequent evaluations were performed on the phage clones by phage binding ELISA. All phage clones tested from the second round of solution biopanning had higher specificity for L. monocytogenes 4b than for L. innocua and three other foodborne pathogens (Salmonella spp., Escherichia coli and Campylobacter jejuni). Further evaluation with five other Listeria spp. revealed that one phage clone in particular, expressing peptide GRIADLPPLKPN, was highly specific for L. monocytogenes with at least 43-fold more binding capability to L. monocytogenes 4b than to any other Listeria sp. This proof-of-principle study demonstrates how a combination of surface, solution and subtractive biopanning was used to maximise binder specificity. L. monocytogenes-specific binders were obtained which could have potential application in novel detection tests for L. monocytogenes, benefiting both the food and medical industries. © 2013 Morton et al.

Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm

Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A(g7) molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A(g7) have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all A(g7) data sets with high accuracy. A binding score matrix representing peptide binding motif to A(g7) was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Peptide binding to the HLA-DRB1 supertype:a proteochemometrics analysis

A proteochemometrics approach was applied to a set of 2666 peptides binding to 12 HLA-DRB1 proteins. Sequences of both peptide and protein were described using three z-descriptors. Cross terms accounting for adjacent positions and for every second position in the peptides were included in the models, as well as cross terms for peptide/protein interactions. Models were derived based on combinations of different blocks of variables. These models had moderate goodness of fit, as expressed by r2, which ranged from 0.685 to 0.732; and good cross-validated predictive ability, as expressed by q2, which varied from 0.678 to 0.719. The external predictive ability was tested using a set of 356 HLA-DRB1 binders, which showed an r2(pred) in the range 0.364-0.530. Peptide and protein positions involved in the interactions were analyzed in terms of hydrophobicity, steric bulk and polarity.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

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.

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0:a quantitative molecular docking study

Background: HLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 - 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pKa of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0. Results: The X-ray structure of the peptide - HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides. Conclusions: The higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His 79β. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins. © 2012 Patronov et al.; licensee BioMed Central Ltd.

Coating of biomaterial scaffolds with the collagen-mimetic peptide GFOGER for bone defect repair

Healing large bone defects and non-unions remains a significant clinical problem. Current treatments, consisting of auto and allografts, are limited by donor supply and morbidity, insufficient bioactivity and risk of infection. Biotherapeutics, including cells, genes and proteins, represent promising alternative therapies, but these strategies are limited by technical roadblocks to biotherapeutic delivery, cell sourcing, high cost, and regulatory hurdles. In the present study, the collagen-mimetic peptide, GFOGER, was used to coat synthetic PCL scaffolds to promote bone formation in critically-sized segmental defects in rats. GFOGER is a synthetic triple helical peptide that binds to the [alpha]2[beta]1 integrin receptor involved in osteogenesis. GFOGER coatings passively adsorbed onto polymeric scaffolds, in the absence of exogenous cells or growth factors, significantly accelerated and increased bone formation in non-healing femoral defects compared to uncoated scaffolds and empty defects. Despite differences in bone volume, no differences in torsional strength were detected after 12 weeks, indicating that bone mass but not bone quality was improved in this model. This work demonstrates a simple, cell/growth factor-free strategy to promote bone formation in challenging, non-healing bone defects. This biomaterial coating strategy represents a cost-effective and facile approach, translatable into a robust clinical therapy for musculoskeletal applications.

Prandial subcutaneous injections of glucagon-like peptide-1 cause weight loss in obese human subjects

Recombinant glucagon-like peptide-1 (7–36)amide (rGLP-1) was recently shown to cause significant weight loss in type 2 diabetics when administered for 6 weeks as a continuous subcutaneous infusion. The mechanisms responsible for the weight loss are not clarified. In the present study, rGLP-1 was given for 5d by prandial subcutaneous injections (PSI) (76nmol 30min before meals, four times daily; a total of 302·4nmol/24h) or by continuous subcutaneous infusion (CSI) (12·7nmol/h; a total of 304·8nmol/24h). This was performed in nineteen healthy obese subjects (mean age 44·2 (sem 2·5) years; BMI 39·0 (sem 1·2)kg/m2) in a prospective randomised, double-blind, placebo-controlled, cross-over study. Compared with the placebo, rGLP-1 administered as PSI and by CSI generated a 15% reduction in mean food intake per meal (P=0·02) after 5d treatment. A weight loss of 0·55 (sem 0·2) kg (P<0·05) was registered after 5d with PSI of rGLP-1. Gastric emptying rate was reduced during both PSI (P<0·001) and CSI (P<0·05) treatment, but more rapidly and to a greater extent with PSI of rGLP-1. To conclude, a 5d treatment of rGLP-1 at high doses by PSI, but not CSI, promptly slowed gastric emptying as a probable mechanism of action of increased satiety, decreased hunger and, hence, reduced food intake with an ensuing weight loss.

The expanding roles of the ghrelin-gene derived peptide obestatin in health and disease

Obestatin is a 23 amino acid, ghrelin gene-derived peptide hormone produced in the stomach and a range of other tissues throughout the body. While it was initially reported that obestatin opposed the actions of ghrelin with regards to appetite and food intake, it is now clear that obestatin is not an endogenous ghrelin antagonist of ghrelin, but it is a multi-functional peptide hormone in its own right. In this review we will discuss the controversies associated with the discovery of obestatin and explore emerging central and peripheral roles of obestatin, roles in adipogenesis, pancreatic homeostasis and cancer.