Development and application of synthetic peptides as vaccines

Validation of New ELISA Technique for Detection of Aflatoxin B1 Contamination in Food Products versus HPLC and VICAM

Toxin-contaminated foods and beverages are a major source of illness, may cause death, and have a significant negative economic impact worldwide. Aflatoxin B1 (AFB1) is a potent toxin that may induce cancer after chronic low-level exposure. This study developed a quantitative recombinant AflR gene antiserum ELISA technique for aflatoxin B1 detection in contaminated food products. Aflatoxin B1 residuals from 36 food samples were analyzed with HPLC and VICAM. DNA was extracted from aflatoxin-contaminated samples and the AflR gene amplified using PCR. PCR products were purified and ligated into the pGEM-T vector. Recombinant plasmids were sequenced and transformed into competent E. coli (BL21). Molecular size and B-cell epitope prediction for the recombinant protein were assessed. The purified protein was used to induce the production of IgG antibodies in rabbits. Serum IgG was purified and labeled with alkaline phosphatase. Finally, indirect-ELISA was used to test the effectiveness of polyclonal antibodies for detection of aflatoxin B1 in food samples.

Identification of amino acid propensities that are strong determinants of linear B-cell epitope using neural networks

BACKGROUND

Identification of amino acid propensities that are strong determinants of linear B-cell epitope is very important to enrich our knowledge about epitopes. This can also help to obtain better epitope prediction. Typical linear B-cell epitope prediction methods combine various propensities in different ways to improve prediction accuracies. However, fewer but better features may yield better prediction. Moreover, for a propensity, when the sequence length is k, there will be k values, which should be treated as a single unit for feature selection and hence usual feature selection method will not work. Here we use a novel Group Feature Selecting Multilayered Perceptron, GFSMLP, which treats a group of related information as a single entity and selects useful propensities related to linear B-cell epitopes, and uses them to predict epitopes.

METHODOLOGY/ PRINCIPAL FINDINGS

We use eight widely known propensities and four data sets. We use GFSMLP to rank propensities by the frequency with which they are selected. We find that Chou's beta-turn and Ponnuswamy's polarity are better features for prediction of linear B-cell epitope. We examine the individual and combined discriminating power of the selected propensities and analyze the correlation between paired propensities. Our results show that the selected propensities are indeed good features, which also cooperate with other propensities to enhance the discriminating power for predicting epitopes. We find that individually polarity is not the best predictor, but it collaborates with others to yield good prediction. Usual feature selection methods cannot provide such information.

CONCLUSIONS/ SIGNIFICANCE

Our results confirm the effectiveness of active (group) feature selection by GFSMLP over the traditional passive approaches of evaluating various combinations of propensities. The GFSMLP-based feature selection can be extended to more than 500 remaining propensities to enhance our biological knowledge about epitopes and to obtain better prediction. A graphical-user-interface version of GFSMLP is available at: http://bio.classcloud.org/GFSMLP/.

High-throughput prediction of protein antigenicity using protein microarray data

MOTIVATION

Discovery of novel protective antigens is fundamental to the development of vaccines for existing and emerging pathogens. Most computational methods for predicting protein antigenicity rely directly on homology with previously characterized protective antigens; however, homology-based methods will fail to discover truly novel protective antigens. Thus, there is a significant need for homology-free methods capable of screening entire proteomes for the antigens most likely to generate a protective humoral immune response.

RESULTS

Here we begin by curating two types of positive data: (i) antigens that elicit a strong antibody response in protected individuals but not in unprotected individuals, using human immunoglobulin reactivity data obtained from protein microarray analyses; and (ii) known protective antigens from the literature. The resulting datasets are used to train a sequence-based prediction model, ANTIGENpro, to predict the likelihood that a protein is a protective antigen. ANTIGENpro correctly classifies 82% of the known protective antigens when trained using only the protein microarray datasets. The accuracy on the combined dataset is estimated at 76% by cross-validation experiments. Finally, ANTIGENpro performs well when evaluated on an external pathogen proteome for which protein microarray data were obtained after the initial development of ANTIGENpro.

AVAILABILITY

ANTIGENpro is integrated in the SCRATCH suite of predictors available at http://scratch.proteomics.ics.uci.edu.

CONTACT

pfbaldi@ics.uci.edu

A general strategy for epitope mapping by direct MALDI-TOF mass spectrometry using secondary antibodies and cross-linking

The combination of limited proteolysis and MALDI-TOF mass spectrometry has become an important tool for the determination of epitopes but works best with highly purified antibodies. Here we report the use of capture antibodies to reduce the need for purification of the antibody in the mass spectrometric determination of the epitope. In this new method, a secondary Fc-specific antibody, covalently bound to Sepharose beads, is used to capture the primary antibody (the antibody of interest). After capture, the two antibodies are cross-linked. The antigen is then bound to the immobilized antibodies and subjected to proteolysis using several successive proteinases. In this study, this strategy is demonstrated with a crude mouse anti-ACTH IgG solution and adrenocorticotropin (ACTH). Comparing this strategy with previous methods where the antibody is bound directly to activated beads, the new method (1) results in a higher binding capacity of the bound antibody to ACTH, (2) does not require purification of the antibody of interest, and (3) dramatically reduces the chemical background in the MALDI mass spectra.

Contributions of mass spectrometry to structural immunology

Mass spectrometry has made important contributions to the field of immunology in the past decade. A variety of mass spectrometric-based techniques have been applied to study the structures of macromolecules that play a vital role in the immune response. These include traditional molecular mass measurements to identify post-translational modifications and structural heterogeneity, mass mapping of proteolysis products, sequencing by tandem mass spectrometry and conformational analysis. Antigen-antibody and other immune complexes have been detected by mass spectrometry, providing an avenue to study macromolecular assemblies that are important to immune function. By virtue of the ability of mass spectrometry based techniques to analyze complex biological mixtures, mass spectrometry has also been employed to identify and sequence protein epitopes important in both the humoral and cellular immune responses. This has been achieved through a combination of immunoaffinity and mass spectrometric techniques, and the coupling of high-performance chromatographs to mass spectrometers. These approaches are important for the identification of pathogens and show promise for the early diagnosis of disease associated with viral and bacterial infection and malignancy. These investigations will enable the mechanisms associated with normal and impaired immune function to be elucidated. Mass spectrometry has been utilized to characterize the structure of peptide mimics, multiple antigenic peptides and other constructs in the design of synthetic immunogens. Information derived from these studies will aid in the development of novel therapeutics and vaccines.

Direct identification of protein epitopes by mass spectrometry without immobilization of antibody and isolation of antibody-peptide complexes

A rapid new approach is described that combines the selectivity and sensitivity of immunoaffinity and mass spectrometric based techniques for mapping protein epitopes. The approach alleviates the need to immobilize antibody, extract the antibody-peptide complex, and dissociate bound peptide, which are requirements of other methods. It avoids problems associated with limited proteolysis of an antigen-antibody complex particularly in the vicinity of the binding domain which can hinder identification of the epitope. Epitopic peptides are identified from a direct comparison of the matrix-assisted laser desorption ionization mass spectra of the antibody reaction mixture and an unreacted control. Samples are prepared for mass spectrometric analysis by heat-assisted and electrospray deposition to afford reproducible spectra that enable epitopic peptides to be identified in complex mixtures analyzed at the femtomole level. Indirect evidence is presented to suggest that the antibody-peptide complex is resilient to both sample deposition and the ionization event. The utility and sensitivity of the approach are illustrated for the lysozyme model. IgG-binding domains of human lysozyme are identified, and one epitope is refined to six residues that comprise part of an extended beta-loop region.

A semi-empirical method for prediction of antigenic determinants on protein antigens

Analysis of data from experimentally determined antigenic sites on proteins has revealed that the hydrophobic residues Cys, Leu and Val, if they occur on the surface of a protein, are more likely to be a part of antigenic sites. A semi-empirical method which makes use of physicochemical properties of amino acid residues and their frequencies of occurrence in experimentally known segmental epitopes was developed to predict antigenic determinants on proteins. Application of this method to a large number of proteins has shown that our method can predict antigenic determinants with about 75% accuracy which is better than most of the known methods. This method is based on a single parameter and thus very simple to use.