Immune epitope database analysis resource

The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages

Identification of B Cell Epitopes of Alcohol Dehydrogenase Allergen of Curvularia lunata

BACKGROUND/OBJECTIVE: Epitope identification assists in developing molecules for clinical applications and is useful in defining molecular features of allergens for understanding structure/function relationship. The present study was aimed to identify the B cell epitopes of alcohol dehydrogenase (ADH) allergen from Curvularia lunata using in-silico methods and immunoassay. METHOD: B cell epitopes of ADH were predicted by sequence and structure based methods and protein-protein interaction tools while T cell epitopes by inhibitory concentration and binding score methods. The epitopes were superimposed on a three dimensional model of ADH generated by homology modeling and analyzed for antigenic characteristics. Peptides corresponding to predicted epitopes were synthesized and immunoreactivity assessed by ELISA using individual and pooled patients' sera. RESULT: The homology model showed GroES like catalytic domain joined to Rossmann superfamily domain by an alpha helix. Stereochemical quality was confirmed by Procheck which showed 90% residues in most favorable region of Ramachandran plot while Errat gave a quality score of 92.733%. Six B cell (P1-P6) and four T cell (P7-P10) epitopes were predicted by a combination of methods. Peptide P2 (epitope P2) showed E(X)(2)GGP(X)(3)KKI conserved pattern among allergens of pathogenesis related family. It was predicted as high affinity binder based on electronegativity and low hydrophobicity. The computational methods employed were validated using Bet v 1 and Der p 2 allergens where 67% and 60% of the epitope residues were predicted correctly. Among B cell epitopes, Peptide P2 showed maximum IgE binding with individual and pooled patients' sera (mean OD 0.604±0.059 and 0.506±0.0035, respectively) followed by P1, P4 and P3 epitopes. All T cell epitopes showed lower IgE binding. CONCLUSION: Four B cell epitopes of C. lunata ADH were identified. Peptide P2 can serve as a potential candidate for diagnosis of allergic diseases

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

<p>Abstract</p> <p>Background</p> <p>The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.</p> <p>Results</p> <p>We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides.</p> <p>Conclusions</p> <p>The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at <url>http://bordnerlab.org/RTA/</url>.</p

High-Throughput Analysis of Synthetic Peptides for the Immunodiagnosis of Canine Visceral Leishmaniasis

Globally, the number of new human cases of visceral leishmaniasis (VL) is estimated to be approximately 500,000 per year. This is the most severe of all forms of leishmaniasis, and the zoonotic form of VL, caused by Leishmania infantum (also known as Leishmania chagasi), represents 20% of human visceral leishmaniasis worldwide; additionally, its prevalence is increasing in urban and peri-urban areas of the tropics. In Brazil, the identification and elimination of infected dogs, which act as a reservoir for Leishmania parasites, is a control measure employed in addition to the use of insecticides against the vectors and the identification and treatment of infected humans. Currently, the diagnostic methods employed to identify infected animals are not able to detect all of these dogs, which compromises the effectiveness of control measures. Moreover, one of the most important issues in controlling VL is the difficulty of diagnosing asymptomatic dogs, which act as parasite reservoirs. Therefore, to contribute to the improvement of the diagnostic methods for CVL, we aimed to identify and characterize new antigens that were more sensitive and specific and could be applied in epidemiologic surveys

Identification of a Highly Conserved H1 Subtype-Specific Epitope with Diagnostic Potential in the Hemagglutinin Protein of Influenza A Virus

Subtype specificity of influenza A virus (IAV) is determined by its two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). For HA, 16 distinct subtypes (H1–H16) exist, while nine exist for NA. The epidemic strains of H1N1 IAV change frequently and cause annual seasonal epidemics as well as occasional pandemics, such as the notorious 1918 influenza pandemic. The recent introduction of pandemic A/H1N1 IAV (H1N1pdm virus) into humans re-emphasizes the public health concern about H1N1 IAV. Several studies have identified conserved epitopes within specific HA subtypes that can be used for diagnostics. However, immune specific epitopes in H1N1 IAV have not been completely assessed. In this study, linear epitopes on the H1N1pdm viral HA protein were identified by peptide scanning using libraries of overlapping peptides against convalescent sera from H1N1pdm patients. One epitope, P5 (aa 58–72) was found to be immunodominant in patients and to evoke high titer antibodies in mice. Multiple sequence alignments and in silico coverage analysis showed that this epitope is highly conserved in influenza H1 HA [with a coverage of 91.6% (9,860/10,767)] and almost completely absent in other subtypes [with a coverage of 3.3% (792/23,895)]. This previously unidentified linear epitope is located outside the five well-recognized antigenic sites in HA. A peptide ELISA method based on this epitope was developed and showed high correlation (χ2 = 51.81, P<0.01, Pearson correlation coefficient R = 0.741) with a hemagglutination inhibition test. The highly conserved H1 subtype-specific immunodominant epitope may form the basis for developing novel assays for sero-diagnosis and active surveillance against H1N1 IAVs

Identification of a Highly Conserved H1 Subtype-Specific Epitope with Diagnostic Potential in the Hemagglutinin Protein of Influenza A Virus

Subtype specificity of influenza A virus (IAV) is determined by its two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). For HA, 16 distinct subtypes (H1–H16) exist, while nine exist for NA. The epidemic strains of H1N1 IAV change frequently and cause annual seasonal epidemics as well as occasional pandemics, such as the notorious 1918 influenza pandemic. The recent introduction of pandemic A/H1N1 IAV (H1N1pdm virus) into humans re-emphasizes the public health concern about H1N1 IAV. Several studies have identified conserved epitopes within specific HA subtypes that can be used for diagnostics. However, immune specific epitopes in H1N1 IAV have not been completely assessed. In this study, linear epitopes on the H1N1pdm viral HA protein were identified by peptide scanning using libraries of overlapping peptides against convalescent sera from H1N1pdm patients. One epitope, P5 (aa 58–72) was found to be immunodominant in patients and to evoke high titer antibodies in mice. Multiple sequence alignments and in silico coverage analysis showed that this epitope is highly conserved in influenza H1 HA [with a coverage of 91.6% (9,860/10,767)] and almost completely absent in other subtypes [with a coverage of 3.3% (792/23,895)]. This previously unidentified linear epitope is located outside the five well-recognized antigenic sites in HA. A peptide ELISA method based on this epitope was developed and showed high correlation (χ2 = 51.81, P<0.01, Pearson correlation coefficient R = 0.741) with a hemagglutination inhibition test. The highly conserved H1 subtype-specific immunodominant epitope may form the basis for developing novel assays for sero-diagnosis and active surveillance against H1N1 IAVs