Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research

Identification of new meningococcal serogroup B surface antigens through a systematic analysis of neisserial genomes

The difficulty of inducing an effective immune response against the Neisseria meningitidis serogroup B capsular polysaccharide has lead to the search for vaccines for this serogroup based on outer membrane proteins. The availability of the first meningococcal genome (MC58 strain) allowed the expansion of high-throughput methods to explore the protein profile displayed by N. meningitidis. By combining a pan-genome analysis with an extensive experimental validation to identify new potential vaccine candidates, genes coding for antigens likely to be exposed on the surface of the meningococcus were selected after a multistep comparative analysis of entire Neisseria genomes. Eleven novel putative ORF annotations were reported for serogroup B strain MC58. Furthermore, a total of 20 new predicted potential pan-neisserial vaccine candidates were produced as recombinant proteins and evaluated using immunological assays. Potential vaccine candidate coding genes were PCR-amplified from a panel of representative strains and their variability analyzed using maximum likelihood approaches for detecting positive selection. Finally, five proteins all capable of inducing a functional antibody response vs N. meningitidis strain CU385 were identified as new attractive vaccine candidates: NMB0606 a potential YajC orthologue, NMB0928 the neisserial NlpB (BamC), NMB0873 a LolB orthologue, NMB1163 a protein belonging to a curli-like assembly machinery, and NMB0938 (a neisserial specific antigen) with evidence of positive selection appreciated for NMB0928. The new set of vaccine candidates and the novel proposed functions will open a new wave of research in the search for the elusive neisserial vaccine.

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.

Critical role of glycosylation in determining the length and structure of T cell epitopes

Background

Using a combined in silico approach, we investigated the glycosylation of T cell epitopes and autoantigens. The present systems biology analysis was made possible by currently available databases (representing full proteomes, known human T cell epitopes and autoantigens) as well as glycosylation prediction tools.

Results

We analyzed the probable glycosylation of human T cell epitope sequences extracted from the ImmuneEpitope Database. Our analysis suggests that in contrast to full length SwissProt entries, only a minimal portion of experimentally verified T cell epitopes is potentially N- or O-glycosylated (2.26% and 1.22%, respectively). Bayesian analysis of entries extracted from the Autoantigen Database suggests a correlation between N-glycosylation and autoantigenicity. The analysis of random generated sequences shows that glycosylation probability is also affected by peptide length. Our data suggest that the lack of peptide glycosylation, a feature that probably favors effective recognition by T cells, might have resulted in a selective advantage for short peptides to become T cell epitopes. The length of T cell epitopes is at the intersection of curves determining specificity and glycosylation probability. Thus, the range of length of naturally occurring T cell epitopes may ensure the maximum specificity with the minimal glycosylation probability.

Conclusion

The findings of this bioinformatical approach shed light on fundamental factors that might have shaped adaptive immunity during evolution. Our data suggest that amino acid sequence-based hypo/non-glycosylation of certain segments of proteins might be substantial for determining T cell immunity/autoimmunity.

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

BACKGROUND

The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.

RESULTS

Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods.

CONCLUSION

The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

Towards in silico design of epitope-based vaccines

BACKGROUND

Epitope-based vaccines (EVs) make use of immunogenic peptides (epitopes) to trigger an immune response. Due to their manifold advantages, EVs have recently been attracting growing interest. The success of an EV is determined by the choice of epitopes used as a basis. However, the experimental discovery of candidate epitopes is expensive in terms of time and money. Furthermore, for the final choice of epitopes various immunological requirements have to be considered.

METHODS

Numerous in silico approaches exist that can guide the design of EVs. In particular, computational methods for MHC binding prediction have already become standard tools in immunology. Apart from binding prediction and prediction of antigen processing, methods for epitope design and selection have been suggested. We review these in silico approaches for epitope discovery and selection along with their strengths and weaknesses. Finally, we discuss some of the obvious problems in the design of EVs.

CONCLUSION

State-of-the-art in silico approaches to MHC binding prediction yield high accuracies. However, a more thorough understanding of the underlying biological processes and significant amounts of experimental data will be required for the validation and improvement of in silico approaches to the remaining aspects of EV design.

Immunoinformatic comparison of T-cell epitopes contained in novel swine-origin influenza A (H1N1) virus with epitopes in 2008-2009 conventional influenza vaccine

In March 2009 a novel swine-origin influenza A (H1N1) virus (S-OIV) emerged in Mexico and the Western United States. Vaccination with conventional influenza vaccine (CIV) does not result in cross-reactive antibodies, however, the disproportionate number of cases (37%) occurring among persons younger than 50 years old suggested that adaptive immune memory might be responsible for the relative lack of virulence in older, healthy adults. Using EpiMatrix, a T-cell epitope prediction and comparison tool, we compared the sequences of the three hemagglutinin (HA) and neuraminidase (NA) proteins contained in 2008-2009 CIV to their counterparts in A/California/04/2009 (H1N1) looking for cross-conserved T-cell epitope sequences. We found greater than 50% conservation of T helper and CTL epitopes between novel S-OIV and CIV HA for selected HLA. Conservation was lower among NA epitopes. Sixteen promiscuous helper T-cell epitopes are contained in the S-OIV H1N1 HA sequence, of which nine (56%) were 100% conserved in the 2008-2009 influenza vaccine strain; 81% were either identical or had one conservative amino acid substitution. Fifty percent of predicted CTL epitopes found in S-OIV H1N1 HA were also found in CIV HA sequences. Based on historical performance, we expect these epitope predictions to be 93-99% accurate. This in silico analysis supports the proposition that T-cell response to cross-reactive T-cell epitopes, due to vaccination or exposure, may have the capacity to attenuate the course of S-OIV H1N1 induced disease-in the absence of cross-reactive antibody response. The value of the CIV or live-attenuated influenza vaccine containing the 2008-2009 vaccine strains, as defense against H1N1, could be further tested by evaluating human immune responses to the conserved T-cell epitopes using PBMC from individuals infected with H1N1 and from CIV vaccinees.

Emerging strengths in Asia Pacific bioinformatics

The 2008 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998, was organized as the 7th International Conference on Bioinformatics (InCoB), jointly with the Bioinformatics and Systems Biology in Taiwan (BIT 2008) Conference, Oct. 20-23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, InCoB is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this InCoB 2008 meeting followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei Conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts.