MHCPred 2.0

The immunoinformatics of cancer immunotherapy

We review here the developments in the field of immunoinformatics and their present and potential applications to the immunotherapeutic treatment of cancer. Antigen presentation plays a central role in the immune response, and as a result in immunotherapeutic methods such as adoptive T-cell transfer and antitumor vaccination. We therefore extensively review the current technologies of antigen presentation prediction, including the next generation predictors, which combine proteasomal processing, transporter associated with antigen processing and major histocompatibility complex (MHC)-binding prediction. Minor histocompatibility antigens are also relevant targets for immunotherapy, and we review the current systems available, SNEP and SiPep. Here, antigen presentation plays a key role, but additional types of data are also incorporated, such as single nucleotide polymorphism data and tissue/cell-type expression data. Current systems are not capable of handling the concept of immunodominance, which is critical to immunotherapy, but efforts have been made to model general aspects of the immune system. Although tough challenges lie ahead, when measuring the field of immunoinformatics on its contributions thus far, one can expect fruitful developments in the future.

Computational analysis and identification of amino acid sites in dengue E proteins relevant to development of diagnostics and vaccines

We have identified 72 completely conserved amino acid residues in the E protein of major groups of the Flavivirus genus by computational analyses. In the dengue species we have identified 12 highly conserved sequence regions, 186 negatively selected sites, and many dengue serotype-specific negatively selected sites. The flavivirus-conserved sites included residues involved in forming six disulfide bonds crucial for the structural integrity of the protein, the fusion motif involved in viral infectivity, and the interface residues of the oligomers. The structural analysis of the E protein showed 19 surface-exposed non-conserved residues, 128 dimer or trimer interface residues, and regions, which undergo major conformational change during trimerization. Eleven consensus T(h)-cell epitopes common to all four dengue serotypes were predicted. Most of these corresponded to dengue-conserved regions or negatively selected sites. Of special interest are six singular sites (N(37), Q(211), D(215), P(217), H(244), K(246)) in dengue E protein that are conserved, are part of the predicted consensus T(h)-cell epitopes and are exposed in the dimer or trimer. We propose these sites and corresponding epitopic regions as potential candidates for prioritization by experimental biologists for development of diagnostics and vaccines that may be difficult to circumvent by natural or man-made alteration of dengue virus.

Predicted epitopes of Lig A of Leptospira interrogans by bioinformatics method: a clue for further vaccine development

Leptospirosis is an important tropical infection. Providing sufficient antibiotic drugs and development and approval of new vaccines are the control of the possible emerging pandemic of this pathogen. For Leptospira interrogans, leptospiral Ig-like A (LigA) LigA may have value in serodiagnosis or as a protective immunogen in novel vaccines. Here, the author reports the preliminary data from the computation analysis of Lig A to find potential T-cell epitopes using a new bioinformatics tool. Among all alleles, the results from A0203, A0301 and DRB0101 show significant lower IC50 than other alleles. The 307FCISIFLSM315 corresponding to DRB0101 allele is the peptide with the best binding affinity. The determined peptides are useful for further vaccine development because it can reduce the time and minimize the total number of required tests to find the possible proper epitopes, the target for vaccine development.

A modular concept of HLA for comprehensive peptide binding prediction

A variety of algorithms have been successful in predicting human leukocyte antigen (HLA)-peptide binding for HLA variants for which plentiful experimental binding data exist. Although predicting binding for only the most common HLA variants may provide sufficient population coverage for vaccine design, successful prediction for as many HLA variants as possible is necessary to understand the immune response in transplantation and immunotherapy. However, the high cost of obtaining peptide binding data limits the acquisition of binding data. Therefore, a prediction algorithm, which applies the binding information from well-studied HLA variants to HLA variants, for which no peptide data exist, is necessary. To this end, a modular concept of class I HLA-peptide binding prediction was developed. Accurate predictions were made for several alleles without using experimental peptide binding data specific to those alleles. We include a comparison of module-based prediction and supertype-based prediction. The modular concept increased the number of predictable alleles from 15 to 75 of HLA-A and 12 to 36 of HLA-B proteins. Under the modular concept, binding data of certain HLA alleles can make prediction possible for numerous additional alleles. We report here a ranking of HLA alleles, which have been identified to be the most informative. Modular peptide binding prediction is freely available to researchers on the web at http://www.peptidecheck.org .

EpiJen: a server for multistep T cell epitope prediction

BACKGROUND

The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online.

RESULTS

To identify epitopes, a source protein is passed through four steps: proteasome cleavage, TAP transport, MHC binding and epitope selection. At each stage, different proportions of non-epitopes are eliminated. The final set of peptides represents no more than 5% of the whole protein sequence and will contain 85% of the true epitopes, as indicated by external validation. Compared to other integrated methods (NetCTL, WAPP and SMM), EpiJen performs best, predicting 61 of the 99 HIV epitopes used in this study.

CONCLUSION

EpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods. These methods aim to reduce subsequent experimental work by improving the success rate of epitope prediction.