MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets—termed T-cell epitope hotspots. MULTIPRED is available at

PRED(BALB/c): a system for the prediction of peptide binding to H2(d) molecules, a haplotype of the BALB/c mouse

PRED(BALB/c) is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I (H2-K(d), H2-L(d) and H2-D(d)) and class II (I-E(d) and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PRED(BALB/c) is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PRED(BALB/c) is available at

Evolutionarily Conserved Protein Sequences of Influenza A Viruses, Avian and Human, as Vaccine Targets

10.1371/journal.pone.0001190PLoS ONE21

Comprehensive analysis of T cell epitope discovery strategies using 17DD yellow fever virus structural proteins and BALB/c (H2d) mice model

AbstractImmunomics research uses in silico epitope prediction, as well as in vivo and in vitro approaches. We inoculated BALB/c (H2d) mice with 17DD yellow fever vaccine to investigate the correlations between approaches used for epitope discovery: ELISPOT assays, binding assays, and prediction software. Our results showed a good agreement between ELISPOT and binding assays, which seemed to correlate with the protein immunogenicity. PREDBALB/c prediction software partially agreed with the ELISPOT and binding assay results, but presented low specificity. The use of prediction software to exclude peptides containing no epitopes, followed by high throughput screening of the remaining peptides by ELISPOT, and the use of MHC-biding assays to characterize the MHC restrictions demonstrated to be an efficient strategy. The results allowed the characterization of 2 MHC class I and 17 class II epitopes in the envelope protein of the YF virus in BALB/c (H2d) mice

Neural models for predicting viral vaccine targets

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve - AROC > 0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC >; 0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED

SARS coronavirus nucleocapsid immunodominant T-cell epitope cluster is common to both exogenous recombinant and endogenous DNA-encoded immunogens

Correspondence between the T-cell epitope responses of vaccine immunogens and those of pathogen antigens is critical to vaccine efficacy. In the present study, we analyzed the spectrum of immune responses of mice to three different forms of the SARS coronavirus nucleocapsid (N): (1) exogenous recombinant protein (N-GST) with Freund's adjuvant; (2) DNA encoding unmodified N as an endogenous cytoplasmic protein (pN); and (3) DNA encoding N as a LAMP-I chimera targeted to the lysosomal MHC II compartment (p-LAMP-N). Lysosomal trafficking of the LAMP/N chimera in transfected cells was documented by both confocal and immunoelectron microscopy. The responses of the immunized mice differed markedly. The strongest T-cell IFN-gamma and CTL responses were to the LAMP-N chimera followed by the pN immunogen. In contrast, N-GST elicited strong T cell IL-4 but minimal IFN-gamma responses and a much greater antibody response. Despite these differences, however, the immunodominant T-cell ELISpot responses to each of the three immunogens were elicited by the same N peptides, with the greatest responses being generated by a cluster of five overlapping peptides, N76-114, each of which contained nonameric H2(d) binding domains with high binding scores for both class I and, except for N76-93, class II alleles. These results demonstrate that processing and presentation of N, whether exogenously or endogenously derived, resulted in common immunodominant epitopes, supporting the usefulness of modified antigen delivery and trafficking forms and, in particular, LAMP chimeras as vaccine candidates. Nevertheless, the profiles of T-cell responses were distinctly different. The pronounced Th-2 and humoral response to N protein plus adjuvant are in contrast to the balanced IFN-gamma and IL-4 responses and strong memory CTL responses to the LAMP-N chimera. (C) 2005 Elsevier Inc. All rights reserved

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

Background: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. Results: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population. Conclusion: Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design