Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus

T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood, We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.

Mice expressing the E7 oncogene of HPV16 in epithelium show central tolerance, and evidence of peripheral anergising tolerance, to E7-encoded cytotoxic T-lymphocyte epitopes

In order to derive mice which expressed both the E7 open reading frame transgene of human papillomavirus type 16 in skin and MHC class 1 restriction elements for several E7-encoded cytotoxic T-lymphocyte (CTL) epitopes, K14.HPV16E7 mice which express E7 in basal keratinocytes were crossed to the F1 generation with A2.1 K-b transgenic mice which express the MHC binding cleft domains of human HLA A*0201, and murine H-2(b). F1 mice (denoted K14E7xA2.1) expressed E7 in the thymus at least as early as 2-5 days before birth. Immunisation of FVBxA2.1 control mice (transgenic for HLA A*0201 and H-2(b) but not for E7), with two HLA A*0201-restricted epitopes of E7 and one H-2(b)-restricted CTL epitope of E7, gave strong primary CTL responses recognising epitope-pulsed or constitutively E7-expressing syngeneic target cells. In contrast, in immunised K14E7xA2.1 mice, the CTL responses to the H-2(b) epitope and one of the HLA A*0201 CTL epitopes were strongly down-regulated, and to the other HLA A*0201 epitope, completely abolished, as demonstrated by percentage specific killing by bulk splenocyte cultures in cyrotoxicity assays, and by CTL precursor frequency analysis, In thymus-transplanted bone marrow radiation chimeras in which the immune system of K14E7xA2.1 mice was replaced by a FVBxA2.1 immune system, specific immunisation did not result in reemergence of strong E7-directed CTL responses. In agreement with these in vitro findings, specific immunisation failed to significantly alter the course of E7-associated tumour development in K14E7xA2.1 mice. These data are consistent with a model of central deletional CTL tolerance to E7-encoded epitopes recognised in the context of two distinct MHC class 1 restriction elements, and with the possibility of peripheral T-cell anergy maintained by expression of E7 in the skin. (C) 1998 Academic Press.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Computational binding assays of antigenic peptides

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1-5%.

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-All binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes. (C) 2001 by Elsevier Science Inc.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

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

PREDBALB/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 PREDBALB/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). PREDBALB/c is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

A peptide-binding motif for I-A(g7), the class II major histocompatibility complex (MHC) molecule of NOD and Biozzi AB/H mice

The class II major histocompatibility complex molecule I-A(g7) is strongly linked to the development of spontaneous insulin-dependent diabetes mellitus (IDDM) in non obese diabetic mice and to the induction of experimental allergic encephalomyelitis in Biozzi AB/H mice. Structurally, it resembles the HLA-DQ molecules associated with human IDDM, in having a non-Asp residue at position 57 in its beta chain. To identify the requirements for peptide binding to I-A(g7) and thereby potentially pathogenic T cell epitopes, we analyzed a known I-A(g7)-restricted T cell epitope, hen egg white lysozyme (HEL) amino acids 9-27. NH2- and COOH-terminal truncations demonstrated that the minimal epitope for activation of the T cell hybridoma 2D12.1 was M12-R21 and the minimum sequence for direct binding to purified I-A(g7) M12-Y20/K13-R21. Alanine (A) scanning revealed two primary anchors for binding at relative positions (p) 6 (L) and 9 (Y) in the HEL epitope. The critical role of both anchors was demonstrated by incorporating L and Y in poly(A) backbones at the same relative positions as in the HEL epitope. Well-tolerated, weakly tolerated, and nontolerated residues were identified by analyzing the binding of peptides containing multiple substitutions at individual positions. Optimally, p6 was a large, hydrophobic residue (L, I, V, M), whereas p9 was aromatic and hydrophobic (Y or F) or positively charged (K, R). Specific residues were not tolerated at these and some other positions. A motif for binding to I-A(g7) deduced from analysis of the model HEL epitope was present in 27/30 (90%) of peptides reported to be I-A(g7)-restricted T cell epitopes or eluted from I-A(g7). Scanning a set of overlapping peptides encompassing human proinsulin revealed the motif in 6/6 good binders (sensitivity = 100%) and 4/13 weak or non-binders (specificity = 70%). This motif should facilitate identification of autoantigenic epitopes relevant to the pathogenesis and immunotherapy of IDDM.

Antibody levels to the class I and II epitopes of the M protein and myosin are related to group A streptococcal exposure in endemic populations

Rheumatic fever (RF)/rheumatic heart disease (RHD) and post-streptococcal glomerulonephritis are thought to be autoimmune diseases, and follow group A streptococcal (GAS) infection. Different GAS M types have been associated with rheumatogenicity or nephritogenicity and categorized into either of two distinct classes (I or II) based on amino acid sequences present within the repeat region ('C' repeats) of the M protein. Sera from ARF patients have previously been shown to contain elevated levels of antibodies to the class I-specific epitope and myosin with the class I-specific antibodies also being cross-reactive to myosin, suggesting a disease association. This study shows that immunoreactivity of the class I-specific peptide and myosin does not differ between controls and acute RF (ARF)/RHD in populations that are highly endemic for GAS, raising the possibility that the association is related to GAS exposure, not the presence of ARF/RHD. Peptide inhibition studies suggest that the class I epitope may be conformational and residue 10 of the peptide is critical for antibody binding. We demonstrate that correlation of antibody levels between the class I and II epitope is due to class II-specific antibodies recognizing a common epitope with class I which is contained within the sequence RDL-ASRE. Our results suggest that antibody prevalence to class I and II epitopes and myosin is associated with GAS exposure, and that antibodies to these epitopes are not an indicator of disease nor a pathogenic factor in endemic populations.

Limitations of HLA-transgenic mice in presentation of HLA-restricted cytotoxic T-cell epitopes from endogenously processed human papillomavirus type 16 E7 protein

We investigated the use of mice transgenic for human leucocyte antigen (HLA) A*0201 antigen-binding domains to test vaccines composed of defined HLA A*0201-restricted cytotoxic T-lymphocyte (CTL) epitopes of human papillomavirus (HPV) type 16 E7 oncoprotein. HPV is detected in >90% of cervical carcinomas. HPV16 E7 oncoprotein transforms cells of the uterine cervix and functions as a tumour-associated antigen to which immunotherapeutic strategies may be directed. We report that although the HLA A*0201 E7 epitope peptides function both to prime for E7 CTL responses, and to sensitize target cells for E7-directed CTL killing in situations where antigen processing is not required, the epitopes are not processed out of either endogenously expressed or immunization-introduced E7, by the mouse antigen-processing and presentation machinery. Thus (1) CTL induced by HLA A*0201 peptide immunization killed E7 peptide-pulsed target cells, but did not kill target cells expressing whole E7; (2) immunization with whole E7 protein did not elicit CTL directed to HLA A*0201-restricted E7 CTL epitopes; (3) HLA A*0201-restricted CTL epitopes expressed in the context of a DNA polytope vaccine did not activate E7-specific T cells either in 'conventional' HLA A*0201 transgenic (A2.1K(b) ) mice, or in HHD transgenic mice in which expression of endogenous H-2 class 1 is precluded; and (4) HLA A*0201 E7 peptide epitope immunization was incapable of preventing the growth of an HLA A*0201- and E7-expressing tumour. There are generic implications for the universal applicability of HLA-class 1 transgenic mice for studies of human CTL epitope presentation in murine models of human infectious disease where recognition of endogenously processed antigen is necessary. There are also specific implications for the use of HLA A2 transgenic mice for the development of E7-based therapeutic vaccines for cervical cancer.

Promiscuous CTL recognition of viral epitopes on multiple human leukocyte antigens: Biological validation of the proposed HLA A24 supertype

Multiple HLA class I alleles can bind peptides with common sequence motifs due to structural similarities in the peptide binding cleft, and these groups of alleles have been classified into supertypes. Nine major HLA supertypes have been proposed, including an A24 supertype that includes A*2301, A*2402, and A*3001. Evidence for this A24 supertype is limited to HLA sequence homology and/or similarity in peptide binding motifs for the alleles. To investigate the immunological relevance of this proposed supertype, we have examined two viral epitopes (from EBV and CMV) initially defined as HLA-A*2301-binding peptides. The data clearly demonstrate that each peptide could be recognized by CTL clones in the context of A*2301 or A*2402; thus validating the inclusion of these three alleles within an A24 supertype. Furthermore, CTL responses to the EBV epitope were detectable in both A*2301(+) and A*2402(+) individuals who had been previously exposed to this virus. These data substantiate the biological relevance of the A24 supertype, and the identification of viral epitopes with the capacity to bind promiscuously across this supertype could aid efforts to develop CTL-based vaccines or immunotherapy. The degeneracy in HLA restriction displayed by some T cells in this study also suggests that the dogma of self-MHC restriction needs some refinement to accommodate foreign peptide recognition in the context of multiple supertype alleles.

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 http:// antigen.i2r.a-star.edu.sg/ multipred/.

Effect of sequence variation in Plasmodium falciparum Histidine-Rich protein 2 on binding of specific monoclonal antibodies: Implications for rapid diagnostic tests for malaria

This Article Right arrow Full Text Right arrow Full Text (PDF) Right arrow Supplemental material Right arrow Alert me when this article is cited Right arrow Alert me if a correction is posted Services Right arrow Similar articles in this journal Right arrow Similar articles in PubMed Right arrow Alert me to new issues of the journal Right arrow Download to citation manager Right arrow Reprints and Permissions Right arrow Copyright Information Right arrow Books from ASM Press Right arrow MicrobeWorld Citing Articles Right arrow Citing Articles via HighWire Right arrow Citing Articles via Google Scholar Google Scholar Right arrow Articles by Lee, N. Right arrow Articles by McCarthy, J. Right arrow Search for Related Content PubMed Right arrow PubMed Citation Right arrow Articles by Lee, N. Right arrow Articles by McCarthy, J. Right arrow Pubmed/NCBI databases * Substance via MeSH Previous Article | Next Article Journal of Clinical Microbiology, August 2006, p. 2773-2778, Vol. 44, No. 8 0095-1137/06/$08.00+0 doi:10.1128/JCM.02557-05 Copyright © 2006, American Society for Microbiology. All Rights Reserved. Effect of Sequence Variation in Plasmodium falciparum Histidine- Rich Protein 2 on Binding of Specific Monoclonal Antibodies: Implications for Rapid Diagnostic Tests for Malaria{dagger} Nelson Lee,1,2 Joanne Baker,2 Kathy T. Andrews,1 Michelle L. Gatton,1,3 David Bell,4 Qin Cheng,2,3 and James McCarthy1* Australian Centre for International and Tropical Health and Nutrition, Queensland Institute of Medical Research and School of Population Health, University of Queensland, Queensland, Australia,1 Department of Drug Resistance and Diagnostics, Australian Army Malaria Institute, Brisbane, Australia,2 Malaria Drug Resistance and Chemotherapy, Queensland Institute of Medical Research, Queensland, Australia,3 World Health Organization, Regional Office for the Western Pacific, Manila, Philippines4 Received 8 December 2005/ Returned for modification 23 February 2006/ Accepted 26 May 2006 The ability to accurately diagnose malaria infections, particularly in settings where laboratory facilities are not well developed, is of key importance in the control of this disease. Rapid diagnostic tests (RDTs) offer great potential to address this need. Reports of significant variation in the field performance of RDTs based on the detection of Plasmodium falciparum histidine-rich protein 2 (HRP2) (PfHRP2) and of significant sequence polymorphism in PfHRP2 led us to evaluate the binding of four HRP2-specific monoclonal antibodies (MABs) to parasite proteins from geographically distinct P. falciparum isolates, define the epitopes recognized by these MABs, and relate the copy number of the epitopes to MAB reactivity. We observed a significant difference in the reactivity of the same MAB to different isolates and between different MABs tested with single isolates. When the target epitopes of three of the MABs were determined and mapped onto the peptide sequences of the field isolates, significant variability in the frequency of these epitopes was observed. These findings support the role of sequence variation as an explanation for variations in the performance of HRP2-based RDTs and point toward possible approaches to improve their diagnostic sensitivities