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.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

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/.

CandiVF - Candida albicans virulence factor database

Candida albicans is a pathogen commonly infecting patients who receive immunosuppressive drug therapy, long-term catheterization, or those who suffer from acquired immune deficiency syndrome (AIDS). The major factor accountable for pathogenicity of C. albicans is host immune status. Various virulence molecules, or factors, of are also responsible for the disease progression. Virulence proteins are published in public databases but they normally lack detailed functional annotations. We have developed CandiVF, a specialized database of C. albicans virulence factors (http://antigen.i2r.a-star.edu.sg/Templar/DB/CandiVF/) to facilitate efficient extraction and analysis of data aimed to assist research on immune responses, pathogenesis, prevention, and control of candidiasis. CandiVF contains a large number of annotated virulence proteins, including secretory, cell wall-associated, membrane, cytoplasmic, and nuclear proteins. This database has in-built bioinformatics tools including keyword and BLAST search, visualization of 3D-structures, HLA-DR epitope prediction, virulence descriptors, and virulence factors ontology.

Fusing subunit antigens to interleukin-2 and encapsulating them in liposomes improves their antigenicity but not their protective efficacy

Subunit vaccines commonly lack sufficient immunogenicity to stimulate a comprehensive protective immune response in vivo. We have investigated the potential of specific cytokines (interleukin-2) and particulate delivery systems (liposomes) to enhance antigenicity. Here we report that the IgG1 and IFN-gamma responses to a subunit antigen, consisting of a T and B-cell epitope from Influenza haemagglutinin, can be improved when it is both fused to interelukin-2 and encapsulated in liposomes. However, this vaccine formulation was not able to protect animals against a challenge with live Influenza A/PR/8/34 virus. The addition of more potent immune stimulators may be necessary to improve responses. (c) 2005 Elsevier Ltd. All rights reserved.

Potent T cell response to a class I-binding 13-mer viral epitope and the influence of HLA micropolymorphism in controlling epitope length

The BZLF1 antigen of Epstein-Barr virus includes three overlapping sequences of different lengths that conform to the binding motif of human leukocyte antigen (HLA) B*3501. These 9-mer ((56)LPOGQLTAy(64)), 11-mer ((54)EPLPQGQLTAy(64)), and 13-mer ((52)LPEPLPQGQLTAY(64)) peptides all bound well to B*3501; however, the CTL response in individuals expressing this HILA allele was directed strongly and exclusively towards the 11-mer peptide. In contrast, EBV-exposed donors expressing HLA B*3503 showed no significant CTL response to these peptides because the single amino acid difference between B*3501 and B*3503 within the F pocket inhibited HLA binding by these peptides. The extraordinarily long 13-mer peptide was the target for the CTL response in individuals expressing B*3508, which differs from B*3501 at a single position within the D pocket (B*3501, 156 Leucine; B*3508, 156 Arginine). This minor difference was shown to enhance binding of the 13-mer peptide, presumably through a stabilizing interaction between the negatively charged glutamate at position 3 of the peptide and the positively charged arginine at HLA position 156. The 13-mer epitope defined in this study represents the longest class I-binding viral epitope identified to date as a minimal determinant. Furthermore, the potency of the response indicates that peptides of this length do not present a major structural barrier to CTL recognition.

TCR{alpha} Genes Direct MHC Restriction in the Potent Human T Cell Response to a Class I-Bound Viral Epitope

The underlying generic properties of {alpha}β TCRs that control MHC restriction remain largely unresolved. To investigate MHC restriction, we have examined the CTL response to a viral epitope that binds promiscuously to two human leukocyte Ags (HLAs) that differ by a single amino acid at position 156. Individuals expressing either HLA-B*3501 (156Leucine) or HLA-B*3508 (156Arginine) showed a potent CTL response to the 407HPVGEADYFEY417 epitope from EBV. Interestingly, the response was characterized by highly restricted TCR β-chain usage in both HLA-B*3501+ and HLA-B*3508+ individuals; however, this conserved TRBV9+ β-chain was associated with distinct TCR {alpha}-chains depending upon the HLA-B*35 allele expressed by the virus-exposed host. Functional assays confirmed that TCR {alpha}-chain usage determined the HLA restriction of the CTLs. Structural studies revealed significant differences in the mobility of the peptide when bound to HLA-B*3501 or HLA-B*3508. In HLA-B*3501, the bulged section of the peptide was disordered, whereas in HLA-B*3508 the bulged epitope adopted an ordered conformation. Collectively, these data demonstrate not only that mobile MHC-bound peptides can be highly immunogenic but can also stimulate an extremely biased TCR repertoire. In addition, TCR {alpha}-chain usage is shown to play a critical role in controlling MHC restriction between closely related allomorphs.

Structural basis and prediction of substrate specificity in protein serine/threonine kinases

The large number of protein kinases makes it impractical to determine their specificities and substrates experimentally. Using the available crystal structures, molecular modeling, and sequence analyses of kinases and substrates, we developed a set of rules governing the binding of a heptapeptide substrate motif (surrounding the phosphorylation site) to the kinase and implemented these rules in a web-interfaced program for automated prediction of optimal substrate peptides, taking only the amino acid sequence of a protein kinase as input. We show the utility of the method by analyzing yeast cell cycle control and DNA damage checkpoint pathways. Our method is the only available predictive method generally applicable for identifying possible substrate proteins for protein serine/threonine kinases and helps in silico construction of signaling pathways. The accuracy of prediction is comparable to the accuracy of data from systematic large-scale experimental approaches.

Recombinant Kunjin virus replicon vaccines induce protective T-cell immunity against human papillomavirus 16 E7-expressing tumour

The persistence of the E7 oncoprotein in transformed cells in human papillomavirus (HPV)-associated cervical cancer provides a tumour-specific antigen to which immunotherapeutic strategies may be directed. Self-replicating RNA (replicon) vaccine vectors derived from the flavivirus Kunjin (KUN) have recently been reported to induce T-cell immunity. Here, we report that inclusion of a CTL epitope of HPV16 E7 protein into a polyepitope encoded by a KUN vector induced E7-directed T-cell responses and protected mice against challenge with an E7-expressing epithelial tumour. We found replicon RNA packaged into virus-like particles to be more effective than naked replicon RNA or plasmid DNA constructed to allow replicon RNA transcription in vivo. Protective immunity was induced although the E7 CTL epitope was subdominant in the context of other CTL epitopes in the polyepitope. The results demonstrate the efficacy of the KUN replicon vector system for inducing protective immunity directed towards a virally encoded human tumour-specific antigen, and for inducing multi-epitopic CTL responses. (C) 2004 Elsevier Inc. All rights reserved.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Prediction of protein B-factor profiles

The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B-factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B-factor distribution (B-factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high-resolution proteins, our method predicts the B-factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B-factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B-factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence-based prediction methods. (C) 2005 Wiley-Liss, Inc.