AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: .

Hepatitis C virus (HCV) envelope glycoproteins E1 and E2 are important targets for the host immune response. The genes encoding these proteins exhibit a high degree of variability that gives rise to differing phenotypic traits, including alterations in receptor-binding affinity and immune recognition and escape. In order to elucidate patterns of adaptive evolution during chronic infection, a panel of full-length E1E2 clones was generated from sequential serum samples obtained from four chronically infected individuals. By using likelihood-based methods for phylogenetic inference, the evolutionary dynamics of circulating HCV quasispecies populations were assessed and a site-by-site analysis of the d N /d S ratio was performed, to identify specific codons undergoing diversifying positive selection. HCV phylogenies, coupled with the number and distribution of selected sites, differed markedly between patients, highlighting that HCV evolution during chronic infection is a patient-specific phenomenon. This analysis shows that purifying selection is the major force acting on HCV populations in chronic infection. Whilst no significant evidence for positive selection was observed in E1, a number of sites under positive selection were identified within the ectodomain of the E2 protein. All of these sites were located in regions hypothesized to be exposed to the selective environment of the host, including a number of functionally defined domains that have been reported to be involved in immune evasion and receptor binding. Dated-tip methods for estimation of underlying HCV mutation rates were also applied to the data, enabling prediction of the most recent common ancestor for each patient's quasispecies. INTRODUCTIONHepatitis C virus (HCV) is a positive-sense RNA virus and is the sole member of the genus Hepacivirus of the family Flaviviridae (Lindenbach & Rice, 2001). Globally, approximately 170 million people are at risk of liver disease due to chronic HCV infection, with an estimated 3 million individuals newly infected per annum (WHO, 1999). Of those infected, a reported 80 % fail to clear the virus, a significant number of whom will go on to develop severe liver disease, including cirrhosis and hepatocellular carcinoma (Alter et al., 1992;Muller, 1996;Saito et al., 1990).HCV circulates within an infected host as a heterogeneous viral population containing genetically distinct, but closely related variants, known as quasispecies (Bukh et al., 1995;Martell et al., 1992). The propensity for genetic change is associated primarily with the error-prone nature of the RNA-dependent RNA polymerase, together with the high HCV replicative rate in vivo (Fukumoto et al., 1996; Neumann et al., 1998;Ramratnam et al., 1999;Zeuzem, 2000). Chronic infection arises, at least in part, through the outgrowth of immune-escape mutants (Farci et al., 2000;Frasca et al., 1999; Majid et al., 1999;Ray et al., 1999;Wang & Eckels, 1999). The envelope glycoprotein genes display some of the highest levels of HCV genetic heterogeneity, with E2 exh...

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide-MHC binding affinity. The ISC-PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide-MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms--q2, SEP, and NC--ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).