Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

Establishment of a quantitative ELISA capable of determining peptide - MHC class I interaction.

Many different assays for measuring peptide-MHC interactions have been suggested over the years. Yet, there is no generally accepted standard method available. We have recently generated preoxidized recombinant MHC class I molecules (MHC-I) which can be purified to homogeneity under denaturing conditions (i.e., in the absence of any contaminating peptides). Such denatured MHC-I molecules are functional equivalents of "empty molecules". When diluted into aqueous buffer containing beta-2 microglobulin (beta2m) and the appropriate peptide, they fold rapidly and efficiently in an entirely peptide dependent manner. Here, we exploit the availability of these molecules to generate a quantitative ELISA-based assay capable of measuring the affinity of the interaction between peptide and MHC-I. This assay is simple and sensitive, and one can easily envisage that the necessary reagents, standards and protocols could be made generally available to the scientific community.

Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor-stratified calibrations.

Peptides are key immune targets. They are generated by fragmentation of antigenic proteins, selected by major histocompatibility complex (MHC) molecules and subsequently presented to T cells. One of the most selective requirements is that of peptide binding to MHC. Accurate descriptions and predictions of peptide-MHC interactions are therefore important. Quantitative matrices representing MHC class I specificity can be used to search any query protein for the presence of MHC binding peptides. Assuming that each peptide residue contributes to binding in an additive and sequence independent manner, such "crude" matrix-driven predictions can be expressed as a quantitative estimates of binding strength. Crude matrix-driven predictions are reasonably uniform (i.e. precise), however, there is a general tendency towards overestimating binding (i.e. being inaccurate). To evaluate and possibly improve predictions, we have measured the MHC class I binding of a large number of peptides. In an attempt to further improve predictions and to include sequence dependency, we subdivided the panel of peptides according to whether the peptides had zero, one or two primary anchor residues. This allowed us to define unique anchor-stratified calibrations, which led to predictions of improved precision and accuracy.

Tumor-associated antigens identified by mRNA expression profiling induce protective anti-tumor immunity.

Defined tumor-associated antigens (TAA) are attractive targets for anti-tumor immunotherapy. Here, we describe a novel genome-wide approach to identify multiple TAA from any given tumor. A panel of transplantable thymomas was established from an inbred p53-/- mouse strain. The resulting tumors were examined for gene expression by mRNA microarray scanning. This analysis revealed heterogeneity of the tumors in agreement with the assumption that they represent different tumorigenic events. Several genes were overexpressed in one or more of the tumors. To examine whether overexpressed genes might be used to identify TAA, mice were immunized with mixtures of peptides representing putative cytotoxic T cell epitopes derived from one of the gene products. Indeed, such immunized mice were partially protected against subsequent tumor challenge. Despite being immunized with bona fide self antigens, no clinical signs of autoimmune reactions were observed. Thus, it appears possible to evaluate the entire metabolism of any given tumor and use this information rationally to identify multiple epitopes of value in the generation of tumor-specific immunotherapy. We expect that human tumors express similar tumor-specific metabolic imprints, which may be used to identify patient-specific arrays of TAA. This may enable a multi-epitope based immunotherapy with improved prospects of clinical tumor rejection.

Identifying cytotoxic T cell epitopes from genomic and proteomic information: "The human MHC project.".

Complete genomes of many species including pathogenic microorganisms are rapidly becoming available and with them the encoded proteins, or proteomes. Proteomes are extremely diverse and constitute unique imprints of the originating organisms allowing positive identification and accurate discrimination, even at the peptide level. It is not surprising that peptides are key targets of the immune system. It follows that proteomes can be translated into immunogens once it is known how the immune system generates and handles peptides. Recent advances have identified many of the basic principles involved. The single most selective event is that of peptide binding to MHC, making it particularly important to establish accurate descriptions and predictions of peptide binding for the most common MHC variants. These predictions should be integrated with those of other steps involved in antigen processing, as these become available. The ability to translate the accumulating primary sequence databases in terms of immune recognition should enable scientists and clinicians to analyze any protein of interest for the presence of potentially immunogenic epitopes. The computational tools to scan entire proteomes should also be developed, as this would enable a rational approach to vaccine development and immunotherapy. Thus, candidate vaccine epitopes might be predicted from the various microbial genome projects, tumor vaccine candidates from mRNA expression profiling of tumors ("transcriptomes") and auto-antigens from the human genome.

Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins.

A simple and fast free energy scoring function (Fresno) has been developed to predict the binding free energy of peptides to class I major histocompatibility (MHC) proteins. It differs from existing scoring functions mainly by the explicit treatment of ligand desolvation and of unfavorable protein-ligand contacts. Thus, it may be particularly useful in predicting binding affinities from three-dimensional models of protein-ligand complexes. The Fresno function was independently calibrated for two different training sets: (a) five HLA-A0201-peptide structures, which had been determined by X-ray crystallography, and (b) three-dimensional models of 37 H-2K(k)-peptide structures, which had been obtained by knowledge-based homology modeling. For both training sets, a good cross-validated fit to experimental binding free energies was obtained with predictive errors of 3-3.5 kJ/mol. As expected, lipophilic interactions were found to contribute the most to HLA-A0201-peptide interactions, whereas H-bonding predominates in H-2K(k) recognition. Both cross-validated models were afterward used to predict the binding affinity of a test set of 26 peptides to HLA-A0204 (an HLA allele closely related to HLA-A0201) and of a series of 16 peptides to H-2K(k). Predictions were more accurate for HLA-A2-binding peptides as the training set had been built from experimentally determined structures. The average error in predicting the binding free energy of the test peptides was 3.1 kJ/mol. For the homology model-derived equation, the average error in predicting the binding free energy of peptides to K(k) was significantly higher (5.4 kJ/mol) but still very acceptable. The present scoring function is thus able to predict with a good accuracy binding free energies from three-dimensional models, at the condition that the backbone coordinates of the MHC-bound peptide have first been determined with an accuracy of about 1-1.5 A. Furthermore, it may be easily recalibrated for any protein-ligand complex.

Complete protection against lethal Toxoplasma gondii infection in mice immunized with a plasmid encoding the SAG1 gene.

Infection with the protozoan parasite Toxoplasma gondii is transmitted to humans from infected animals by tissue cysts and oocysts excreted by cats. Immunization with inactivated parasites or recombinant proteins has at best shown partial protection. We constructed a plasmid expressing the SAG1 surface antigen of T. gondii, p1tPASAG1, and showed that animals immunized with the plasmid produce anti-SAG1 antibodies which recognize the native SAG1. Mice immunized with p1tPASAG1 showed 80 to 100% protection against challenge with the non-cyst-producing, virulent RH isolate, compared to an 80% mortality in mice immunized with empty plasmid, which is the greatest efficacy of any vaccine against T. gondii produced so far. The SAG1 molecule was analyzed for potential cytotoxic T-lymphocyte (CTL) epitopes, and four peptides with the best fit were synthesized. The ability of the peptides to stimulate gamma interferon production by CD8(+) T cells from p1tPASAG1-immunized mice was tested in an ELISPOT assay, and one new CTL epitope was identified. Adoptive transfer of CD8(+) T cells from p1tPASAG1-immunized to naïve mice showed partial protection. In conclusion, DNA vaccination with p1tPASAG1 gave effective protection in mice against T. gondii infection and the protection could be adoptively transferred by purified CD8(+) T cells.