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Pathogens have presented a major challenge to individuals and populations of living organisms, probably as long as there has been life on earth. They are a prime object of study for at least three reasons: (1) Understanding the way of pathogens affords the basis for preventing and treating the diseases they cause. (2) The interactions of pathogens with their hosts afford valuable insights into the working of the hosts’ cells, in general, and of the host’s immune system, in particular. (3) The co-evolution of pathogens and their hosts allows for transferring knowledge across the two interacting species and affords valuable insights into how evolution works, in general. In the past decade computational biology has started to contribute to the understanding of host-pathogen interaction in at least three ways which are summarized in the subsequent sections of this chapter.

Rationale: We have previously demonstrated that bioinformatics tools such as artificial neural networks (ANNs) are capable of performing pathogen‐, genome‐ and HLA‐wide predictions of peptide–HLA interactions. These tools may therefore enable a fast and rational approach to epitope identification and thereby assist in the development of vaccines and immunotherapy. A crucial step in the generation of such bioinformatics tools is the selection of data representing the event in question (in casu peptide–HLA interaction). This is particularly important when it is difficult and expensive to obtain data. Herein, we demonstrate the importance in selecting information‐rich data and we develop a computational method, query‐by‐committee, which can perform a global identification of such information‐rich data in an unbiased and automated manner. Furthermore, we demonstrate how this method can be applied to an efficient iterative development strategy for these bioinformatics tools. Methods: A large panel of binding affinities of peptides binding to HLA A*0204 was measured by a radioimmunoassay (RIA). This data was used to develop multiple first generation ANNs, which formed a virtual committee. This committee was used to screen (or ‘queried’) for peptides, where the ANNs agreed (‘low‐QBC’), or disagreed (‘high‐QBC’), on their HLA‐binding potential. Seventeen low‐QBC peptides and 17 high‐QBC peptides were synthesized and tested. The high‐ or low‐QBC data were added to the original data, and new high‐ or low‐QBC second generation ANNs were developed, respectively. This procedure was repeated 40 times. Results: The high‐QBC‐enriched ANN performed significantly better than the low‐QBC‐enriched ANN in 37 of the 40 tests. Conclusion: These results demonstrate that high‐QBC‐enriched networks perform better than low‐QBC‐enriched networks in selecting informative data for developing peptide–MHC‐binding predictors. This improvement in selecting data is not due to differences in network training performance but due to the difference in information content in the high‐QBC experiment and in the low‐QBC experiment. Finally, it should be noted that this strategy could be used in many contexts where generation of data is difficult and costly.

An effective SARS vaccine is likely to include components that can induce specific cytotoxic T‐cell (CTL) responses. The specificities of such responses are governed by HLA‐restricted presentation of SARS‐derived peptide epitopes. Exact knowledge of how the immune system handles protein antigens would allow for the identification of such linear sequences directly from genomic/proteomic sequence information. The latter was recently established when a causative coronavirus (SARS CoV) was isolated and full‐length sequenced. Here, we have combined advanced bioinformatics and high‐throughput immunology to perform an HLA supertype, genome‐wide scan for SARS‐specific cytotoxic T cell epitopes. The scan includes all nine human HLA supertypes in total covering >99% of all major human populations. For each HLA supertype, we have selected the 15 top candidates for test in biochemical‐binding assays. At this time (approximately 6 months after the genome was established), we have tested the majority of the HLA supertypes and identified almost 100 potential vaccine candidates. These should be further validated in SARS survivors and used for vaccine formulation. We suggest that immunobioinformatics may become a fast and valuable tool in rational vaccine design.