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Bayesian Immunogenicity Inference Tool for ELISpot

BIITE was developed to solve a problem in identifying immunogenic combinations of HLA (human leukocyte antigen) class II molecules with peptides.

HLA molecules present intracellular peptides found within a cell on the cell surface, to ellicit an immune response if the peptide is recognised as foreign. There are a number of different HLA molecules expressed in human cells, and identifying precisely which combination of HLA molecule and peptide results in an immune response is key in a number of research areas including infectious, autoimmune, allergic or oncogenic disease, host defence, pathogenesis and epitope vaccine design.

HLA class II molecules present a particular challenge in identifying their peptide complexes as they have a high polymorphism and multiple loci (meaning an individual can have between 3 and 14 different HLA class II molecules), as well as there being additional uncertainty in attributing HLA molecule - T-cell interaction (due to varibility in expression, cell surface presentation and strong linkage disequilibrium). Another issue is that the binding groove is open-ended, accommodating peptides of varying sizes.



BIITE is designed for use on experimental data, rather than to make in silico predictions. It analyses data from all HLA class II molecules vs the same peptide simultaneously, applying Bayesian methods to output a curve that can be interpreted as a probability density function of the immunogenicity.

Strictly necessary inputs are the ELISpot experiment outcomes for each subject, together with their genotype for the loci of interest. Optionally, the user can also provide prior information to the algorithm; this is done on an HLA molecule-per-molecule basis. 



Fig 1. Outline of the algorithm. [From Boelen et al. 2016, see below]

We aim to describe the immunogenicity of a peptide:HLA combination as a number between 0 and 1. For each subject, the outcome of the ELISpot assay is known, together with a list of their HLA alleles. The user can add prior knowledge in the form of a prior distribution. In the example, the prior knowledge describes the belief that HLA class II allele A is not immunogenetic in combination with the peptide, while HLA class-II allele B is. The output of the algorithm consists of posterior marginal distributions of the values Ej. In the example, it is found that allele A is more immunogenic that allele B in combination with the peptide.



More information on BIITE, including application in 2 ELISpot datasets, can be found in the following publication:

Boelen et al. BIITE: A Tool to Determine HLA Class II Epitopes from T Cell ELISpot Data, PLoS Comput Biol. 2016 Mar 8;12(3):e1004796 DOI:10.1371/journal.pcbi.1004796



Internal case number 8156

BIITE (Bayesian Immunogenicity Inference Tool for ELISpot)

A Metropolis-Hastings implementation to infer which peptide:HLA-II combinations are immunogenic, from ELISpot data

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