Telling self from non-self: Learning the language of the Immune System Rose Hoberman and Roni Rosenfeld BioLM Workshop May 2003

Understanding the Immune System The Goals: characterize the differences between the languages of self vs. non-self explain (and predict) which self proteins (or regions of proteins) are auto-reactive, which proteins are highly allergenic, ... create better predictors of immunogenicity Possible applications: vaccine development treating auto-immune diseases co-opt the immune system for cancer therapy

Focus on T cells Essential component of the adaptive immune system kill virus-infected cells stimulate B cells to produce antibodies coordinate entire adaptive response Amenable to sequence-based analysis T cell’s recognize short amino acid chains

Specificity of T cells Through a process of DNA splicing each T cell has a unique surface molecule called a T cell receptor (TCR) recognizes a unique pattern A T cell epitope the region of an antigen capable of eliciting a T cell response short peptide (amino acid chain) derived from a protein antigen.

Predicting Epitopes Many proteins are not immunogens Even an immunogenic protein might have only one or a few T cell epitopes We have millions of T cells, each of which recognizes only a few patterns How can we predict epitopes?

Two Possible Constraints Machinery for generating and displaying peptides Many peptides will never even be presented to T cells Process of maintaining self-tolerance T cells should not attack cells displaying only peptides derived from self proteins

TCR-MHC-Peptide Binding

Modelling the Peptide Pipeline Binding and cleavage databases over 10,000 synthetic and pathogen-derived peptides ~400 MHC I and II alleles Prediction methods position specific probability matrices neural networks peptide threading Large amount of data and body of research

Two Possible Constraints Machinery for turning proteins into peptides Many peptides will never even be presented to T cells Self-tolerance T cells should not attack cells displaying only self proteins

Self Tolerance T cells originate in the bone marrow then migrate to the Thymus where they mature Selection of T cells through binding to self MHC-peptides in thymus Strong binders are killed (clonal deletion) Remaining T cells are (usually) no longer self-reactive

Finding Immunogenic Regions of Proteins Method 1: learn to predict which peptides will be generated, transported, and bound with MHC molecules Method 2: learn to discriminate self from non-self and use these models to classify each possible peptide

Related Work Compositional bias and mimicry toward the nonself proteome in immunodominant T cell epitopes of self and nonself antigens Ristori G, Salvetti M, Pesole G, Attimonelli M, Buttinelli C, Martin R, Riccio P. FASEB J. 14, 431--438 (2000).

Self-Reactive Protein Multiple Sclerosis (MS) is caused by the destruction of the Myelin sheets which surround nerve cells T cells erroneously attack the Myelin Basic Protein (MBP) on the surface of the Myelin cells Well-studied protein; known which regions are immunogenic

Unigram Models Ristori et al created two sets (self/non-self)... Human genomes Microbial genomes (Bacteria/Viruses) We created three sets... Human Pathogenic bacteria Non-pathogenic bacteria

A Simple Self/Non-Self Predictor For each window of size ~7-15 Calculate the probability that the subsequence was generated by each unigram distribution (running average) The ratio of the two probabilities gives a prediction of the degree of expected immune response Similar to Betty’s segmentation by ratio of short-range/long-range models

Prediction of IP Values (Ristori et. al.)

Pathogenic vs. Non-Pathogenic

A Simple Extension Do amino acid physical and chemical properties have any predictive power? bulkiness and hydrophobicity measures result in better predictions on MBP than self/non-self but Ristori et al claim that their predictions are better than any previous work Question: which existing prediction model works best?

Where to Go From Here? Understand relative performance and strengths/weaknesses self/non-self modelling more traditional epitope prediction methods how to combine these methods what is the right evaluation function?

Future Work features data modelling higher level n-grams expression level of genes exploit the differences between pathogen/non-pathogen as well as self/non-self data auto-immune proteins epitopes of known pathogens, ... modelling more powerful than simple ratio of probabilities