telling self from non-self: learning the language of the immune system rose hoberman and roni...
TRANSCRIPT
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-selfexplain (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 developmenttreating auto-immune diseasesco-opt the immune system for cancer therapy
Focus on T cells
Essential component of the adaptive immune system
kill virus-infected cellsstimulate B cells to produce antibodiescoordinate entire adaptive response
Amenable to sequence-based analysisT 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 responseshort peptide (amino acid chain) derived from a protein antigen.
Predicting Epitopes
Even an immunogenic protein might have only one or a few T cell epitopesWe have millions of T cells, each of which recognizes only a few patternsHow can we predict epitopes?
Many proteins are not immunogens
Two Possible Constraints
Machinery for generating and displaying peptides
Many peptides will never even be presented to T cells
Process of maintaining self-toleranceT cells should not attack cells displaying only peptides derived from self proteins
Modelling the Peptide Pipeline
Binding and cleavage databases over 10,000 synthetic and pathogen-derived peptides~400 MHC I and II alleles
Prediction methodsposition specific probability matricesneural networkspeptide 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-toleranceT 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 matureSelection 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 cellsT cells erroneously attack the Myelin Basic Protein (MBP) on the surface of the Myelin cellsWell-studied protein; known which regions are immunogenic
Unigram Models
Ristori et al created two sets (self/non-self)...1. Human genomes2. Microbial genomes (Bacteria/Viruses)
We created three sets...1. Human2. Pathogenic bacteria3. Non-pathogenic bacteria
A Simple Self/Non-Self Predictor
For each window of size ~7-15Calculate 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 responseSimilar to Betty’s segmentation by ratio of short-range/long-range models
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-selfbut 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 modellingmore traditional epitope prediction methodshow to combine these methodswhat is the right evaluation function?