Computational Immunology An Introduction

1. Computational ImmunologyAn Introduction Rose Hoberman BioLM Seminar April 2003

2. Overview • Brief intro to adaptive immune system • B and T cells • Achieving specificity • Antibodies, TCR, MHC molecules • Maintaining tolerance to self • Clonal selection/deletion in the thymus • Paper: • Compositional bias and mimicry toward the nonself proteome in immunodominant T cell epitopes of self and nonself antigens.

3. Innate and Adaptive • Both identify and attack foreign tissues and organisms • Have different strengths • In a constant dialogue with each other • Complement each other

4. Innate Immunity • Recognize classes of pathogens, not a specific organism • Always respond to a pathogen in the same manner • all plants, animals, insects... have an innate immune system • example: complement binds to mannose on bacterial cell walls, flagging for phagocytosis

6. Adaptive Immunity • Memory • enables vaccination and resistance to reinfection by the same organism • Specificity • distinguish foreign cells from self • distinguish foreign cells from one another ... the focus of this talk

7. The Major Players • B cells • produce antibodies which bind to pathogens and disable them or flag them for destruction by the innate system • T cells • kill infected cells • coordinate entire adaptive response

8. B cell Specificity • ImmunoGlobulin (Ig) molecules • Thousands on surface of each B cell • Ig are essentially just bound antibodies • 10^15 Ig types • Through a complicated process of DNA rearrangement ... • Each B cell’s Ig molecules recognize a unique three dimensional epitope

9. Specificity of T cells • Each T cell has a unique surface molecule called a T cell receptor (TCR) • Through similar process of DNA splicing... • Like Ig’s, each cell’s TCRs recognizes a unique pattern (10^7 TCR types) • But a T cell epitope is a short amino acid chain (a peptide), not part of a folded protein

10. Predicting Epitopes • Even an immunogenic protein might have only one or a few epitopes • We have millions of T and B cells, each of which recognizes only a few proteins • How can we predict epitopes? • i.e. for vaccine development, cancer treatment... • Many proteins are not immunogens

11. Two Possible Constraints • Machinery for turning proteins into peptides • Many peptides will never even be presented to T cells • Self-tolerance • T and B cells should not attack self proteins

12. Peptide Generation • Cytosolic proteins are degraded by a large protease complex called the proteasome • Peptides of around 8-11 a.a. are transported by TAP proteins into the ER • In the ER, a small number of peptides are bound to MHC class I molecules • These MHC-peptide complexes are shipped to the cell surface to be surveyed by T cells

13. Peptide Generation

14. MHC Diversity • Three loci code for MHC Class I molecules and six loci for the MHC Class II molecules • Most polymorphic genes in vertebrates • Diversity is concentrated in peptide binding groove Locus Alleles A ~220 ~110 ~460 1,~360 22, 48 20, 96 C B DR DQ DP

15. MHC-Peptide Binding

16. TCR-MHC-Peptide Binding

17. Learn MHC Binding Patterns • Binding databases • over 10,000 synthetic and pathogen-derived peptides • ~400 MHC I and II alleles • some qualitative affinity data • some TAP binding and T cell epitopes • Prediction methods • motifs • position specific probability matrices • neural networks • peptide threading

18. Self Tolerance • T cells originate in the bone marrow then migrate to the Thymus where they mature • Selection of T cells through binding to common MHC-self peptides in thymus • strong binders are killed (clonal deletion) • weak binders die from lack of stimulation (clonal selection) • Remaining T cells are no longer self-reactive (with about 10 caveats) • many self-reactive T cells • danger theory

19. Finding Immunogenic Regions of Proteins • Motivation • vaccine development • drug development for auto-immune diseases • developing techniques to co-opt the immune system for cancer therapy • 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 • unigrams.pdf • MBP unigram probability ratios

20. Molecular Mimicry • Protein fragment from a pathogen (or food) sometimes resembles part of a self protein • Stimulates the immune system of susceptible individuals (depending on MHC type) to attack the self protein • Can result in auto-immune disease • Shouldn’t these T cells have been filtered out? • Why isn’t the result immune ignorance?

21. Brief Paper Overview 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.

22. Unigram Models Ristori... • Human proteome • Microbial proteomes (Bacteria/Viruses) We tried... • Human proteome • Pathogenic bacteria • Non-pathogenic bacteria unigrams.pdf

23. 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

24. 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 • The ratio of the two gives a prediction of the degree of expected immune response • probability ratios for MBP

25. Where to Go From Here? • Go beyond the unigram • higher level n-gram • amino acid classes • other ideas • Combine methods 1 and 2 • use to evaluate immune response dependent on an individual’s MHC alleles • Evaluation metric • classification or estimation task? • More data