Computational ImmunologyComputational ImmunologyAn IntroductionAn Introduction

Rose Hoberman

BioLM Seminar April 2003

OverviewOverview

• 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.

Innate and AdaptiveInnate and Adaptive

• Both identify and attack foreign tissues and organisms

• Have different strengths

• In a constant dialogue with each other

• Complement each other

Innate ImmunityInnate 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

Adaptive ImmunityAdaptive 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

The Major PlayersThe 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

B cell SpecificityB 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

Specificity of T cellsSpecificity 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

Predicting Predicting EpitopesEpitopes

• 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

Two Possible ConstraintsTwo 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

Peptide GenerationPeptide 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

Peptide GenerationPeptide Generation

MHC DiversityMHC 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

A

C

B

DR

DQ

DP

Locus Alleles~220

~110

~460

1,~360

22, 48

20, 96

MHC-Peptide BindingMHC-Peptide Binding

TCR-MHC-Peptide TCR-MHC-Peptide BindingBinding

Learn MHC Binding PatternsLearn 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

Self ToleranceSelf 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

Finding Immunogenic Finding Immunogenic Regions of ProteinsRegions 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

Molecular MimicryMolecular 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?

Brief Paper OverviewBrief 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.

Unigram ModelsUnigram Models

Ristori...1. Human proteome2. Microbial proteomes (Bacteria/Viruses)

We tried...1. Human proteome2. Pathogenic bacteria3. Non-pathogenic bacteria

unigrams.pdf

Self-Reactive ProteinSelf-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

A Simple Self/Non-Self A Simple Self/Non-Self PredictorPredictor

• 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

Where to Go From Here?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