Computational%20Immunology%20An%20Introduction

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Computational ImmunologyAn Introduction

• Rose Hoberman
• BioLM Seminar April 2003

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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.

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Innate and Adaptive

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

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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

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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

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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

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B cell Specificity

• ImmunoGlobulin (Ig) molecules
• Thousands on surface of each B cell
• Ig are essentially just bound antibodies
• 1015 Ig types
• Through a complicated process of DNA
  rearrangement ...
• Each B cells Ig molecules recognize a unique
  three dimensional epitope

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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 Igs, each cells TCRs recognizes a unique
  pattern (107 TCR types)
• But a T cell epitope is a short amino acid chain
  (a peptide), not part of a folded protein

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Predicting Epitopes

• Many proteins are not immunogens

• 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...

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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

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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

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Peptide Generation
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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
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MHC-Peptide Binding

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TCR-MHC-Peptide Binding

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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

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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

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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

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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
• Shouldnt these T cells have been filtered out?
• Why isnt the result immune ignorance?

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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.

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Unigram Models

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

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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

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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

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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
  individuals MHC alleles
• Evaluation metric
• classification or estimation task?
• More data