Transcript Computational Immunology An Introduction

Computational Immunology
An Introduction
Rose Hoberman
BioLM Seminar April 2003
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.
Innate and Adaptive
• Both identify and attack foreign
tissues and organisms
• Have different strengths
• In a constant dialogue with each
other
• Complement each other
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
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
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
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
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
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...
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
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
Peptide Generation
MHC Diversity
• Three loci code for MHC Locus Alleles
Class I molecules and
~220
A
six loci for the MHC
~110
C
Class II molecules
~460
B
• Most polymorphic genes DR 1,~360
in vertebrates
DQ 22, 48
• Diversity is concentrated DP 20, 96
in peptide binding groove
MHC-Peptide Binding
TCR-MHC-Peptide
Binding
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
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 selfreactive (with about 10 caveats)
– many self-reactive T cells
– danger theory
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
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?
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.
Unigram Models
Ristori...
1. Human proteome
2. Microbial proteomes (Bacteria/Viruses)
We tried...
1. Human proteome
2. Pathogenic bacteria
3. Non-pathogenic bacteria
unigrams.pdf
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
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
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