statistical physics of t cell receptor selection and function thesis committee meeting, 04/15/2009...
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Statistical physics of T cell receptor selection and
function
Thesis committee meeting, 04/15/2009
Andrej Košmrlj
Physics DepartmentMassachusetts Institute of Technology
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Immune system
Flexible system to combat diverse pathogens
Mis-regulation leads to autoimmune diseases
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Multiple Sclerosis Diabetes
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Antigen presentation and recognition
APCs internally process self and foreign proteins, cut them to short peptides (8-15 aa) and present them on the surface
antigen recognition: strong binding of TCR to antigenic pMHC
self pMHC bind TCR more weakly
Antigen presenting cell T cell
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T cell receptors
most T cell express distinct TCR - stochastic gene rearrangement process
TCR antigen recognition:degeneracy - each TCR can recognize many antigenic peptidesspecificity - TCR recognition of antigen is specific for single point amino acid mutations
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Development in thymus
strongbinding
weakbinding
Negative selection deletes strongly binding autoimmune TCR
Positive selection results in weak binding of TCR to endogenous pMHC – implicated in survival, MHC restriction.
Palmer et al, Nature (2006)
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Specificity of antigen recognition
• Specificity to antigenic peptide:
• Many peptides: mutations destroy activation – specific T-cells
• Single peptide: mutations don’t matter – cross-reactive T-cells
Huesby et al, Cell (2005)
P-1
P2
P3
P5
P-1
P2
P3
P5
Many self-peptides One self-peptide
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Model
Surviving T cells: Eint > EN for all peptides; Eint < EP for at least one peptide
TCR-peptide contacts:
self peptides in thymus:
PNAS (2008)PRL (submitted)
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Extreme value distribution
increasing M(self-peptides)
TCRs withweaker
amino acids
Selection condition:
Probability of TCR selection:
Properties of
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Amino acid composition of selected TCRs
WEAKSTRONG
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Selected TCRs are enriched withweak amino acids
WEAKSTRONG
Abhishek Jha
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Frustration leads to specific TCR repertoire
One peptide
EN < E < EP
selected
E < EN
negatively selected
Many peptides
Solution: special class of sequences enriched with weak amino acids that distribute moderate interactions through the entire sequence
Optimizing interactions with one peptide leads to “bad” interactions with another
More hotspots – specific T cell repertoire
TCR
peptide
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Recall specificity of antigen recognition
• Specificity to antigenic peptide:
• Many peptides: mutations destroy activation – specific T-cells
• Single peptide: mutations don’t matter – cross-reactive T-cells
Huesby et al, Cell (2005)
P-1
P2
P3
P5
P-1
P2
P3
P5
Many self-peptides One self-peptide
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Specificity mirrors experiments
Hot spot: more than half the mutations of a peptide amino acid destroy reactivity
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Increased number of moderately interacting contacts
B3K 506 TCRC57BL/6 derived
MHC + peptide specific
YAe62.8 TCRIAb-SP derived
MHC + peptide degenerate
Increased number of moderate interactions
Decreased number of strong interactions
Huesby et al, Nat. Immunol (2006)
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TCR RECOGNITION ~ STATISTICAL SCAN OF A BAR CODE
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Large N limit (1/2)
Selection condition:
Scaling in the large peptide (N) limit:
Statistical mechanics:
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Large N limit (2/2)
Selection condition:
Hamiltonian minimization:
Amino acid composition of selected TCRs:STRONG AA
WEAK AA
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Phase diagram
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Selected TCRs are enriched withweak amino acids
WEAKSTRONG
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Future project
HIV elite controllers (long term non-progressors) are associated with special MHC alleles (e.g. HLA B57). TCRs restricted by B57 allele are more cross-reactive - helps recognizing HIV mutants.
HLA B57 allele bind fewer peptide than most other alleles.
Our model: thymic selection with fewer self-peptides leads to more cross-reactive TCRs.
Connect thymic selection to viral dynamics model (Elizabeth Read) to explain differences in the acute phase of viral infection
HIV
Acknowledgements
Arup K. Chakraborty
Mehran Kardar
Abhishek K. Jha
Elizabeth L. ReadThesis committee meeting, 04/15/2009
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Antigen presentation and recognition
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dendritic cell
How does our immune system work ?
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UNDERSTANDING ADAPTIVE IMMUNITY
Flexible system to combat diverse pathogens
Mis-regulation leads to autoimmune diseases
Multiple Sclerosis
The challenge: develop principlesprinciples that govern the emergence of an immune response or autoimmunity and design rulesdesign rules for therapies
Diabetes
Theory/computation Experiments
statistical mechanics genetics biochemistry imagingchemical kinetics
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Model
Surviving T cells: E>EN for all peptides; E < EP for at least one peptide
TCR-peptide contacts:
self peptides in thymus:
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Amino acid frequencies of recognized antigens
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Distribution of single site contact energies
between reactive T cells and antigen
B3K 506 TCRC57BL/6 derived
MHC + peptide specific
YAe62.8 TCRIAb-SP derived
MHC + peptide degenerate
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Distribution of single site contact energies
between reactive T cells and antigen
Increased number of moderate interactions
Decreased number of strong interactions
Selection with many peptides increases the number of moderate contacts between TCR and peptide amino acids
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Recall hotspots in experiments
Hot spot: more than half the mutations of a peptide amino acid destroy reactivity
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How long is peptide?
Human proteome P ≈107
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TCR RECOGNITION IS LIKESCANNING A BAR CODE
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Model
TCR pMHC
MHC peptide conserved variable
Miyazawa-Jernigan
Surviving T cells: E<EN for all peptides; E > EP for at least one peptide
Pairwise interactions
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Fraction of selected T cells
Negative selection dominates
Positive selection dominates
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Fraction of selected T cells
Negative selection dominates
Positive selection dominates