Statistical physics of T cell receptor selection and function Thesis committee meeting, 04/15/2009 Andrej Košmrlj Physics Department Massachusetts Institute

Statistical physics of T cell receptor selection and

function

Thesis committee meeting, 04/15/2009

Andrej Košmrlj

Physics DepartmentMassachusetts Institute of Technology

Andrej Košmrlj, 04/15/2009 2

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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


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


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


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)


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


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)


Extreme value distribution

increasing M(self-peptides)

TCRs withweaker

amino acids

Selection condition:

Probability of TCR selection:

Properties of


Amino acid composition of selected TCRs

WEAKSTRONG


Selected TCRs are enriched withweak amino acids

WEAKSTRONG

Abhishek Jha


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


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


Specificity mirrors experiments

Hot spot: more than half the mutations of a peptide amino acid destroy reactivity


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)


TCR RECOGNITION ~ STATISTICAL SCAN OF A BAR CODE


Large N limit (1/2)


Scaling in the large peptide (N) limit:

Statistical mechanics:


Large N limit (2/2)


Hamiltonian minimization:

Amino acid composition of selected TCRs:STRONG AA

WEAK AA


Phase diagram


Selected TCRs are enriched withweak amino acids

WEAKSTRONG


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



dendritic cell

How does our immune system work ?


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:


Amino acid frequencies of recognized antigens


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


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


Recall hotspots in experiments

Hot spot: more than half the mutations of a peptide amino acid destroy reactivity


How long is peptide?

Human proteome P ≈107


TCR RECOGNITION IS LIKESCANNING A BAR CODE


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


Fraction of selected T cells

Negative selection dominates

Positive selection dominates


Fraction of selected T cells

Negative selection dominates

Positive selection dominates

Documents

Statistical physics of T cell receptor selection and function Thesis committee meeting, 04/15/2009 Andrej Košmrlj Physics Department Massachusetts Institute