Computational Vaccinology Darren R Flower [email protected].

Computational Vaccinology

Darren R Flower

http://www.jenner.ac.uk/res-bio.htm

[email protected]

COMPUTATIONAL VACCINOLOGY

SUBUNITVACCINE

EPITOPEVACCINE

Vaccines induce protective immunity, an enhanced adaptive immune response to re-infection.

WHOLEORGANISM

World classdatabase

Antigens,B cell and T cell

epitopesPeptide binding,Protein-Protein

Interactions

ImprovedPrediction

Class I and IIT cell epitope

prediction

B cell epitopesand Antigens

Experimentalverification

and data discovery

test predictionand generate

new binding data

COMPUTATIONAL VACCINOLOGY

TCR, MHC and co-receptors on the surface of T-cell and antigen-presenting cell.

T-cells have T cell receptors in their membranes that bind to the protein fragments presented by the MHC proteins.

T cells recognise the presence of foreign protein and hence pathogenic micro-organisms and then destroy them.

T-cell, TCR and MHC

Peptide MHC binding is just like the binding of drugs to other receptors

We can use QSAR and molecular dynamics (MD) simulation to examine, model and predict MHC-peptide interaction

TCR-peptide-MHC complex

DATA DRIVENMODELLING:

QSAR

Irini Doytchinova

Channa HattutawagamaValerie WalshePingPing Guan

QSARQUANTITATIVE STRUCTURE

ACTIVITY RELATIONSHIP

STRUCTURALDESCRIPTION

andBIOLOGICAL

RESPONSE

ROBUSTMULTIVARIATE

STATISTICS

PREDICTIVEQSAR

MODEL

+ IC50spIC50 exp

pIC5

pred

Y=X*W*(P’*W)-1*C’+F

CoMSIA model

5

6

7

8

9

5 6 7 8 9

experimental pIC50

r = 0.783

pred

icte

d pI

C50

CoMFA model

5

6

7

8

9

5 6 7 8 9

experimental pIC50

r = 0.694

pred

icte

d pI

C50

Training set102

peptides

Test set50

peptides

152 peptideswith affinity tothe HLA-A2.1

Training set102

peptides

Test set50

peptides

152 peptideswith affinity tothe HLA-A2.1

r2pred < 0.5

NC = 6 q2 = 0.480 r2 = 0.911

r2pred = 0.679

NC = 5 q2 = 0.542 r2 = 0.870

Comparison ofCoMFA and CoMSIA

for HLA-A*0201

Hydrophobic Map

Hydrogen Bond Map

Steric Map

Electrostatic Map

NC = 7 q2 = 0.683 r2 = 0.891 n = 236

Full CoMSIA Analysis of HLA-A*0201

HN

N

H O

H

NN

N

O

H O

H O

NN

N

H O

H O

H O

H

N

O

H OH

O

P1

P2

P3

P4

P5

P6

P7

P8

P9

2i

7

1ii1i

8

1ii

9

1ii50 PPPPPconstpIC

HLA-A*0201: NC = 5 q2 = 0.337 r2 = 0.898 n = 340

ADDITIVE METHOD FOR AFFINITY PREDICTION

STRUCTURE DRIVENMODELLING:ATOMISTIC

MOLECULAR DYNAMICSIMULATION

Shunzhou Wan

DESIGN MUTANTS

Point MutantsChimeras

Deletion Mutants

Fusion Proteins

PREDICT

DYNAMIC PROPERTIES

Molecular Dynamics Simulations

PREDICT LIGANDS

Small Molecule DockingDrug Design

PREDICT

PROTEIN - PROTEIN

INTERACTIONS

Large Molecule Docking

ANTIBODYBINDING

MHC-peptide BINDING

PREDICTTHERMODYNAMICS

OF BINDING

PREDICTCOMPLEX

BEHAVIOUR

High Performance Computing& Biomolecular Simulations

Simulations of Biomolecular Systems includeproteins, nucleic acids, drug-receptor interactions,

protein folding, and a few examples of more complex systems, such protein-membrane interactions.

Most simulations done on desktop workstations and “small” parallel machines (~32 processors)

Long time scales and large systems generally intractable

HPC and the GRID allow us, for the first time, to do things properly

Simulated systems are LARGE: 30,000-300,000 atoms

Simulation timescales are LONG: In nanoseconds, even microsecond1

Requires high performance computing

We use scalable codes LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) & NAMD

on large parallel machines (up to 1000+ nodes)

Large Scale Molecular Dynamics

1. Duan Y, et al., Science 1998, 282:740-744.

MD scaling performance (LAMMPS)

Parallelising the AMBER software scales very poorly in our hands

MHC-peptide complexes

HLA-A*0201:MAGE-A4 complex

Simulated using AMBER force field in LAMMPS

a)

…

1- 2 domainsperiodic boundaryno constraints

Rognan et al. (1992) Proteins 13, 70-85

b) c)

1- 2 domainsperiodic boundaryconstraints on backbone

Meng et al. (1997) Int. Immunol. 9, 1339-1346

all domainsspherical boundaryfix all atoms out of sphereconstraints on outer buffer region of sphere

Michielin et al. (2002) J. Mol. Biol. 324, 547-569

MHC-peptide complexes: What has been done?

Can the a3 and b2m domains

and/or their movement

be neglected in simulations?

MHC-peptide complexes

MHC-peptide complexes: Simulation models

Partial model30,574 atomsNo constraints

Full model58,825 atomsNo constraints

Many authors1 regards this system as being out of reach of MD simulation

- "much too large"

- "relevant time scales inaccessible"

But, with scalable codes and tightly coupled massively parallel machines ...

1. Nojima et al., Chem Pharm Bull (Tokyo) 2002 50(9), 1209-1214.

Amber 98 Force Field

MHC-peptide complexes: Simulation models

... for the 58,825 atom

model (whole model),

we can perform 1 ns

simulation in 17 hours'

wall clock time on 256

processors of Cray T3E

using LAMMPS

MHC-peptide complexes: Results

For the partial

system, about 300ps

were required for

equilibration, while

the whole system

required about

600ps, equilibration

here being a function

of the size of the

system. RMS deviation from x-ray structure versus simulation time (ps). Above: partial MHC-peptide system; Below: whole MHC-peptide system. Solid line: mainchain of protein; Dotted line: mainchain of peptide.

MHC-peptide complexes: Results

In the partial system simulation, the middle sheets (4, 5) at the bottom of groove bulge towards the peptide, while 1, 2 and 8 turn aside from it, whereas the whole system simulation does not exhibit these effects.

View of the b-sheets of the average structures from the partial system simulation (blue) and the whole system simulation (yellow), compared with the x-ray structure (red). From top to bottom, the sheets are juxtaposed from the N-terminal to the C-terminal of the peptide. The view is directly onto the peptide-binding side.

MHC-peptide complexes: Results

The loop regions have large deviations from the x-ray structure, while the two long helices and the -sheets have relatively small deviations.

RMS deviations of peptide (top) and

antigen-binding site of MHC protein

(bottom) from x-ray structure. Solid line:

whole system simulation; Dashed line:

partial system simulation.

Peptide Fluctuation vs Thermal B-Factor

B FACTOR

The larger deviations observed with the peptide in the partial system indicate that the peptide is considerably less tightly bound in the partial system than in the whole system.

MHC-peptide complexes: Conclusions

For 58,825 atoms system, 1 ns simulation can be performed in 17 hours'

wall clock time on 256 processors of T3E.

More accurate results are obtained by simulating the whole complex

than just a part of it.

The 3 and 2m domains have a significant influence on the structural

and dynamical features of the complex, which is very important for

determining the binding efficiencies of epitopes.

We are now doing TCR-peptide-MHC simulations

(~ 100,000 atom model) using NAMD.

WE WANT USE MD TO ADDRESS FUNDAMENTAL PROBLEMS IN IMMUNOLOGY

MHCs are polymorphic – there are hundreds of individual alleles with in the human population

each with a different peptide binding specificity

Use MD to identify binding epitopes anduse these to design and develop novel vaccines

Also want to use MD to examine more complex systems such as the Immunological Synapse

which are not accessible to direct experimental analysis

Bioinformatics Group

Irini DoytchinovaShunzhou Wan

Helen McSparronValerie WalshePingPing GuanMartin BlytheDebra Taylor

EJIVR

Seph Borrow

Outside

Peter Coveney (UCL)

ACKNOWLEDGEMENTS

Funding from EPSRC (RealityGrid, CSAR)

Jenner Institute (GSK, BBSRC, MRC, DOH)