T-cell epitope prediction by molecular dynamics simulations Irini Doytchinova Medical University of...

T-cell epitope predictionby molecular dynamics

simulations

Irini Doytchinova

Medical University of Sofia

Copyright © 1997 Ivo Ivanov

School of PharmacyMedical University of Sofia

Vaccines and Epitopes

live attenuated or killed pathogens

subunitvaccines

epitope-basedvaccines

Epitope is a continuous or non-continuous sequence ofa protein that is recognized by and interacts with other protein.

linearepitope

conformationalepitope

Т-limphocyte

В-limphocyte

Antigen processing pathways

Intracellular pathway Extracellular pathway

T-cell epitope prediction

in vitro and in vivo tests clinical tests

Epitope-based vaccine development

in silico prediction

100 aa 92 overlapping nonamer peptides 10 nonamer

peptides

T-cell epitope prediction

T-cell epitope prediction is a critical step in the development of epitope-based vaccines. As the veracity of

the predictions improves, the subsequent expensive “wet lab”

work becomes faster, more efficient and more successful.

in silico prediction in vitro and in vivo tests clinical tests

Epitope-based vaccine development

Biology Informatics

Bioinformatics

ImmunologyImmunoinformatics

Immunoinformatics approaches

Sequence-based methods Structure-based methods

peptide pIC50exp

ILDPFPVTV 8.654

ALDPFPPTV 8.170

VLDPFPITV 8.139

................ .......

LLDPFPPPV 7.442

ILDPIPPTV 7.296

LLDDFPVTV 7.155

ILDPLPPTV 7.145

YLFPGPVTA 6.305

Affinity = f (Chemical Structure)

Motif-based, QMs, ANN, SVM

Affinity = f (Interaction energy)

Molecular dockingMolecular dynamics

Our immunoinformatics tools

http://www.pharmfac.net/ddg

MHC class II binding prediction by molecular dynamics

Combinatorial libraryΔG

PKYVKQNTLKLAT + 0.456PKXVKQNTLKLAT - 0.123PKYVKXNTLKLAT …PKYVKQNXLKLAT …PKYVKQNTXKLAT …PKYVKQNTLKXAT …

1 4 6 7 9A … … … … … C … … … … …D … … … … …E … … … … …… … … … … …

External validation

QM

Peptide – HLA-DP2 protein complex(DPA1*0103 red, DPB1*0101 blue)

pdb code: 3lqz, April 2010

Combinatorial library

p1

p2p3

p4

p5

p6p7

p8

p9

RK FHYLPFLPS TGGS

9 positions x 19 amino acids + 1 original ligand = 172 ligands

MD simulations

Problems to solve:1. Which energy to use for prediction?2. How long to equilibrate the system?

GROMACS is developed by Herman Berendsens group, Groningen University. GROMACS 4.0.7: Hess, et al. (2008) J. Chem. Theory Comput. 4: 435-447.

pdb to gmx

neutralize the charge with counterions

create a box around the complex

energy minimization

fill the box with water molecules

position-restrained MD

MD with simulated annealing

record the interaction energies

Force field: GROMOS96 53a6

side:1 nm

NA+

20 ps

100-310K

LJ-SR & Coul-SR

Which energy to use for prediction ?

0

10

20

30

40

LJ-SR Coul-SR Sum

sensitivity

Test set n = 1932known binders to HLA-DRB1*0101originating from 122 foreign proteins

bindersall

binderspredictedtrueysensitivit

Lennard-Jones short-range potential gives better prediction than Coulomb short-range potential.

Sensitivities were calculated over the top 5% of the predicted affinities of all overlapping peptides originating from one protein.

How long to equilibrate the system?

Time/accuracy trade-off: 1 ns calculated for 11 hours

MD-based Quantitative Matrices (MD-QMs)

• Normalized position per position (QMnpp)

• Normalized over all positions (QMnap) minmax

inorm,i XX

XXX

Favourable amino acids have positive values, disfavourable aa take negative ones.

External validation

Test set of 457 known binders to HLA-DP2 proteinoriginating from 24 foreign proteins

Immune Epitope Database: http://www.immuneepitope.org

Peptide score

Score = Xp1 + Xp2 + Xp3 + Xp4 + Xp5 + Xp6 + Xp7 + Xp8 + Xp9

MGHRTYYKL 0.567GHRTYYKLP 1.245HRTYYKLPR 2.935RTYYKLPRT -0.769TYYKLPRTT 3.719YYKLPRTTN 1.543YKLPRTTNV 0.451KLPRTTNVD 2.039

TYYKLPRTT 3.719 HRTYYKLPR 2.935 KLPRTTNVD 2.039 YYKLPRTTN 1.543GHRTYYKLP 1.245MGHRTYYKL 0.567YKLPRTTNV 0.451 RTYYKLPRT -0.769

Peptide score

ranking

top 5%

External validation

QMnap predicts better than QMnpp.bindersallbinderspredictedtrue

ysensitivit

Influence of flanking residues

p1

p2p3

p4

p5

p6p7

p8

p9

RK FHYLPFLPS TGGS

13 positions x 19 amino acids + 1 original ligand = 248 ligands

p-1

p-2 p+1

p+2

External validation

bindersallbinderspredictedtrue

ysensitivit Addition of flanking residues terms does not improve the predictive ability.

Addition of cross terms

p1

p2p3

p4

p5

p6p7

p8

p9

RK FHYLPFLPS TGGS

Score = Xp1 + Xp2 + Xp3 + Xp4 + Xp5 + Xp6 + Xp7 + Xp8 + Xp9

+ Xp1p2 + Xp2p3 + Xp3p4 + Xp4p5 + Xp5p6 + Xp6p7 + Xp7p8 + Xp8p9

External validation

Addition of cross terms slightly improves the predictive ability.

bindersall

binderspredictedtrueysensitivit

Influence of anchor residues

p1p4

p6p9

RK FHYLPFLPS TGGS

5 positions x 19 amino acids + 1 original ligand = 96 ligands

p7

External validation

Anchor-based QM is better predictor than all position-based QM. bindersall

binderspredictedtrueysensitivit

Anchor residues + cross terms

p1p4

p6p9

RK FHYLPFLPS TGGS

p7

Score = Xp1 + Xp4 + Xp6 + Xp7 + Xp9

+ Xp1p4 + Xp4p6 + Xp6p7 + Xp7p9

External validation

bindersallbinderspredictedtrue

ysensitivit Combination between anchor positions and cross terms improves the prediction.

Acknowledgements

• Ivan Dimitrov • Mariyana Atanasova• Panaiot Garnev

Department of ChemistrySchool of PharmacyMedical University of Sofia

• Peicho PetkovSchool of PhysicsUniversity of Sofia

• Darren R. Flower Aston University, Birmingham, UK

All models are wrong but some are useful.

George E. P. Box, 1987

Professor of Statistics, University of Wisconsin