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![Page 1: T-cell epitope prediction by molecular dynamics simulations Irini Doytchinova Medical University of Sofia School of Pharmacy Medical University of Sofia.](https://reader036.fdocuments.us/reader036/viewer/2022070411/56649c765503460f9492aa71/html5/thumbnails/1.jpg)
T-cell epitope predictionby molecular dynamics
simulations
Irini Doytchinova
Medical University of Sofia
Copyright © 1997 Ivo Ivanov
School of PharmacyMedical University of Sofia
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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
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Antigen processing pathways
Intracellular pathway Extracellular pathway
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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
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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
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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
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Our immunoinformatics tools
http://www.pharmfac.net/ddg
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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
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Combinatorial library
p1
p2p3
p4
p5
p6p7
p8
p9
RK FHYLPFLPS TGGS
9 positions x 19 amino acids + 1 original ligand = 172 ligands
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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
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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.
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How long to equilibrate the system?
Time/accuracy trade-off: 1 ns calculated for 11 hours
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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.
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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%
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External validation
QMnap predicts better than QMnpp.bindersallbinderspredictedtrue
ysensitivit
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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
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External validation
bindersallbinderspredictedtrue
ysensitivit Addition of flanking residues terms does not improve the predictive ability.
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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
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External validation
Addition of cross terms slightly improves the predictive ability.
bindersall
binderspredictedtrueysensitivit
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Influence of anchor residues
p1p4
p6p9
RK FHYLPFLPS TGGS
5 positions x 19 amino acids + 1 original ligand = 96 ligands
p7
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External validation
Anchor-based QM is better predictor than all position-based QM. bindersall
binderspredictedtrueysensitivit
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Anchor residues + cross terms
p1p4
p6p9
RK FHYLPFLPS TGGS
p7
Score = Xp1 + Xp4 + Xp6 + Xp7 + Xp9
+ Xp1p4 + Xp4p6 + Xp6p7 + Xp7p9
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External validation
bindersallbinderspredictedtrue
ysensitivit Combination between anchor positions and cross terms improves the prediction.
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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