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T-cell epitope prediction by molecular dynamics simulations

Discover the intricate process of T-cell epitope prediction for developing epitope-based vaccines through molecular dynamics simulations and advanced immunoinformatics techniques. Learn about sequence-based and structure-based methods, affinity prediction, peptide interactions with HLA proteins, and the impact on vaccine development. Explore the challenges, solutions, and advancements transforming the field of immunology. External validation results and insights from leading researchers provide valuable knowledge for future studies.

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T-cell epitope prediction by molecular dynamics simulations

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  1. School of Pharmacy Medical University of Sofia T-cell epitope predictionby molecular dynamics simulations Irini Doytchinova Medical University of Sofia

  2. Vaccines and Epitopes live attenuated or killed pathogens subunit vaccines epitope-based vaccines Т-limphocyte conformational epitope Epitope is a continuous or non-continuous sequence of a protein that is recognized by and interacts with other protein. linear epitope В-limphocyte

  3. Antigen processing pathways Intracellular pathwayExtracellular pathway

  4. T-cell epitope prediction Epitope-based vaccine development 100 aa 92 overlapping nonamer peptides 10 nonamer peptides in silicoprediction in vitro andin vivo testsclinical tests

  5. T-cell epitope prediction Epitope-based vaccine development in silicopredictionin vitro andin vivo testsclinical tests 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. Immunology Immunoinformatics Bioinformatics Biology Informatics

  6. Immunoinformatics approaches Sequence-based methods Structure-based methods Affinity = f(Chemical Structure) Motif-based, QMs, ANN, SVM Affinity = f(Interaction energy) Moleculardocking Molecular dynamics

  7. Our immunoinformatics tools http://www.pharmfac.net/ddg

  8. MHC class II binding prediction by molecular dynamics Combinatorial library ΔG PKYVKQNTLKLAT+ 0.456 PKXVKQNTLKLAT - 0.123 PKYVKXNTLKLAT … PKYVKQNXLKLAT … PKYVKQNTXKLAT … PKYVKQNTLKXAT … QM 1 4 6 7 9 A … … … … … C … … … … … D … … … … … E … … … … … …… … … … … Peptide – HLA-DP2 protein complex (DPA1*0103 red, DPB1*0101 blue) pdb code: 3lqz, April 2010 External validation

  9. Combinatorial library p3 p8 p2 p5 p7 p9 p4 p6 p1 RK FHYLPFLPS TGGS 9 positions x 19 amino acids + 1 original ligand = 172 ligands

  10. MD simulations 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 Force field: GROMOS96 53a6 side: 1 nm create a box around the complex fill the box with water molecules energy minimization NA+ neutralize the charge with counterions position-restrained MD 20 ps MD with simulated annealing 100-310K Problems to solve: 1. Which energy to use for prediction? 2. How long to equilibrate the system? record the interaction energies LJ-SR & Coul-SR

  11. Which energy to use for prediction ? Test set n = 1932 known binders to HLA-DRB1*0101 originating from 122 foreign proteins Sensitivities were calculated over the top 5% of the predicted affinities of all overlapping peptides originating from one protein. Lennard-Jones short-range potential gives better prediction than Coulomb short-range potential.

  12. How long to equilibrate the system? Time/accuracy trade-off: 1 ns calculated for 11 hours

  13. MD-based Quantitative Matrices (MD-QMs) • Normalized position per position (QMnpp) • Normalized over all positions (QMnap) Favourable amino acids have positive values, disfavourable aa take negative ones.

  14. External validation Test set of 457 known binders to HLA-DP2 protein originating from 24 foreign proteins Immune Epitope Database: http://www.immuneepitope.org Score = Xp1 + Xp2 + Xp3 + Xp4 + Xp5 + Xp6 + Xp7 + Xp8 + Xp9 Peptide score Peptide score top 5% MGHRTYYKL 0.567 GHRTYYKLP 1.245 HRTYYKLPR 2.935 RTYYKLPRT -0.769 TYYKLPRTT 3.719 YYKLPRTTN 1.543 YKLPRTTNV 0.451 KLPRTTNVD 2.039 TYYKLPRTT 3.719 HRTYYKLPR 2.935 KLPRTTNVD 2.039 YYKLPRTTN 1.543 GHRTYYKLP 1.245 MGHRTYYKL 0.567 YKLPRTTNV 0.451 RTYYKLPRT -0.769 ranking

  15. External validation QMnap predicts better than QMnpp.

  16. Influence of flanking residues p3 p8 p-2 p2 p5 p+1 p-1 p+2 p7 p9 p4 p6 p1 RKFHYLPFLPSTGGS 13 positions x 19 amino acids + 1 original ligand = 248 ligands

  17. External validation Addition of flanking residues terms does not improve the predictive ability.

  18. Addition of cross terms p3 p8 p2 p5 p7 p9 p4 p6 p1 RK FHYLPFLPS TGGS Score = Xp1 + Xp2 + Xp3 + Xp4 + Xp5 + Xp6 + Xp7 + Xp8 + Xp9 + Xp1p2 + Xp2p3 + Xp3p4 + Xp4p5 + Xp5p6 + Xp6p7+Xp7p8 + Xp8p9

  19. External validation Addition of cross terms slightly improves the predictive ability.

  20. Influence of anchor residues p7 p9 p4 p6 p1 RK FHYLPFLPS TGGS 5 positions x 19 amino acids + 1 original ligand = 96 ligands

  21. External validation Anchor-based QM is better predictor than all position-based QM.

  22. Anchor residues + cross terms p7 p9 p4 p6 p1 RK FHYLPFLPS TGGS Score = Xp1 + Xp4 + Xp6 + Xp7 + Xp9 + Xp1p4 + Xp4p6 + Xp6p7 + Xp7p9

  23. External validation Combination between anchor positions and cross terms improves the prediction.

  24. Acknowledgements • Ivan Dimitrov • Mariyana Atanasova • Panaiot Garnev Department of Chemistry School of Pharmacy Medical University of Sofia • Peicho Petkov School of Physics University 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

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