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Bioinformatics of Disease: immune epitope prediction

Bioinformatics of Disease: immune epitope prediction. Shoba Ranganathan Professor and Chair – Bioinformatics Dept. of Chemistry and Biomolecular Sciences & Adjunct Professor Biotechnology Research Institute Dept. of Biochemistry Macquarie University Yong Loo Lin School of Medicine

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Bioinformatics of Disease: immune epitope prediction

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  1. Bioinformatics of Disease: immune epitope prediction Shoba Ranganathan Professor and Chair – Bioinformatics Dept. of Chemistry and Biomolecular Sciences & Adjunct Professor Biotechnology Research Institute Dept. of Biochemistry Macquarie University Yong Loo Lin School of Medicine Sydney, Australia National University of Singapore, Singapore (shoba.ranganathan@mq.edu.au) (shoba@bic.nus.edu.sg) Visiting scientist @ Institute for Infocomm Research (I2R), Singapore

  2. Bioinformatics is ….. • Bioinformatics is the study of living systems through computation

  3. Sequences Networks, pathways and systems Structures Genetics and populations Genomes Transcriptomes Data in Bioinformatics (in the main)and their management and analysis Databases, ontologies Data & text mining Algorithms Maths/Stats Physics/ Chemistry Evolution and phylogenetics

  4. Overview of my research • Genome analysis • Transcriptome analysis • Protein/Proteome analysis • Systems Biology • Immunoinformatics • Genome-phenome mapping • Biodiversity Informatics

  5. 5. What is Immunoinformatics? • Using Bioinformatics to address problems in Immunology • Application of bioinformatics to accelerate immune system research has the potential to deliver vaccines and address immunotherapeutics. • Computational systems biology of immune response

  6. Immunology Computer Science Biology Immunoinformatics

  7. Basic immunology Clinical immunology -omics Networks, pathways, and systems IMMUNOINFORMATICS Artificial intelligence Cell biology Physics/ Chemistry Databases Algorithms Maths/Stats

  8. Basic immunology Clinical immunology Networks, pathways and systems -omics Summary Genetics and populations • Introduction • Structural Immunoinformatic Database development • Data Analysis • Computational models • Applications

  9. The immune system • Composed of many interdependent cell types, organs, and tissues to • protect the body from infections (bacterial, parasitic, fungal, or viral) and • arrest abnormal growth and differentiation • Inappropriate immune responses lead to allergies and autoimmunity • 2nd most complex system in the human body

  10. Genomics vs. Immunomics • Genomics: solving the genome puzzle • 104 genes coding for 106 products • Immunomics: understanding immune response • 102-103 genes leading to >1012 products • Enormous diversity in immunomics has implications for immune function and modulation

  11. It is a numbers game…. • >1013 MHC class I haplotypes (IMGT-HLA) • 107-1015 T cell receptors (Arstila et al., 1999) • >109 combinatorial antibodies (Jerne, 1993) • 1012 B cell clonotypes (Jerne, 1993) • 1011 linear epitopes composed of nine amino acids • >>1011 conformational epitopes

  12. T cell mediated adaptive immune response • Specific peptide residues critical for stimulating cellular immune responses • Major histocompatibility complex (MHC) molecules (Human Leukocyte Antigen or HLA in humans) bind and present short antigenic peptides to T cell receptors, for inspection • Antigen presentation is by two classes of MHC (class I and class II) • Those peptides that bind to specific MHC and trigger T cell recognition (T cell epitopes) are targets for vaccine and immunotherapy development

  13. How to generate a T cell-mediated immune response 3. T cell receptor 2. MHC 1. Epitope

  14. Major histocompatibility complex Gene structure of the human MHC 3D structure of the human MHC MHC Class II MHC Class I

  15. MHC Class I for endogenous peptides Figure by Eric A.J. Reits

  16. MHC class II for exogenous peptides Figure by Eric A.J. Reits

  17. Antigen processing pathway: peptides, MHC, T-cells • Degradation of antigen • Peptide binding to MHC • Recognition of peptide-MHC complex by T-cells Yewdell et al. Ann. Rev Immunol (1999) 0.05% chance of immunogenicity 50% CTL response 20% processed 0.5% bind MHC

  18. Physico-chemical properties affect MHC-peptide binding

  19. Epitope prediction º “Fishing”

  20. Computational models can help identify T cell epitopes • Suggest candidate epitopes by in silico screening of entire proteins and even proteomes with specificity at: • the allele level • the supertype level • disease-implicated alleles alone. • Minimize the number of wet-lab experiments • Cut down the lead time involved in epitope discovery and vaccine design

  21. Predicting MHC-binding peptides Tong, Tan and Ranganathan (2007) Briefings in Bioinformatics 8: 96-108 • Sequence-based approach • Pattern recognition techniques • binding motif, matrices, ANN, HMM, SVM • Main limitations: • Require large amount of data for training • Preclude data with limited sequence conservation • Structure-based approach • Rigid backbone modeling techniques • Flexible docking techniques • Main advantage: large training datasets unnecessary

  22. Our aim: Structure-based prediction of MHC-binding peptides

  23. Why structure? • Great potential to: • generate biologically meaningful data for analysis • predict candidate peptides for alleles that have not been widely studied, where sequence-based approaches fail or are not attempted • predict binding affinity of peptides • predict non-contiguous epitopes • Structure determination through experimental methods is both expensive and time-consuming • Has not been extensively studied due to high computational costs and development complexity

  24. Existing Structure-based Prediction Techniques • Protein Threading [Altuvia et al. 1995; Schueler-Furman et al. 2000] • Homology Modeling [Michielin et al. 2000] • Rigid/Flexible Docking [Rosenfeld et al. 1993; Sezerman et al. 1996; Rognan et al. 1999; Desmet et al. 2000; Michielin et al. 2003]

  25. Hypothesis for epitope selection • Peptides bound to MHC alleles are similar to substrates bound to enzymes • “Lock-and-key” mechanism for peptide selection • Shape • Size • Electrostatic characteristics

  26. Basic immunology Sequences Structures Genetics and populations Databases, ontologies • Introduction • Structural Immunoinformatic Database development • Data Analysis • Computational models • Applications

  27. MPID:MHC-Peptide Interaction DatabaseGovindarajan et al. (2003) Bioinformatics, 19: 309-310 RDB of 82 curated pMHC complexes (Class I: 64 & Class II:18)

  28. Peptide/MHC interaction characteristics Gap volume Interface area Peptide Length Interface area Gap Volume Interacting Residues Intermolecular hydrogen bonds Gap index =

  29. MPID-T: MHC-Peptide-T Cell Receptor Interaction DatabaseTong et al. (2006) Applied Bioinformatics, 5: 111-114 • 187 curated pMHC • 16 with TCR • Human:110, Murine:74 and Rat:3 • Alleles: 40 (interface area, H bonds, gap volume and gap index)

  30. Distribution of MHC by allele 101 new entries 187 entries (Human: 110; Murine: 74; Rat: 3) 134 non-redundant entries (class I: 100; class II: 34) 121 class I and 41 class II entries 26 HLA alleles (class I: 18; class II: 8) 14 rodent alleles (class I: 8; class II: 6) 16 TCR/peptide/MHC complexes

  31. Peptide/MHC binding motifs Polar Amide Basic Acidic Hydrophobic • Conserved peptide properties in solution structures • Classified according to • Alleles • Peptide length

  32. How to obtain structures of experimentally unsolved alleles? • There were only36crystal structures of unique MHC (2006) alleles vs.1765 unique MHC alleles identified in IMGT/HLA database • Structure determination through experimental methods is both expensive and time-consuming • Homology model building for alleles with no structural data!

  33. Structures • Introduction • Structural Immunoinformatic Database development • Data Analysisof pMHC Class I complexes • Computational models • Applications Data & text mining Maths/Stats

  34. MHC Class I superfamilies have different interaction characteristics Single linkage cluster analysis of 68 pMHC Class I complexes from 13 alleles (all available A and B)

  35. Details Data • 68 peptide–HLA complexes spanning 13 classes I alleles from MPID-T Hierarchical clustering • Hierarchical clustering using the agglomerative algorithm. • Distance between structures computed by single-linkage method (MATLAB version 7.0) based on the separation between the each pair of data points. • Nearest neighbors merged into clusters. • Smaller clusters were then merged into larger clusters based on inter-cluster distances, until all structures are combined. • Last 3 levels considered for defining HLA class I supertypes. Interaction parameters • Significant for the characterization of peptide/MHC interface: • Intermolecular hydrogen bonds • pMHC Interface area • Binding characteristics of HLA supertypes analyzed • Gap volume • Gap index

  36. Do the Class I alleles aggregate into “superfamilies” using receptor-ligand interaction patterns? Legend B27 B44 B7 B62 B8

  37. MHC Class I superfamilies from receptor-ligand interactions 80 HLA class I complexes 13 class I alleles Five descriptors Hierarchical clustering using nearest neighbor algorithm 77% consensus with data from other groups Supertype definition: receptor structure, ligand binding motifs, or receptor-ligand interaction patterns Tong, Tan and Ranganathan (2007) Bioinformatics, 23: 177-183 Legend B27 B44 B7 B62 B8

  38. Structures Sequences • Introduction • Structural Immunoinformatic Database development • Data Analysis • Computational models • Applications Physics/ Chemistry Maths/Stats

  39. Two-step approach to predict MHC-binding peptides • Finding the best fit conformation (docking) of peptides within the MHC binding groove • Screening potential binders from the background

  40. y   x C N C Ca z O R Docking is a computationally exhaustive procedure • Large number of possible peptide conformations • 3 global translational degrees of freedom • 3 global rotational degrees of freedom • 1 conformational degree of freedom for each rotatable bond >1010 possible conformations for a 10-residue peptide

  41. Conservation of nonamer peptide backbone conformation • Class I peptides • N-termini residues 0.02 – 0.29 Å • C-termini residues 0.00 – 0.25 Å • Class II binding registers • Only 9 residues fit in the binding groove • N-termini residues 0.01 – 0.22 Å • C-termini residues 0.02 – 0.27 Å

  42. Rapid docking of peptide to MHC Tong, Tan & Ranganathan (2004) Protein Sci. 13:2523-2532 1 2 3

  43. Benchmarking with existing techniques aRMSD of peptide backbone obtained from respective authors. bRMSD of peptide backbone obtained in our work from redocking bound complexes and single template respectively.

  44. Quantitative separation of binders from non-binders: empirical free energy scoring function • DQ3.2binvolved in several autoimmune diseases: • Celiac disease • insulin-dependent diabetes mellitus • IDDM-associated periodontal disease • autoimmune polyendocrine syndrome type II

  45. Quantitative separation of binders from non-binders: empirical free energy scoring function Gbind = αGH + βGS + GEL + C • Gbind = binding free energy • GH = hydrophobic term • GS = decrease in side chain entropy • GEL = electrostatic term • C = entropy change in system due to external factors • α, β, γ optimized by least-square multivariate regression with experimental binding affinities (IC50) of MHC-peptides in training dataset (Rognan et al., 1999)

  46. Test case: MHC Class II DQ8 • DQ3.2b(DQA1*0301/DQB1*0302)is involved in several autoimmune diseases: • Celiac disease • insulin-dependent diabetes mellitus • IDDM-associated periodontal disease • autoimmune polyendocrine syndrome type II

  47. Data used • Structure: 1JK8 - DQ3.2β–insulin B9-23 complex • Dataset I: 127 peptides with experimentally determined IC50 values [70 high-affinity (IC50 <500 nM), 13 medium-affinity (500 nM < IC50 < 1500 nM )and 23 low-affinity (1500 < IC50 < 5000 nM) binders and 21 non-binders (5000 < IC50)] derived from biochemical studies. • 87 with known binding registers. • Dataset II: 12 Dermatophagoides pternnyssinus (Der p 2) peptides with experimental T-cell proliferation values from functional studies, with 7 peptides eliciting DQ3.2β-restricted T-cell proliferation.

  48. Scoring: Training & testing datasets • Training • 56 binding conformations with known registers • 30 non-binding conformations from 3 non-binders • Testing • Test set 1 – 68 peptides from biochemical studies • 16 strong ; 13 medium; 21 weak; 18 non-binders • Test set 2 – 12 peptides from functional studies • 7 elicit T-cell proliferation

  49. Screening class II binding register: a sliding window approach E285B 112-126 peptide Y Q T I E E N I K I F E E D A

  50. 4-step protocol used A B C D

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