structural immunoinformatics – two case studies

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Medical University of SofiaFaculty of Pharmacy. Structural Immunoinformatics two case studies. M. Atanasova , I. Dimitrov, A. Patronov, I. Doytchinova. Regional Conference in Supercomputing Applications in Science and Industry, 20-21 Sept. 2011, Sunny Beach, Bulgaria. - PowerPoint PPT Presentation

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  • Structural Immunoinformatics two case studies M. Atanasova, I. Dimitrov, A. Patronov, I. DoytchinovaMedical University of SofiaFaculty of PharmacyRegional Conference in Supercomputing Applications in Science and Industry, 20-21 Sept. 2011, Sunny Beach, Bulgaria

  • Medical University of SofiaFaculty of PharmacyImmunoinformastic Approaches: Sequence - basedStructure - basedpeptide pIC50exp ILDPFPVTV8.654ALDPFPPTV8.170VLDPFPITV8.139.......................

    FLDPFPATV8.270Affinity = f (Chemical Structure)

    Motif-based, QMs, ANN, SVMAffinity = f (Interaction energy)

    Molecular dockingMolecular dynamicsImmunoinformatics (Computational Immunology, Theoretical Immunology)

  • Medical University of SofiaFaculty of PharmacyCase study 1: T-cell epitope prediction of proteins from Boophilus microplus Boophilusmicroplus tick.Ticks are hematophagous parasites that feed on variety of domestic animals. B. microplus tick: a hard tick; transmits lethal pathogens;causes disease and death.Collaborator: University of Pretoria, SAAim:to predict peptides originating from B. microplus and binding with high affinity to murine MHC class II proteins IAd and IEd.

  • Medical University of SofiaFaculty of PharmacyCase study 1: T-cell epitope prediction of proteins from Boophilus microplus Approaches used Sequence - basedStructure - basedMHCPred and RANKPEP servers for MHC class II binding predictionMolecular docking calculations

  • Medical University of SofiaFaculty of PharmacyCase study 1: T-cell epitope prediction of proteins from Boophilus microplus Selection of high immunogenic B. microplus proteins by VaxiJen server.Presentation of the selected proteins as sets of overlapping peptides.1.2.B. Microplus numberProtein peptides

    Contig282859Contig742093CK18162461Selection of input X-ray structures of complexes of murine MHC II protein with a peptide. Ova/IAd (pdb code: 2iad)HB/IEk (pdb code:1iea)Homology modelingof IEk to IEd structure3.Optimization of complexes of each peptide with each MHC II protein.Dockingcalculationsbiding site - 6; Chemscore scoring function; fixed protein and peptide backbone; ranking by score; GOLD v.5.0.2.Workflow:

  • Medical University of SofiaFaculty of PharmacyCase study 1: T-cell epitope prediction of proteins from Boophilus microplus Binding affinity prediction to IAd by MHCPred and RANKPEP MHCPred: Predicted binders with IC50 < 50 nMare highlighted in green.RANKPEP: Predicted binders with binding threshold: 7.10 are highlighted in purple.

  • Medical University of SofiaFaculty of PharmacyCase study 1: T-cell epitope prediction of proteins from Boophilus microplus Binding affinity prediction to IAd and IEd by Molecular docking IAdIEdThe top 2 best clusters of binders are highlighted in magenta.

  • Medical University of SofiaFaculty of PharmacyCase study 1: T-cell epitope prediction of proteins from Boophilus microplus Peptides selected for further experimental studies

  • Medical University of SofiaFaculty of PharmacyCase study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1) Aim: to generate quatitative matrices (QMs) for prediction of peptides binding to SLA-1 Swine Influenza in pigs:

    An acute respiratory disease;

    High morbidity depending on the immune status;

    Can results in important economic losses.CReSACentre de Recerca en Sanitat Animal

  • Modeled proteins: SLA-1*0101 SLA-1*0401SLA-1*0501 SLA-1*1101

    7 anchor positions X 19 aa = 133 + 1 original ligand = 134 peptides

    biding site - 6; Chemscore scoring function; fixed protein and ligand apart from the residues from the tested peptide position; ranking by lowest RMS; GOLD v.5.0.2.

    normalization of the binding energies and compilation into QMs.Medical University of SofiaFaculty of Pharmacy Workflow:

    Homology modeling of SLA-1 from HLA*0201 (pdb:3pwj).

    2. Construction of combinatorial library of peptides.

    3. Molecular docking of peptides to SLA proteins.

    4. Forming of docking score-based QMs (DS-QMs). Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

  • Medical University of SofiaFaculty of Pharmacy Workflow:

    Homology modeling of SLA-1 from HLA*0201 (pdb:3pwj).

    2. Construction of combinatorial library of peptides.

    3. Molecular docking of peptides to SLA proteins.

    4. Forming of docking score-based QMs (DS-QMs). Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

  • Medical University of SofiaFaculty of PharmacyCase study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

    SLA allelePocket 2 profileP2 accepts0101Tyr9, Ala24, Val34, Met45, Glu63, Lys66, Gln67Leu, Met, Asn0501Ser9, Ala24, Val34, Met45, Glu63, Lys66, Gln67Trp, Leu, Phe0401Tyr9, Ala24, Val34, Met45, Glu63, Asn66, Val67Leu, Met, Thr1101Ser9, Glu24, Val34, Met45, Glu63, Arg66, Val67Leu, Met, Ile

  • Medical University of SofiaFaculty of PharmacyCase study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

    SLA allelePocket 2 profileP2 accepts0101Tyr9, Ala24, Val34, Met45, Glu63, Lys66, Gln67Leu, Met, Asn0501Ser9, Ala24, Val34, Met45, Glu63, Lys66, Gln67Trp, Leu, Phe0401Tyr9, Ala24, Val34, Met45, Glu63, Asn66, Val67Leu, Met, Thr1101Ser9, Glu24, Val34, Met45, Glu63, Arg66, Val67Leu, Met, Ile

  • Medical University of SofiaFaculty of PharmacyCase study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1) SIV proteins screenedto predict SLA binders:- hemagglutinin (HA)- nucleocapsid protein (NP)- matrix protein 1 (M1)- polymerase PB1 (PB1)

  • Thank you for your attention!

    The first step in the workflow chart is the selection of high immunogenic b. microplus proteins by VaxiJen server. VaxiJen is a server for prediction of protective antigens as antigen classification is solely based on the physicochemical properties of proteins.

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