Computer Simulation of T-Cell Activation

Transcription

1 ISIM1 Computational Immunology Computer Simulation of T-Cell Activation W. Schreiner, M. Cibena, U. Omasits, M. Berger, R. Kobler, M. Neumann, O. Steinhauser Section for Biomedical Computersimulation and Bioinformatics Core Unit for Medical Statistics and Informatics Medical University of Vienna

2 Survey ISIM1 Project 1: Simulation of epitope - MHC - TCR binding & interaction Project 2: Simulation of Cooperative TCR - activation Both projects are carried out within the Austrian Computational Grid Initiative

3 Project 1: Epitope - MHC - TCR interaction ISIM1 Computational technique: molecular dynamics 3 large Molecules: MHC epitope (self smhc, patho pmhc) TCR many H 2 O (up to ) Step1: Simulation of MHC - epitope - interaction Step2: Simulation of pmhc - TCR - interaction

4 Molecular Dynamics - Objectives ISIM1 understand the molecular details of peptide-mhc binding explore binding mechanism & specificity of T-Cell-Receptor calculate binding free energy: dg bind Epitope prediction & Epitope design

5 ISIM1 Molecular Dynamics of a pmhc-complex Complex Procedure: 1. Coordinates get the starting coordinates from the RCSB Protein Data Bank 2. Simplification simplify the system due to computational efficiency 3. Water-Box solvate the pmhc-complex in a box of water molecules 4. Warm-Up heat-up the system to equilibrate the water molecules 5. Molecular Dynamics run 1000ps (1ns) MD-simulation to equilibrate the pmhc-complex a brief animated example of the main steps in a molecular dynamics simulation

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16 Simulation analysis - RMSD Root Mean Square Deviation RMSD (nm) 0,35 0,30 0,25 0,20 0,15 0,10 0,05 0,00 Root Mean Square Deviation Time (ps) antigen backbone MHC backbone characterizes the mean structural deviation from the starting configuration at each time (after a least-squares fit) reflects the quality of the MD-simulation

17 Simulation analysis - RMSD Root Mean Square Deviation RMSD (nm) 0,35 0,30 0,25 0,20 0,15 0,10 0,05 0,00 Root Mean Square Deviation Time (ps) Equilibrium reached after ~250ps antigen backbone MHC backbone characterizes the mean structural deviation from the starting configuration at each time (after a least-squares fit) reflects the quality of the MD-simulation

18 Simulation analysis - COM Center of mass distance between MHC and nonamer COM distance (nm) 1,2 Center of mass distance 1,1 1,0 0,9 0,8 0,7 0, Time (ps) depicts a possible movement of the epitope relative to the MHC: out of cleft closer into the cleft

19 Simulation analysis - COM Center of mass distance between MHC and nonamer COM distance (nm) 1,2 Center of mass distance 1,1 1,0 0,9 0,8 0,7 0, COM distance (nm) Center of mass distance 1,1 1,0 0,9 0,8 0,7 0, Time (ps) Time (ps) 1,2 stable unstable

20 Simulation analysis - RMSF Root Mean Square Fluctuation of the nonamer residues 0,12 Root Mean Square Fluctuations 0,10 RMSF (nm) 0,08 0,06 0,04 0,02 0,00 N-term Antigen Residue C-term. RMSF = standard deviation of the atom positions over time tightly bonded residues have low RMSF values

21 Simulation analysis - SASA Solvent Accessible Surface Area of the nonamer residues 1,0 Solvent Accessible Surface Area realtive surface exposure 0,8 0,6 0,4 0,2 0, N-term. C-term. Residue shows the non-buried part of each residue-surface very exposed residues are potential TCR-binding residues buried residues are anchors to the MHC (main anchors: P2 & P9 secondary anchors: P1 & P3)

22 Future Work in pmhc - MolDyn simulate pmhc-complex with mutated epitope simulate a ternary pmhc-tcr complex calculate binding free energy

23 Project 2: Simulation of Cooperative TCR - activation Simulation model: concepts Computational technique Simulation runs Evaluation of target quantities Prospects

24 Simulation model: Concepts APC - TC - interface (modeled as 2-dimensional contact surface) 1 APC furnished with 60 MHC/µm² smhc: self-epitope loaded pmhc: patho-epitope loaded 1 TC furnished with 160 TCRs/ µm² Sizes of APC and TC are scalable Keep density of MHCs and TCRs constant large cells many MHCs/TCRs slow simulation... How large cells do we need to simulate to obtain realistic responses? Do we need full size TCs to reproduce cooperative activation mechnisms of synapse?

25 Motions of Molecules stochastic, brownean motions reproduce measured diffusion coefficients specifically for each CDx: free MHCs within APC free TCRs within TC joint (slower) motion of MHC-TCR complexes motions under rigid/periodic boundary conditions

26 Periodic Boundary Conditions (PBC) TCR 2 TCR 2 TCR 2 distance under PBC TCR 2 TCR 1 TCR 2 TCR 1 TCR 2 TCR 1 normal distance TCR 2 TCR 1 TCR 2 TCR 1 TCR 2 TCR 1 TCR 1 TCR 1 TCR 1

27 Goodies of PBCs Possible benefits of Periodic Boundary Conditions (PBC) A spherical TC with diameter = 15µm has a surface of 700µm² and with 160 TCRs/ µm² there are TCRs With PBCs, particles may move freely, like in a very large system (do not bounce against boundaries) PBC provide a finite (!) system without boundaries, immitating an infinite (i.e. much larger) system. if the model should actually be larger then it is possible to simulate: PBC at least imitate a larger system than actually realized long range interactions may be better represented than with RBC cooperative mechanisms (e.g. mutual inhibition among smhc- TCR complexes) may be of the long - range - type and benefit from PBC

28 TC creeping over APC Creeping = relative movement of TC w/respect to MHCs MHC-movements are biased downwards to emulate creeping under RBC: MHCs accumulate at bottom of APC (unrealistic)

29 TC creeping over APC / ctd. under PBC: realistic draft of TCRs relative to MHCs TCRs encounter ever so more MHCs to facilitate scanning!

30 Computational & Modeling Technique multi-particle stochastic simulation implemented in object oriented (OO) concepts (JAVA) each CDx is derived from a general molecule class and furnished with specific features, e.g. states of phosphorylation mutual interactions between CDx and Cdy are derived from a general interaction class interactions modeled via Unified Modeling Language (UML) grid-partitioning of cells to increase simulation speed data output/interfaces via XML Evaluation in SAS (Statistical Analysis Systems)

31 Computational & Modeling Technique Aim: One Program for many models. Krogsgaard M, Davis MM. How T-cells 'see' antigen. Nat.Immunol. 2005; 6: Chan C, George AJ, Stark J. Cooperative enhancement of specificity in a lattice of T cell receptors. Proc.Natl.Acad.Sci.U.S.A 2001; 98:

32 Computational & Modeling Technique The variety of models is coped with via a hierachical OO- Software Concept. Sharpe AH, Freeman GJ. The B7- CD28 superfamily. Nat.Rev.Immunol. 2002; 2: Love PE, Shores EW. ITAM multiplicity and thymocyte selection: how low can you go? Immunity. 2000; 12:

33 One Program for Many Models features of all interaction models (superclass) features of specific interaction model (sub-class) Interaction: MHC - epitope considered static (smhc or pmhc) smhc/pmhc induce different parameters (e.g. half-lives) for TCR-activation Interaction: smhc-tcr dock/undock TCR-activation levels none/partial/ full specific ITAM - patterns different Interaction: pmhc-tcr parameter values dock/undock TCR-activation levels none/partial/ full specific ITAM - patterns

34 Specific CDs Inherit General Properties from Molecule-Superclass Features of all interaction models (superclass) eatures of specific interaction model (sub-class) Interaction: TCR-TCR mutual inhibition of activation smhc-tcr inhibits neighbours within certain range docked TCR prevents other TCR from docking (inhibits sequential proofreading) mutual enhancment of activation pmhc-tcr enhances neighbour TCR activation pmhc-tcr protects neighbour TCR from inhibition Interaction: CDx - CDy not modeled as yet

35 Simulation Runs staged simulation Equilibration (smhcs and TCRs) cooperative development of TCR activation Observation of fully developed activation? (hopefully) stage 1 adding stage 2 stage 3 pmhcs

36 Timecourse of Target quantities

37 Lifetime of TCR- Docking and Activation - Stages Survival - concepts from clinical biostatistics my be applied to TCR-lifetimes

38 Is MHC-TCR docking promiscuous? (When? Why?)

39 Future Work Simulation of TCR-Activation Consider more / Other CDs CD4 CD28 other fellows? Modeling special Encounter Mechanisms now: simple probabilities distance - dependent probabilities probabilities with memory Spatial heterogeneity of TC/APC-membranes: Lipid Rafts, including spatio-temporal dynamics Use the modeling approach to explain simultaneous sensitivity and specificity of epitope recognition

40 Computational Immunology Team This research project is supported by: M.Cibena M.Berger W.Schreiner U.Omasits Computational Support: R. Kobler (Parallel Computing, JKU) Senior Advisors: M.Neumann (Experimental Physics), O.Steinhauser (Structural Chemistry), A.Schimpl (Immunology)