Cartoon modeling of proteins by sdfwerte

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									Cartoon modeling of proteins
       Fred Howell and Dan Mossop
                              ANC
                        Informatics
Overview

   Why / how to model intracellular processes?
   Examples: MCell, Stochsim, Virtual Cell

   Cartoon models
   Where's the data on structure / interactions?

   A new 3D protein interaction simulator
        post synaptic density self-assembly
        vesicle formation
        vesicle transport


   Futures & speculations
Why / how to model intracellular processes?

   Ordered soup of ~1,000,000 different types of macromolecules
   Complex and specific network of interactions
   Ion channels and complexes the tip of the iceberg (croutons?)

   Much work on gene networks / intracellular pathways
   Mostly ignores spatial effects (well mixed pool / kinetics)

   Hypothesis of mechanisms typically involve cartoon descriptions /
    precise shapes / jigsaw-like interactions of proteins

   Computer models typically don't
Intracellular pathway modeling

   Single mixed pool:
        Rate equations / kinetics (as differential equations)
        Stochastic simulators (Stochsim)
   A number of connected compartments
        Virtual cell
   Individual molecules / brownian motion
        MCell


   ... but none of them take into account the actual shapes of proteins!
Single protein modeling

   The great protein folding problem - what shapes can the sequence
    form?
        Uses molecular dynamics (motion of each atom in the molecule) to try
         and predict low energy folding conformations of primary sequence
             hard, not there yet
        Intermediate protein modeling - recognise characteristic subsequences
         of amino acids, guess substructures like alpha helices, beta sheets
             promising, not there yet


   Timescales of femto- and pico- seconds

   ... data available from crystallography on some proteins (PDB)
   ... predicting binding sites is very hard
Cartoon models

   Typically used to hypothesise mechanisms
Getting data on protein shapes

   PDB: coordinates of each atom in protein

   One possibility: cluster analysis to reduce to a number of subunits
Getting data on protein interactions

   This is harder

   Ideally would like binding sites, bond angles, bond strengths

   Typically get "A does / does not interact with B (probably)"

   ... but the situation is set to improve as more data becomes
    available in databases
So, how to build models?

   Cheat - use a mixture of real and hypothesised model proteins
A new protein interaction simulator


     proteins modeled as simplified 3D structures including a number of
      subunits / binding sites / conformational states
     water not modeled explicitly
     proteins moved by brownian motion
     bonding / state transition probabilities set as parameters
     collision detection
     in version 1 protein complexes modeled as rigid structures
     membranes modeled as a restriction to 2D diffusion of membrane
      bound proteins (still free to rotate)
Example models

   (1) Formation of the post synaptic density - a model of recruitment of
    AMPA receptors to the vicinity of activated NMDA receptors

   (2) Self assembly of clathrin coated vesicles

   (3) Transport of vesicles using kinesin
The common theme

   Throw together an unordered collection of proteins, with specific
    binding sites, interactions and probabilities

   Evolve the system through time

   See if complex shapes and processes emerge
Example 1 - post synaptic density
    NMDAr          AMPAr





               CAM KII
    glue
Example 2 - Vesicle formation

   Clathrin:-
Example 4 - Kinesin

   Input - a motor protein model, stable states / transitions / binding
    cause it to walk up microtubules carrying its payload
Details of simulator (and approaches tried)

   Fluid dynamics?
   DPD?
   MD?
   Monte-carlo?
Simulator design:

   XML model description (protein shapes, initial state, binding sites
    and probabilities)
   Java simulation engine for state updates
   Java3D visualisation
Futures: modeling technology

   Add spring constants to bonds (rather than completely rigid)

   More sophisticated models of membranes (rather than a 2D
    restriction on diffusion)

   Efficient cytoskeleton models?

   Explicit water? Small ions?

   Auto generation from databases of protein shapes and interactions?
Futures: applications

   DNA replication machinery (helicase / polymerase)
   Snares / vesicle docking / budding (a model of Golgi apparatus?)
   Full molecular model of a dendritic spine receiving an burst of
    transmitter
   Ribosome operation
   Entire process of cell division (dna replication + microtubule
    formation + motor protein separation + control sequences)
   Self assembly of viruses from their coat proteins
A model of parallel processing?

   How does this ordered soup of proteins maintain a such a large
    number of tightly synchronised feedback control systems?

   Could it be a useful model of computation in its own right? The well
    mixed case is:-
        we have a memory of 1,000,000 different variables (one per protein)
        we have specific probabilties of transitions between these
        we have mechanisms for synthesising and destroying proteins


   Adding 3D structure we also get:-
        some combinations of these variables form substructures with specific
         properties
        interactions depend on where the proteins are
Conclusions

   We can build 3D models of protein systems to test and visualise
    hypothesis about how structures can form

   We still don't have a good way to model all the intracellular
    complexity

   Perhaps we should focus on molecular models of viruses and
    bacteria before attempting eukaryotic cells?



   Thanks to Dan Mossop for doing all the work

								
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