Transcript Cartoon modeling of proteins

Cartoon modeling of proteins
Fred Howell and Dan Mossop
ANC
Informatics
Overview
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Why / how to model intracellular processes?
Examples: MCell, Stochsim, Virtual Cell
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Cartoon models
Where's the data on structure / interactions?
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A new 3D protein interaction simulator
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post synaptic density self-assembly
vesicle formation
vesicle transport
Futures & speculations
Why / how to model intracellular processes?
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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)
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Hypothesis of mechanisms typically involve cartoon descriptions /
precise shapes / jigsaw-like interactions of proteins
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Computer models typically don't
Intracellular pathway modeling
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Single mixed pool:
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A number of connected compartments
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Virtual cell
Individual molecules / brownian motion
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Rate equations / kinetics (as differential equations)
Stochastic simulators (Stochsim)
MCell
... but none of them take into account the actual shapes of proteins!
Single protein modeling
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The great protein folding problem - what shapes can the sequence
form?
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Uses molecular dynamics (motion of each atom in the molecule) to try
and predict low energy folding conformations of primary sequence
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hard, not there yet
Intermediate protein modeling - recognise characteristic subsequences
of amino acids, guess substructures like alpha helices, beta sheets
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promising, not there yet
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Timescales of femto- and pico- seconds
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... data available from crystallography on some proteins (PDB)
... predicting binding sites is very hard
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Cartoon models
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Typically used to hypothesise mechanisms
Getting data on protein shapes
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PDB: coordinates of each atom in protein
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One possibility: cluster analysis to reduce to a number of subunits
Getting data on protein interactions
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This is harder
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Ideally would like binding sites, bond angles, bond strengths
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Typically get "A does / does not interact with B (probably)"
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... but the situation is set to improve as more data becomes
available in databases
So, how to build models?
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Cheat - use a mixture of real and hypothesised model proteins
A new protein interaction simulator
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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
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(1) Formation of the post synaptic density - a model of recruitment of
AMPA receptors to the vicinity of activated NMDA receptors
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(2) Self assembly of clathrin coated vesicles
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(3) Transport of vesicles using kinesin
The common theme
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Throw together an unordered collection of proteins, with specific
binding sites, interactions and probabilities
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Evolve the system through time
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See if complex shapes and processes emerge
Example 1 - post synaptic density
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NMDAr
glue
AMPAr
CAM KII
Example 2 - Vesicle formation
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Clathrin:-
Example 4 - Kinesin
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Input - a motor protein model, stable states / transitions / binding
cause it to walk up microtubules carrying its payload
Details of simulator (and approaches tried)
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Fluid dynamics?
DPD?
MD?
Monte-carlo?
Simulator design:
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XML model description (protein shapes, initial state, binding sites
and probabilities)
Java simulation engine for state updates
Java3D visualisation
Futures: modeling technology
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Add spring constants to bonds (rather than completely rigid)
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More sophisticated models of membranes (rather than a 2D
restriction on diffusion)
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Efficient cytoskeleton models?
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Explicit water? Small ions?
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Auto generation from databases of protein shapes and interactions?
Futures: applications
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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?
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How does this ordered soup of proteins maintain a such a large
number of tightly synchronised feedback control systems?
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Could it be a useful model of computation in its own right? The well
mixed case is:
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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:
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some combinations of these variables form substructures with specific
properties
interactions depend on where the proteins are
Conclusions
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We can build 3D models of protein systems to test and visualise
hypothesis about how structures can form
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We still don't have a good way to model all the intracellular
complexity
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Perhaps we should focus on molecular models of viruses and
bacteria before attempting eukaryotic cells?
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Thanks to Dan Mossop for doing all the work