NIH, NIDDK, LBG
Travis Hoppe, Allen Minton, Di Wu
(deck source)
against
Human serum albumin, Ovalbumin, Lysozyme, Bovine Serum Albumin respectively.
How do we model the interaction between proteins on a larger scale? Can we predict aggregates?
Phase separations lead to sudden fundamental changes in liquid structure and local density.
This is usually really important.
Match experimental data with results
of computational models.
Classify common protein solution behavior
from coarse-grained models.
Predict behavior of mutations and unknown solutions.
Necessary first step for a verifiable model.
For rotationally invariant molecules
(where is the pairwise interaction energy)but in general...
How do you model a
Need an expression for the interaction energy
This is not the free energy, but the enthalpy
Important terms:
How do you model a
Second-order effects?
Must decide if this approximation is valid for the system.
modeling the
modeling the
Coulomb's Law (point charge)
Correction for dielectrics?
First order approximation to screening effects.
Charge strength decays exponentially due to ions.
Describes the electrostatic interaction between a charge distribution and an ionic solution.
Can be linearized and solved on a computer efficiently.
Splits space into regions of discrete .
Start with the crystallized PDB Structure (HSA)
Typically (in the absence of ions)
,
Electrostatic field
Determine a region of excluded volume.
Spherical Harmonic decomposition for large distances.
Best fit macrocharges to replicate the field.
Good approximation of the near field, poor up close.
Captures the anisotropic field
especially near the isoelectric point.
Macrocharge approximations make for reasonable
models of large protein solutions.
Excluded volume for modeled as a hard sphere.
Representation of the protein electrostatics
in an ionic solution for a given pH.
Our charge distributions are not isotropic anymore,
we must to compute this:
There are many pairwise orientations.
Blind sampling may miss specific interactions.
Need to know at different if we want to scale the model.
counts the number of ways
we can get a particular energy,
efficiently calculate with Wang-Landau.
Calculate the non-ideality of a protein molecule after including both the excluded volume and electrostatics.
Predict the second-virial coefficient as a function of pH values, protein concentrations, binary mixtures, and salt concentrations.
If experimental results agree, use the model in higher-order simulations to predict phase behavior via Gibb's ensembles.
results for square-well potentials shown
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## How does it work?
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and [links](http://thoppe.github.io/)!
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