Post on 02-Jun-2015
Comparing the electrostatic properties of protein active sites and other Cresset research
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Agenda
> Looking at 3D RISM> Work by Mark Mackey and Paolo Tosco
> Approaches to Protein Fields> Work by Andy Smith, Susana Tomasio
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New use for the XED force field?
3D-RISM
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3D-RISM
> Analytical method for working out where water goes (Ornstein-Zernike equation)
> Conceptually equivalent to running an infinite-time MD simulation on the solvent and extracting the solvent particle densities
ℎ 𝑟𝑟12,𝜔𝜔1,𝜔𝜔2= 𝑐𝑐 𝑟𝑟12,𝜔𝜔1,𝜔𝜔2
+ 𝜌𝜌�𝑑𝑑𝑟𝑟3𝑑𝑑𝜔𝜔3𝑐𝑐 𝑟𝑟13,𝜔𝜔1,𝜔𝜔3 ℎ(𝑟𝑟32,𝜔𝜔3,𝜔𝜔2)
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3D-RISM
> Analytical method for working out where water goes (Ornstein-Zernike equation)
> Conceptually equivalent to running an infinite-time MD simulation on the solvent and extracting the solvent particle densities
> Horribly complicated maths> GPL implementation in Amber Tools> Output is grid containing particle densities> Thermodynamic analysis to assign “happiness”
to each water
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> Results depend on the potential function from solvent to solute u(r12, Ω1, Ω2)
> In practise, this means vdW + electrostatics> Results only as good as your potential functions> Does the XED description of electrostatics
improve the results?
Problems
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Electrostatics from Molecular Mechanics
> XED force field – eXtended Electron Distribution> Multipoles via additional monopoles
> Huckel> separation of π and σ charges – substituent effects> find bond orders and assign hybridization – analogue N atoms
> Full MM Force Field with excellent coverage of organic chemistry and proteins
> Minimization, Conformations etc.> Additional atoms cost more than ACC> Cheaper than other multipole methods> Local polarization
H
0.5
-0.5
+0.9+0.1
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Comparing XED with GAFF – Hydrogen Density
XED GAFF
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Comparing XED with GAFF – Hydrogen Density
XED GAFF
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Comparing XED with GAFF – Hydrogen Density
XED GAFF
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Comparing XED with GAFF – Oxygen Density
XED GAFF
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RISM with XED Conclusions
> Initial results look promising
> Water patterns around small molecules look better with XED
> Does this extend to protein environments?> Does it change the ‘happiness’ factor?
> We’ll keep you posted
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A quick refresher
Field points
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Field Points & Scoring
Calculate interaction energy potential with charged oxygen probe. Contour this potential
Field points indicate potential sites where protein atoms want to sit
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Field Points & Scoring
Minimize Oxygen probe from many starting points onto surface of ligandCharge on probe determines which field we are calculating.
-
-
+
Field points indicate potential sites where protein atoms want to sit
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Similarity Metric Relies on Field Values
A B
2ABBA
ABEEE →→ +
=BBAA
ABAB EE
ES+
=2
∑ ×=→Afp
ABABA fppositionFfpsizeE ))(()(
EA→B = “Energy”, SAB = Similiarity
A B
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Example Ligand field – 1FVT
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Fields and More
Proteins
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Protein Field Problem
> Can’t use the ligand field points to sample the protein field> Most should be inside the protein surface
> Same problem will exist for protein field points> Protein field points will show where ligand atoms want
to be> Most should be inside the ligand surface
> But we can still compare proteins to proteins> Predict protein similarity
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Validation
> Find all dissimilar proteins that bind the same ligand> Show that the field for these is similar
> What ligands?> ATP ?
> Yuk!> NADP ?
> Yuk!> Estradiol ?
> Yuk!
Lots of similar proteins with similar ligands, few diverse proteins with synthetic ligands
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Problems
> Protein preparation a key step> Time consuming> Required automated process
> How to handle highly charged proteins> Scaling?
> How to handle water?> Remove?
> How to handle metals?> Point charge?
> How to define the active site?>Where is it, and where does it stop?
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Charged Proteins
> Traditional ligand approach – uniformly scaled formal charges (Native)> All formal charges are divided by 8
> Uniformly scaled formal charges with a distance dependant dielectric (Native-DDD)
> Remove or scale formal charges further for solvated charged residues (Varichg)> Fully solvated formal charges: charge scaler=80> Buried in the protein: charge scaler=2
> Varichg with a distance dependent dielectric
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Some Good Results – 1FVT Native
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Some Good Results – 4MBS DDD
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Some Good Results – 1OIT DDD+Scaling
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Still Work in Progress
> New thinking for protein preparation> Checking of Asn/Gln, Ser/Thr/Tyr orientations> Possible integration with RISM work
> Validate through protein similarity rather than ligand binding ?
> Still need to understand when each field generation method works and why> DDD looks good, but is not best for all proteins
> Need ideas on how to close the ligand-protein field gap> Would be nice to compare ligand and protein fields directly,
but you can’t
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Some Good Results
Ligand field =
Protein field =
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mark@cresset-group.comsusana@cresset-group.com
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