Fast Methods for Simulation of Biomolecule Electrostatics · 2019-11-20 · Biomolecule...
Transcript of Fast Methods for Simulation of Biomolecule Electrostatics · 2019-11-20 · Biomolecule...
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Fast Methods for Simulation of Biomolecule Electrostatics
Shihhsien KuoMichael Altman
Jaydeep BardhanBruce TidorJacob White
November 12, 2002
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Outline
Ø Problem statementØ Finite-difference approach and problemsØ Integral equation method and advantagesØ Fast solver implementationØ Computational resultsØ Conclusion and Future work
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The Problem of Drug Design
Receptor Ligand
+
Complex
Designed drug molecule
Given protein molecule
• Electrostatics• Solvation• Van der Waals forces• Covalent bonding• Hydrophobic effect
Electrostatics
Salt water
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Ligand(drug molecule)
++
-
--
-
+ +
+
Receptor (protein molecule)
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+
+
+
+
+
- -
-
Electrostatics View
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Minimize Electrostatic Binding Energy
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+
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+- -
-++-
---
+ +
++
+
+
+
+
+- -
-++-
---
+ +
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Higher energy Lower energy
Determine the charge distribution in the ligandso that it is “Energetically Optimized” to bind
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ligand receptorbinding desolvation desolvation interactionE E E E= + −
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Ligand(drug molecule)
++
-
--
-
+ +
+
Receptor (protein molecule)
+
+
+
+
+
+
- -
-
An Electrostatic Analysis Problem
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-++
H2O
Na+
Cl_
_
_
_
_
_
_
+
+
+
+ ++
+
++
+
+ - ++
-++
- ++
-+
+
-++
-+
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- ++
-++-+
+
-+
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-++
-+
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Salt water
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Homogeneoussalt water
highε
Macromolecularsurface of ligand or ligand-receptor complex
lowε
A Simplified Physical Description
Point charges of ligand in homogeneous dielectric
( ) 2( ) ( ) ( ) ( ) ( ) ( ) 0r r r r r rε ϕ ε κ ϕ ρ∇ ∇ − + =r r r r r rilinearized Poisson-Boltzmann equation:
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homogeneous dielectric
Molecular Surface Representation
macromolecular atoms
point charges at atom centers
probe solvent molecule
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Simplified Mathematical Model: Inside Macromolecule
Macromolecularsurface
2
1
( ) ( )N
iin i
i low
qr r rϕ δ
ε=
∇ = − −∑r r r
Poisson equation
lowε
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Simplified Mathematical Model: Salt Water Outside
Macromolecularsurface
2 2( ) ( )out outr rϕ κ ϕ∇ =r r
Linearized Poisson-Boltzmann equation
highε
Inverse Debye screening length to model ions
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Interface Condition
Boundary Conditions:
( ) ( )in outo r or r
n nϕ ϕ
ε∂ ∂
=∂ ∂
r r( ) ( )in o out or rϕ ϕ=r r
highr
low
εε
ε=
lowε
highε
n
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Why Use this Simplified Model?
n Atomistic Level Simulation is too expensiven Salt ions and water molecules treated
individually
n Continuum Model Matches Well with Experimental Data
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Standard Finite-Difference Method
*
lowε lowε
lowεlowε
lowε
highε
highε
highε
highε
highε
lowε
lowε
Problem 3: Inexact Boundary Conditions
Problem 2: Poor Point Charge Approximation
Problem 1: Inaccurate Molecular Surface
set up boundary conditions andsolve for grid potentials
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Integral equation: Interior Problem
MacromolecularSurface Ω
1
( ) ( ; ) ( ; ) ( ) ( ; )N
in in kin in in k
k low
G qr r r G r r r dr G r r
n nϕ
ϕε=Ω
∂ ∂ ′ ′ ′ ′ ′− = ∂ ∂ ∑∫
r r r r r r r r r
1( ; )
4inG r rr rπ
′ =′−
r r r r
Homogeneous low dielectric medium
lowε
Translation invariant Green’s function
( )inG r r′= −r r
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Integral equation: Exterior Problem
( ) ( ; ) ( ; ) ( ) 0out outout out
Gr r r G r r r dr
n nϕ
ϕΩ
∂ ∂ ′ ′ ′ ′ ′− + = ∂ ∂ ∫r r r r r r r
; ( )( )4
r r
to t ou ue
G r rr
G r rr
κ
π
′− −
′ =′
′= −−
r r rr rr r r
NOTE: κ is real,electrostatic not fullwave problem
nMatch boundary conditions
( )in
rϕ ′r 1( )in
rrn
ϕε
∂′
∂r
Homogeneous high dielectric medium
highε
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Advantages For Integral Equation Formulation
n Directly discretize surfaces
1( ) ( ; ) ( ; ) ( ) 0out in
in outr
Gr r r G r r r dr
n nϕ
ϕεΩ
∂ ∂′ ′ ′ ′ ′− + = ∂ ∂ ∫
r r r r r r r1
( ) ( ; ) ( ; ) ( ) ( ; )N
in in kin in in k
k low
G qr r r G r r r dr G r r
n nϕ
ϕε=Ω
∂ ∂ ′ ′ ′ ′ ′− = ∂ ∂ ∑∫
r r r r r r r r r
n Point charges treated exactly
n Handles infinite exterior
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Standard piecewise constant collocation discretization method
( ) ( )in j jj
r a B rϕ ≈ ∑r r
( ) ( )inj j
j
r b B rn
ϕ∂≈
∂ ∑r r
r ∈Ωr
§ Piecewise constant basis functions
B10
B15
B2
§ Collocation points at panel centroids
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Matrix Equation
1 4
0
Nk
k i k
in
o
in
tu out
j
j
aq
DD
S rb
rS
π=
=
−
∑ r r
14
j
inij
ipanel
S drr rπ
′= −′−∫
rr r
14
i
j
r routij
r ipanel
eS dr
r r
κ
ε π
′− −
′=′−∫
r r rr r
14
j
inij
ipanel
D drn r rπ
∂ ′= ′ ′∂ − ∫
rr r
4
i
j
r routij
ipanel
eD dr
n r r
κ
π
′− − ∂ ′= − ′ ′∂ − ∫
r r rr r
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A sphere molecule: comparison with analytical result
100
10
1
ERROR
(%)
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Iterative solver' ' '( ) ( ; ) ( )r K r r r drϕ σ≡ ∫
r r r r r
[ ],i j j iσ ϕ Κ =
Discretization
IterativeSolver
matrix-vector multiply black box
j guessσ ,i j j guess
σ Κ
improve guess solution
GaussianElimination
[ ]1
,j i j iσ ϕ−
= Κ
for i = 1:n-1 for j = i+1:n
Kj,i = Kj,i / Ki,ifor k = i+1:n
Kj,k = Kj,k - Kj,i / Ki,kend
endend
3( )NΟ 2( )NΟ per iteration
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Use Fast Integral Equation Solver
n Multiple Green’s functions
n Translation Invariant kernel
( ; )4
r r
oute
G r rr r
κ
π
′− −
′ =′−
r rr r r r
1( ; )
4inG r rr rπ
′ =′−
r r r r
( log )N NΟ Matrix-vector multiply
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Pre-corrected FFT algorithm
Picture courtesy of J. Phillips
matrix-vector multiply black box
j guessσ ,i j j guess
σ Κ
pre-corrected FFT
charge distribution
potential due to space-invariant kernel
4) direct interaction and correction among near neighbors
(4)(1)
1) projection of panel charges onto grid charges
(2)
2) grid potentials due to grid charges are computed by FFT
(3)3) potentials on panel centroids are
interpolated from grid potentials
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[ ] [ ] [ ]11,i j ijP Pσ ϕ−− = Κ
[ ] ,i jP ≈ Κ Need to find a good preconditioner
And solve
hopefully better conditioned than [Ki,j]
Qsi molecule Ecm protein
Preconditioner on Two Examples
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Preconditioner result: Qsi molecule
OO
O OO O
OO O
O OO
II
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Preconditioner result: Ecm protein
O OO O
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Accuracy comparison with DelPhi
18596
5842
9330
# of salt panels
82868
34114
17204
# of dielectric panels
ECM
TSA
Water
-653.88-646.42
-34.75-34.62
-3.17-3.14
DelPhipFFT
Esolvation (kcal/mol)
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Convergence Results of Ecm Protein
pFFT DelPhi
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Binding energy calculation of a protein-peptide complex
Energy calculated (kcal/mol)
14.51
14.52
Rdesolvation
131.03131.0324.47DelPhi
130.91130.8024.47pFFT
(L->R)interaction(R->L)interactionLdesolvation
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Conclusions and Future work
ØWorking on coupling to charge optimization problem in drug design
ØExtending formulation to include more complicated geometry (inner cavities in macromolecule)
ØFine tuning existing pre-corrected FFT code
ØCarefully selected Integral Formulationresults in Fast Solver for BiomoleculeElectrostatics