protein docking
DESCRIPTION
Protein Docking. Rong Chen Boston University. L. L. L. L. R. R. L. R. R. R. The Lowest Binding Free Energy D G. water. R. Fast Fourier Transform. R. Discretize. Complex Conjugate. R. Correlation function. L. Rotate. Discretize. L. L. Fast Fourier Transform. Surface. - PowerPoint PPT PresentationTRANSCRIPT
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Protein Docking
Rong ChenBoston University
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BU Bioinformatics
Protein Docking Using FFT
R
L L
RR
LRotate
Fast Fourier Transform
Complex Conjugate
Discretize
Discretize
Fast FourierTransform
Surface Interior
Correlation function
21 11( , ) ( , ) ( , ) IFT{IFT[ ( , )] DFT[ ( , )]}
l
N N
mScore o p R l m L l o m p R l m L l o m p
N
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BU Bioinformatics
Rotational Sampling
• Evenly distributed Euler angles
Sampling Interval Number of angles20° 1,80015° 3,60012° 9,00010° 14,4008° 27,0006° 54,0004° 180,000
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BU Bioinformatics
Performance Evaluation
• Success Rate: given the number of predictions(Np), success rate is the percentage of complexes in the benchmark for which at least one hit has been obtained.
• Hit Count: the average number of hits over all complexes at a particular Np.
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BU Bioinformatics
Rotational Sampling Density
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
6 8 10 12 14 16 18 20Rotational Sampling Interval
Succ
ess
Rat
e
Np=1000 Np=500 Np=200Np=100 Np=50 Np=20Np=10
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BU Bioinformatics
Rotational Sampling Density
0
5
10
15
20
0 100 200 300 400 500 600 700 800 900 1000
Number of Predictions
Hit
Cou
nt
6°
20°
15°
12°
10°
8°
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BU Bioinformatics
Protein Docking Using FFT
R
L L
RR
LRotate
Fast Fourier Transform
Complex Conjugate
Discretize
Discretize
Fast FourierTransform
Surface Interior
Correlation function
21 11( , ) ( , ) ( , ) IFT{IFT[ ( , )] DFT[ ( , )]}
l
N N
mScore o p R l m L l o m p R l m L l o m p
N
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BU Bioinformatics
Protein Docking Using FFT
Surface Interior Binding SiteY Translation
Cor
rela
tion
X Translation
IFFT
Increase the speed by 107
L
R
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BU Bioinformatics
An Effective Binding Free Energy Function
vdW desol elec const
vdW
desol
elec
const
ΔG=ΔE +ΔG +ΔE +ΔG
ΔE :
ΔG :
ΔE :
ΔG :
van der Waals energy; Shape complementarityDesolvation energy; HydrophobicityElectrostatic interaction energyTranslational, rotational and vibrational free energy changes
desolΔG = N ΔG
N :
ΔG :
i ii
i
i
Number of atom pairs of type iDesolvation energy for an atom pair of type i
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BU Bioinformatics
9i 9i 9i 9i 9i
9i 9i 9i 9i 9i
9i 9i 9i
9i 9i 9i
9i 9i 9i 9i 9i
9i 9i 9i 9i 9i 11
1 11 1 1
11
1 11
1 11
11
11
1 11 1 1
1
1 1 9i
1 1 9i 9i 1
1 1
1
9i 1
RGSC LGSC
Grid-based Shape Complementarity
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BU Bioinformatics
RPSC LPSC
1+3i
1+3i 1+3i 1+9i
1+3i 1+3i 1+9i 1+9i 1+3i
1+3i 1+3i
1+3i
1+9i 1+3i
3i 3i 3i 3i 3i
3i 9i 3i 3i 3i
3i 9i 3i
3i 9i 3i
3i 9i 3i 3i 3i
3i 3i 3i 3i 3i 22
3 32 3 2
23
5 23
5 23
23
22
3 32 3 2 1
1
1
11
1
PairwiseShape Complementarity
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BU Bioinformatics
PSC vs. GSC on Success Rate
PSC vs. GSC for Unbound Docking
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 10 100 1000
Number of Predictions
Succ
ess
Rat
e
PSC
GSC
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BU Bioinformatics
PSC vs. GSC on Hit CountPSC vs. GSC for Unbound Docking
0
1
2
3
4
5
6
0 100 200 300 400 500 600 700 800 900 1000
Number of predictions
Hit
Cou
nt
PSC
GSC
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BU Bioinformatics
An Effective Binding Free Energy Function
vdW desol elec const
vdW
desol
elec
const
ΔG=ΔE +ΔG +ΔE +ΔG
ΔE :
ΔG :
ΔE :
ΔG :
van der Waals energy; Shape complementarityDesolvation energy; HydrophobicityElectrostatic interaction energyTranslational, rotational and vibrational free energy changes
desolj
ΔG = N ΔG
N :
ΔG :
ij iji
ij
ij
Number of atom pairs of type i-jDesolvation energy for an atom pair of type i-j
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BU Bioinformatics
Impact of Desolvation and Electrostatics
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 10 100 1000Number of Predictions
Succ
ess
Rat
e
PSCPSC+DesolvationPSC + Desolvation + Electrostatics
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BU Bioinformatics
Impact of Desolvation and Electrostatics
0
1
2
3
4
5
6
7
8
0 100 200 300 400 500 600 700 800 900 1000Number of Predictions
Hit
Cou
nt
PSCPSC+DEPSC + Desolvation + Electrostatics
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BU Bioinformatics
Other available Docking Software
• Fast Fourier Transform or FFT (Katchalski-Katzir, Sternberg, Vakser, Ten Eyck groups)
• Computer vision based method (Nussinov group, 1999)
• Boolean operations (Palma et al., 2000)• Polar Fourier correlations (Ritchie & Kemp,
2000)• Genetic algorithm (Gardiner, Burnett groups)• Flexible docking (Abagyan, 2002)
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BU Bioinformatics
3D-Dock
• Michael J.E. Sternberg, Imperial Cancer Research Fund, London, UK.
• FTDock: Grid-based shape complementarity, FFT.
• RPScore: empirical pair potential.• MultiDock: refinement.• http://www.bmm.icnet.uk/docking/index.html
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BU Bioinformatics
GRAMM
• Ilya A. Vakser, State University of New York at Stony Brook.
• Geometric fit and hydrophobicity• FFT• Low resolution docking• http://reco3.ams.sunysb.edu/gramm/
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BU Bioinformatics
DOT
• Lynn F. Ten Eyck, University of California, San Diego.
• Grid-based shape complemetarity, elctrostatics• FFT• http://www.sdsc.edu/CCMS/Papers/
DOT_sc95.html
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BU Bioinformatics
ICM
• Ruben Abagyan, The Scripps Research Institute, La Jolla.
• Pseudo-Brownian rigid-body docking• Biased Probability Monte Carlo
Minimization of the ligand interacting side-chains.
• http://abagyan.scripps.edu/lab/web/man/frames.htm
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BU Bioinformatics
HEX
• Dave Ritchie, University of Aberdeen, Aberdeen, Scotland, UK
• spherical polar Fourier correlations • http://www.biochem.abdn.ac.uk/hex/
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BU Bioinformatics
Approach Overview
PDB1 PDB2
PDB Processing
ZDOCK: Initial-stage Docking
RDOCK: Refinement-stage Docking
Clustering
Final 10 predictions
Bio
logi
cal i
nfor
mat
ion