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Protein Docking Rong Chen Boston University

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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 Presentation

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Page 1: Protein Docking

Protein Docking

Rong ChenBoston University

Page 2: Protein Docking

BU Bioinformatics

The Lowest Binding Free Energy G

water

RL

RL

LR

L

R

LR

Page 3: Protein Docking

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

Page 4: Protein Docking

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

Page 5: Protein Docking

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.

Page 6: Protein Docking

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

Page 7: Protein Docking

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

20°

15°

12°

10°

Page 8: Protein Docking

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

Page 9: Protein Docking

BU Bioinformatics

Protein Docking Using FFT

Surface Interior Binding SiteY Translation

Cor

rela

tion

X Translation

IFFT

Increase the speed by 107

L

R

Page 10: Protein Docking

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

Page 11: Protein Docking

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

Page 12: Protein Docking

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

Page 13: Protein Docking

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

Page 14: Protein Docking

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

Page 15: Protein Docking

BU Bioinformatics

Why PSC works better than GSC?

Page 16: Protein Docking

BU Bioinformatics

A

B

C

D

Why PSC works better than GSC?

Page 17: Protein Docking

BU Bioinformatics

A Receptor-Ligand ComplexA Receptor-Ligand Complex

Page 18: Protein Docking

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

Page 19: Protein Docking

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

Page 20: Protein Docking

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

Page 21: Protein Docking

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)

Page 22: Protein Docking

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

Page 23: Protein Docking

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/

Page 24: Protein Docking

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

Page 25: Protein Docking

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

Page 26: Protein Docking

BU Bioinformatics

HEX

• Dave Ritchie, University of Aberdeen, Aberdeen, Scotland, UK

• spherical polar Fourier correlations • http://www.biochem.abdn.ac.uk/hex/

Page 27: Protein Docking

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

Page 28: Protein Docking

BU Bioinformatics

Example:

• CAPRI Target 6: α-amylase / Camelid VHH domain