1.
Improvement of the Protein-Protein Docking Prediction
by Introducing a Simple Hydrophobic Interaction Model:
an Application to Interaction Pathway Analysis
Masahito Ohue† ‡ Yuri Matsuzaki† Takashi Ishida† Yutaka Akiyama†
† Graduate School of Information Science and Engineering,
Tokyo Institute of Technology
‡ JSPS Research Fellow
The 7th IAPR International Conference on
Pattern Recognition in Bioinformatics
November 9th, 2012, Tokyo, Japan

2.
Summary
• MEGADOCK is a PPI network prediction system
using all-to-all protein docking calculation
– Required a rapidly calculation for all-to-all prediction
– MEGADOCK is 8.8 times faster than ZDOCK
• We proposed new docking score model
added a hydrophobic interaction keeping rapidness
• We achieved the better level of accuracy without
increasing calculation time
2012/11/09 PRIB2012 2

3.
Introduction

4.
Background
• Proteins play a key role in biological events
ex) Signal transduction
SOS
Ras Raf1
http://en.wikipedia.org/wiki/Epidermal_growth_factor_receptor
Many proteins display their biological functions
by binding to a specific partner protein at a specific site
2012/11/09 PRIB2012 4

5.
Background
• PDB crystal structures are currently increasing
but interacted structures are not enough
Structural coverage of interactions Modified from Stein A, 2011.
While there is structural data for a large part of the interactors (60–80%), the portion of interactions
having an experimental structure or a template for comparative modeling is limited (less than 30%)
→ Computational protein docking is very important
2012/11/09 PRIB2012 5

6.
Background
• Protein docking is useful of all-to-all protein-
protein interaction prediction
• 1000 proteins’ combinations
→500,500 (1000x1001/2)
• 500,500 PPI predictions require
43,000 CPU days when using ZDOCK*
• On 400 nodes x 12 cores CPU/node
→ still require 9 days
*Mintseris J, et al. Proteins, 2007.
Need a fast protein docking software for all-to-all
protein-protein interaction prediction
2012/11/09 PRIB2012 6

7.
Background
• MEGADOCK has a rapid protein docking engine
150
Avg docking time [min]
120
All-to-all PPI Prediction
90 in 1000 proteins
60 9 days → 1 day
ZDOCK 3.0 MEGADOCK
30
0
MEGADOCK ZDOCK 2.3 ZDOCK 3.0
• However, MEGADOCK’s docking accuracy is
worse compared to ZDOCKs
• Our purpose is developing more accurate protein
docking method with rapidness
2012/11/09 PRIB2012 7

8.
Protein Docking Problem
Protein monomeric Protein complex
structure pair (predicted structure)
Docking calculation
2012/11/09 PRIB2012 8

9.
MEGADOCK Docking Engine Overview
Receptor
Receptor voxelization
Worker
Node 1 Ligand
Master
Node Receptor FFT
Worker
Node 2
Loop for Ligand rotations …
Worker
Node 3
OpenMP Parallelization
MPI Parallelization Ligand voxelization
Ligand FFT
Convolution
Score calculation (Inverse FFT)
Save high scoring docking poses …
2012/11/09 PRIB2012 9

10.
3-D Grid Convolution by FFT
• 3-D Grid Convolution Katchalski-Katzir E, et al. PNAS, 1992.
Ligand Receptor Δθ is 15 degree → 3600
patterns of Ligand rotations
(2-D image)
Parallel translation
Docking Score
Convolution
Convolution using FFT
2012/11/09 PRIB2012 10

11.
Previous Score Model (MEGADOCK)
• MEGADOCK’s docking score Ohue M, et al. IPSJ TOM, 2010.
Convolution
rPSC ELEC
– Calculate shape complementarity and electrostatics
with only 1 convolution
2012/11/09 PRIB2012 11

12.
rPSC
• rPSC (real Pairwise Shape Complementarity) Ohue M, et al. IPSJ TOM, 2010.
– Shape complementarity score using only real value
(2-D image)
1 2 3 3 3 2 1
2 -27 -27 -27 -27 -27 2 Good point!
3 -27 -27 -27 -27 -27 2 1 1
1 1
3 -27 -27 -27 5 2 1 1 1 1 1 1
1 1 2 2 1
3 -27 -27 -27 5 2 1 1 1 1 1 1
1 1 2 2 1
3 -27 -27 -27 -27 -27 2 1
1 1
1
2 -27 -27 -27 -27 -27 2 Ligand
1 2 3 3 3 2 1 Penalty
Receptor
Receptor rPSC 2012/11/09 PRIB2012 Ligand rPSC 12

13.
Comparison of Convolutions
• ZDOCK 3.0 convolution Mintseris J, et al. Proteins, 2007.
Shape Complementarity = Fit neatly +i Clash
Electrostatics = Coulomb force
Desolvation free energy 1 = atom a’s effect +i atom b’s effect
Desolvation free energy 2 = atom c’s effect +i atom d’s effect
・・・
Desolvation free energy 6 = atom k’s effect +i atom l’s effect
• MEGADOCK convolution
Docking Score = Shape Complementarity
(rPSC) +i Coulomb force
2012/11/09 PRIB2012 13

14.
Accurate and Fast Docking
• For more accurate protein docking
– Considering hydrophobic interaction
– Previous methods require high calculation cost
• ZDOCK 2.3・・・2 convolutions
• ZDOCK 3.0・・・8 convolutions
• For keeping rapid calculation
– We should keep 1 time convolution
• We proposed simple hydrophobic interaction
model with keeping 1 convolution
2012/11/09 PRIB2012 14

15.
Materials and Methods

16.
Proposed Model
• Implementation of hydrophobic interaction
– Used Atomic Contact Energy score
• Atomic Contact Energy (ACE) Zhang C, et al. J Mol Biol, 1997.
– The empirical atomic pairwise potential for estimating
desolvation free energies
– Used by ZDOCK, FireDock*, etc. *Andrusier N, et al. Proteins, 2007.
• How to add the ACE to MEGADOCK?
– Add the averaged ACE value (non-pairwise) of surface
atom of Receptor
Modified rPSC Model
2012/11/09 PRIB2012 16

17.
Modification of rPSC Model
• Considered hydrophobic surface of the receptor
using non-pairewise ACE
1+H 2+H 3+H 3+H 3+H 2+H 1+H
2+H -27 -27 -27 -27 -27 2+H
3+H -27 -27 -27 -27 -27 2+H 1 1
3+H -27 -27 -27 5+H 2+H 1+H 1 1 1 1 1
3+H -27 -27 -27 5+H 2+H 1+H 1 1 1 1 1
3+H -27 -27 -27 -27 -27 2+H 1 1
Ligand
2+H -27 -27 -27 -27 -27 2+H
1+H 2+H 3+H 3+H 3+H 2+H 1+H
Receptor
Sum of near atoms’ ACE (space)
2012/11/09 PRIB2012 (inside of the receptor) 17

18.
Pairwise Potential
• Using table of contact energy
Receptor Ligand
eiN × Set 1 on
FFT position of
N atoms eij N Cα C …
N -0.724 -0.903 -0.722 …
Receptor Ligand
Cα -0.119 -0.842 -0.850 …
eiCα × Set 1 on
FFT position of C -0.008 -0.077 -0.704 …
Cα atoms … … … … …
Receptor Ligand
eiC × Set 1 on
FFT position of
←Need (#type of atoms/2) times convolutions
C atoms
・
・
・
18

19.
Averaged (Non-Pairwise) Potential
• Using averaged contact energy
eij N Cα C … ei
Receptor Ligand
N -0.724 -0.903 -0.722 … -0.495
ei Set 1 on
×
FFT position of Cα -0.119 -0.842 -0.850 … -0.553
all atoms
C -0.008 -0.077 -0.704 … -0.464
↑ 1 time FFT … … … … … …
Pros : high speed calculation
Cons: poor accuracy
19

20.
Experimental Settings
• Dataset: Protein-protein docking benchmark 4.0
– 176 complex structures Hwang H, et al. Proteins, 2010.
(include both bound/unbound form)
1CGI receptor
bound Predicted 1CGI
(re-docking) using 1CGI_r and 1CGI_l
PDB ID: 1CGI
1CGI ligand
unbound 2CGA
(real problem) Predicted 1CGI
using 2CGA and 1HPT
1HPT
2012/11/09 PRIB2012 20

21.
Proposed Docking Score
• New docking score
– Calculate 3 physico-chemical effects
by only 1 convolution calculation
2012/11/09 PRIB2012 21

22.
Experimental Settings
• How to evaluate accuracy? → Success Rank
Docking score Ligand-RMSD
1st
S=2132.1 35.1Å
Success Rank
2nd
S=1802.3 2.3Å of this case is 2
3rd
S=1623.5 4.1Å Near native solution
(L-RMSD < 5Å)
・・・
Native
・・・
3600th
S=300.8 24.8Å
Ligand-RMSD (root mean square deviation)
RMSD with respect to the X-ray structure, calculated for the all heavy atoms of the ligand residues
when only the receptor proteins (predicted structure and X-ray structure) are superimposed.
2012/11/09 PRIB2012 22

23.
Results and Discussions

24.
Docking Calculation Time
On TSUBAME 2.0, 1 CPU core (Intel Xeon 2.93 GHz)
400
Total time for 176 docking [hr]
300
200
good
100
Comparable
0
MEGADOCK Proposed ZDOCK 2.3 ZDOCK 3.0
x8.8 faster than ZDOCK 3.0 / x3.8 faster than ZDOCK 2.3
2012/11/09 PRIB2012 24

25.
Accuracy in Bound Set
ex) The number of cases of “Success Rank ≦ 100”
good is 147 (84%(147/176) of all cases).
100%
80%
Success Rate
60%
ZDOCK 3.0
40% ZDOCK 2.3
Proposed
20%
MEGADOCK
0%
1 10 100 1000
Number of Predictions
Achieved the same level of accuracy as ZDOCK 3.0
2012/11/09 PRIB2012 25

26.
Accuracy in Unbound Set
good ex) The number of cases of “Success Rank ≦ 1000”
100% is 46 (26%(46/176) of all cases).
ZDOCK 3.0
80% ZDOCK 2.3
Success Rate
Proposed
60% MEGADOCK
40%
20%
0%
1 10 100 1000
Number of Predictions
Achieved the same level of accuracy as ZDOCK 2.3
2012/11/09 PRIB2012 26

27.
Prediction Example
PDB ID: 1BVN
(Unbound Success Rank:
11(MEGADOCK)→1(Proposed))
Receptor: α-amylase
Ligand: Tendamistat
Another angle
(α-amylase inhibitor)
The 1st rank predicted structure
by MEGADOCK (wrong position)
Hydrophobicity scale*
hydrophilic hydrophobic
2012/11/09 PRIB2012 *Eisenberg D, et al. J Mol Biol, 1984. 27

28.
Application to Pathway Analysis
• Bacterial chemotaxis signaling pathway prediction
– Predicting “interact or not” from docking results*
* Matsuzaki Y, et al. JBCB, 2009.
• Method ZRANK
Rank of
energy score: ZR
S1 ZR1
Docking S2 ZR2 Mean of S μ
calculation S.D. of S σ
S3 ・
・
・
・
・
・
ZR3,273
S6,000
・
・
・
2012/11/09 PRIB2012 28

29.
Application to Pathway Analysis
• Bacterial chemotaxis signaling pathway prediction
– Predicting “interact or not” from docking results*
* Matsuzaki Y, et al. JBCB, 2009. d A(L) B R W D Y C Z X FliG FliM FliN
Tsr ◆ ◆ ◆ ◆ ◆
A(L)
◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ Known PPI
B ◆ ◆ ◆ ◆ ◆ Predicted
R ◆ ◆
W ◆ ◆ ◆ ◆ ◆ ◆
D ◆ ◆ ◆ ◆
Y ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆
C ◆ ◆ ◆ ◆ ◆
Z ◆ ◆ ◆ ◆
X ◆ ◆ ◆ ◆ ◆ ◆
FliG ◆ ◆ ◆ ◆ ◆
FliM ◆
– Results (F-measure)
FliN ◆ ◆ ◆
MEGADOCK Proposed ZDOCK 3.0
0.43 0.45 0.49
2012/11/09 PRIB2012 29

30.
Conclusion

31.
Conclusion
• We proposed novel docking score including
simple hydrophobic interaction
• We achieved the better level of accuracy without
increasing calculation time
– Same level of accuracy as ZDOCK 2.3
– 8.8 times faster than ZDOCK 3.0
3.8 times faster than ZDOCK 2.3
• Future works
– Developing a hydrophobic interaction model considering
both receptor and ligand protein
– Applying to large PPI network and cross-docking of
structure ensemble
2012/11/09 PRIB2012 31

32.
Acknowledgements
• Akiyama Lab. Members
• This work was supported in part by TECH chan
– a Grant-in-Aid for JSPS Fellows, 23・8750
– a Grant-in-Aid for Research and Development of The Next-Generation
Integrated Life Simulation Software
– Educational Academy of Computational Life Sciences
from the Ministry of Education, Culture, Sports, Science and
Technology of Japan (MEXT).
32