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Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
Accurate protein-protein docking with rapid calculation
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Accurate protein-protein docking with rapid calculation

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In PRIB2012 Talk (http://prib2012.org) …

In PRIB2012 Talk (http://prib2012.org)

<reference>
Masahito Ohue, Yuri Matsuzaki, Takashi Ishida, Yutaka Akiyama: "Improvement of the Protein-Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: an Application to Interaction Pathway Analysis", In Proceedings of The 7th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB2012), Lecture Note in Bioinformatics 7632, 178-187, Springer Heidelberg, 2012.
http://link.springer.com/chapter/10.1007%2F978-3-642-34123-6_16

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  • 1. Improvement of the Protein-Protein Docking Predictionby 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 FellowThe 7th IAPR International Conference onPattern Recognition in BioinformaticsNovember 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 ProblemProtein 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 LigandMasterNode Receptor FFT Worker Node 2 Loop for Ligand rotations … Worker Node 3 OpenMP ParallelizationMPI 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 translationDocking 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 24.8ÅLigand-RMSD (root mean square deviation)RMSD with respect to the X-ray structure, calculated for the all heavy atoms of the ligand residueswhen 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 200good 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 Setgood 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 ExamplePDB ID: 1BVN(Unbound Success Rank: 11(MEGADOCK)→1(Proposed))Receptor: α-amylaseLigand: 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

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