1. A new tri-objective model for the flexible docking problem
J-C. BOISSON1, L. JOURDAN1, E-G. TALBI1 and D.HORVATH2
1Project Team INRIA DOLPHIN, Lille, France.
2 « Laboratoire de Glycobiologie Structurale et Fonctionnelle », CNRS, Lille1, France.
J-C. BOISSON META 2008

2. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
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J-C. BOISSON META 2008

3. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
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4. + Molecular docking HIV-1 PROTEASE + XK263
RECEPTOR +
XK263 Inhibitor
LIGAND
Molecular docking  prediction of the optimal complex receptor/ligand according to chemical and geometric properties.
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5. Molecular docking Docking simulation:
rigid  no conformation modification of the molecules.
semi-flexible  one of the two molecules may have its conformation modified during the process (generally the ligand).
flexible  conformational modifications for the both molecules.
Several sites can exist for docking the ligand.
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6. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
23/04/2018
J-C. BOISSON META 2008

7. ANR Dock project ANR  french research agency.
Dock project  3-year project about protein structure prediction and docking.
One of the objectives of the Dock project :
Find new multi-objective models for the flexible docking.
Implement them with effective (parallel) optimization methods.
Propose these methods to the community.
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8. Docking@Grid: conformational sampling and docking on grids
PSP = protein structure prediction
Ligand files
Conformations
Ligand PSP
User
Docking
Receptor PSP
Receptor files
Conformations
More information on :
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9. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
23/04/2018
J-C. BOISSON META 2008

10. A tri-objective model (1/8)
An energy objective  qualify the stability of the gained complexes.
A geometric objective  describe the degree of penetration of the ligand into the site.
A robustness objective  ability of the complex to resist to small perturbations.
A-A Tantar, N. Melab, E-G. Talbi and B. Toursel. A Parallel Hybrid Genetic Algorithm for Protein Structure Prediction on the Computational Grid. Elsevier Science, Future Generation Computer Systems, 23(3): , 2007.
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11. A tri-objective model (2/8)
1. Ligand/site complex energy
Force field used = originaly based on the Consistent Valence Force Field (CVFF)
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12. A tri-objective model (3/8)
2. Complex surface  3 possibilities:
 Van Der Waals surface (a: blue),
 solvant accessible surface (b: red),
 Connolly surface (c: green).
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13. A tri-objective model (4/8)
2. Complex surface  3 possibilities:
 Van Der Waals surface,
 solvant accessible surface
 Connolly surface.
 SASA
Solvent Accessible Surface Area
Original paper
S.M. Le Grand and K.M. Merz, Jr. Rapid Approximation to Molecular Surface Area via the Use of Boolean Logic and Look-Up Tables. Journal of Computational Chemistry, 14(3): (1993).
Recent paper using SASA
A. Leaver-Fay, G.L. Butterfoss, J. Snoeyink and B. Kuhlman. Maintaining solvent accessible surface area under rotamer substitution for protein design. Journal of Computational Chemistry, 28(8): (2007).
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14. A tri-objective model (5/8)
SASA
= 6201 Å2
SASA
= 5548 Å2
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15. A tri-objective model (6/8)
3. Complex robustness
G =
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16. A tri-objective model (7/8): sampling around optimal complex (ligand rotation)
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17. A tri-objective model (8/8): sampling around optimal complex (ligand translation)
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18. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
23/04/2018
J-C. BOISSON META 2008

19. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design  the genetic algorithms (GA)
Data and results.
Conclusions and perspectives.
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20. Motivation for choosing the GA
Efficient multi-objective GA scheme are available.
These algorithms allow to gain a set of solutions of good compromise.
They have a good power of exploration of the search space.
The GA are easy to parallelize.
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21. Solution representation
Site
Ligand X1 Y1 Z1 X2 Y2 Z2 X3 Y3 Z3 . XN YN ZN
X’1
Y’1
Z’1
X’2
Y’2
Z’2
X’3
Y’3
Z’3 .
X’N
Y’N
Z’N
« docking complex »
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22. Indicator-Based EA (IBEA) [Zitzler et al. 2004]
Initialization initial population P.
Fitness assignment quality indicator Qi :
Fitness (x) = Qi (x , P\{x})
Diversity preservation  none.
Selection binary tournament.
Recombination and mutation operators.
Replacement remove the worst individual and update fitness values until |P| = N.
Elitism archive A of potentially efficient solutions.
Output archive A.
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23. Recombination operator
Parents
S1 + L1
S2 + L2
Children
S1 + L2
S2 + L1
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24. Classic docking mutations
Translation
Rotation of a torsion angle
Rotation
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25. Advanced docking mutations (1/2)
Reverse mutation  big ligand rotation to avoid bad results due to symmetry in it.
Big and small rotation/translation windows  adapting the impact of ligand rotations and translations during the docking process.
SMO mutation (Several Mutations in One)  mechanism to make several modifications without evaluate an individual in order to gain access to new search space areas.
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26. Advanced docking mutation (2/2)
Hybridizing mechanisms :
Local search based on ligand rotations.
Local search based on torsion rotations.
 goal : minimizing the complex energy.
(mono-objective local searches)
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27. PARAllel and DIStributed Evolving Objects EO
PEO MO
MOEO
 Evolving Object (EO) for population of solution based metaheuristics : EA, PSO.
 Moving Objects (MO) for solution base metaheuristics : HC, SA, TS, ILS, VNS, …
 Multi-Objective EO (MOEO) for multi-objective evolutionary algorithms : NSGA-II, SPEA, IBEA, …
 ParadisEO (PEO)  EO, MO and MOEO on clusters and/or grids.
S. Cahon, N. Melab and E-G. Talbi, ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, vol. 10(3), pp , May 2004.
A. Liefooghe, M. Basseur, L. Jourdan and E-G. Talbi. ParadisEO-MOEO: A Framework for Multi-Objective Optimization. Proceedings of EMO’2007, pages , LNCS, Springer-Verlag, 2007.
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28. Performance assessment
Comparison of the final complexes with the crystallographic one.
Root Mean Square Deviation (RMSD) computation :
with :
 n the number of heavy atoms.
 dx, dy and dz the deviation between complex and model structures for x, y and z coordinate.
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J-C. BOISSON META 2008

29. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
23/04/2018
J-C. BOISSON META 2008

30. Data and results Tests on 6 instances of the CDCC-Astex clean list:
6rsa, 1mbi, 2tsc, 1htf, 1dog and 2mcp.
Instances prepared with the Chimera software :
Extraction of the ligand from the site to have a « seed » ligand used for population initialisation.
Correct results obtained (average RMSD = 2 Å) but not good enough comparing to standard docking algorithms (generally < 1 Å).
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J-C. BOISSON META 2008

31. Outline Molecular docking. ANR Dock project & Docking@GRID.
A new tri-objective model.
Algorithm design.
Data and results.
Conclusions and perspectives.
23/04/2018
J-C. BOISSON META 2008

32. Conclusions and perspectives (1/2)
A new tri-objective has been proposed.
It appears to be able to give promising results on 6 instances.
It needs :
To be tested on other instances.
To have its behaviour improved.
But it is an exploratory study …
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33. Conclusions and perspectives (2/2)
It is only one (the first) model designed.
Eight multi-objective models have been designed:
Bi and tri objective models.
Using two different force fields:
CVFF.
Autodock 4.0.
Tested on the same instances for twelve operator configurations of the parallel GA.
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34. Questions ?
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