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New models for the docking problem

Jean-Charles BOISSON. New models for the docking problem. Image from the ArgusLab software. Outline. The docking problem: Protein Structure Prediction (PSP). Protein Folding Problem (PFP). Existing software and visualization tools. Existing Models: Coding. Evaluation function. Methods.

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New models for the docking problem

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  1. Jean-Charles BOISSON New models for the docking problem Image from the ArgusLab software

  2. J.C. BOISSON Docking workshop March, 11-14 2007 Outline The docking problem: Protein Structure Prediction (PSP). Protein Folding Problem (PFP). Existing software and visualization tools. Existing Models: Coding. Evaluation function. Methods. Model already used in the team. New models ???

  3. J.C. BOISSON Docking workshop March, 11-14 2007 The docking problem (DP) How can a ligand be « docked » in a binding site ? Only one conformation for the ligand and the site: Rigid docking. The ligand can have several (or a lot of) conformations for the site: Semi-flexible docking. The ligand and the site can have several (or a lot of ) conformations: Flexible docking.

  4. J.C. BOISSON Docking workshop March, 11-14 2007 Application of the DP: drug design Designing a new drug: Find the ligand that can interact with a given site. The number of existing ligands is enormous (million). All the ligands can not be tested in vitro. How do we choose the right ones to test ? Randomly. Thanks to expert opinion. … Automatic docking: Allows to screen the right ligands for a site. The remain ligands can be tested with a high confidence. Drug designing is boosted !!!

  5. J.C. BOISSON Docking workshop March, 11-14 2007 DP: mix of 2 sub-problems In the flexible DP: The ligand and the site have to modify their shape to find the best complementarity How do we find the new conformations ? The ligand and the site have to form a stable complex How do we find the best complex conformation ?  PFP  PSP

  6. J.C. BOISSON Docking workshop March, 11-14 2007 The Protein Structure Prediction From a protein in its linear form (amino acid chain), find the final 2D or 3D structure. A problem with a large search space. No unique solution  there are several equivalent solutions (due to the environment). Very consuming process for « real » protein.

  7. J.C. BOISSON Docking workshop March, 11-14 2007 The Protein Folding Problem How do a protein exactly fold itself to gain its 3D structure ? Finding the moves to reach a stable state. A problem with a large search space. Very time consuming process… A paradox  in the real life, fastest known proteins can fold themselves in only 1 μs.

  8. J.C. BOISSON Docking workshop March, 11-14 2007 DP difficulties The size of the search space due to all the conformations of the ligand and the site. The most stable states for the ligand and the site do not necessary form the right complex. The best ligand/site complex ( with the minimal energy) is not necessary the right one. Several sites can exist… Need to find ALL the complexes of minimal energy.

  9. J.C. BOISSON Docking workshop March, 11-14 2007 Existing software and visualization tools Population based algorithm: Autodock. GOLD. Constructive algorithm: FlexX. DOCK. PostDock. B.D. Bursulaya, M. Totrov, R. Abagyan, and C.L. Brooks. Comparative study of several algorithms for flexible ligand docking. Journal of Computer-Aided Molecular Design, 17(11) :755-763, November 2003. E.A. Wiley and G. Deslongchamps. PostDock : A novel visualization tool for the analysis of molecular docking. Computing and Visualization in Science, 2005. Published online.

  10. J.C. BOISSON Docking workshop March, 11-14 2007 Existing models: coding (1/2) Simplified coding  the lattice models: HP-model (most used). Very generic modelling. The shape of the lattice allows to limit the number of potential conformation. The linked evaluation methods  Allow to compute « real » proteins. A solution  coordinates of each amino acid. K.A. Dill. Theory for the Folding and Stability of Globular Proteins. Biochemistry, 24 :1501-1509, 1985.

  11. J.C. BOISSON Docking workshop March, 11-14 2007 Existing models: coding (2/2) Complex models  atomic models 3D coordinates or torsion angle representations. Realistic modelling. The linked evaluation methods  limitation to small proteins (few amino acids). Respectful of all energetic interaction.

  12. J.C. BOISSON Docking workshop March, 11-14 2007 Evaluation (1/2) Simplified models: No real energetic model. Based on two global evaluation functions: Maximisation of the H-H contact number. Protein surface minimisation. M.T. Hoque, M. Chetty, and L.S. Dooley. A Hybrid Genetic Algorithm for 2D FCC Hydrophobic-Hydrophilic Lattice Model to Predict Protein Folding. Proceedings of Australian Joint Conference on Artificial Intelligence (AI 2006), LNAI 4303 :867-876, 2006.

  13. J.C. BOISSON Docking workshop March, 11-14 2007 Evaluation (2/2) Complex models : Energetic model  force field utilisation: CHARMM, AMBER, … Combination of energetic terms: Van der Waals, Coulomb, Valence, Desolvatation. And force terms: Torsions, Bonds. For the same terms, using different force fields give different results.

  14. J.C. BOISSON Docking workshop March, 11-14 2007 Existing Multi-objective models (1/3) Bi-objective model (most used): Bonded atom energy: Link energy. Valence energy. Torsion energy Non bonded atom energy: Van der Walls energy. Coulomb energy. Desolvatation energy. V. Cutello, G. Narzi, and G. Nocisia. A Class of Pareto Archived Evolution Strategy Algorithms Using Immune Inspired Operators for Ab-Initio Protein Structure Prediction. Proceedings of Evo Workshops 2005, LNCS 3449 :54-63, 2005 V. Cutello, G. Narzi, and G. Nocisia. A multi-objective evolutionary approach to the protein structure prediction problem. Journal of the Royal Society Interface, 2005. Published online.

  15. J.C. BOISSON Docking workshop March, 11-14 2007 Existing Multi-objective models (2/3) Another bi-objective model: Energetic interaction between the ligand and the site.  Δinter Energetic interaction between the atoms of the ligand.  Δintra Using the Autodock evaluation function. S. Janson and D. Merkle. A New Multi-objective Particle Swarm Optimization Algorithm Using Clustering Applied to Automated Docking. Proceedings of Second International Workshop, HM 2005, LNCS 3636 :128-141, 2005.

  16. J.C. BOISSON Docking workshop March, 11-14 2007 Existing Multi-objective models (3/3) Tri-objective model: Bonded atom energy. Non bonded atom energy Shape completion. Ordinary the shape completion is only a conformational filter before the docking process. A. Oduguwa, A. Tiwari, S. Fiorentino, and R. Roy. Multi-Objective Optimisation of the Protein-Ligand Docking Problem in Drug Discovery. In GECCO’06, July 8-12 2006. Seattle, Washington, USA. M.E.B. Yamagishi, N.F. Martins, G. Neshich, W. Cai, X. Shao, A. Beautrait, and B. Maigret. A fast surface-matching procedure for protein-ligand docking. Journal of Molecular Modelling, 12 :965-972, 2006.

  17. J.C. BOISSON Docking workshop March, 11-14 2007 Used Resolution methods First algorithms  Metropolis algorithms. Currently used: Genetic algorithms (Autodock, GOLD, …): Autodock  hybridizing GA/local search Lamarckian genetic algorithm Constructive algorithms (Flex, DOCK, …). Others proposed methods: Swarm particle. Constraint programming.

  18. J.C. BOISSON Docking workshop March, 11-14 2007 Model already used in the team A Bi-objective model for PSP. Based on the bonded and non bonded energy model. A Lamarckian genetic algorithm for the resolution. Using grid5000 power to compute complete protein folding. A-A. Tantar, N. Melab, E-G. Talbi, and B. Toursel. Solving the Protein Folding Problem with a Bicriterion Genetic Algorithm on the Grid. In Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID’06), page 43, 2006.

  19. J.C. BOISSON Docking workshop March, 11-14 2007 Finding new models (1/X) Respectful of the repulsive/attractive system linked to the docking. Bi-objective model: Repulsive energies: Repulsive Van der Waals energy. Coulomb energy. Hydrophobic contact. Attractive energies: Attractive Van der Walls energy. Desolvatation energy.

  20. J.C. BOISSON Docking workshop March, 11-14 2007 Finding new models (2/X) Finding new criteria: Shape matching/completion. Hydrogen bridge number. Surface criterion: Connolly surface. Van Der Waals surface. Lee-Richards surface. J. Ryu, R. Park, and DS. Kim. Connolly Surface on an Atomic Structure via Voronoi Diagram of Atoms. Journal of Computer Science and Technology, 21(2) : 255-260, 2006.

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