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Comparative Evaluation of 11 Scoring Functions for Molekular Docking

Comparative Evaluation of 11 Scoring Functions for Molekular Docking. Authors: Renxiao Wang, Yipin Lu and Shaomeng Wang. Presented by Florian Lenz. Today‘s Docking Programs. 1. Sampling 2. Selecting Scoring function are needed for both! Guiding the sampling Evaluating the results.

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Comparative Evaluation of 11 Scoring Functions for Molekular Docking

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  1. Comparative Evaluation of 11 Scoring Functions for Molekular Docking Authors: Renxiao Wang, Yipin Lu and Shaomeng Wang Presented by Florian Lenz

  2. Today‘s Docking Programs • 1. Sampling • 2. Selecting • Scoring function are needed for both! • Guiding the sampling • Evaluating the results

  3. Previous Studies • Compared combinations of docking programs / scoring functions • one combination fails: blame the Scoring Function, the Docking Program, or the combination? • Even if all the functions are tested under the same conditions: A unmonitored sampling process could yield inadequate samples

  4. Solution • Only use ONE docking program, and a wide range of parameters • Monitor the sampling results • 100 different complexes • Three kinds of tests: • Reproduce experimental determined structure • Reproduce experimental determined binding affinities • Describe a funnel shaped energy surface

  5. Selecting the test cases • Starting point: 230 complexes • Only these with a resolution better then 2.5 Å are used (172) • Creating a diverse ensemble (100)

  6. Sampling • AutoDock using Genetic Algorithms • Protein-Conformation is fixed • Ligand: • Every rotatable single bond may rotate • Flexibility of cyclic part is neglected • Translation: 0.5 Å, Rotation: 15°, Torsion: 15° • Docking Box: 30x30x30 Å around the observed binding position • For each complex: 100 sampled conformation and the „real“ conformation

  7. Monitoring • Repetition: Aim is notto find energy minimum, but to create a diverse test set • RMSD must cover a wide range (0 to 15 Å) • # of clusters between 30 and 70 • Enough results near the “real” position and meaningful conformations. • Key Parameter: Length of the GA-Runs • Too short -> Results are too close to initial position • Too long -> Results enrich at very few clusters

  8. Problems with too long/short runs • For every complex, the numbers of generations have to be determined separately • If even 200 generations don‘t lead to a satisfying result, the complex is discarded

  9. Example for a monitored ensemble

  10. The 11 scoring functions • 3 force-field based: AutoDock, G-Score and D-Score • 6 empirical: LigScore, PLP, LUDI, F-Score, ChemScore and X-Score • Knowledge-based: PMF and DrugScore

  11. First Tests: Docking Accuracy • „How close is the ligand in the best scored solution to its “real” position?“

  12. 1. Tests: Docking Accuracy

  13. Type of Interaction vs. Docking Accuracy (CVDW)(VDW) + (CH-bond)(HB) + (Chydrophobic)(HS) + (Crotor)(RT)+C0

  14. Consensus Scoring Example: 1st place with X-Score, 7th place with LigScore = ((1+7)/2=) 4th place X-Score+LigScore

  15. 2nd Test: Binding Affinity Prediction • dj is the distance between the rank by score and the rank by free energy for complex number j • Rs = 1 correspond to a perfect correlation • Rs= -1 correspond to a perfect inverse correlation • Rs = 0 correspond to a complete disorder • Compare the ranking by scores with the ranking of the free energies. • Using Spearman Correlation:

  16. 2nd Test: Binding Affinity Prediction Best Result: X-Score (Rs = 0.660 4th best result: G-Score (Rs = 0.569)

  17. 2nd Test: Binding Affinity Prediction

  18. 3rd Test: Funnel Shaped Energy Surface • How does the Ligand reach the binding pocket of the Protein? • Theory stems from Protein Folding • Ligand is guided by decreasing free energy • Scoring functions should show a correlation between RMSD Value and score

  19. 3rd Test: Funnel Shaped Energy Surface Example: PDB Entry 1cbx (Carboxypeptidase with Benzylsuccinate) X-Score (Rs: 0.877) LigScore (Rs: 0.135)

  20. 3rd Test: Funnel Shaped Energy Surface

  21. Side Result: The Outliers • In seven ensembles, none of the 11 function was able to pick a conformation with a RMSD below 2.0 Å • Analysis of these shows the general problems of today’s scoring functions • Indirect interactions (1CLA, 2CLA, 3CLA) • Very shallow groove instead of binding pocket (1THA, 1RGL, 1TET)

  22. Indirect Interactions • In samples, water molecules are not included • F-Score predicted that the ligand binds on the surface • DrugScore, LigScore and PLP found another little hole in the protein to put the ligand in

  23. Very shallow groove • Correct “binding pocket” • But only partial overlapping and wrong orientation

  24. Most important results • Empirical Function worked best in Docking Accuracy • Consensus scoring of the six best functions greatly improves the success rate (above 80%) • Prediction of Binding Affinities was less encouraging • There are examples, to which none function could find a good solution to

  25. Thank You

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