Comparative Evaluation of 11 Scoring Functions for Molekular Docking

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Comparative Evaluation of 11 Scoring Functions
for Molekular Docking

• Authors Renxiao Wang, Yipin Lu and Shaomeng Wang

Presented by Florian Lenz
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Todays Docking Programs

• 1. Sampling
• 2. Selecting
• Scoring function are needed for both!
• Guiding the sampling
• Evaluating the results

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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

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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

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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)

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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

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Monitoring

• Repetition Aim is not to 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 -gt Results are too close to initial
  position
• Too long -gt Results enrich at very few clusters

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Problems with too long/short runs

• For every complex, the numbers of generations
  have to be determined separately
• If even 200 generations dont lead to a
  satisfying result, the complex is discarded

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Example for a monitored ensemble
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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

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First Tests Docking Accuracy

• How close is the ligand in the best scored
  solution to its real position?

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1. Tests Docking Accuracy
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Type of Interaction vs. Docking Accuracy

• (CVDW)(VDW) (CH-bond)(HB) (Chydrophobic)(HS)
  (Crotor)(RT)C0

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Consensus Scoring

• Example
• 1st place with X-Score, 7th place with LigScore
  ((17)/2) 4th place X-ScoreLigScore

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2nd Test Binding Affinity Prediction

• Compare the ranking by scores with the ranking of
  the free energies.
• Using Spearman Correlation

• 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

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

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3rd Test Funnel Shaped Energy Surface
Example PDB Entry 1cbx (Carboxypeptidase with
Benzylsuccinate)
X-Score (Rs 0.877)
LigScore (Rs 0.135)
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3rd Test Funnel Shaped Energy Surface
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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
  todays scoring functions
• Indirect interactions (1CLA, 2CLA, 3CLA)
• Very shallow groove instead of binding pocket
  (1THA, 1RGL, 1TET)

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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

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Very shallow groove

• Correct binding pocket
• But only partial overlapping and wrong orientation

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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

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Thank You