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Julia Salas

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Julia Salas. CS379a. 1-24-06. Aim of the Study. To survey the docking and scoring algorithms ... 'Reasonable' bond distances/angles. Correct atom hybridization ... – PowerPoint PPT presentation

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Title: Julia Salas


1
  • Julia Salas
  • CS379a
  • 1-24-06

2
Aim of the Study
  • To survey the docking and scoring algorithms
    available today
  • Evaluate protocols for three tasks
  • 1. Prediction of the conformation of ligand
    bound to protein target
  • 2. Virtual screening of database to identify
    leads
  • 3. Prediction of binding affinities
  • General Methods
  • Investigate several docking programs using a
    variety of different target types
  • Use a large set of closely related compounds
    (compound set) for each target type

3
Target Types/Targets Used
  • Target Types 7 protein classes represented
  • Targets 8 proteins of interest to GSK
  • Variety Diversity of mechanisms, binding site
    shape, binding site chemical environment

4
Compound Sets Used (Ligands)
  • Goal Represent a typical pharmaceutical compound
    collection
  • Compound/Ligand Sets 1303 compounds
  • 150-200 closely related compounds
  • Compounds have experimentally determined
    affinities
  • Affinities of compounds in a single set span a
    min of 4 orders of magnitude
  • Each set has shown biological activity towards
    target protein
  • Each set has a max of 20 inactive and 20
    extremely active compounds
  • Each set has published (2-54) cocrystal
    structures with the target protein

5
Compound Sets Used (Ligands)
  • zdc

6
Docking and Scoring Algorithms
  • Docking Algorithms
  • Evaluated 10 programs with different algorithms
    and scoring functions
  • 19 protocols total
  • Procedure
  • Each method evaluated by an expert, no time
    restrictions or other constraints
  • Evaluators did not have cocrystal structures,
    only ligand structure and protein active site
    residues
  • Same ligand starting structure
  • Optimized to a (local) min
  • Reasonable bond distances/angles
  • Correct atom hybridization
  • 4 structures provided (differ in ionization)
  • SMILES (text-based) structure description

7
Analysis of Docking Programs and Scoring Functions
  • 19 protocols evaluated on three tasks
  • 1. Prediction of the conformation of ligand
    bound to protein target
  • 2. Virtual screening of database to identify
    leads
  • 3. Prediction of binding affinities

8
Prediction of Ligand Conformation Bound to
Protein Target
  • Compare predictions to (136) cocrystal structures
    using
  • 1. rmsd for heavy atoms
  • 2. Volume overlap Tanimoto similarity index
  • Two standards for success rmsd within
  • 2Å (correct orientation) Black Bars
  • 4Å (within binding site) Gray Bars
  • Can evaluate both the scoring function and the
    overall methods

IX, ID Vol overlap integrals for crystal and
docked structure OX,DVol overlap between crystal
and docked pose 0 Tvol 1
9
Prediction of Ligand Conformation Bound to
Target Conclusions
  • The good
  • Docking programs could generate crystal
    conformations
  • For all (-HCVP) targets, at least one program
    could dock 40 of ligands within 2
  • 90 of ligands could be docked with 4Å with 100
    docked in correct location
  • The bad
  • Program with best performance changes target to
    target
  • Scoring function lead to consistently incorrect
    predictions
  • HCVP had very weak predictions

10
Virtual Screening of Database to Identify Leads
  • Ability to identify the active compounds
  • Enrichment How quickly did the protocol identify
    the active compound vs. random chance?
  • Success Identify at least 50 of the active
    compounds within the top 10 of the score-ordered
    list ? halfway between random and max.
  • Lead Identification Cost analysishow many
    compounds do you need to screen to find at least
    one active compound from each class?
  • All active compound classes IDd within top 10
  • Percent actives vs. percent compounds
    screened measured

11
Prediction of Binding Affinities
  • Calculated docking scores compared to measured
    affinity
  • Docking scores were autoscaled and then compared
  • Conclusions
  • No statistically significant correlation between
    scoring function and measured affinity

12
Conclusions and Discussion Questions
  • Docking programs were able to generate poses that
    resemble cocrystal structures
  • Largest difficulties were in determining the
    small molecule structure, not placing ligand in
    binding site
  • Scoring functions were not successful in
    predicting the best structures
  • Active compounds could be identified in a pool of
    decoys
  • Docking scores could not be correlated to
    affinity
  • Question 1 What factors may have contributed to
    the failure of these programs to predict small
    molecule conformation?
  • Question 2 The failure of the programs to
    predict HCVP structures was attributed to the
    enzymes large active site. Why? Additionally,
    should flexibility/dynamics be considered?
  • Question 3 Compound classes were defined by
    similar backbone structure. Although all
    compounds in a class had measured affinities, can
    we assume they all have the same binding mode?
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