julia salas cs379a 1-24-06
DESCRIPTION
Julia Salas CS379a 1-24-06. 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 - PowerPoint PPT PresentationTRANSCRIPT
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Julia Salas
CS379a
1-24-06
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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
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Target Types/Targets Used
• Target Types:Target Types: 7 protein classes represented
• Targets: 8 proteins of interest to GSK
• Variety: Diversity of mechanisms, binding site shape, binding site chemical environment
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Goal: Represent a typical pharmaceutical compound collection
Compound Sets Used (Ligands)
• 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
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Compound Sets Used (Ligands)
• zdc
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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
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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
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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,D=Vol overlap between crystal and docked pose
0 ≤ Tvol ≤ 1
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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
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Virtual Screening of Database to Identify Leads
• Ability to identify the active compounds1. 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.
2. Lead Identification: Cost analysis…how many compounds do you need to screen to find at least one active compound from each class?
• All active compound classes ID’d within top 10%• Percent actives vs. percent compounds screened
measured
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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
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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 enzyme’s 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?