Download - VLife SCOPE for Lead Optimization
© VLife Sciences Technologies Pvt. Ltd. All rights reserved 1
Agenda
Background: Why SCOPE like method is required?
SCOPE methodology
Case study of PTP1B inhibitors
Highlights of SCOPE
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Why SCOPE.??
Key requirements of drug discovery: ligand screening and prioritization or clues for ligand design improvements
Docking: Useful tool for screening and provides reasonable geometry for receptor ligand complex Known problems in using these tools Poor correlation between binding energy and activity
Scoring functions in docking are not sensitive for prioritization
Binding energy and docking scores do not provide any clues in ligand design for improvement in activity
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Ligand based studyStructure based study
SCOPE Flowchart
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ProteinProtein
Residue wise Interactions via Docking score
Residue wise Interactions via Docking score
LigandLigand
Complexes Complexes
CocrystalsCocrystals
QSAR ModelQSAR Model Key Residueal Interaction
Key Residueal Interaction
Design & ScreeningDesign & Screening
Residue wise interactions are utilized as descriptors, f(Exp. Activity)
In short - QSAR model of the docked or co-crystallized poses
Key residues modulate the activity of Ligand
Predict the activity of unknown compounds as screening of large databases
SCOPE Methodology
Use PLP scoring function for energy contributions
Calculate steric and hydrogen bond (HB) energy terms for each residue
Energy terms are populated in QSAR like worksheet
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CASE STUDYUse of SCOPE for development of PTP1B inhibitors
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Protein tyrosine phosphatase 1B (PTP1B)
Negative regulator in insulin and leptin signaling pathways Inhibitors of PTP1B are anti-diabetic agents
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PTP1B inhibitor case study
Collection of co-crystallized structures from Protein Data Bank (PDB) PTP1B activity data for co-crystallized ligands collected from PDBbind
database Number of co-crystallized structures taken – 48 Eight chemical classes of ligands & activity (pKd) variation over five log
units (3.64 to 8.74) Steric and HB terms for each residue calculated using SCOPE module of
VLifeMDS
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PTP1B inhibitor case study
Energy terms are populated in QSAR like worksheet (116 / 61) Four chemical classes in training and four chemical classes in test set
Training = 28; Test = 20 Model building using simulated annealing coupled partial least squares
regression Validation of models using
Test set (co-crystallized) External validation set (which are not co-crystallized)
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External validation set
22 molecules – collected from 10 literature sources – belongs to seven chemical classes
Docked into suitable pdb using manual docking For each external set ligand, find PDB cocrystallized ligand with maximum
similarity & use the corresponding PDB for docking Align common portion of external ligand on PDB ligand Uncommon part is explored for conformational flexibility within receptor
Val_r2 used to assess predictive power of model
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Summary of models
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Fitness plot
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PLS Contribution of descriptors
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SCOPE identifies importance of interacting residues and required
type of interaction
ST = StericHB = Hydrogen Bond
Interactions justified by SCOPE
High Active Low Active
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Steric H bond interactions
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StericH-bond
Both
Pointers to enhance activity from SCOPE
H-Bond
Steric groups
Ile219Ile219Val49Val49
Met258Met258
Arg47Arg47
Lys120Lys120
SCOPE identifies sites of lead optimization & provides clues for scaffold growth
SCOPE Ranking Performance
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SCOPE Offers excellent accuracy while Ranking the dataset
SCOPE Prediction Performance
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SCOPE Offers superior prediction performance arising out of insensitive docking score based methods
11 Molecules11 Molecules
19 Molecules19 Molecules
7 Molecules7 Molecules
11 Molecules11 Molecules
Learning from PTP1B study
To achieve higher activity, ligand should make interactions with the following residues (in order of priority)Arg47 > Ile219 > Val49 > Lys120 > Met258 >Thr263 > Asp48 > Gly220 > Gly259 > Phe182 > Tyr20 > Arg24
Ligand should have least interaction with Ala217
Scope enables reliable ranking of ligands
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Highlights
SCOPE generates QSAR of docked/co-crystalized structures, using residue wise energy terms available in scoring function, e.g. HB, steric, etc.
Identifies important residues and their contribution for binding of ligands
Allows quantitative estimation of activity of new ligands
Enables lead optimization by providing clues for scaffold growth
Allows screening and prioritization of compound databases for chosen receptor
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References
SCOPE is propritary method of VLife Sciences Technologies Pvt. Ltd. References
Rationalizing Protein–Ligand Interactions for PTP1B Inhibitors Using Computational Methods Chem Biol Drug Des 2009; 74: 582–595
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