VLife SCOPE for Lead Optimization

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




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

For more information Email : [email protected] www.VLifeSciences.com

VLife SCOPE for Lead Optimization

Technology

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