VLife SCOPE for Lead Optimization
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VLife SCOPE for Lead Optimization

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VLifeSCOPE is Structure Based Compound Optimization, Prioritization & Evolution. It brings together two powerful approaches namely - comparative binding energy analysis based method for lead......

VLifeSCOPE is Structure Based Compound Optimization, Prioritization & Evolution. It brings together two powerful approaches namely - comparative binding energy analysis based method for lead optimization and score based approach for activity prediction.

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  • 1.  
  • 2. Agenda
    • Background: Why SCOPE like method is required?
    • SCOPE methodology
    • Case study of PTP1B inhibitors
    • Highlights of SCOPE
  • 3. 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
  • 4. S COPE Flowchart Ligand based study Structure based study Protein Residue wise Interactions via Docking score Ligand Complexes Cocrystals QSAR Model Key Residueal Interaction Design & 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
  • 5. SCOPE Methodology
    • C ombination of S tructure and L igand based drug design methods
    • Stage I
    • Makes use of crystal or docking pose of ligand in receptor cavity
    • In this pose, calculate residue wise interactions of ligand with
    • active site residues (energy score)
    • Stage II
    • Generate quantitative model of activity as a function of energy
    • components of binding score for each residue
    • Allows identification of important residues responsible for
    • modulating the ligand activity
    Aid in lead optimization through improving interactions with these important residues
  • 6. 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
  • 7. CASE STUDY
    • Use of SCOPE for development of PTP1B inhibitors
  • 8. Protein tyrosine phosphatase 1B (PTP1B)
    • Negative regulator in insulin and leptin signaling pathways
    • Inhibitors of PTP1B are anti-diabetic agents
  • 9. 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
  • 10. 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)
  • 11. 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
  • 12. Summary of models
  • 13. Fitness plot
  • 14. PLS Contribution of descriptors SCOPE identifies importance of interacting residues and required type of interaction ST = Steric HB = Hydrogen Bond
  • 15. I nteractions justified by SCOPE
    • High Active
    • Low Active
  • 16. Steric H bond interactions Steric H-bond Both
  • 17. Pointers to enhance activity from SCOPE H-Bond Steric groups SCOPE identifies sites of lead optimization & provides clues for scaffold growth Ile219 Val49 Met258 Arg47 Lys120
  • 18. SCOPE Ranking Performance SCOPE Offers excellent accuracy while Ranking the dataset
  • 19. SCOPE Prediction Performance SCOPE Offers superior prediction performance arising out of insensitive docking score based methods 11 Molecules 19 Molecules 7 Molecules 11 Molecules
  • 20. 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
  • 21. 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
  • 22. References
    • SCOPE is p ropritary 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 : yogeshw@vlifesciences.com www.VLifeSciences.com