VLife SCOPE for Lead Optimization


Published on

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.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

VLife SCOPE for Lead Optimization

  1. 2. Agenda <ul><li>Background: Why SCOPE like method is required? </li></ul><ul><li>SCOPE methodology </li></ul><ul><li>Case study of PTP1B inhibitors </li></ul><ul><li>Highlights of SCOPE </li></ul>
  2. 3. Why SCOPE.?? <ul><li>Key requirements of drug discovery: ligand screening and prioritization or clues for ligand design improvements </li></ul><ul><li>Docking: Useful tool for screening and provides reasonable geometry for receptor ligand complex </li></ul><ul><ul><li>Known problems in using these tools </li></ul></ul><ul><ul><li>Poor correlation between binding energy and activity </li></ul></ul><ul><li>Scoring functions in docking are not sensitive for prioritization </li></ul><ul><li>Binding energy and docking scores do not provide any clues in ligand design for improvement in activity </li></ul>
  3. 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 <ul><li>Residue wise interactions are utilized as descriptors, f(Exp. Activity) </li></ul><ul><li>In short - QSAR model of the docked or co-crystallized poses </li></ul><ul><li>Key residues modulate the activity of Ligand </li></ul><ul><li>Predict the activity of unknown compounds as screening of large databases </li></ul>
  4. 5. SCOPE Methodology <ul><li>C ombination of S tructure and L igand based drug design methods </li></ul><ul><li>Stage I </li></ul><ul><li>Makes use of crystal or docking pose of ligand in receptor cavity </li></ul><ul><li>In this pose, calculate residue wise interactions of ligand with </li></ul><ul><li>active site residues (energy score) </li></ul><ul><li>Stage II </li></ul><ul><li>Generate quantitative model of activity as a function of energy </li></ul><ul><li>components of binding score for each residue </li></ul><ul><li>Allows identification of important residues responsible for </li></ul><ul><li>modulating the ligand activity </li></ul>Aid in lead optimization through improving interactions with these important residues
  5. 6. SCOPE Methodology <ul><li>Use PLP scoring function for energy contributions </li></ul><ul><li>Calculate steric and hydrogen bond (HB) energy terms for each residue </li></ul><ul><li>Energy terms are populated in QSAR like worksheet </li></ul>
  6. 7. CASE STUDY <ul><li>Use of SCOPE for development of PTP1B inhibitors </li></ul>
  7. 8. Protein tyrosine phosphatase 1B (PTP1B) <ul><li>Negative regulator in insulin and leptin signaling pathways </li></ul><ul><li>Inhibitors of PTP1B are anti-diabetic agents </li></ul>
  8. 9. PTP1B inhibitor case study <ul><li>Collection of co-crystallized structures from Protein Data Bank (PDB) </li></ul><ul><li>PTP1B activity data for co-crystallized ligands collected from PDBbind database </li></ul><ul><li>Number of co-crystallized structures taken – 48 </li></ul><ul><li>Eight chemical classes of ligands & activity (pKd) variation over five log units (3.64 to 8.74) </li></ul><ul><li>Steric and HB terms for each residue calculated using SCOPE module of VLifeMDS </li></ul>
  9. 10. PTP1B inhibitor case study <ul><li>Energy terms are populated in QSAR like worksheet (116 / 61) </li></ul><ul><li>Four chemical classes in training and four chemical classes in test set </li></ul><ul><ul><li>Training = 28; Test = 20 </li></ul></ul><ul><li>Model building using simulated annealing coupled partial least squares regression </li></ul><ul><li>Validation of models using </li></ul><ul><ul><li>Test set (co-crystallized) </li></ul></ul><ul><ul><li>External validation set (which are not co-crystallized) </li></ul></ul>
  10. 11. External validation set <ul><li>22 molecules – collected from 10 literature sources – belongs to seven chemical classes </li></ul><ul><li>Docked into suitable pdb using manual docking </li></ul><ul><ul><li>For each external set ligand, find PDB cocrystallized ligand with maximum similarity & use the corresponding PDB for docking </li></ul></ul><ul><ul><li>Align common portion of external ligand on PDB ligand </li></ul></ul><ul><ul><li>Uncommon part is explored for conformational flexibility within receptor </li></ul></ul><ul><li>Val_r2 used to assess predictive power of model </li></ul>
  11. 12. Summary of models
  12. 13. Fitness plot
  13. 14. PLS Contribution of descriptors SCOPE identifies importance of interacting residues and required type of interaction ST = Steric HB = Hydrogen Bond
  14. 15. I nteractions justified by SCOPE <ul><li>High Active </li></ul><ul><li>Low Active </li></ul>
  15. 16. Steric H bond interactions Steric H-bond Both
  16. 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
  17. 18. SCOPE Ranking Performance SCOPE Offers excellent accuracy while Ranking the dataset
  18. 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
  19. 20. Learning from PTP1B study <ul><li>To achieve higher activity, ligand should make interactions with the following residues (in order of priority) </li></ul><ul><li>Arg47 > Ile219 > Val49 > Lys120 > Met258 >Thr263 > Asp48 > Gly220 > Gly259 > Phe182 > Tyr20 > Arg24 </li></ul><ul><li>Ligand should have least interaction with Ala217 </li></ul><ul><li>Scope enables reliable ranking of ligands </li></ul>
  20. 21. Highlights <ul><li>SCOPE generates QSAR of docked/co-crystalized structures, using residue wise energy terms available in scoring function, e.g. HB, steric, etc. </li></ul><ul><li>Identifies important residues and their contribution for binding of ligands </li></ul><ul><li>Allows quantitative estimation of activity of new ligands </li></ul><ul><li>Enables lead optimization by providing clues for scaffold growth </li></ul><ul><li>Allows screening and prioritization of compound databases for chosen receptor </li></ul>
  21. 22. References <ul><li>SCOPE is p ropritary method of VLife Sciences Technologies Pvt. Ltd. </li></ul><ul><li>References </li></ul><ul><ul><li>Rationalizing Protein–Ligand Interactions for PTP1B Inhibitors Using Computational Methods Chem Biol Drug Des 2009 ; 74: 582–595 </li></ul></ul><ul><li>For more information </li></ul><ul><ul><li>Email : yogeshw@vlifesciences.com www.VLifeSciences.com </li></ul></ul>