GQSAR for GPCR Studies


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GQSAR is a breakthrough patent pending methodology that significantly enhances the use of QSAR as an approach for new molecule design. As a predictive tool for activity, this method is significantly superior to conventional 3D and 2D QSAR. Here we explain application of GQSAR for optimizing GPCR compounds in non congeneric series.

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  • 4pathClusterCount^2 (-1.47, -1.11): Substituent with branched structure PSAExclPandS (2.62, 2.92): Substituent with polar surface area. Substituent with sp3 hybridized nitrogen atom substituted with two heavy atoms
  • R3-4pathClusterCount*R1-T==2: R3 substituent with branched substituents along with presence of sp2 hybridized atoms separated with two topological bond distance at R1 R3-smr*R1-T==6: R3 Substituent`s molar refractivity (i.e. bulk) along with presence of sp2 hybridized atoms separated with six topological bond distance at R1 R3-chi3Cluster*R1-T=C1: R3 substituent`s chi3cluster topological index along with presence of sp2 hybridized carbon atom counts at R1 R3-SsCH3count*R1-TCF5 : R3 substituent with sp3 hybridized carbon atom attached to one heavy atom along with presence of F atom separated from C by five topological bond distance at R1
  • GQSAR for GPCR Studies

    1. 1. Fragment Based-GQSAR for GPCR Studies Presenter: Kundan B. Ingale Application Scientist
    2. 2. <ul><ul><li>Mediate biological signalling in health/disease </li></ul></ul><ul><li>Commercially validated - 40% of top 100 drugs </li></ul><ul><li>< 2% of proteins in PDB </li></ul><ul><li>Difficult to crystallize or too big for NMR </li></ul><ul><li>Other Issues </li></ul><ul><ul><li>Non-alpha helices </li></ul></ul><ul><ul><li>Loops may contain other secondary structures and domains </li></ul></ul><ul><ul><li>Bias towards TM proteins that are easy to crystallize </li></ul></ul><ul><ul><li>Energetics of TM proteins not completely understood (polar interactions or van der Waals interactions play role in role in helix-helix interaction) </li></ul></ul>GPCR…
    3. 3. Challenges in Multi-target ligand design for GPCRs <ul><li>Design of Multi Targeted ligands: </li></ul><ul><ul><li>Advantages over single drug for single target </li></ul></ul><ul><ul><li>Ligands that simultaneously bind to 5HT1A and 5HTT have shown good promise in treatment of major depression </li></ul></ul><ul><li>Can structure based method be used ? </li></ul><ul><li>Are Ligand based methods useful ? </li></ul><ul><ul><li>Shape Based Comparisons ? </li></ul></ul><ul><ul><li>Pharmacophore based ? </li></ul></ul><ul><ul><li>QSAR ? </li></ul></ul><ul><ul><ul><li>What Next ..? </li></ul></ul></ul>
    4. 4. Key elements of GQSAR <ul><li>Where is GQSAR useful </li></ul><ul><li>Lead optimization by using site specific clues from GQSAR model </li></ul><ul><li>Scaffold hopping by choosing </li></ul><ul><ul><ul><li>groups/fragments satisfying descriptor </li></ul></ul></ul><ul><ul><ul><li>ranges of actives in the dataset </li></ul></ul></ul><ul><li>Novel library generation along with predicted activity of ligands </li></ul><ul><li>Alignment independent fragment based QSAR modeling </li></ul><ul><li>Conformer independent method </li></ul><ul><li>GQSAR models generation for both congeneric and non-congeneric data </li></ul><ul><li>Provides site specific clues </li></ul><ul><li>Patent pending method </li></ul>Group QSAR: For lead optimization <ul><li>Publication references </li></ul><ul><li>QSAR Combi Science 2009, 28:36–51 </li></ul><ul><li>J Mol Graph Mod 2010;28:683-694 </li></ul>Fig: GQSAR Workflow
    5. 5. 10/10/11 Dataset for GQSAR <ul><li>BindingDB database: 162 molecules (5HT1A receptor and 5HTT Inhibitory activities) </li></ul><ul><li>Biological activity: Binding affinity data (Ki nM) </li></ul>Class Number of molecules Activity (Ki nM) Min Max C1 (Piperidine) 69 HT1A 0.91 3200.00 HTT 0.24 9006.00 C2 (Piperazine) 56 HT1A 0.12 475.00 HTT 1.30 3900.00 C3 (non ring N atom) 26 HT1A 2.00 1470.00 HTT 0.50 4700.00 C4 ( 1,2,3,6-tetrahydropyridine) 8 HT1A 10.90 92.60 HTT 19.80 387.00 C5 ( azabicyclo[3.2.1]oct-3-ene) 3 HT1A 127.70 357.00 HTT 8.50 33.00
    6. 6. 10/10/11 Representative Molecules Piperazine (C2) Non Ring Nitrogen (C3) 1,2,3,6-tetrahydropyridine (C4) azabicyclo[3.2.1]oct-3-ene (C5) Piperidine (C1)
    7. 7. Fragmentation Pattern Fragment R1 (aromatic region): aromatic ring connected with the core of the molecule i.e. fragment R2 Fragment R2 (anchor region): substituent present in the center of the molecule Fragment R3 (flexible region): substituent connected to other end of the fragment R2
    8. 8. Relationship between 5HT1A and 5HTT inhibition Design and optimize molecules for multi-target activity r2 = 0.051 Fig: Scatter Plot of pKi_5HT1A Vs pKi_5HTT
    9. 9. Data Processing and Model building <ul><li>Biological Activity: negative logarithm of binding affinity i.e. pKi (nM) </li></ul><ul><li>Descriptors: 2D group based descriptors and their squared terms </li></ul><ul><li>Training set: 93 molecules ( From scaffold C2-C5) </li></ul><ul><li>Test set : 69 molecules (From scaffold : C1) </li></ul><ul><li>GQSAR enables identification of common set of descriptors influencing the binding of ligands to both the targets </li></ul><ul><li>GQSAR model: (without Fragment Interaction Descriptors) </li></ul><ul><ul><li>62% (r 2 = 0.620) of variation in the 5HT1A activity </li></ul></ul><ul><ul><li>49 % (r 2 = 0.490) of variation in the 5HTT activity </li></ul></ul>
    10. 10. 10/10/11 GQSAR Model <ul><li>Fragment Interaction Descriptors: </li></ul><ul><ul><li>Example : R1_slogp*R2_smr: Product of log P of fragment R1 and molar refraction of fragment R2. </li></ul></ul><ul><li>GQSAR model: (with Fragment Interaction Descriptors) </li></ul><ul><ul><li>71% ( r 2 = 0.710) of variation in the 5HT1A activity </li></ul></ul><ul><ul><li>83% ( r 2 = 0.830) of variation in the 5HTT activity </li></ul></ul>Fig: Contribution Plot for descriptors in GQSAR equation
    11. 11. Model Representation
    12. 12. Model Validation <ul><li>Test Set : 69 molecules (chemical class not present in the training set). </li></ul><ul><li>Model Applicability Domain Check: 50 molecules out of 69 </li></ul><ul><li>Prediction Correctness: molecules predicted within ±1 log units </li></ul><ul><ul><li>5HT1A: 46 (92%), </li></ul></ul><ul><ul><li>5HTT: 40 (80%) </li></ul></ul><ul><ul><li>Prediction accuracy: > 80% with a new scaffold </li></ul></ul>
    13. 13. <ul><li>Aromatic region (R1) Descriptors: R1-4pathClusterCount (6.33, 1.8)* </li></ul><ul><li>Anchor region (R2) Descriptors: R2-PSAExclPandS (2.62, 2.92)* </li></ul><ul><li>Flexible region (R3) Descriptors: </li></ul><ul><ul><li>R3-4pathClusterCount^2 (-1.47, -1.11)* </li></ul></ul><ul><ul><li>R3-SssNHE-index^2 (-4.39, -3.73)* </li></ul></ul>10/10/11 Model Interpretation * Figures in bracket indicate contribution of descriptor towards 5HTT and 5HT1A respectively ↑ Branched substitution ↑ Polar surface ↓ Branched Substitution ↓ H-don N atom attached to 2 heavy atoms pKi (5HTT): -0.97; pKi(5HT1A) : 0.04
    14. 14. 10/10/11 10/10/11 <ul><li>Aromatic (R1) and Flexible (R3) regions interaction descriptors: </li></ul><ul><li>R3-4pathClusterCount*R1-T==2 (-19.63, -8.22) </li></ul><ul><li>R3-smr*R1-T==6 (-6.83, -14.75) </li></ul>Model Interpretation (Interaction Descriptors) * Figures in bracket indicate contribution of descriptor towards 5HTT and 5HT1A respectively pKi (5HTT): -0.59; pKi(5HT1A) : -0.3 ↓ sp2 atoms separated by 2 bonds ↓ Branched substitution ↓ sp2 atoms separated by 6 bonds ↓ Molar refractivity
    15. 15. 10/10/11 Summary and Conclusion <ul><li>GQSAR method can be successfully applied to non-congeneric series of molecules </li></ul><ul><li>With GQSAR, one can identify common set of descriptors that influence the multi-targeted activities of ligands </li></ul><ul><li>GQSAR method provides site specific clues for Lead optimization </li></ul><ul><li>GQSAR method can be effectively used to design Multi Targeted ligands </li></ul>10/10/11
    16. 16. References <ul><ul><li>GQSAR is patented by VLife Sciences Technologies Pvt. Ltd. </li></ul></ul><ul><ul><li>References: </li></ul></ul><ul><ul><ul><li>&quot;Group Based QSAR (G-QSAR) : Mitigating Interpretation Challenges in QSAR”, QSAR & Combinatorial Science, 28(1),36–51(2009) </li></ul></ul></ul><ul><ul><ul><li>&quot;A Comprehensive Structure-Activity Analysis of Protein Kinase B-alpha (Akt1) Inhibitors“, Journal of Molecular Graphics and Modelling, (2010) doi: 10.1016/j.jmgm.2010.01.007 </li></ul></ul></ul><ul><ul><li>For more information : </li></ul></ul>
    17. 17. <ul><li>VLife Sciences Technologies Pvt Ltd </li></ul><ul><ul><li>101-102 Pride Purple Coronet, Baner Road, Pune 411 045 (MS) India </li></ul></ul><ul><ul><li>Tel / Fax : +91 20 2729 1590/1 </li></ul></ul><ul><ul><li>Email : </li></ul></ul>Copyright © 2005 VLife Sciences Technologies Pvt. Ltd. All Rights Reserved. VLife Sciences, VLife Logo ,and all other VLife product names and slogans are trademarks or registered trademarks of VLife Sciences Technologies Pvt. Ltd.
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