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PockDrug at ISB 2014

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Presentation of PockDrug model

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PockDrug at ISB 2014

  1. 1. 1 BORREL Alexandre PockDrug: a new pocket druggability prediction model ables to overcome pocket estimation uncertainties ISB Winter School 2014 - Levi 24-11-2014
  2. 2. 2 What is druggability ? « The viability of a drug target depends on two components: biological relevance and chemical tractability.  The concept of druggability was introduced to describe the second component, and it is defined as the ability of a target to bind a drug-like molecule with a therapeutically useful level of affinity. » Definitions Perola et al. 2012  Journal of Chemical Information and Modeling, 52(4), 1027–1038. « The term ‘druggability’ usually refers to the likelihood of finding orally bioavailable small molecules  that bind to a particular target in a disease-modifying way » Edfeldt et al. 2011 Drug Discovery Today, 16(7-8), 284–7.  Orally bioavailable small molecules Target
  3. 3. Ligand 3 What are drug-like molecules ? Drug-like molecule Orally bioavailable small molecules tend to have properties within certain parameters e.g., Lipinski et al. 1997 Advanced Drug Delivery Reviews 23 (2-25)  In order to bind such compounds, a protein should have a binding site with complementary properties ● Mwt <= 500 Da ● LogP <= 5 ● H-bond Acceptors <= 10 ● H-bond Donors <= 5 « Rule of 5 » Weight: 381 Da LogP : 3.9 ... Celecoxib (CEL) Pocket (from 1OQ5) Volume: 361 Å3 Surface: 296 Å2 ... General concept of model Pocket
  4. 4. Importance of assessing druggability Estimates suggest that around 10-15% of human genome may be druggable (with small molecule approach) and 600-1500 potential targets Adapted from Hopkins, A. A. L., & Groom, C. R. C.  (2002). Nature Reviews - prioritize potential targets - avert targets that are unlikely to bind small molecules with high affinity (optimize experimental screenings) Druggability is important to: Brown, D., & Superti-Furga, G. (2003). Drug Discovery Today, 8(23), 1067–1077 Human genome ~30,000 Druggable Genome ~30,000 Diesase modifying Genes ~3,000 Drug targets ~ 600-1,500 4
  5. 5. 5 How do you predict the pocket druggability ? From target structures Step1: Identifying cavities or pockets (pockets estimation) 3 main steps: Step2: Compute pocket properties Step3: Apply a statistical model Less druggable DruggablePockets Polarity, ... Geometry, ... Step1 Step2 Step3
  6. 6. 6 Dataset ? NRDLD (Non Redundant set of Druggable and Less Druggable binding sites) 71 druggables 44 less druggables HTS NMR screening PDBBind Database screeningExperimental data Adapted from Krasowski, A. et al. (2011).  Journal of Chemical  Information and Modeling, 51(11), 2829–42. Widely used by others druggability studies. Apo (Apo proteins set) From “Druggable Cavity Directory”, 139 apo protein are extracted. 132 druggables 7 less druggables
  7. 7. 7 Estimations Pocket estimation (step1) We decided to use 3 different pocket estimations. prox: by taking the ligand information, the pocket is the protein atoms close to the ligand, only for holo pocket. fpocket: geometric algorithm based on Voronoi tessellation DoGSite: based on a Difference of Gaussian (DoG) approach which originates from image processing. Le Guilloux, V. et al. (2009). BMC  Bioinformatics, 10, 168 Volkamer, A. et al. (2010).Journal of  Chemical Information and Modeling,  50(11), 2041–52.  « ...different pocket detection methods can assign different sizes and/or numbers of pockets for the same  structure. » Gao, M., & Skolnick, J. (2013). Bioinformatics  (Oxford, England), 29(5), 597–604.
  8. 8. 8 Descriptors Pocket characteristic features (step2) A set of 52 descriptors are computed. Scores of polarity, hydrophobicity, ... Geometry, distance, volume, shape, ... K H A LN W d d' Inertia Residues compositions Perola et al. 2012 (Chemical information and modeling)  Kyte et al. 1992 (molecular biology)  Petitjean 1996 (Chemical information and modeling) Pérot et al. 2008 (Drug Discovery Today)
  9. 9. 9 Goal 2. Pocket estimations prox fpocket DoGSite 52 descriptors 3. Pocket descriptors 1. Datasets 71 druggables 44 less druggables NRDLD 132 druggables 7 less druggables Apo 48 druggables 26 less druggables Train 23 druggables 14 less druggables Test
  10. 10. 10 Goal 2. Pocket estimations prox fpocket DoGSite 52 descriptors 3. Pocket descriptors 1. Datasets 71 druggables 44 less druggables NRDLD 132 druggables 7 less druggables Apo 48 druggables 26 less druggables Train 23 druggables 14 less druggables Test Unique generic model available on several pocket estimations
  11. 11. 11 Pocket variability PCA from three pockets set from estimated by prox, fpocket and DoGSite and computed from 52 pockets descriptors. Same binding site defined by different estimations have different properties but .... The Overlap Score are weak: prox-fpocket = 30 % ± 14 % prox-DoGSite = 28 % ± 14 % fpocket-DoGSite = 30 % ± 16 %
  12. 12. 12 Variability VS Druggability
  13. 13. 13 In spite of different estimations druggable and less druggable pockets are grouped in different area. Globally same properties in despite of estimation Aromaticity Geometry Polarity Hydrophobicity Variability VS Druggability
  14. 14. 14 Statistic protocol – Step 3 Train set 52 pocket descriptors Pocket characterized Train pocket set 1. Define training and test set by estimator
  15. 15. 15 Statistic protocol – Step 3 52 pocket descriptors Combination of n descriptors among 52 ninit = 2 X LDA models built If n=2, X=2,652 If n=3, X = 23,426 Train set 2. compute Linear Discriminant Analysis (LDA) models with n descriptorsPocket characterized Train pocket set 1. Define training and test set by estimator
  16. 16. 16 Statistic protocol – Step 3 Train set 52 pocket descriptors Combination of n descriptors among 52 ninit = 2 Matthew's Coefficient Correlation 3. select best models with minimal number of descriptors Best model performances On train set and cross validation Build model from best LDA models if MCC loo (n)>MCC loo (n-1) else Consensus PockDrug Pocket characterized Train pocket set X LDA models built If n=2, X=2,652 If n=3, X = 23,426 1. Define training and test set by estimator 2. compute Linear Discriminant Analysis (LDA) models with n descriptors
  17. 17. Statistic protocol – Step 3 fpocket Set train test Loo Acc 86 % 86 % 85 % MCC 0.67 0.71 0.65 MCC close to 0.70 on train, test and by Loo Overcom e the pocket uncertainties ? Select model built from fpocket
  18. 18. 18 1. Test model on other pockets estimated by other estimators. Validation – Step 3 2. Test model on apo pocket set MCC close to 0.70 on train, test and by Loo Protein sets Estimations Pocket descriptors Pocket sets characterized Performances prox-test fpocket-test DoGSite-test fpocket-apo fpocket Test DoGSite DoGSite-apo prox apo Consensus PockDrug fpocket Set train test Loo Acc 86 % 86 % 85 % MCC 0.67 0.71 0.65
  19. 19. Performances 1. Robust on estimations Consensus PockDrug prox- test DoGSite -test fpocket- test Acc 95 % 87 % 87 % MCC 0.89 0.73 0.71 Good performances overcomes pocket estimations 19
  20. 20. Performances fpocket- score DoGSite- Scorer Acc 76 % 76 % MCC 0.51 0.54 1. Robust on estimations 2. Comparison + 0.20 in term of MCC Consensus PockDrug prox- test DoGSite -test fpocket- test Acc 95 % 87 % 87 % MCC 0.89 0.73 0.71 Good performances overcomes pocket estimations 20
  21. 21. Performances fpocket- apo DoGSite- apo Acc 91 % 94 % Mcc 0.45 0.53 fpocket- score DoGSite- Scorer Acc 76 % 76 % MCC 0.51 0.54 1. Robust on estimations 2. Comparison 3. Apo pockets + 0.20 in term of MCC Successful in apo pocket Consensus PockDrug prox- test DoGSite -test fpocket- test Acc 95 % 87 % 87 % MCC 0.89 0.73 0.71 Good performances overcomes pocket estimations 21
  22. 22. 22 Characteristics in PockDrug 4 keys properties Hydrophobicity ++++ Geometric +++ Contact (H-bond donnor-acceptor) ++ Aromaticity + Hydrophobicity Geometry Aromatic Contact Correlation radar (prox-fpocket) PockDrug include high correlated descriptors
  23. 23. PockDrug results 23 Acetylcholinesterase complexed with Huprine Geometry Hydrophobicity Aromatic 0.82 +/- 0.09 By pocket, we have a druggable probability with a confidence index Results by pocket Druggable probability (Mean) Confidence (SD)
  24. 24. 24 PockDrug - website Using pocket estimated Using new target
  25. 25. 25 PockDrug - website Pocket Descriptors - geometry - physicochemical properties Probability & confidence
  26. 26. 26 PockDrug - website http://pockdrug.rpbs.univ-paris-diderot.fr/ Feedbacks - Questions, comments, ...
  27. 27. 27 Conclusion Druggability models Perspectives - Efficient - Less depend of pocket estimation (for estimations tested) - Efficient on an apo pocket set - Test the model on other estimators - Improve the quality of pocket characteristic features, propose new pocket descriptors, e.g. pocket solvation, pocket flexibility, … - Propose a similar protocol for other ligand type e.g. Small molecules not drug- like, peptide, …
  28. 28. 28 Acknowledgments Pr. Camproux & Dr. Xhaard Computation Drug Discovery group Supervisors Others contributors Dr. Regad & Dr. Petitjean Web site Abi Hussein Hiba, Bécot Jérôme
  29. 29. 29 Acknowledgments Thank you for your attention Organizing Committee ISB
  30. 30. 30 PockDrug - website http://pockdrug.rpbs.univ-paris-diderot.fr/ Feedbacks - Questions, comments, ...
  31. 31. 31 Druggability model 0.82 +/- 0.09 Pocket from 1OQ5 Results by pocket By pocket, we have a druggable probability with a confidence index.
  32. 32. 32 Performances Desaphy, J. et al. (2012). Journal of Chemical Information and Modeling, 52(8), 2287–99.
  33. 33. 33 Statistic protocol (2) Pockets estimated by fpocket Matthew's Coefficient Correlation Choose the models with combination of 3 descriptors
  34. 34. 34 Statistic protocol (3) 2 models are generated and discuss - Observated-PockDrug (from pockets estimated by prox4), data not shows - Predicted-PockDrug (from pockets estimated by fpocket) Druggability model are a combination of best LDA models with: - best performances on other pocket sets - with a minimal number of descriptors Best model performances 52 pocket descriptors Pockets characterized train set test set X LDA models built if n = 2, X = 2 652 if n = 3, X = 23 426 Built Drug-model among X best LDA models Combinations of n descriptors among 52 ninit = 2 n = n + 1 if MCC loo(n) >MCC loo(n-1) else OPE - prox4 Pocket set Model construction Performances Estimators PPE - fpocket - DoGSite Apo - Apo139 Protein sets Holo - NRDLD Pocket descriptors Druggability model Validation Pocket sets characterized prox4-NRDLD fpocket-NRDLD DoGSite-NRDLD fpocket-Apo139 DoGSite-Apo139 and MCC test(n) >MCC test(n-1) on test, train set and by leave one out (loo)
  35. 35. 35 Dataset NRDLD (Non Redundant set of Druggable and Less Druggable bindind sites) Non-Redundant set of Druggable and Less Druggable binding sites (NRDLD set) 71 druggables 44 less druggables HTS - 43 druggable - 17 non druggable NMR screening - 35 druggables - 37 non druggables PDBBind Database screening Experimental data Cheng et al (2007). Nature Biotechnology, 25(1), 71–5 Hajduk et al. (2005). Journal of Medicinal Chemistry, 48(7), 2518–25. Wang et al. (2005). Journal of Medicinal Chemistry, 48(12), 4111–9. From Krasowski, A. et al. (2011). Journal of Chemical Information and Modeling, 51(11), 2829–42. Widely used by others druggability studies. Apo139 From DCD database, 139 apo protein are extracted. 132 druggables 7 less druggables
  36. 36. 36 Druggability predictions: background « ...different pocket detection methods can assign different sizes and/or numbers of pockets for the  same structure. » Gao, M., & Skolnick, J. (2013). Bioinformatics  (Oxford, England), 29(5), 597–604. Step1: Step2: -Lots of estimator algorithms: - energy levels - geometric features - sequence alignment - Appropriate descriptor set - Best statistical protocol - machine learning ? - descriptors selection ? - validation ? Step3: - Dataset

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