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Multi Target Bioactivity Models in Pipeline Pilot

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  • 1. Multi-target bioactivity models in Pipeline Pilot Using ligand and target information Gerard JP van Westen Pipeline Pilot UGM (17-1-2013)
  • 2. Cool things to do with PP • Multi-target bioactivity models ▫ The why… ▫ The how… ▫ The results… (time permitting)
  • 3. The why.. a target is never alone… • Drug targets often have similar paralogs ▫ Selectivity is required • Viral targets often mutate leading to resistance ▫ Broad activity is required • Non-similar proteins have been shown to share ligands ▫ E.g. acetylcholine and serotonin
  • 4. Molecular Similarity Efavirenz, EFV (NNRTI) Emtricitabine, FTC (NRTI) Lamivudine, 3TC (NRTI) 2
  • 5. Molecular Similarity Emtricitabine, FTC (NRTI) 1.0 0.9 0.3 0.9 1.0 0.4 0.3 0.4 1.0 2 2
  • 6. Sequence Similarity Emtricitabine, FTC (NRTI) 1.0 0.9 0.3 0.9 1.0 0.4 0.3 0.4 1.0 Phenylalanine Tyrosine Arginine
  • 7. Sequence Similarity Emtricitabine, FTC (NRTI) 1.0 0.9 0.3 0.9 1.0 0.4 0.3 0.4 1.0 FYI IYF WTF FYI IYF WTF
  • 8. The how… what is PCM ? • Proteochemometric modeling combines both a ligand descriptor and target descriptor GJP van Westen, JK Wegner et al. MedChemComm (2011),16-30, 10.1039/C0MD00165A
  • 9. What is PCM ? • Proteochemometric modeling combines both a ligand descriptor and target descriptor GJP van Westen, JK Wegner et al. MedChemComm (2011),16-30, 10.1039/C0MD00165A Bio-Informatics
  • 10. What is PCM ? • Proteochemometric modeling combines both a ligand descriptor and target descriptor Bio-Informatics GJP van Westen, JK Wegner et al. MedChemComm (2011),16-30, 10.1039/C0MD00165A
  • 11. PCM using Pipeline Pilot • For this work we use mostly: ▫ Chemistry (circular fingerprints) ▫ Data modeling ▫ R statistics components (machine learning) • Lacking was a protein descriptor type component… • (In addition I missed some validation components…) ▫ Matthews Correlation Coefficient ▫ R2 to a line through the origin (R2 zero)
  • 12. Target descriptors • Simple way to derive protein descriptors 1. Select the binding pocket 2. Align the relevant residues 3. Convert to physicochemical properties
  • 13. Target descriptors
  • 14. • PP component can create different protein descriptors 1. ProtFP Feature: J. Med. Chem. 2012, 55, 7010-7020 ; BMC Bioinformatics 2012, Submitted 2. ProtFP PCA: BMC Bioinformatics 2012, Submitted 3. Z-Scales : J. Med. Chem. 1998, 41, 2481-2491 4. VHSE : Biopolymers 2005, 80, 775-786 5. ST-Scales : Amino Acids 2010, 38, 805-816 6. T-Scales : J. Mol. Struct. 2007, 830, 106-115 7. MS-WHIM J. Chem. Inf. Comp. Sci. 1999 39, 525-533 8. FASGAI : Eur. J. Med. Chem. 2009, 44, 1144-1154 9. Blosum62 : J. Comp. Biol. 2009, 16, 5, 703-723 Target Descriptors Revised version of paper to be submitted
  • 15. Visualized in PP
  • 16. Visualized in PP 4 1 2 3 5 Feature Based
  • 17. Visualized in PP 4 1 2 3 5 Feature Based 4 5 3 2 1 Physicochemical Properties
  • 18. • The example is using Z-scales by Sandberg et al. • Uses a PCA to derive 5 principal components that describe amino acid similarity ▫ Based on side chain physicochemical properties • We use first 3 ▫ 1 – Lipophilicity ▫ 2 – Size ▫ 3 – Charge / Polarity M Sandberg, L Eriksson J Med Chem (1998) 41: 2481 - 2491 Target Descriptors
  • 19. • Dataset Provide by Tibotec and Virco • Antivirogram® assay • Patient data • Reverse Transcriptase and Protease sequences • Fold Change in –logIC50 Target Amino acids Binding Site Drug Class Drugs Mutant Sequences Data points Reverse Transcriptase 400* Orthosteric NRTI 8 10,501 72,727 Reverse Transcriptase 400* Allosteric NNRTI 4 10,723 35,249 Protease 99 Orthosteric PI 9 27,081 180,162 Example Data set GJP van Westen, A Hendriks et al. PLoS Comp Biol (2013) Accepted / In press
  • 20. Example Data set
  • 21. Methods
  • 22. Results
  • 23. • What is important to our models? • What residue position? • What mutation is present at that position? • How much is contributed to resistance? • Bioactivity spectra can be obtained from these models Feature Importance
  • 24. Feature Importance
  • 25. • Currently we have applied the technique using PP to: • Adenosine receptors (human + rat) • HIV inhibitors (preclinical lead optimization) • HIV inhibitors (clinical drugs) • OATP1 inhibitors • Aminergic GPCRs • … Data sets
  • 26. Acknowledgements • Ad IJzerman • Andreas Bender • Alwin Hendriks • Herman van Vlijmen • Joerg Wegner • Anik Peeters • John Overington • George Papadatos
  • 27. Multi-target bioactivity models in Pipeline Pilot Using ligand and target information Gerard JP van Westen www.gjpvanwesten.nl Pipeline Pilot UGM (17-1-2013)
  • 28. Model validation (classification) • PP lacked a component to calculate correlation coefficients between two properties in the data stream in (binary) classification.
  • 29. Model validation (regression) • PP lacked a component to calculate correlation coefficients between two properties in the data stream in regression. (R2 zero, etc) A. Tropsha; Predictive Quantitative Structure-Activity Relationships Modeling; in Handbook of Chemoinformatics Algorithms (2010) J. Faulon and A. Bender; Editors.
  • 30. Ligand Descriptors • Scitegic Circular Fingerprints ▫ Circular, substructure based fingerprints ▫ Maximal radius of 3 bonds from central atom ▫ Each substructure is converted to a molecular feature Carbon Oxygen Substructure
  • 31. Fingerprints Carbon Oxygen Substructure
  • 32. Fingerprints CC C Carbon Oxygen Substructure
  • 33. Fingerprints C AA A CC C N OC C Carbon Oxygen Substructure
  • 34. Fingerprints C AA A CC C C N O Carbon Oxygen Substructure