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Multi Target Bioactivity Models in Pipeline Pilot
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
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
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
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
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