Toxicological Relationships Between Proteins Obtained From

a Molecular Spam Filter

Florian Nigsch & John Mitchell

F. Nigsch, et al., J. Chem. Inf. Model., 48, 306-318 (2008)

F. Nigsch, et al., Toxicology and Applied Pharmacology, 231, 225-234 (2008)

F. Nigsch, et al., J. Chem. Inf. Model., 48, 2313-2325 (2008)

Toxicological Relationships Between Proteins Obtained From

a Molecular Spam Filter

Florian Nigsch & John Mitchell

F. Nigsch, et al., J. Chem. Inf. Model., 48, 306-318 (2008)

F. Nigsch, et al., Toxicology and Applied Pharmacology, 231, 225-234 (2008)

F. Nigsch, et al., J. Chem. Inf. Model., 48, 2313-2325 (2008)

Toxicological Relationships Between Proteins Obtained From

a Molecular Spam Filter

Florian Nigsch & John Mitchell

Now at Novartis Institutes, Boston

Toxicological Relationships Between Proteins Obtained From

a Molecular Spam Filter

Florian Nigsch & John Mitchell

Soon moving to University of St Andrews

Spam

• Unsolicited (commercial) email

• Approx. 90% of all email traffic is spam

• Where are the legitimate messages?

• Filtering

Analogy to Drug Discovery

• Huge number of possible candidates

• Virtual screening to help in selection process

Properties of Drugs

• High affinity to protein target

• Soluble

• Permeable

• Absorbable

• High bioavailability

• Specific rate of metabolism

• Renal/hepatic clearance?

• Volume of distribution?

• Low toxicity

• Plasma protein binding?

• Blood-Brain-Barrier penetration?

• Dosage (once/twice daily?)

• Synthetic accessibility

• Formulation (important in development)

Multiobjective Optimisation

Bioactivity Synthetic accessibility

Permeability

Toxicity

Metabolism

Solubility

Huge number of candidates …

Multiobjective Optimisation

Bioactivity Synthetic accessibility

Permeability

Toxicity

Metabolism

Solubility

U S E L

E S S

Drug

Huge number of candidates … most of which are useless!

Winnow Algorithm

• Invented in late 1980s by Nick Littlestone to learn Boolean functions

• Name from the verb “to winnow”– High-dimensional input data

• Natural Language Processing (NLP), text classification, bioinformatics

• Different varieties (regularised, Sparse Network Of Winnow - SNOW, …)

• Error-driven, linear threshold, online algorithm

Winnow Algorithm

• Invented in late 1980s by Nick Littlestone to learn Boolean functions

• Name from the verb “to winnow”– High-dimensional input data

• Natural Language Processing (NLP), text classification, bioinformatics

• Different varieties (regularised, Sparse Network Of Winnow - SNOW, …)

• Error-driven, linear threshold, online algorithm

Winnow Algorithm

• Invented in late 1980s by Nick Littlestone to learn Boolean functions

• Name from the verb “to winnow”– High-dimensional input data

• Natural Language Processing (NLP), text classification, bioinformatics

• Different varieties (regularised, Sparse Network Of Winnow - SNOW, …)

• Error-driven, linear threshold, online algorithm

Winnow Algorithm

• Invented in late 1980s by Nick Littlestone to learn Boolean functions

• Name from the verb “to winnow”– High-dimensional input data

• Natural Language Processing (NLP), text classification, bioinformatics

• Different varieties (regularised, Sparse Network Of Winnow - SNOW, …)

• Error-driven, linear threshold, online algorithm

Winnow Algorithm

• Invented in late 1980s by Nick Littlestone to learn Boolean functions

• Name from the verb “to winnow”– High-dimensional input data

• Natural Language Processing (NLP), text classification, bioinformatics

• Different varieties (regularised, Sparse Network Of Winnow - SNOW, …)

• Error-driven, linear threshold, online algorithm

Feature Space - Chemical Space

m = (f1,f2,…,fn)

f1

f2

f3

Feature spaces of high dimensionality

COX2

f2

f3

f1

DHFR

CDK1CDK2

Combinations of Features

Combinations of molecular features

to account for synergies.

Features of Molecules

Based on circular fingerprints

Training Example

WorkflowFor predicting protein targets

Protein Target Prediction

• Which protein does a given molecule bind to?• Virtual Screening• Multiple endpoint drugs - polypharmacology• New targets for existing drugs• Prediction of adverse drug reactions (ADR)

– Computational toxicology

Protein Target Prediction

• Which protein does a given molecule bind to?• Virtual Screening• Multiple endpoint drugs - polypharmacology• New targets for existing drugs• Prediction of adverse drug reactions (ADR)

– Computational toxicology

Protein Target Prediction

• Which protein does a given molecule bind to?• Virtual Screening• Multiple endpoint drugs - polypharmacology• New targets for existing drugs• Prediction of adverse drug reactions (ADR)

– Computational toxicology

Protein Target Prediction

• Which protein does a given molecule bind to?• Virtual Screening• Multiple endpoint drugs - polypharmacology• New targets for existing drugs• Prediction of adverse drug reactions (ADR)

– Computational toxicology

Protein Target Prediction

• Which protein does a given molecule bind to?• Virtual Screening• Multiple endpoint drugs - polypharmacology• New targets for existing drugs• Prediction of adverse drug reactions (ADR)

– Computational toxicology

Predicted Protein Targets

• Selection of 233 classes from the MDL Drug Data Report

• ~90,000 molecules• 15 independent

50%/50% splits into training/test set

Predicted Protein Targets

Cumulative probability of correct prediction within the three top-ranking predictions: 82.1% (±0.5%)

Computational Toxicology

• Model for target prediction

• Annotated library of toxic molecules– MDL Toxicity

database

– ~150,000 molecules

– Standardisation

– MySQL database

• For each molecule we predict the likely target

• Correlations between predicted protein targets and known toxicity codes– Canonical (23)

– Full (490)

Toxicological Relationships Outline (1)

• Protein target prediction allows us to link (predictively) 150,000 toxic organic molecules to 233 specific protein targets

• Each target is treated as a single protein, although may be sets of related proteins)

• Toxicological databases link (experimentally) these 150,000 molecules to 23 toxicity classes

• Combining these two sources of data matches the 233 proteins with the 23 toxicity classes

Toxicological Relationships Outline (1)

• Protein target prediction allows us to link (predictively) 150,000 toxic organic molecules to 233 specific protein targets

• Each target is treated as a single protein, although may be sets of related proteins

• Toxicological databases link (experimentally) these 150,000 molecules to 23 toxicity classes

• Combining these two sources of data matches the 233 proteins with the 23 toxicity classes

Toxicological Relationships Outline (1)

• Protein target prediction allows us to link (predictively) 150,000 toxic organic molecules to 233 specific protein targets

• Each target is treated as a single protein, although may be sets of related proteins

• Toxicological databases link (experimentally) these 150,000 molecules to 23 toxicity classes

• Combining these two sources of data matches the 233 proteins with the 23 toxicity classes

Toxicological Relationships Outline (1)

• Protein target prediction allows us to link (predictively) 150,000 toxic organic molecules to 233 specific protein targets

• Each target is treated as a single protein, although may be sets of related proteins

• Toxicological databases link (experimentally) these 150,000 molecules to 23 toxicity classes

• Combining these two sources of data matches the 233 proteins with the 23 toxicity classes

Toxicological Relationships Outline (2)

• For each protein target, we have a profile of association with the 23 toxicity classes

• Proteins with similar profiles are clustered together

• We demonstrate that these clusters of proteins can be physiologically meaningful.

Toxicological Relationships Outline (2)

• For each protein target, we have a profile of association with the 23 toxicity classes

• Proteins with similar profiles are clustered together

• We demonstrate that these clusters of proteins can be physiologically meaningful.

Toxicological Relationships Outline (2)

• For each protein target, we have a profile of association with the 23 toxicity classes

• Proteins with similar profiles are clustered together

• We demonstrate that these clusters of proteins can be physiologically meaningful.

Predictions Obtained

L70 - Changes in liver weight<LiverY07 - Hepatic microsomal oxidase<Enzyme inhibitionM30 - Other changes<Kidney, Urether, and BladderL30 - Other changes<Liver

Target PredictionHighest ranking class IS predicted protein target

Protein code j

Toxicity codes i

Result matrix R = (rij)rij incremented for each prediction.

( )Protein targetsT

oxcodes

r11 r12

r21

…

Toxicity Annotations

CANONICAL TOXICITY CODES (23)

FULL TOXICITY CODES (490)Y41 : Glycolytic < Metabolism (intermediary) < Biochemical

Proteins by Toxicity

• Cardiac - G1. Kainic acid receptor2. Adrenergic alpha23. Phosphodiesterase III4. cAMP Phosphodiesterase5. O6-Alkylguanine-DNA

alkyltransferase

• Vascular - H

1. Angiotensin II AT2

2. Dopamine (D2)

3. Bombesin

4. Adrenergic alpha2

5. 5-HT antagonist

Top 5 Proteins by Toxicity

68 distinct proteins for 23 toxicity classes, i.e., 3.0 proteins per canonical toxicity code.

Lanosterol 14alpha-Methyl Demethylase 5 Glucose-6-phosphate Translocase 4 IL-6 4 Benzodiazepine Antagonist 3 Kainic Acid Receptor 3

Proteins and their connectivities

Clustering of Toxicity Classes

Clustering of toxicity classes: based on predicted protein associations from the result matrix

Correlation Between Toxicity Classes

Correlations between toxicity classes: 23 by 23 correlation matrix

Correlations between proteins: 233 by 233 correlation matrix

Correlation Between Proteins

Correlations between proteins: 233 by 233 correlation matrix

Cluster 1 (proteins 6-11)

Correlation Between Proteins

We will look at two specific clusters, which are called Cluster 1 and Cluster 4.

• Carbonic Anhydrase Inhibitor

• Estrogen Receptor Modulator

• LHRH Agonist• Aromatase Inhibitor• Cysteine Protease

Inhibitor• DHFR Inhibitor

• Cluster 1 (proteins 6-11)

• Within-cluster correlation (without auto-correlation)r = 0.95

Cluster 1

• Carbonic Anhydrase Inhibitor

• Estrogen Receptor Modulator

• LHRH Agonist• Aromatase Inhibitor• Cysteine Protease

Inhibitor• DHFR Inhibitor

• Cluster 1 (proteins 6-11)

• Within-cluster correlation (without auto-correlation)r = 0.95

Cluster 1

• Carbonic Anhydrase Inhibitor

• Estrogen Receptor Modulator

• LHRH Agonist• Aromatase Inhibitor• Cysteine Protease

Inhibitor• DHFR Inhibitor

Cluster 1

• Within-cluster correlation (without auto-correlation)r = 0.95

Proteins involved in breast cancer

Cluster 1

Proteins involved in breast cancer

Cluster 1

Computational ToxicologyCA

ER LHRH

Aromatase Cysteine Prot.

DHFR

Tissue-specific transcripts of human steroid sulfatase are under control of estrogen signaling pathways in breast carcinoma, Zaichuk 2007

“aim of this study was to characterize carbonic anhydrase II (CA2), as novel estrogen responsive gene” Caldarelli 2005

The Transactivation Domain AF-2 but Not the DNA-Binding Domain of the Estrogen Receptor Is Required to Inhibit Differentiation of Avian Erythroid Progenitors, Marieke von Lindern 1998

This led to premature expression of CAII, a possible explanation for the toxic effects of overexpressed ER.

Cathepsin L Gene Expression and Promoter Activation in Rodent Granulosa Cells, Sriraman 2004

showed that cathepsin L expression in granulosa cells of small, growing follicles in- creased in periovulatory follicles after human chorionic gonadotropin stimulation.

Controversies of adjuvant endocrine treatment for breast cancer and recommendations of the 2007 St Gallen conference, Rabaglio 2007

Merchenthaler 2005

Summary of aromatase inhibitor trials: The past and future, Goss 2007 Regulation of collagenolytic cysteine

protease synthesis by estrogen in osteoclasts, Furuyama 2000

Antimalarials?

Induction by estrogens of methotrexate resistance in MCF-7 breast cancer cells, Thibodeau 1998

Literature-based links between these proteins

Breast Cancer Proteins

and now Cluster 4 …

Cluster 4

This cluster links treatment of stomach ulcers to loss of

bone mass!

This cluster links treatment of stomach ulcers to loss of

bone mass!

Proton Pump Inhibitors etc.

Correlation above 0.98

Proton Pump Inhibitors etc.

Correlation above 0.99

Correlation above 0.98

Proton Pump Inhibitors etc.

• Proton pump inhibitors used to limit production of gastric acid

• PTH is important in the developent/regulation of osteoclasts (cells for bone resorption)

• PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass

Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

PTH = Parathyroid hormone (84 aa mini-protein)

Proton Pump Inhibitors etc.

• Proton pump inhibitors used to limit production of gastric acid

• PTH is important in the developent/regulation of osteoclasts (cells for bone resorption)

• PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass

Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

PTH = Parathyroid hormone (84 aa mini-protein)

Proton Pump Inhibitors etc.

• Proton pump inhibitors used to limit production of gastric acid

• PTH is important in the developent/regulation of osteoclasts (cells for bone resorption)

• PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass

Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

PTH = Parathyroid hormone (84 aa mini-protein)

Proton Pump Inhibitors etc.

• Proton pump inhibitors used to limit production of gastric acid

• PTH is important in the developent/regulation of osteoclasts (cells for bone resorption)

• PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass

Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

PTH = Parathyroid hormone (84 aa mini-protein)

Proton Pump Inhibitors etc.

• Proton pump inhibitors used to limit production of gastric acid

• PTH is important in the developent/regulation of osteoclasts (cells for bone resorption)

• PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass

Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

Conclusions

• Successful adaptation of algorithm formerly not used in this area

• Benchmark confirms usability, speed & memory requirements

• Can find correct protein targets for molecules

• Hence link proteins together via ligand-binding properties and associations of ligands with toxicities

• Identify toxicological relationships between proteins

Conclusions

• Successful adaptation of algorithm formerly not used in this area

• Benchmark confirms usability, speed & memory requirements

• Can find correct protein targets for molecules

• Hence link proteins together via ligand-binding properties and associations of ligands with toxicities

• Identify toxicological relationships between proteins

Conclusions

• Successful adaptation of algorithm formerly not used in this area

• Benchmark confirms usability, speed & memory requirements

• Can find correct protein targets for molecules

• Hence link proteins together via ligand-binding properties and associations of ligands with toxicities

• Identify toxicological relationships between proteins

Conclusions

• Successful adaptation of algorithm formerly not used in this area

• Benchmark confirms usability, speed & memory requirements

• Can find correct protein targets for molecules

• Hence link proteins together via ligand-binding properties and associations of ligands with toxicities

• Identify toxicological relationships between proteins

Conclusions

• Successful adaptation of algorithm formerly not used in this area

• Benchmark confirms usability, speed & memory requirements

• Can find correct protein targets for molecules

• Hence link proteins together via ligand-binding properties and associations of ligands with toxicities

• Identify toxicological relationships between proteins

Acknowledgements

• Jos Tissen• Bernd van Buuren• Silvia Miret

» Unilever» Cambridge

• Andreas Bender• Hamse Mussa• Jeremy Jenkins

Funding - Unilever