Invited talk presented at the Educational Session on "the use and abuse of chemical probes" chaired by Prof. Paul Workman about the Probe Miner resource and its use to help scientists assess and select chemical probes using publicly available data.
1. in partnership with
Probe Miner
Harnessing large-scale public data for the objective
assessment of chemical probes
Albert A. Antolin, Joe E. Tym, Angeliki Komianou, Ian Collins, Paul Workman & Bissan Al-Lazikani
Department of Data Science and Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK.
2018 AACR National Meeting, Chicago, USA.
2. Disclosure Information
AACR National Meeting
Albert A. Antolin
I have no personal financial relationships to disclose.
Employee of ICR which has multiple commercial interactions
and
I will not discuss off-label use and/or investigational use in my
presentation.
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3. Previous LiteratureOnline search enginesCompound vendor catalogs
Limitations of current approaches to chemical probe selection
3
Liu, et al. Clin Cancer Res, 2012
Blagg & Workman, Cancer Cell, 2017
4. The Chemical Probes Portal and potential for synergies
• Expert-curated resource that recommends probes for specific targets
4
Arrowsmith, et. al., Nat Chem Biol, 2015
Synergistic
Genuine PARP
inhibitor
5. Can we exploit large-scale public data
for the objective assessment and prioritization of
bioactive compounds as potential chemical probes?
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6. Methods: Sources of Data
• canSAR (https://cansar.icr.ac.uk/)
• Integrated multidisciplinary curated data
6
Tym, et. al., Nucleic Acids Res., 2016
>150,000 visitors in 2016
> 2.1 M chemical compounds
14.6 M bioactivity data points
2.8 M mutations from patients
> 228,000 clinical trials
7. Chemical probes for the human proteome
• Systematically and objectively analyzing compounds available in public
databases as chemical probes
7
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
https://cansar.icr.ac.uk/
8. The Druggable proteome 8
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
22 – 40%
Druggable proteome
Nature Rev. Drug Discov. 2013
Sci. Trans Med., 2016
9. Probing the liganded proteome 9
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
22 – 40%
Druggable
11%
Liganded
2,220 proteins
How well can we
probe the biology
of this liganded
proteome?
10. Assessing the quality of chemical probes in public databases
Minimum-quality criteria
10
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
22 – 40%
11%
Target
Potency
≤ 100 nM
Target
Selectivity
10-fold
>1 protein
Cell
Potency
< 10 µM
Workman & Collins, Chem. Biol., 2010
11. How much of the liganded proteome can we probe? 11
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
22 – 40%
11%
Target
Potency
74%
Target
Selectivity
40%
Cell
Potency
55%
12. Minimally acceptable probes
Target Potency,
Cell Potency
& Target Selectivity
9%
We can probe for less than 2% of the human proteome
We don’t have the appropiate tools for target validation
12
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
22 – 40%
11%
13. Minimally acceptable probes
Target Potency,
Cell Potency
& Target Selectivity
9%
We can probe for less than 2% of the human proteome
We don’t have the appropriate tools for target validation
13
Antolin, et. al., Cell Chem Biol., 2018
Human proteome
∼20,171 proteins
NAR, 2016
22 – 40%
11%
1.4%
We need more and
better chemical probes
to cover the proteome
14. Objective and quantitative assessment of chemical probes
From fitness factors to scores
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Workman & Collins, Chem. Biol., 2010
15. Objective and quantitative assessment of chemical probes
From fitness factors to scores
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Antolin, et. al., Cell Chem Biol., 2018
• Compound-protein affinity values
• Compound-cell line affinity values
• Compound chemical structures
6 Chemical Probe Scores
16. Probe Miner website resource
User-friendly resource for the objective assessment of chemical probes
16
Antolin, et. al., Cell Chem Biol., 2018
http://probeminer.icr.ac.uk
17. Overview page
Summaries of the data and statistical analysis using our algorithm
17
Antolin, et. al., Cell Chem Biol., 2018
19. Overview Page
Compound viewer interactively linked to the distribution
19
Antolin, et. al., Cell Chem Biol., 2018Antolin, et. al., Cell Chem Biol., 2018
20. Overview Page
Compound viewer interactively linked to the distribution
20
Antolin, et. al., Cell Chem Biol., 2018Antolin, et. al., Cell Chem Biol., 2018
21. Individual chemical probe page
Extended details on raw data, protein affinity profile and cross-references
21
Antolin, et. al., Cell Chem Biol., 2018
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24. Probe Miner and The Chemical Probes Portal:
Overall synergy
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Antolin, et. al., Cell Chem Biol., 2018
Larger number of
publications and data
assessed
Information from journals not covered
by public medicinal chemistry
databases and in depth analysis of
selectivity and in vivo data.
25. ABCC8: wider coverage of Probe Miner
Probe Miner 2,220 targets; The Chemical Probes Portal > 140 targets
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26. PDPK1: the value of expert curation in the Portal
and the challenge of automatically assessing selectivity
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Challenging
comparison
when information
varies significantly
#1 #2
27. SMYD2: The value of regular updates of information 27
Data
Update
28. Future Plans
• We will maintain Probe Miner and update it regularly to ensure topicality
• We are already liaising with The Chemical Probes Portal to identify new
probes for expert assessment
• We are already starting to extract and include selected chemical biology data
from a wider range of journals that are currently missing from medicinal
chemistry databases
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29. Conclusions
• Probe Miner: objective assessment of potential chemical
probes from large-scale literature data
• We do not have enough high-quality chemical probes:
selectivity is the biggest hurdle
• We urgently need to test selectivity more thoroughly and improve
how this data is captured in public databases
• Synergy from the complimentary use of the experience and
knowledge of experts with large-scale computational data
analysis.
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<2%
30. in partnership with
Thank you!
Bissan Al-Lazikani Paul Workman
DEPARTMENT OF DATA SCIENCE
Joe Tym
Angeliki Komianou
Elizabeth Coker
Costas Mitsopoulos
Carmen Rodriguez-Gonzalvez
Veronica Garcia-Perez
Sheng Yu
Catherine Fletcher
Sebastian Poetsrl
James Campbell
Patrizio di Micco
STMP Team
Paul Clarke
Chi Zhang
Alexia Hervieu
Ian Collins
