TDRtargets.org: an open-access resource for prioritizing possible drug targets

and linking them to possible inhibitors

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Gregory J. Crowther1 and Fernán Agüero2

with Santiago J. Carmona2, M. Paula Magariños2, Dhanasekaran Shanmugam3, Maria A. Doyle4, Christiane Hertz-Fowler5, Matthew Berriman5, Solomon Nwaka6,

Stuart A. Ralph4, David S. Roos3, John P. Overington7, and Wesley C. Van Voorhis1

1University of Washington, 2Universidad de San Martín, 3University of Pennsylvania, 4University of Melbourne,

5Wellcome Trust Sanger Institute, 6TDR / World Health Organization, and 7European Bioinformatics Institute

Overview of TDRtargets.org

• Established in 2007 with funding from TDR division of World Health Organization

• Open-access database to facilitate target-based drug development for “neglected diseases”

• More details: F. Agüero et al., Nat. Rev. Drug Discov. 7: 900-7, 2008

Disease Reference Pathogen

African sleeping sickness Trypanosoma brucei

Chagas disease Trypanosoma cruzi

Filariasis Brugia malayi

Leishmaniasis Leishmania major

Leprosy Mycobacterium leprae

Malaria Plasmodium falciparum, P. vivax

Schistosomiasis Schistosoma mansoni

Toxoplasmosis Toxoplasma gondii

Tuberculosis Mycobacterium tuberculosis

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Target-based drug development

Identify possible drug targets (proteins).

Express and purify targets.

Solve targets’ 3D structures with bound compounds.Confirm that compounds kill

cells via the associated targets.

Preclinical (animal) testing: efficacy, ADME, toxicity.

Screen for compound-target associations.

Optimize compounds for selective inhibition.

TDRtargets.org

Sample Criterion

WeightCriterion met by Protein X?

Assayable 20 Yes

Crystallizable 10 No

Druggable 30 Yes

Essential 25 Yes

Overview of TDRtargets.org

• Original goal: facilitate identification of proteins with traits of good drug targets.

predicted from protein binding pockets and similarities to known

drug targets (A. Hopkins, B. Al-Lazikani, J. Overington)

orthology is used to make inferences about incompletely studied proteins (D. Roos)

according to sigma.com and brenda-enzymes.orgaccording to Protein

Data Bank (pdb.org)

Weighting allows proteins to be ranked based on many criteria without discarding those that lack some desired criteria; e.g.,

1. Protein Y (75 points)2. Protein Z (45 points)3. Protein X (30 points)

Overview of TDRtargets.org

• Original goal: facilitate identification of proteins with traits of good drug targets.

Sample Criterion

Weight Protein X Protein Y Protein Z

Assayable 20 Yes Yes Yes

Crystallizable 10 Yes No No

Druggable 30 No Yes No

Essential 25 No Yes Yes

A gene page

Examples of prioritizing targets

“Identification of attractive drug targets in neglected-disease pathogens using an in silico approach” (G. J. Crowther et al., PLoS Negl. Trop. Dis. 4: e804, 2010)

• made good lists of promising drug targets in several species (http://www.tdrtargets.org/published/browse/366)

• compared to lists previously published by others

• explored plusses and minuses of bioinformatics-based rankings

Figure 2: A summary of the multiparameter search queries presented in this study.

Criterion Weight

is a protein 1

has associated PubMed publications 20

has a solved crystal structure 20

has a ModBase 3D model 10

has a druggability index ≥ 0.4 20

has a compound desirability index > 0.2 10

has a precedent for assayability 20

classified by KEGG as a glycolytic/gluconeogenic enzyme 1000

glycolytic flux control (based on M. A. Albert et al., 2005) glyceraldehyde-3-phosphate dehydrogenase (1.2.1.12)

glycerol-3-phosphate dehydrogenase (1.1.1.8)

glycerol-3-phosphate oxidase (1.1.99.5)

phosphoglycerate mutase (5.4.2.1)

aldolase (4.1.2.13)

enolase (4.2.1.11)

phosphoglycerate kinase (2.7.2.3)

pyruvate kinase (2.7.1.40)

40

30

30

30

10

10

10

10

Criteria for Table 6 (T. brucei glycolysis)U

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Rank Gene ID Gene product Weight

1 Tb927.1.700 phosphoglycerate kinase 1101

1 Tb11.02.3210 triosephosphate isomerase 1101

1 Tb927.6.4300glyceraldehyde 3-phosphate

dehydrogenase, glycosomal1101

1 Tb927.6.4280glyceraldehyde 3-phosphate

dehydrogenase, glycosomal1101

5 Tb927.1.710 phosphoglycerate kinase 1081

5 Tb09.211.0540 fructose-1,6-bisphosphate, cytosolic 1081

5 Tb10.70.5800 hexokinase 1081

5 Tb10.70.5820 hexokinase 1081

9 Tb927.3.3270ATP-dependent phosphofructokinase,

6-phospho-1-fructokinase1071

9 Tb10.70.1370fructose-bisphosphate aldolase,

glycosomal1071

9 Tb927.1.3830glucose-6-phosphate isomerase,

glycosomal1071

9 Tb10.70.4740 enolase 1071

13 Tb927.1.720 phosphoglycerate kinase 1061

13 Tb10.6k15.3850glyceraldehyde 3-phosphate

dehydrogenase, cytosolic1061

high flux control; validation in PMID 19748525

low flux control, but validation in PMID 20405000

Table 6: Prioritization of glycolytic enzymes in T. brucei.

Examples of prioritizing targets

Use of TDRtargets.org to plan and inform experimental work:

• Picking M. tuberculosis and helminth targets for biochemical screens in Shanghai (TDR)

• Picking the T. brucei Glycogen Synthase Kinase 3 as a promising target (UW, Pfizer, Serono, TDR)

• Picking multiple T. brucei targets for genetic validation via RNAi (Ken Stuart, Meg Phillips)

• Picking Plasmodium targets for biochemical screens of antimalarial compounds (Medicines for Malaria Venture, GlaxoSmithKline, Novartis)

Examples of prioritizing targets: Discussion

• Old targets vs. new targets– High rankings of well-known targets suggests that search

strategies are reasonable…– But if all top-ranked targets are well-known, what’s the point?

• False negatives– Examples:

• Plasmodium cytochrome b• helminth acetylcholine & GABA receptors, Glu-gated Cl- channel,

SLO-1 K+ channel

– Possible explanations (non-exclusive):• Targets found through phenotypic screens but do not meet usual criteria

for target-based approach• Assumption that loss-of-function phenotype is best• Total pool of viable targets greatly exceeds the clinically validated ones

Examples of prioritizing targets: Discussion

• False positives– Plasmodium enoyl-ACP reductase (FabI)

• Ranks #2 in Table 4 of PLoS NTD paper• Nonessential for blood-stage growth!• Significance of low, not-tightly-regulated expression during blood stage?

– M. tuberculosis pantothenate kinase (PanK or CoaA)• Ranks in top 100 of Table 5 of PLoS NTD paper• Screens found potent enzyme inhibitors, but none kill cells (C. E. Barry)• Enzyme activity vastly exceeds what is required for growth (C. E. Barry)

• No list is canonical– Researchers have legitimate differences of opinion

• Helminths: penalize proteins with human orthologs, or not?• M. tuberculosis: target information-processing proteins?

– Rankings should change as new data arrive– Make your own!

Emerging challenges in drug discovery

• How can we link “cell-active” compounds (discovered in whole-cell screens) to specific targets?

• How can we study novel proteins that don’t have known inhibitors?

► Importance of compound-target links! ◄

Recent and forthcoming progress on TDRtargets.org:1. Add infrastructure for handling chemical data.

2. Expand the number of bioactive compounds in the database.

3. Link compounds with targets (via literature curation and informatics).

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Text-based searches

Substructure/similarity searches

• DrugBank: ~4,000 FDA-approved drugs

• Starlite/ChEMBL: >500,000 bioactive compounds- includes information on targets (protein, cellular)

• Antimalarial compounds reported by • GlaxoSmithKline (~14,000)• Novartis (~5,400)• St. Jude (~3,400)

Chemical data sources

Target-compound links: 1° associations

• All curated from the literature• TDRtargets.org curation

• focused on neglected diseases• focused on protein targets

• Inpharmatica/ChEMBL curation (J. Overington)• not focused on particular organisms or diseases, but

biased towards chemical literature• the target of a compound can be any biological object:

• a protein (e.g., HIV protease)• a cell line (e.g., HeLa cells)

Target-compound links: 2° associations

Informatics-based predictions rather than experimental data

• Currently available: predictions from orthology

Human glycogen synthase kinase 3

N-[5-(3-pyridyl)-2H-pyrazolo[3,4-b] pyridin-3-yl] butyramide

1° association(IC50 = 11 nM)

orthologs

Bm1_49835 (B. malayi)

LmjF18.0270 (L. major)

PFC0525c (P. falciparum)

PVX_119725 (P. vivax)

Smp_008260.1 (S. mansoni)

Tb927.10.13780 (T. brucei)

Tc00.1047053507993.80 (T.

cruzi)

TGME49_065330 (T. gondii)

  • Coming up: predictions based on docking simulations, compound similarities, etc.

  

 

2° associations

What would make TDRtargets.orgeven more useful and popular?

• More screening data (e.g., for M. tuberculosis)?

• Additional ways to link compounds and targets?

• Additional datasets (e.g., transcriptomics) for prioritizing targets, and better user interface via closer alignment with EuPathDB.org?

• Other ideas?

2° association . . .Upgrade to 1°?

Summary

• TDRtargets.org is an open-access database that facilitates target-based drug development for neglected diseases.

• Targets may be prioritized with weighted searches of multiple criteria.

• The main goal of the website is NOT to establish “canonical” top-10 lists, but to let visitors use their own criteria to find targets that are attractive to them.

• A focus of ongoing work is the use of curation and informatics to link compounds and targets.