New Target Prediction and Visualization Tools New Target Prediction and Visualization Tools Incorporating Open Source Molecular Fingerprints Incorporating Open Source Molecular Fingerprints

For TB Mobile Version 2For TB Mobile Version 2

Sean EkinsSean Ekins1, 2 1, 2 , Alex M. Clark, Alex M. Clark33 and Malabika Sarker and Malabika Sarker44

1Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA.

2Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.3Molecular Materials Informatics, 1900 St. Jacques #302, Montreal Quebec, Canada H3J 2S1

4SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.

.

Tuberculosis kills 1.6-1.7m/yr (~1 every 8 seconds)

1/3rd of worlds population infected!!!!

streptomycin (1943)streptomycin (1943)para-para-aminosalicyclic acid (1949)aminosalicyclic acid (1949)isoniazid (1952) isoniazid (1952) pyrazinamide (1954)pyrazinamide (1954)cycloserine (1955)cycloserine (1955)ethambutol (1962)ethambutol (1962)rifampicin (1967)rifampicin (1967)

Multi drug resistance in 4.3% of cases Multi drug resistance in 4.3% of cases

Extensively drug resistant increasing Extensively drug resistant increasing incidenceincidence

one new drug (bedaquiline) in 40 yrs one new drug (bedaquiline) in 40 yrs

TB key pointsTB key points

Tested >350,000 molecules Tested ~2M 2M >300,000>1500 active and non toxic Published 177 100s 800

Big Data: Screening for New Tuberculosis Treatments Big Data: Screening for New Tuberculosis Treatments

How many will become a new drug?How do we learn from this big data?

What are the targets for these molecules?

Others have likely screened another 500,000

Pathway analysisBinding site similarity to Mtb proteinsDockingBayesian Models - ligand similarity

Predicting the target/s for small moleculesPredicting the target/s for small molecules

Multi-step processMulti-step process

1.1.Identification of essential Identification of essential in vivoin vivo enzymes of enzymes of Mtb Mtb involved intensive involved intensive literature mining and manual curation, to extract all the genes essential for literature mining and manual curation, to extract all the genes essential for MtbMtb growth growth in vivo in vivo across speciesacross species..

2.2.Homolog information was collated from other studies.Homolog information was collated from other studies.

3.3.Collection of metabolic pathway information involved using TBDB.Collection of metabolic pathway information involved using TBDB.

4.4.Identifying molecules and drugs with known or predicted targetsIdentifying molecules and drugs with known or predicted targets involved searching the CDD databases for manually curated data. The involved searching the CDD databases for manually curated data. The structures and data were exported for combination with the other data.structures and data were exported for combination with the other data.

5.5.All data were combined with URL links to literature and TBDB and All data were combined with URL links to literature and TBDB and deposited in the CDD database.deposited in the CDD database.

Initially over 700 molecules in datasetInitially over 700 molecules in dataset

Dataset Curation: TB molecules and target information Dataset Curation: TB molecules and target information database connects molecule, gene, pathway and literaturedatabase connects molecule, gene, pathway and literature

Sarker et al., Pharm Res 2012, 29, 2115-2127.

TB molecules and target information database connects TB molecules and target information database connects molecule, gene, pathway and literaturemolecule, gene, pathway and literature

iPhone Android

TB Mobile 1. layout on iPhone and AndroidTB Mobile 1. layout on iPhone and Android

14 First line drugs active against 14 First line drugs active against MtbMtb evaluated in evaluated in TB Mobile app and the top 3 molecules shownTB Mobile app and the top 3 molecules shown

Confirms all in TB Mobile and retrieved

Predicted targets of GSK TB hits months Predicted targets of GSK TB hits months earlier using TB Mobile earlier using TB Mobile

GSK report hits Dec 2012GSK report hits Dec 2012

2424thth Jan 2013 http://goo.gl/9LKrPZ Jan 2013 http://goo.gl/9LKrPZ

GSK predict targets Oct 2013GSK predict targets Oct 2013

Ekins et al., Tuberculosis 94: 162-169 (2014)

Predicted targets Predicted targets using TB Mobile using TB Mobile

No verification No verification yetyet

PCA of 745 compounds with Mtb targets (blue) and 1200 PCA of 745 compounds with Mtb targets (blue) and 1200 Mtb active and non cytotoxic hits compounds (yellow) Mtb active and non cytotoxic hits compounds (yellow)

Chemical property space of TB Mobile compoundsChemical property space of TB Mobile compounds

Ekins et al., Tuberculosis 94: 162-169 (2014)

PCA of 745 compounds with Mtb targets (blue) and 177 PCA of 745 compounds with Mtb targets (blue) and 177 GSK Mtb leads (yellow) GSK Mtb leads (yellow)

Chemical property space of TB Mobile and GSK lead Chemical property space of TB Mobile and GSK lead compoundscompounds

Ekins et al., J Chem Inf Model 53: 3054 (2013)

TB Mobile 2. layout on iPhoneTB Mobile 2. layout on iPhone

About CDDAbout CDDMolecule searchMolecule search

FiltersFilters

Action Menu Molecule prediction Clustering About TB Mobile Action Menu Molecule prediction Clustering About TB Mobile

Control blockControl block

Compound listCompound list

Text searchText search

TB Mobile 2. iPhone vs TB Mobile 1. Android TB Mobile 2. iPhone vs TB Mobile 1. Android Molecule Detail and LinksMolecule Detail and Links

iPhone Android

BookmarkBookmark

copycopy

open-inopen-in

clustercluster

closeclose

TB Mobile 2. iPhone vs TB Mobile 1. TB Mobile 2. iPhone vs TB Mobile 1. Android Similarity Searching in the appAndroid Similarity Searching in the app

iPhone Android

TB Mobile 2. iPhone vs TB Mobile 1. Android TB Mobile 2. iPhone vs TB Mobile 1. Android Filtering and Sharing FunctionsFiltering and Sharing Functions

Each molecule can be copied to the clipboard then opened with other apps (e.g. MMDS, MolPrime, MolSync, ChemSpider, and from these exported via Twitter or email) or shared via Dropbox.

TB Mobile 2. – Filtering and Sharing TB Mobile 2. – Filtering and Sharing FunctionsFunctions

Data can also be filtered by target name, pathway name, essentiality and human ortholog

PCA of 745 compounds with Mtb PCA of 745 compounds with Mtb targets (blue) and 60 new targets (blue) and 60 new compounds (yellow) compounds (yellow)

Chemical property space of screening hits and Chemical property space of screening hits and molecules evaluated in TB Mobile 2.molecules evaluated in TB Mobile 2.

PCA of 745 compounds with Mtb PCA of 745 compounds with Mtb targets (blue) and 20 new test targets (blue) and 20 new test compounds (yellow) compounds (yellow)

GeneGene CountCount GeneGene CountCount

Rv0283Rv0283 11 Rv0678Rv0678 11

Rv1211Rv1211 11 Rv1685cRv1685c 11

Rv1885cRv1885c 44 Rv3160cRv3160c 11

Rv3161cRv3161c 11 TB27.3 (Rv0577)TB27.3 (Rv0577) 22

ald (Rv2780)ald (Rv2780) 22 alr (Rv3423c)alr (Rv3423c) 88

aroD (Rv2537c)aroD (Rv2537c) 1414 aspS (Rv2572c)aspS (Rv2572c) 11

atpE (Rv1305)atpE (Rv1305) 22 blaC (Rv2068c)blaC (Rv2068c) 11

clpB (Rv0384c)clpB (Rv0384c) 11 clpC (Rv3596c)clpC (Rv3596c) 11

cyp121 (Rv2276)cyp121 (Rv2276) 22 cyp130 (Rv1256c)cyp130 (Rv1256c) 22

cyp51 (Rv0764c)cyp51 (Rv0764c) 22 cysH (Rv2392)cysH (Rv2392) 1010

cysS (Rv2130c)cysS (Rv2130c) 11 dacB2 (Rv2911)dacB2 (Rv2911) 11

dapA (Rv2753c)dapA (Rv2753c) 1212 deaD (Rv1253)deaD (Rv1253) 11

def (Rv0429c)def (Rv0429c) 1414 dfrA (Rv2763c)dfrA (Rv2763c) 33

dinG (Rv1329c)" (count=1)dinG (Rv1329c)" (count=1) 11 dlaT (Rv2215)dlaT (Rv2215) 22

dnaA (Rv0001)" (count=1)dnaA (Rv0001)" (count=1) 11 dnaB (Rv0058)dnaB (Rv0058) 11

dnaE2 (Rv3370c)" (count=1)dnaE2 (Rv3370c)" (count=1) 11 dprE1 (Rv3790)dprE1 (Rv3790) 88

dprE2" (count=1)dprE2" (count=1) 11 drpE2 (Rv3791)drpE2 (Rv3791) 22

dxr (Rv2870c)dxr (Rv2870c) 11 dxs1 (Rv2682C)dxs1 (Rv2682C) 2929embA (Rv3794)embA (Rv3794) 22 embB (Rv3795)embB (Rv3795) 11

embC (Rv3793)embC (Rv3793) 11 engA (Rv1713)engA (Rv1713) 11

era (Rv2364c)era (Rv2364c) 11 ethA (Rv3854c)ethA (Rv3854c) 11

fabG (Rv0242c)fabG (Rv0242c) 22 fabH (Rv0533)fabH (Rv0533) 4848fadD32 (Rv3801c)fadD32 (Rv3801c) 55 fbpC (Rv0129C)fbpC (Rv0129C) 2121folP1 (Rv3608C)folP1 (Rv3608C) 11 folP2 (Rv1207)folP2 (Rv1207) 11

frdA (Rv1552)frdA (Rv1552) 11 ftsZ (Rv2150c)ftsZ (Rv2150c) 33

fusA1 (Rv0684)fusA1 (Rv0684) 33 fusA2 (Rv0120c)fusA2 (Rv0120c) 33

glcB (Rv837c)glcB (Rv837c) 11 glf (Rv3809c)glf (Rv3809c) 4040

glmU (Rv1018c)glmU (Rv1018c) 11 guab2 (Rv3411)guab2 (Rv3411) 11

gyrA (Rv0006)gyrA (Rv0006) 2424 gyrB (Rv0005)gyrB (Rv0005) 99

ilvG (Rv1820)ilvG (Rv1820) 11 infB (Rv2839c)infB (Rv2839c) 11

inhA (Rv1484)inhA (Rv1484) 157157 kasA (Rv2245)kasA (Rv2245) 99

kasB (Rv2246)kasB (Rv2246) 55 ldtMt1 (Rv0116c)ldtMt1 (Rv0116c) 44

ldtMt2 (Rv2518c)ldtMt2 (Rv2518c) 11 lpd (Rv0462)lpd (Rv0462) 55

lppS (Rv2515c)lppS (Rv2515c) 11 mbtA (Rv2384)mbtA (Rv2384) 9595

mca (Rv1082)mca (Rv1082) 2727 mfd (Rv1020)mfd (Rv1020) 11

mmpL3 (Rv0206c)mmpL3 (Rv0206c) 1515 moeW (Rv2338c)moeW (Rv2338c) 11

mshB (Rv1170)mshB (Rv1170) 44 murB (Rv0482)murB (Rv0482) 11

murD (Rv2155c)murD (Rv2155c) 22 nadB (Rv1595)nadB (Rv1595) 11

ndhA (Rv0392c)ndhA (Rv0392c) 11 nrdR (Rv2718c)nrdR (Rv2718c) 22

pH HomeostasispH Homeostasis 55 panC (Rv3602c)panC (Rv3602c) 2020pks13 (Rv3800c)pks13 (Rv3800c) 33 proteasomeproteasome 22

ptpA (Rv2234)ptpA (Rv2234) 3838 ptpB (Rv0153c)ptpB (Rv0153c) 33

purU (Rv2964)purU (Rv2964) 22 qcrB (Rv2196)qcrB (Rv2196) 55

quinol oxidasequinol oxidase 11 recG (Rv2973c)recG (Rv2973c) 11

rplC (Rv0701)rplC (Rv0701) 22 rplJ (Rv0651)rplJ (Rv0651) 33

rpoB (Rv0667)rpoB (Rv0667) 44 sahH (Rv3248c)sahH (Rv3248c) 22

thiL (Rv2977c)thiL (Rv2977c) 106106 tlyA (Rv1694)tlyA (Rv1694) 22

tuf (Rv0685)tuf (Rv0685) 33 uvrA (Rv1638)uvrA (Rv1638) 11

Target distribution Target distribution in TB Mobile 2.in TB Mobile 2.

Open Extended Connectivity FingerprintsOpen Extended Connectivity Fingerprints

ECFP_6 FCFP_6• Collected, Collected,

deduplicated, deduplicated, hashedhashed

• Sparse integersSparse integers

• Invented for Pipeline Pilot: public method, proprietary detailsInvented for Pipeline Pilot: public method, proprietary details• Often used with Bayesian models: many published papersOften used with Bayesian models: many published papers• Built a new implementation: open source, Java, CDKBuilt a new implementation: open source, Java, CDK

– stable: fingerprints don't change with each new toolkit releasestable: fingerprints don't change with each new toolkit release– well defined: easy to document precise stepswell defined: easy to document precise steps– easy to port: already migrated to iOS (Objective-C) for easy to port: already migrated to iOS (Objective-C) for TB MobileTB Mobile app app

• Provides core basis feature for CDD open source model serviceProvides core basis feature for CDD open source model service

•Clark et al., J Cheminformatics 6: 38 (2014)Clark et al., J Cheminformatics 6: 38 (2014)

Testing the fingerprints – comparing to published data Testing the fingerprints – comparing to published data

Dataset Leave one out ROC Published

Reference Leave one out ROC in this study

Combined model (5304 molecules) ECFP_6

fingerprints

N/A N/A 0.77

Combined model (5304 molecules) FCFP_6

fingerprints

0.71 J Chem Inf Model

53:3054-3063.

0.77

MLSMR dual event model (2273 molecules) and ECFP_6 fingerprints

N/A N/A 0.84

MLSMR dual event model (2273 molecules) and FCFP_6 fingerprints

0.86 PLOSONE 8:e63240

0.83

Published models also include 8 additional descriptors as well as fingerprints

•Clark et al., J Cheminformatics 6: 38 (2014)Clark et al., J Cheminformatics 6: 38 (2014)

Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6 Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6 fingerprints; (b) modified Bayesian estimators for active and inactive compounds; fingerprints; (b) modified Bayesian estimators for active and inactive compounds; (c) structures of selected binders.(c) structures of selected binders.

For each listed target with at least two binders, it is first assumed that all of the For each listed target with at least two binders, it is first assumed that all of the molecules in the collection that do not indicate this as one of their targets are molecules in the collection that do not indicate this as one of their targets are inactive. inactive.

In the app we used ECFP_6 fingerprints In the app we used ECFP_6 fingerprints

Building Bayesian models for each targetBuilding Bayesian models for each target

Predict targetsPredict targetsCluster moleculesCluster molecules

Open in MMDSOpen in MMDS

Bayesian predictions, data export and clusteringBayesian predictions, data export and clustering

Clark et al., J Cheminformatics, 6: 38 (2014)

Draw structures either in app, paste or open from other apps e.g. MMDS

TB Mobile ranks content TB Mobile can use built in target Bayesian models to

predict target Take a screenshot of results Output bayesian model predictions to MMDS Compare to published data Annotate results, tabulate

Process used to evaluate TB MobileProcess used to evaluate TB Mobile

We have curated an additional set of 20 molecules that have activity We have curated an additional set of 20 molecules that have activity against against MtbMtb and were identified by HTS or other methods and were identified by HTS or other methods

Several targets were not in the databaseSeveral targets were not in the database

Molecules active against Molecules active against MtbMtb evaluated in TB Mobile app evaluated in TB Mobile app

•Clark et al., J Cheminformatics 6: 38 (2014)Clark et al., J Cheminformatics 6: 38 (2014)

Continue to update with more data Continue to update with more data Outreach to increase awareness of app and dataOutreach to increase awareness of app and data Add machine learning algorithms to predict activity (Add machine learning algorithms to predict activity (in in

vitro and in vivo vitro and in vivo whole cell activity)whole cell activity)

Could we appify similar target data for other neglected Could we appify similar target data for other neglected diseases/ targets e.g. malariadiseases/ targets e.g. malaria

What next ?What next ?

In vitro data In vivo data

Target data

ADME/Tox data & Models

Drug-like scaffold creation

TB Prediction Tools TB Publications

Data sources and tools we could integrate

Exposure of CDD content from collaboration with SRIExposure of CDD content from collaboration with SRI More visibility for brand in new placesMore visibility for brand in new places Experiment in small database with focus on content Experiment in small database with focus on content

deliverydelivery A functional app to reach scientists that may not have A functional app to reach scientists that may not have

cheminformatics or bioinformatics trainingcheminformatics or bioinformatics training Pushing the boundaries of what an app can doPushing the boundaries of what an app can do

Benefits of creating TB MobileBenefits of creating TB Mobile

http://goo.gl/vPOKShttp://goo.gl/vPOKS

http://goo.gl/iDJFR

TB Mobile 2– Is on iTunes and TB Mobile 1 is on Google TB Mobile 2– Is on iTunes and TB Mobile 1 is on Google play and are FREEplay and are FREE

http://goo.gl/Goa4e

TB mobile – find out more at www.scimobileapps.com

Papers published on TB Mobile or using Papers published on TB Mobile or using datasetdataset

Ekins et al., Tuberculosis 94: 162-169 (2014)Ekins et al., Tuberculosis 94: 162-169 (2014)

Ekins et al., J Chem Inf Model 53: 3054 (2013)Ekins et al., J Chem Inf Model 53: 3054 (2013)

Clark et al., J Cheminformatics 6:38 (2014)Clark et al., J Cheminformatics 6:38 (2014)

Ekins et al., J Cheminformatics 5:13 (2013)Ekins et al., J Cheminformatics 5:13 (2013)

You can find me @...PAPER ID: 22104 “Collaborative sharing of molecules and data in the mobile age” (final paper number: 43)DIVISION: COMP; DAY & TIME OF PRESENTATION: August 10, 2014 from 4:45 pm to 5:15 pmLOCATION: Moscone Center, West Bldg., Room: 2005 PAPER ID: 22094 “Expanding the metabolite mimic approach to identify hits for Mycobacterium tuberculosis ” (final paper number: 78)DIVISION: COMP: DAY & TIME OF PRESENTATION: August 11, 2014 from 9:00 am to 9:30 amLOCATION: Moscone Center, West Bldg., Room: 2005 PAPER ID: 22120 “Why there needs to be open data for ultrarare and rare disease drug discovery” (final paper number: 48)DIVISION: CINF:SESSION DAY & TIME OF PRESENTATION: August 11, 2014 from 10:50 am to 11:20 amLOCATION: Palace Hotel, Room: Marina PAPER ID: 22183 “Progress in computational toxicology” (final paper number: 125)DIVISION: TOXI: DAY & TIME OF PRESENTATION: August 12, 2014 from 6:30 pm to 10:30 pmLOCATION: Moscone Center, North Bldg. , Room: 134 PAPER ID: 22091 “Examples of how to inspire the next generation to pursue computational chemistry/cheminformatics” (final paper number: 100)DIVISION: CINF: Division of Chemical Information DAY & TIME OF PRESENTATION: August 13, 2014 from 8:25 am to 8:50 amLOCATION: Palace Hotel, Room: Presidio PAPER ID: 22176 “Applying computational models for transporters to predict toxicity” (final paper number: 132)DIVISION: TOXI: DAY & TIME OF PRESENTATION: August 13, 2014 from 9:45 am to 10:05 amLOCATION: InterContinental San Francisco, Room: Grand Ballroom A PAPER ID: 22186 “New target prediction and visualization tools incorporating open source molecular fingerprints for TB mobile version 2” (final paper number: 123)DIVISION: CINF: DAY & TIME OF PRESENTATION: August 13, 2014 from 1:35 pm to 2:05 pmLOCATION: Palace Hotel, Room: California Parlor  

All at CDD, SRI, IDRI and many others …Funding: All at CDD, SRI, IDRI and many others …Funding: 2R42AI088893-02 NIAID, NIH; 9R44TR000942-02 NCATS, NIH; CDD TB has been developed thanks to funding from the Bill and Melinda Gates Foundation (Grant#49852)

Email: ekinssean@yahoo.comEmail: ekinssean@yahoo.com

Slideshare: http://www.slideshare.net/ekinssean Slideshare: http://www.slideshare.net/ekinssean

Twitter: collabchemTwitter: collabchem

Blog: http://www.collabchem.com/Blog: http://www.collabchem.com/

Website: http://www.collaborations.com/CHEMISTRY.HTMWebsite: http://www.collaborations.com/CHEMISTRY.HTM