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Computational Aspects of HTS Planning and Analysis Course Introduction to ChEMBL - Anne Hersey ChEMBL Group EMBL-EBI

Introduction to ChEMBL - BioMedBridges

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Page 1: Introduction to ChEMBL - BioMedBridges

Computational Aspects of HTS Planning and Analysis Course

Introduction to ChEMBL -

Anne Hersey

ChEMBL Group

EMBL-EBI

Page 2: Introduction to ChEMBL - BioMedBridges

Outline • ChEMBL

• Background

• Content

• Identifying compounds binding to a protein target

• Assessing compounds

• Using other resources • Crystal structures

• Druggability algorithms

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Page 3: Introduction to ChEMBL - BioMedBridges

Compound Selection Ta

rget

Annotation Possible ?

Virtual screening

file ChEMBL ChEMBL

Virtual screening

file DrugEBIlity PDBe

No Diversity based list

Yes Activity based hits

Known actives ?

No Select

compounds similar to known

actives

Yes

Are the actives ‘Drug like’ ?

No

Yes

Activity-based

Yes No

Structure based hits

Known structure ?

Yes Select compounds

compatible with binding site

Is the binding site ‘Drug like’ ?

Yes

No Structure-based

3

Page 4: Introduction to ChEMBL - BioMedBridges

What is ChEMBL • Open access database for drug discovery

• Freely available (searchable and downloadable)

• Content:

• Bioactivity data manually extracted from the primary medicinal chemistry literature from journals such as J. Med. Chem.

• Subset of data from PubChem

• Deposited data e.g. neglected disease screening, GSK kinase set

• Bioactivity data is associated with a biological target and a chemical structure

• Compounds are stored in a structure searchable format

• Protein targets are linked to protein sequences in UniProt

• Updated regularly with new data

• Secure searching (https://www.ebi.ac.uk/chembldb )

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Page 5: Introduction to ChEMBL - BioMedBridges

Data Example EP1 Antagonists for Inflammatory Pain A. Hall et al.

Bioorg. Med. Chem. Lett. 19 (2009) 2599–2603

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Page 6: Introduction to ChEMBL - BioMedBridges

View of data in ChEMBL

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Compound Target Activity Assay Lit ref

Page 7: Introduction to ChEMBL - BioMedBridges

Some Numbers (ChEMBL17)

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Page 8: Introduction to ChEMBL - BioMedBridges

Accessing ChEMBL Data

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Page 9: Introduction to ChEMBL - BioMedBridges

Drug Targets

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Data for: ~260 drug targets ~6000 protein targets (single proteins,families and complexes)

Page 10: Introduction to ChEMBL - BioMedBridges

Are there known Active Compounds for my Target?

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From ChEMBL identify compounds that bind to the target Select:

• Potent compounds • Rule of 5 compliant (drug-like) • Ligand efficient molecules

Example (DPP4):

Page 11: Introduction to ChEMBL - BioMedBridges

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Linagliptin

Saxagliptin

Sitacliptin

Alogliptin

Compounds with DPP4 data in ChEMBL Are they drug like?

Page 12: Introduction to ChEMBL - BioMedBridges

Ligand Efficiencies

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LE -RTln(Ki)/Heavy_atoms (Hopkins AL et al DDT; 2004) BEI pKi*1000/MW (Abad-Zapatero C et al DDT 2005) SEI pKi*100/PSA LLE pKi – ALogP (Leeson PD et al NRDD 2007)

In ChEMBL LE calculated for: IC50,Ki,EC50,Kd,XC50,AC50,Potency

Select most ligand efficient compounds

Page 13: Introduction to ChEMBL - BioMedBridges

Other Information about compounds

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Linagliptin bound to DPP4

Compound availability

Page 14: Introduction to ChEMBL - BioMedBridges

Another example

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Sci Transl Med 5, 206ra138 (2013)

Page 15: Introduction to ChEMBL - BioMedBridges

Information on PERK Target

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Page 16: Introduction to ChEMBL - BioMedBridges

Searching by Compound

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Page 17: Introduction to ChEMBL - BioMedBridges

Extending dataset – Similar Targets

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>sp|Q9NZJ5|E2AK3_HUMAN Eukaryotic translation initiation factor 2-alpha kinase 3 OS=Homo sapiens GN=EIF2AK3 PE=1 SV=3 MERAISPGLLVRALLLLLLLLGLAARTVAAGRARGLPAPTAEAAFGLGAAAAPTSATRVPAAGAVAAAEVTVEDAEALPAAAGEQEPRGPEPDDETELRPRGRSLVIISTLDGRIAALDPENHGKKQWDLDVGSGSLVSSSLSKPEVFGNKMIIPSLDGALFQWDQDRESMETVPFTVESLLESSYKFGDDVVLVGGKSLTTYGLSAYSGKVRYICSALGCRQWDSDEMEQEEDILLLQRTQKTVRAVGPRSGNEKWNFSVGHFELRYIPDMETRAGFIESTFKPNENTEESKIISDVEEQEAAIMDIVIKVSVADWKVMAFSKKGGHLEWEYQFCTPIASAWLLKDGKVIPISLFDDTSYTSNDDVLEDEEDIVEAARGATENSVYLGMYRGQLYLQSSVRISEKFPSSPKALESVTNENAIIPLPTIKWKPLIHSPSRTPVLVGSDEFDKCLSNDKFSHEEYSNGALSILQYPYDNGYYLPYYKRERNKRSTQITVRFLDNPHYNKNIRKKDPVLLLHWWKEIVATILFCIIATTFIVRRLFHPHPHRQRKESETQCQTENKYDSVSGEANDSSWNDIKNSGYISRYLTDFEPIQCLGRGGFGVVFEAKNKVDDCNYAIKRIRLPNRELAREKVMREVKALAKLEHPGIVRYFNAWLEAPPEKWQEKMDEIWLKDESTDWPLSSPSPMDAPSVKIRRMDPFATKEHIEIIAPSPQRSRSFSVGISCDQTSSSESQFSPLEFSGMDHEDISESVDAAYNLQDSCLTDCDVEDGTMDGNDEGHSFELCPSEASPYVRSRERTSSSIVFEDSGCDNASSKEEPKTNRLHIGNHCANKLTAFKPTSSKSSSEATLSISPPRPTTLSLDLTKNTTEKLQPSSPKVYLYIQMQLCRKENLKDWMNGRCTIEERERSVCLHIFLQIAEAVEFLHSKGLMHRDLKPSNIFFTMDDVVKVGDFGLVTAMDQDEEEQTVLTPMPAYARHTGQVGTKLYMSPEQIHGNSYSHKVDIFSLGLILFELLYPFSTQMERVRTLTDVRNLKFPPLFTQKYPCEYVMVQDMLSPSPMERPEAINIIENAVFEDLDFPGKTVLRQRSRSLSSSGTKHSRQSNNSHSPLPSN

PERK sequence from Uniprot BLAST search for similar sequences

Page 18: Introduction to ChEMBL - BioMedBridges

Compound Selection Ta

rget

Annotation Possible ?

Virtual screening

file ChEMBL ChEMBL

Virtual screening

file druggability PDBe

No Diversity based list

Yes Activity based hits

Known actives ?

No Select

compounds similar to known

actives

Yes

Are the actives ‘Drug like’ ?

No

Yes

Activity-based

Yes No

Structure based hits

Known structure ?

Yes Select compounds

compatible with binding site

Is the binding site ‘Drug like’ ?

Yes

No Structure-based

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Page 19: Introduction to ChEMBL - BioMedBridges

Structure Based Design

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Page 20: Introduction to ChEMBL - BioMedBridges

Is my Protein Druggable? - • Structure based methods identify cavities in protein crystal

structures and assessing the properties of these cavities

• Rules for properties that indicate a druggable cavity learnt from analysis of co-crystal complexes with drug-like ligands

• Examples of Algorithms:

• PocketFinder – An, Totrov & Abagyan, 2005

• Druggability Indices – Hajduk, Huth & Fesik, 2005

• Rule based method - Perola, Herman & Weiss, 2012

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Page 21: Introduction to ChEMBL - BioMedBridges

DrugEBIlity • https://www.ebi.ac.uk/chembl/drugebility/structure

• All potential pockets in crystal structures from PDB predicted using a pocket-finding algorithm (based on SurfNet, Laskowski 1995)

• Decision tree algorithm trained on known binding pockets for drug-like ligands (e.g., rule-of-five)

• Decision tree used to classify unknown pockets into druggable/undruggable

• Second ‘tractability’ algorithm also trained with more relaxed ligand criteria (e.g., Mwt < 800)

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Page 22: Introduction to ChEMBL - BioMedBridges

Is the Binding Site Druggable?

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Page 23: Introduction to ChEMBL - BioMedBridges

Acknowledgements

• John Overington

• Anna Gaulton

• Mark Davies

• Patricia Bento

• Jon Chambers

• Francis Atkinson

• Louisa Bellis

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• Yvonne Light

• George Papadatos

• Shaun McGlinchey

• Nathan Dedman

• Michal Nowotka

• Ruth Akhtar

• Kaz Ikeda