pharmacophoreguided fragment-based drug...
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Pharmacophore Guided Fragment-Based Drug Design
Pharmacophore Guided Fragment-Based Drug Design
Tien Luu, PhDLead Scientific Specialist, Life [email protected]
Discovery Studio 2.1: New Science and Customized Workflows for Drug Discovery Research10th July 2008
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© 2008 Accelrys, Inc. 2
Fragment-Based Design
•Combination of small fragments results in high diversity
•Addressing sub-site specificity and ligand efficiency
•Low molecular weight compounds represent suitable basis for optimisation of ADME properties
S. Borman, Chemical & Engineering News, June 19, 2006, Volume 84, 25, pp.
56-78; November 28, 2005, Volume 83, 48, pp. 28–30
© 2008 Accelrys, Inc. 3
Why Pharmacophores?
•Smaller fragments may bind in various parts of the binding cavity
•From the way a fragment docks, it may be difficult to discover how the ligand containing that fragment may dock
•The pose of the fragment in a ligand may not be a top hit when the fragment is docked
•When two or more fragments are joined properly, the chances of finding the ligand pose seems greater
•Pharmacophores can direct the fragment to the location occupied when the fragments form a ligand
Babaogly, K, Shoichet, B, Deconstructing fragment-based drug discovery,
Nature Chem. Biol., 2 (12), 2006
© 2008 Accelrys, Inc. 4
Fragment-Based Workflow
Fragment Queries
Fragment
Database
Library
Enumeration
Hit Refinement
Screen / Focus
Database
Scoring &
Prioritisation
Reference Query
Protein Target File
Reference Query
Synthetic Feasibility Filtering
Hit Lists
De-novo Library
Focused Hit List
Refined Hit List
© 2008 Accelrys, Inc. 5
Example: Thymidine Kinase
• Fragment based approach applied to thymidine kinase
1. Protein preparation
2. Pharmacophore construction and link definitions
3. Enumeration and hit refinement
4. Selection of candidate molecules
© 2008 Accelrys, Inc. 6
The Target
•Example: HSV Thymidine Kinase (1kim.pdb)
© 2008 Accelrys, Inc. 7
Protein Preparation
• Fully automated protocol dealing with protein preparation:
1. Cleans the protein/ligand complex– All Hydrogen are added
– Alternate residue conformation are removed
– Names are standardized
– Missing residues completed
2. CHARMm pKa calculation to protonate the protein
http://forums.accelrys.org/eve/forums/a/tpc/f/2011007181/m/3601080603
© 2008 Accelrys, Inc. 8
Describing the Interactions
•3HBD, 1HBA, 1Hydrophobe
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Fragment Queries
•Two options explored– Difference in the resulting molecules expected
– The link atom queries need to be formulated differently
© 2008 Accelrys, Inc. 10
Fragment Linking
•Link atom does not have to be of the same type – Sometimes a different atom type in each fragment is required for
enumeration
OH
OH
NH
O
CH3
ON
H
O
H
O
CH3
NH
N
O
CH3OH
OH
O
H
CH3
NH
O
NH
O O
OH
OH
NH2
© 2008 Accelrys, Inc. 11
Fragment Queries
Strategy 1 Strategy 2
•Link1 and Link2
Shape parameters
can be tuned
© 2008 Accelrys, Inc. 12
Fragment Database
•Commercial, corporate
•Customised
– 120000 compounds
– 20000 fragments
– Conversion into a multi-conf database
© 2008 Accelrys, Inc. 13
Synthetic Feasibility Score
•Molecular Networks (www.molecular-networks.com)
• ISV partner (Pipeline Pilot Components)
•Fast estimation of synthetic accessibility of organic compounds– Scoring of compounds on a scale from 1 and 10
• 1 - Easy to synthesise
• 10 - Difficult to synthesise
•Prioritisation of chemical compounds– Generated by de novo design experiments
– Generated by inverse QSAR/QSPR experiments
– Large virtual compound libraries
Boda, K. et al. Structure and reaction based evaluation of synthetic accessibility. J. Comput.-Aided Mol.
Des. 2007, 21, 311-325.
© 2008 Accelrys, Inc. 14
SYLVIA – Components of Accessibility Scoring
•Structure-based– Molecular graph complexity 1
– Ring complexity 2
– Stereochemical complexity
•Starting material-based– Similarity to starting materials
•Product bond-based– Reaction fitness: based on presence of product reaction center substructures
(RCSS) extracted from reaction databases
1 Bertz, S.H. J. Am. Chem. Soc. 1981, 103, 3599-3601.
2 Gasteiger, J.; Jochum, C. J. Chem. Inf. Comput. Sci. 1979, 19, 43-48.
© 2008 Accelrys, Inc. 15
Fragment Screening
•Customised Database– 20000 compounds
– Synthetic feasibility filtering
(threshold: 4)
– Multiple searches done simultaneously
Query1 Query2
Strategy1 29 31
Strategy2 116 61
Hit List From Fragment-library
© 2008 Accelrys, Inc. 16
Enumerating and Re-Focusing the de-novo Library
Strategy1 159 (899) 146
Strategy2 704 (7076) 388
Enumerated Library Focused Library
Energy or structural filters
are applied �
Number of successful
molecules is not
the simple product of
number of fragments
© 2008 Accelrys, Inc. 17
Hit Refinement
•Focused lists are just a starting point for additional refinement
•Refinement is a Multiple Step procedure
• Is project dependent
• In this example, focused hit lists are:– Docked into the active site of thymidine kinase (CDDOCKER)
• 2-deoxythymidine is used for comparison
– Filter the poses with the pharmacophore
– Evaluated against a library of pharmacophores built from kinase targets –
pharmacological profiling
Pharmacological
profiling
Pharmacophore-based
pose filteringCDOCKER
© 2008 Accelrys, Inc. 18
Hit Refinement – Results: Strategy 1
•DeoxyThymidine CDOCKER energy: 25.05 – 21.96
•For 57 compounds, best pose CDOCKER energy > best pose CDOCKER energy of DeoxyThymidine
© 2008 Accelrys, Inc. 19
Hit Refinement – Results: Strategy 1 continued
•Filtering CDOCKER poses with the pharmacophore
•24 compounds
© 2008 Accelrys, Inc. 20
00.10.20.30.40.50.60.70.80.9
1
TK
(HSV1)
Bcr-Abl CDK2 CDK5 Hck
Kinase target
Profile of 163584
Scaled Fit
00.10.20.30.40.50.60.70.80.9
1
TK
(HSV1)
PDK1 CDK2 CDK5 Pim1
Kinase target
Profile of 164538
Scaled Fit
00.10.20.30.40.50.60.70.80.9
1
TK (HSV1) CDK2
Kinase target
Profile of 224933
Scaled Fit
0
0.10.20.30.40.50.60.7
0.80.9
1
TK (HSV1) CDK2 CDK5
Kinase Target
Profile of 238894
Scaled Fit
Hit Refinement – Results: Strategy 1 continued
© 2008 Accelrys, Inc. 21
Hit Refinement – Results: Strategy 2
•DeoxyThymidine CDOCKER energy: 25.05 – 21.96
•For 180 compounds, best pose CDOCKER energy > best pose CDOCKER energy of DeoxyThymidine
© 2008 Accelrys, Inc. 22
Hit Refinement – Results: Strategy 2 continued
•Filtering CDOCKER poses with the pharmacophore
•131 compounds
© 2008 Accelrys, Inc. 23
0
0.10.20.30.40.50.60.7
0.80.9
1
TK (HSV1) CDK2
Kinase Target
Profile of 109397-17414
Scaled Fit
0
0.10.20.30.40.50.60.7
0.80.9
1
TK (HSV1) CLK1
Kinase Target
Profile of 109397-75086
Scaled Fit
0
0.10.20.30.40.50.60.7
0.80.9
1
TK (HSV1) CLK1
Kinase Target
Profile of 109397-254669
Scaled Fit
0
0.10.20.30.40.50.60.7
0.80.9
1
TK (HSV1) CDK2 c-Met
Kinase Target
Profile of 109397-148362
Scaled Fit
Hit Refinement – Results: Strategy 2 continued
© 2008 Accelrys, Inc. 24
Conclusion
•Easy to set and fast procedure
•Used here for lead hopping, but can also be used for Lead optimization (scaffold + pharmacophore)
•Focused hit list is only the starting point for refinement
•Synthetic feasibility score used to filter the compounds during enumeration phase
© 2008 Accelrys, Inc. 25
Aknowledgements
•Remy Hoffmann
•Konstantin Poptodorov
•Catalyst Development Team– Jon Sutter
– Al Maynard
– Jiabo Li
– Roman Kuchkuda
– Christoph Koelmel
© 2008 Accelrys, Inc. 26
Thank You
•Thank You for attending today’s webinar. If you have any further questions please e-mail me at [email protected]
•You can also contact us using the form on our website: http://accelrys.com/company/contact/
•We will be exhibiting at the following upcoming events:– CHI Protein Kinase Targets (June 23 – 25, Boston, Booth #4)
– CHI Structure Based Design (June 25 – 27, Boston, Booth #7)
– Drug Discovery Technology and Development (August 4 – 7, Boston, Booth
#512)
– ACS Fall 2008 (August 17 – 21, Philadelphia, Booth #211)
•Reminder: The next webinar in this series will be: – Advances in Pharmacophore Modeling and Parallel Screening (Ragi Raghavan)
– July 17, 2008 – 3pm GMT (7am PST) and 10am PST
© 2008 Accelrys, Inc. 27
Discovery Studio 2.1: New Science and Customized Workflows for Drug Discovery Research
• Advances in Protein Modeling and Protein-Protein Interactions in Discovery Studio – Francisco Hernandez-Guzman – June 12, 2008
• Physics Based Protein Ionization and pK Estimation– Francisco Hernandez-Guzman – June 19, 2008
• Towards Increased Accuracy in Computational Drug Discovery with QM/MM– Dipesh Risal – June 26, 2008
• Pharmacophore Guided Fragment-Based Drug Design– Tien Luu – July 10, 2008
• Advances in Pharmacophore Modeling and Parallel Screening– Ragi Raghavan – July 17, 2008
•Workflow Customization with DS Developer Client– Luke Fisher – July 24, 2008
• Tips and Tricks in using Discovery Studio– Al Maynard – July 31, 2008