pharmacophoreguided fragment-based drug...

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


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


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


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


The Target

•Example: HSV Thymidine Kinase (1kim.pdb)


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


Describing the Interactions

•3HBD, 1HBA, 1Hydrophobe


Fragment Queries

•Two options explored– Difference in the resulting molecules expected

– The link atom queries need to be formulated differently


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


Fragment Queries

Strategy 1 Strategy 2

•Link1 and Link2

Shape parameters

can be tuned


Fragment Database

•Commercial, corporate

•Customised

– 120000 compounds

– 20000 fragments

– Conversion into a multi-conf database


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.


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.


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


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


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


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


Hit Refinement – Results: Strategy 1 continued

•Filtering CDOCKER poses with the pharmacophore

•24 compounds


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 2

•DeoxyThymidine CDOCKER energy: 25.05 – 21.96

•For 180 compounds, best pose CDOCKER energy > best pose CDOCKER energy of DeoxyThymidine



•Filtering CDOCKER poses with the pharmacophore

•131 compounds


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



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


Aknowledgements

•Remy Hoffmann

•Konstantin Poptodorov

•Catalyst Development Team– Jon Sutter

– Al Maynard

– Jiabo Li

– Roman Kuchkuda

– Christoph Koelmel


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


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

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