find the needle in the haystack - slovenská akadémia vied · virtual screening department of...
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Tibor Kožár
Clusters, Grids & Molecules:Virtual Screening
Department of BiophysicsInstitute of Experimental Physics
Slovak Academy of SciencesKošice
Slovakia
“FIND THE NEEDLE IN THE HAYSTACK”
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“chemistry universe” estimation:
1060HAYSTACK:
Small Molecules
How big is the haystack?i.e. what’s available for HTS and VTS?
Where’s the lock?
How do clusters/grids help us to be efficient?
“… das Enzym und Glykosid zueinander passen müssen, wie Schloss und Schlüssel, um eine chemische Wirkung
aufeinander ausüben zu können.”
Emil Fischer
LOCK & KEY
What’s the key (ligand)?
QUESTIONS:
Problem of size & complexity
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New Webster’s Dictionary:“drug any substance used in the composition of a medicine”
Drug Discovery & Development Process
DD&D: Expensive, time consuming, with numerous bottlenecks
& low success rate
TARGET
NN 11
TargetIdentifi-cation
LeadIdentifi-cation
LeadOptimi-zation
Pre-clinicalStudies
ClinicalTrials
moleculesmolecules drugdrug
Screening:
mmleadlead
moleculesmolecules
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Where’s the lock?
“Predicting protein druggability”From: Philip J. Hajduk, Jeffrey R. Huth and Christin Tse(DDT • Volume 10, Number 23/24 • December 2005)
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Combinatorial Libraries
Drug Library(WDI ~ 5x104)
Natural ProductsLibrary
Commercial Libraries
~ 105
Small-MoleculeLibraries
How big is the haystack? Library examples:
Publicly AccessibleLibraries e.g. NCI
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2003: 3x106 compounds40 suppliers
2007: 39.8x106 compounds269 suppliers
20.9 x106 unique
How big is the haystack? What can we really purchase?
An Example: CHEMNAVIGATOR(www.chemnavigator.com)
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Available Molecules
N ~ 107
104 possible targets
Predicted Screening Database
~ 1011
Further problems of size & possible solutions
Example for Experimental HTS Laboratory
~ 100 000 ligands per day per 1 proteintarget
“In Silico” SOLUTION:VS (on cluster
and/or grid)
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T
Toxicity profiles of the lead molecules are important to predict the potential side effects of the developed drugs;
AdsorptionDistributionMetabolismExcretion
ADME properties are important in order to understand and predictdrug response effects;
The ideal drug exhibits a balance of potency, selectivity, pharmacokinetics, pharmacodynamics and toxicity profiles;
Appropriate ADME/T properties are major determinants for good leads to become good drugs;
“In Silico” prediction of ADME/T helps to avoid bad drug candidates
ligand+
enzyme-substrate complex
enzyme
DrugsAre more than ligands (binders) to the target
DD&D: More than lock & key
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Rational “IN SILICO” Design Strategies
Structure of the Receptor is not known and no quantitative
information about the biological effect is available
Structure of the Receptor is not known
(Ligand-based Drug Design)KEY
Structure of the Receptor is known
(Receptor-based Drug Design)
CLUSTERS
GRIDSLOCK
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Focused Compounds Sets: 102-104
RECEPTOR BASEDDocking
Combinatorial Docking
Binding modeBinding affinityTransition state
modeling
“In-house” Multiconformational Compounds Libraries: ~2.6 x106
LIGAND BASED2D/3D propertiesDiversity analysis
Drug-likenessADME/T
Pharmacophore searches
QSAR
Integration of “IN SILICO” Strategies
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CADD Resource Integration
Academic Software
(MM, MD, QM)
Core*2 Duo/Quad CLUSTER
GRID
Academic Software
Torque/Maui/MPI
MM & QM
LSF Desktop
(Platform Computing)
GridMP(United Devices)
ingerSoftware for Biomolecular
Modeling
Grid support:
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Schrödinger: a complete suite of software that addresses the challenges in pharmaceutical research:
Prime is an accurate protein structure prediction package;Glide performs accurate, rapid ligand-receptor docking; Liason predicts binding affinity; QSite can be used to study reaction mechanisms within a protein active site; Phase is for ligand-based pharmacophore modeling; QikProp is for ADME properties prediction of drug candidates; LigPrep is a rapid 2D to 3D conversion program that can prepare ligand libraries for further computational analyses;CombiGlide is for focused library design; Epik for accurate enumeration of ligand protonation states in biological conditions;Jaguar is the high-performance ab initio QM application;MacroModel is for molecular modeling;Maestro is the graphical interface.
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The compound is not absorbed when:> 5 H Bond Donors (expressed as sum of OH's NH's)M.W. > 500LogP >5 (MlogP >4.15)> 10 H Bond acceptors (expressed as sum of N's and O's)compound classes that are substrates for biological transporters are
exceptions to the rule.
Basic Filtering based on Lipinski’s rule of 5:
Screens for the quality of the “Haystack”
Ref.: C.A. Lipinski et al, Adv. Drug Del. Rev., 1997, 23, 3-25.
the number of violations of the 95% ranges for known drugs for the descriptors and predicted properties [#stars]
octanol/water partition coefficient [QPlogPo/w]aqueous solubility [QPlogS]Caco-2 cell permeability [BIPCaco & AffyPCaco]MDCK cell permeability [AffyPMDCK]skin permeability [QPlogKp]free energy of solvation in hexadecane [QPlogPC16] free energy of solvation in octanol [QPlogPoct] free energy of solvation in water [QPlogPw]polarizability [QPlogKp]…
More elaborate filtering based on Schrödinger’s QikProp values:
Ref: QikProp, version 3.0, Schrödinger, LLC, New York, NY, 2005.
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Buying the “Haystack”: Quality of Commercial Libraries for HTS
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Examples of DOCKING Algorithms/Programs:
Lead Refinement – Binding Studies
Different protein targetsDifferent classes of synthesized moleculesProtocols to avoid promiscuous inhibitorsAvailability of experimental binding assays
DockAutoDock GoldFlexXGlide…
Differences in the ligand placement algorithm & in scoring functionConsensus scoring
Used in this study in both Cluster & Grid environments
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Carbohydrate-binding studies
Gal – 4:
Gal – 9:
Library of Carbohydrate
Mimetics +Gal – 7:
Gal – 1:
Gal – 3:
PDB coordinates:
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Sequence alignment:
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Glide - Docking of the natural ligand:
Superposed Examples for the “Best” binders:
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Glide Docking Refinement:
Jaguar 6-31G** optimization of selected binders – running time examples:
– Quantum Polarized Ligand Docking (QPLD)protocol
~ 15 min/molecule on Core*2 Quad 2.4 GHz with docking energy improvement for all studied molecules
Before-docking Optimization:
Molecule NAT NDihed Time Procs1 63 13 117 42 55 13 201 43 57 14 412 24 57 13 237 45 76 17 651 4
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• “In Silico” DESIGN AND SCREENING are helpful tools for efficient drug design and development;
• VIRTUAL SCREENING can help to speed-up the DD&D process andsave funds allocated for real HTS;
CONCLUSIONS & OUTLOOKS
• PRICE/PERFORMANCE RATIO of Linux clusters and Grid computingopens new horizons for computerized drug development to be pursued in advance of experimental techniques;
• CADD can guide organic chemistry synthesis efforts (e.g. “In Silico” combinatorial libraries);
• VIRTUAL SCREENING helps to cherry-pick ligands and offers binding mode analysis against different targets;
• technology & knowledge-based integration of resources will result in setting up of VIRTUAL CADD LABORATORIES.
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Experimental Data:• Doc. Peter Kutschy & Prof. Ján Mojžiš – Košice, Slovakia• Prof. Hans-Joachim Gabius & Dr. Sabine Andre – München, Germany
Virtual Laboratory:• Dr. István Komáromi – Debrecen, Hungary
Clustering:• Ing. Ján Astaloš – Košice, Slovakia
Collaborations & Acknowledgements
Funding:• APVV 0514-06• APVV SK-MAD 013-06• VEGA 2/7053/27