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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”
“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
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
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)
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
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)
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)
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
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
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
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:
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.
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.
Buying the “Haystack”: Quality of Commercial Libraries for HTS
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
Carbohydrate-binding studies
Gal – 4:
Gal – 9:
Library of Carbohydrate
Mimetics +Gal – 7:
Gal – 1:
Gal – 3:
PDB coordinates:
Sequence alignment:
Glide - Docking of the natural ligand:
Superposed Examples for the “Best” binders:
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
• “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.
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
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