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