Università degli Studi di MilanoDipartimento di Scienze Farmaceutiche “Pietro Pratesi”

Alessandro Pedretti

GriDock: An MPI-based softwarefor virtual screening in drug discovery

What is the virtual screening ?

• The virtual screening (VS) is a computational approach that can be used in drug discovery processes to find new hit compounds.

• It can be compared to the High-throughput screening (HTS) that is a true experimental approach.

Database of moleculesDatabase of molecules

Database filterDatabase filter

Hit compoundsHit compounds

Virtual screening

Set of moleculesSet of molecules

Experimental assayExperimental assay

Hit compoundsHit compounds

High-throughput screening

Database of moleculesDatabase of molecules

Database filterDatabase filter

Hit compoundsHit compounds

Virtual screening

The database of molecules

• The database must contain molecules that are available in the real world or synthetically accessible in easy way.

• The pharmaceutical industries have got databases built trough the years from researches in some different fields.

• Some databases are publicly available and provided by chemical compound resellers (AKos, Asinex, TimTec, etc) or by non-profit institutions (Kyoto University, NCI, University of Padua, etc).

• The database must contain a large number of molecules in order to do an exhaustive exploration of the chemical space.

The database filter

• The database filter does the virtual test to check if a molecule could be bioactive or not.

• The kind of filter allows to classify the virtual screening approaches in:

Ligand-based

The 3D structure of the biological target is unknown and a set of geometric rules and/or physical-chemical properties (pharmacophore model) obtained by QSAR studies are used to screen the database.

Structure-basedIt involves molecular docking calculations between each molecule to test and the biological target (usually a protein). To evaluate the affinity a scoring function is applied. The 3D structure of the target must be known.

Molecular docking

Ligand Receptor

+

Ligand – receptor complex

Docking software

• The complex quality is evaluated by the score.

GriDock – Main features

• GriDock is a software developed to perform structure-based virtual screenings.

• It’s a front-end to the well known AutoDock software, developed by D.S. Goodsel and A.J. Olson.

• It uses VEGA command-line software to perform file format conversion, database extraction and molecular property calculations.

AutoDock 4AutoDock 4 + VEGAVEGA

GriDock

Virtual screening

• Highly portable C++ code (Linux 32 and 64 bit, Windows 32 and 64 bit).

• It can take full advantages of multi-CPUs/cores systems and GRID-based architectures through its parallel design.

How GriDock works

• Molecular docking.• Score calculation.

Database of moleculesDatabase of molecules

VEGAVEGA

Ligand – receptor complexes

Ligand – receptor complexes

AutoDock 4AutoDock 4

Score analysisScore analysis

Output filesOutput files

• Calculation of the molecular properties.

• Input file generation (PDBQT).

Receptor coord.+ maps

Receptor coord.+ maps

How VEGA works with GriDock

Database of moleculesDatabase of molecules Hydrogens addHydrogens add

Potential attributionPotential attribution

Property calculationProperty calculation

Calculation of chargesCalculation of charges

Search of flexible torsions

Search of flexible torsions

Conversion to PDBQTConversion to PDBQT to AutoDock 4to AutoDock 4

AMBER force field

Gasteiger-Marsili method

GriDock multi-threaded version

GriDock main thread GriDock main thread

VEGAVEGA

AutoDock 4AutoDock 4

Thread 1Thread 1

VEGAVEGA

AutoDock 4AutoDock 4

Thread 2Thread 2

VEGAVEGA

AutoDock 4AutoDock 4

Thread nThread n

DatabaseDatabase ReceptorReceptor

Output files*Output files*

Thr

ead

loop

Symmetric multiprocessing (SMP) provided by pthread library or Windows APIs

• Log file (gridock_DATE.log).• CSV file containing the list of complexes ranked by docking score.• Zip file containing the output complexes generated by AutoDock 4.

Mutex controlled access

GriDock MPI version

Output filesOutput files

GriDock MPI master node GriDock MPI master node

GriDock MPI master node GriDock MPI master node

DatabaseDatabase ReceptorReceptor DatabaseDatabase ReceptorReceptor DatabaseDatabase ReceptorReceptor

VEGAVEGA

AutoDock 4AutoDock 4

Node 2Node 2

VEGAVEGA

AutoDock 4AutoDock 4

Node nNode n

Nod

e lo

op MPIVEGAVEGA

AutoDock 4AutoDock 4

Node 1Node 1

GriDock input requirements

To perform a virtual screening with GriDock, you need:

• The 3D structure of the biological target.

- Protein Data Bank (http://www.rcsb.org).

- Homology modeling.

• The 3D maps of the active site generated by AutoGrid 4

- AutoDockTools / MGLTools (http://mgltools.scripps.edu).

- VEGA ZZ (http://www.vegazz.net).

• One or more databases of 3D structures in SDF or Zip format.

• Ligand.Info: Small-Molecule Meta-Database (http://ligand.info).

• MMsINC (http://mms.dsfarm.unipd.it/MMsINC.html).

• ZINC (http://zinc.docking.org).

The Citrus tristeza virus case

• The Citrus tristeza virus (CTV) is a positive single stranded RNA virus that causes a serious pathology of the citruses.

• Any treatment to save the infected plants is unknown.

• A possible therapeutic target could be the RNA-dependent-RNA polymerase (RdRp) involved in the virus replication.

ssRNA (+) – 5’ prot. mRNAProtease Translation

Early protein

RdRp

prot.Other proteins Protease

(-)RNA

Replicativecomplex

Structuralproteins

Virions

Infected cell

Translation

The RdRp model

The crystal structure doesn’t exist and a homology modeling procedure was performed:

Rough 3D structureRough 3D structure

Primary structurePrimary structureSwissProtQ2XP15

Folding predictionFolding predictionFugue

To the refinementworkflow

To the refinementworkflow

VEGA ZZ+

NAMDRdRp model

Model refinement

Missing residuesMissing residues

Side chains addSide chains add

Hydrogens addHydrogens add

Energy minimizationEnergy minimization

Model readyfor the screening

Model readyfor the screening

Rough modelRough model

VEGA ZZ+

NAMD

30.000 stepsconjugate gradients

Structure checkStructure check

Ramachandran plot

Calculation of the grid maps

AutoDock requires pre-calculated grid maps to evaluate the total interaction energy between the ligand and the target macromolecule.

To do it, we used the script included in the VEGA ZZ package:

Mapping the active siteMapping the active site

RdRp structureRdRp structure Potential attributionPotential attribution

Calculation of chargesCalculation of charges

Apolar hydrogens remove

Apolar hydrogens remove PDBQT filePDBQT file

AutoGrid 4 runAutoGrid 4 run Grid map filesGrid map filesScript file:AutoDock/Receptor.c

Considered databases

All test databases in SDF format were downloaded from http://ligand.info:

• ChemBank

• ChemPDB

• KEGG Ligand

• Anti-HIV NCI

• Drug/likeness NCI

• Not annotate NCI

• AKos GmbH

• Asinex Ltd.

The total number of docked ligands is: ~1,000,000

Test system

Tyan Transport VX50

• # 8 AMD Opteron 875 dual core CPUs @ 2.4 GHz.

• 8 Gb Ram.

• 72 + 150 Gb SATA hard disk.

• Linux 64 bit (CentOS 4).

40,000 ligands/day.

Preliminary results

The top ranked ligands contains in their structure one or more sulfurs.

Sulfonic acid derivatives.These compounds are know to be potent inhibitors of the HIV reverse transcriptase. Some of them are naphtalen polysulfonic acids developed as Anti-HIV (Anti-HIV NCI database).

Conclusions

• We developed a new parallel structure-based virtual screening software able to run on both multi-CPU and GRID systems.

• The complete model of the RNA-dependent-RNA-polymerase of Citrus Tristeza Virus was obtained to perform a virtual screening study.

• Screening ~1,000,000 ligands, potential RdRp inhibitors were found.

• These molecules contains sulfur atoms and, more in details, multiple sulfonic acid moieties.

• Some of them are included in the Anti-HIV class.

• To complete the study, the activity of the found molecules must be experimentally confirmed by biological assays.

Acknowledgments

www.ddl.unimi.itwww.vegazz.net

• Giulio Vistoli

• Cristina Marconi

• Alessandro Lombardo

• Santo Motta

• Francesco Pappalardo

• Emilio Mastriani