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Università degli Studi di Milano Dipartimento di Scienze Farmaceutiche “Pietro Pratesi”. GriDock: An MPI-based software for virtual screening in drug discovery. Alessandro Pedretti. Database of molecules. Set of molecules. Database of molecules. Database filter. Experimental assay.

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Università degli Studi di Milano Dipartimento di Scienze Farmaceutiche “Pietro Pratesi”

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Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

Università degli Studi di Milano

Dipartimento di Scienze Farmaceutiche “Pietro Pratesi”

GriDock: An MPI-based software

for virtual screening in drug discovery

Alessandro Pedretti


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

Database of molecules

Set of molecules

Database of molecules

Database filter

Experimental assay

Database filter

Hit compounds

Hit compounds

Hit compounds

Virtual screening

Virtual screening

High-throughput screening

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.


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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.


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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.


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

+

Docking software

Receptor

  • The complex quality is evaluated by the score.

Ligand – receptor complex

Molecular docking

Ligand


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

+

Virtual screening

AutoDock 4

VEGA

GriDock

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.

  • 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.


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

Database of molecules

  • Calculation of the molecular properties.

  • Input file generation (PDBQT).

VEGA

Receptor coord.

+ maps

  • Molecular docking.

  • Score calculation.

AutoDock 4

Ligand – receptor complexes

Score analysis

Output files

How GriDock works


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

Database of molecules

Hydrogens add

Conversion to PDBQT

to AutoDock 4

How VEGA works with GriDock

Property calculation

Potential attribution

AMBER force field

Gasteiger-Marsili method

Calculation of charges

Search of flexible torsions


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

Thread 1

VEGA

AutoDock 4

GriDock multi-threaded version

Database

Receptor

GriDock main thread

Thread 2

Thread n

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

VEGA

VEGA

Thread loop

AutoDock 4

AutoDock 4

Mutex controlled access

Output files*

  • 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.


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

GriDock MPI master node

Database

Receptor

Database

Receptor

Database

Receptor

Node 1

Node 2

Node n

MPI

Node loop

VEGA

VEGA

VEGA

AutoDock 4

AutoDock 4

AutoDock 4

GriDock MPI master node

Output files

GriDock MPI version


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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).


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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.

Infected cell

Protease

Translation

ssRNA (+) – 5’ prot.

mRNA

Early protein

Replicative

complex

prot.

Protease

Other proteins

Translation

RdRp

Structural

proteins

Virions

(-)RNA


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

SwissProt

Q2XP15

Primary structure

Folding prediction

Fugue

VEGA ZZ

+

NAMD

To the refinement

workflow

RdRp model

The RdRp model

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

Rough 3D structure


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

Ramachandran plot

Model refinement

Rough model

VEGA ZZ

+

NAMD

Missing residues

Side chains add

Hydrogens add

30.000 steps

conjugate gradients

Energy minimization

Structure check

Model ready

for the screening


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

RdRp structure

Potential attribution

Calculation of charges

Apolar hydrogens remove

PDBQT file

Mapping the active site

Script file:

AutoDock/Receptor.c

AutoGrid 4 run

Grid map files

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:


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

40,000 ligands/day.

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).


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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).


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

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.


Universit degli studi di milano dipartimento di scienze farmaceutiche pietro pratesi

www.vegazz.net

www.ddl.unimi.it

Acknowledgments

  • Giulio Vistoli

  • Cristina Marconi

  • Alessandro Lombardo

  • Santo Motta

  • Francesco Pappalardo

  • Emilio Mastriani


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