1. Università degli Studi di Milano Dipartimento di Scienze Farmaceutiche “Pietro Pratesi” Alessandro Pedretti GriDock: An MPI-based software for virtual screening in drug discovery

2. 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 molecules Database filter Hit compounds Virtual screening Set of molecules Experimental assay Hit compounds High-throughput screening Database of molecules Database filter Hit compounds Virtual screening

3. 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.

4. 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-based It 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.

5. Molecular docking Ligand Receptor + Ligand – receptor complex Docking software The complex quality is evaluated by the score.

6. 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 4 + VEGA 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.

7. How GriDock works Molecular docking. Score calculation. Database of molecules VEGA Ligand – receptor complexes AutoDock 4 Score analysis Output files Calculation of the molecular properties. Input file generation (PDBQT). Receptor coord. + maps Receptor coord. + maps

8. How VEGA works with GriDock Database of molecules Hydrogens add Potential attribution Property calculation Calculation of charges Search of flexible torsions Conversion to PDBQT to AutoDock 4 AMBER force field Gasteiger-Marsili method

9. GriDock multi-threaded version GriDock main thread VEGA AutoDock 4 Thread 1 VEGA AutoDock 4 Thread 2 VEGA AutoDock 4 Thread n Database Receptor Output files* Thread 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

10. GriDock MPI version Output files GriDock MPI master node Database Receptor Database Receptor Database Receptor VEGA AutoDock 4 Node 2 VEGA AutoDock 4 Node n Node loop MPI VEGA AutoDock 4 Node 1

11. 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).

12. 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.mRNA ProteaseTranslation Early protein RdRp prot. Other proteins Protease (-)RNA Replicative complex Structural proteins Virions Infected cell Translation

13. The RdRp model The crystal structure doesn’t exist and a homology modeling procedure was performed: Rough 3D structure Primary structure SwissProt Q2XP15 Folding prediction Fugue To the refinement workflow To the refinement workflow VEGA ZZ + NAMD RdRp model

14. Model refinement Missing residues Side chains add Hydrogens add Energy minimization Model ready for the screening Model ready for the screening Rough model VEGA ZZ + NAMD 30.000 steps conjugate gradients Structure check Ramachandran plot

15. 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 site RdRp structure Potential attribution Calculation of charges Apolar hydrogens remove PDBQT file AutoGrid 4 run Grid map files Script file: AutoDock/Receptor.c

16. 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

17. 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.

18. 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).

19. 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.

20. Acknowledgments www.ddl.unimi.itwww.vegazz.net Giulio Vistoli Cristina Marconi Alessandro Lombardo Santo Motta Francesco Pappalardo Emilio Mastriani