Alessandro Pedretti

1. UNIVERSITÁ DEGLI STUDI DI MILANO Facoltà di Scienze del Farmaco Virtual screening and collaborative computing: a new frontier in drug discovery Alessandro Pedretti XI Congreso Venezolano de Química Caracas, June 18, 2013

2. Overview • Collaborative computing applied in a computational chemistry laboratory. • WarpEngine paradigm to distribute the calculations in the local network. • Virtual screening setup to choose the best software and parameters. • Two WarpEngine applications to evaluate its performances. • Short WarpEngine practical session.

3. Computational power What is the collaborative computing Main definition: The “collaborative computing” term includes technologies and informatics resources based on a network communication system that allows the documents and projects to be shared between users. All activities are managed by a variety of devices such as desktops, laptops, tablets and smartphones. In a computational chemistry laboratory: The daily activity of a computational chemist requires not only to share information and data between the users, but also hardware resources.

4. Typical scenario in a lab Internet Servers PCs Firewall • Several PCs with heterogeneous hardware / OSs. • Very high computational power “fragmented” on the local network. • Hard possibility to use all computational power to run a single complex calculation. Network devices Ethernet infrastructure 100-1000 Mbit/s

5. Main features • Parallel computing without the grid paradigm. • Client/server architecture with hot-plug capabilities. • Possibility to perform calculations with different pieces of software without changing the main code. • Expandable by scripting languages. • High-level database interface integrated in the main code supporting the most common SQL database engines (Access, MySQL, SQLite, SQL Server, etc). • Easy configuration by graphic interface. • High performances and security.

6. Property calculation Molecule editing MM / MD calculations Surface mapping Trajectory analysis File format conversion Database engine Plug-in expandability Graphic interface Scripting languages What we need … … to develop WarpEngine: • High-level database interface. • Fast customizable Web server. • Script engine. • Graphic environment.

7. Server scheme Project manager Job manager Database engine VEGA ZZ core Client manager UDP server HTTP server Main program Optional encrypted tunnel provided by WarpGate IP filter PowerNet plug-in To clients TCP/IP, HTTP, broadcast

8. Client scheme PowerNet plug-in Main program Project manager Multithreaded worker VEGA ZZ core UDP client HTTP client To the server TCP/IP, HTTP, broadcast

9. Application fields WarpEngine is easy expandable by scripting languages, hence it’s possible to perform some calculation types: • Semi-empirical calculations • Ab-initio calculations • Rescore of docking poses • Multiple molecular mechanics calculations • Virtual screening

10. Drug discovery and virtual screening Today, the virtual screening is a very common approach to identify hit compounds from large libraries of molecules in the drug discovery process. It can be classified in: • Ligand-basedThe 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 library. • Structure-basedIt involves molecular docking calculations between each molecule to be tested 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.

11. Database Virtual screening Hit compounds Dis-advantages of the virtual screening • Advantages: • Fast (but it depends by the library size). • Possibility to optimize the in-home resources. • Cheap. • Disadvantages: • False positive rate. • Limited chemical space (ligand-based). • Impossibility to discriminate the intrinsic activity (structure-based). • Necessity to confirm the results by experimental assays.

12. Choice of docking software for virtual screening For test purposes, we choose three well known and free docking software: • AutoDock 4.2http://autodock.scripps.edu • AutoDock Vinahttp://vina.scripps.edu • PLANTShttp://www.tcd.uni-konstanz.de/research/plants.php and the acetylcholine esterase (AchE) ligand database from Directory of Useful Decoys (DUD, http://dud.docking.org), containing: • 107 true active molecules • 3892 true inactive molecules All these ligands were docked into AchE crystal structure downloaded from PDB (1EVE) in order to evaluate the predictive power and the performances of each docking software.

13. Hit rate evaluation The hit rate is the measure of the probability to find active ligands into a set of molecules and it can be calculated by the following equation: Considering the whole dataset: The random hit rate is the probability to find an active compound by random choices. In other words, every 100 randomly selected ligands from the data set, there are 2.68 active compounds.

14. Evaluation of virtual screening performances • The performances of each virtual screening software are evaluated by: • sorting the results by the docking score; • calculating the hit rate in a set of top ranked molecules (1%, 2% and 5% of the total data set); • calculating the enrichment factor: Every virtual screening calculation must have at least EF > 1.0 and to be considered enough efficient EF > 2.0. It means that the screening must have performances at least 2-fold better than the random.

15. AutoDock and Vina results • two AutoDock runs were performed: screening and full docking parameters. • one Vina calculation with exhaustiveness set to 7; • both software use a similar scoring function based on Amber force field.

16. PLANTS results • The PLANTS enrichment performances were evaluated by considering: • all three scoring functions (ChemPLP, PLP and PLP95); • two degrees of exhaustiveness (Speed1 and Speed2); • flexible side chains of aminoacids (PLP and Speed2 only).

17. 37 cores 42 Gb ram > 3 Tb storage Hardware for the test • 1 PC configured as client and server: • Quad-core • 9 PC configured as client: • 1 six-core • 7 quad-core • 1 dual-core • 1 single-core • Operating systems: • 6 Windows 7 Pro x64 • 3 Windows 7 Pro • 1 Windows XP Pro • Network connection: • Ethernet 100 Mbs

18. Software & data for the test • APBS – Adaptive Poisson-Boltzmann Solver • Calculation of solvation energy. • PLANTS – Protein-Ligand ANT system • Structure-based virtual screening. Both programs are single-threaded • Database of drugs in .mdb format • 174.398 molecules, average MW 353,70. • Human M2 muscarinic receptor • PDB ID: 3UON.

19. APBS – Solvation energy calculation. • 174.398 molecules, two APBS calculation for each molecule (reference and solvated state). • Time required by a single thread calculation: 13 days 5 hours • Time required by WarpEngine: 8 hours 36 minutes • WarpEngine speed: 339,10jobs / min. • PLANTS – Virtual screening. • 174.398 molecules, M2 target, PLP, speed2. • Time required by a single thread calculation: 36 days 22 hours • Time required by WarpEngine: 1 day 0 hour 1 minute • WarpEngine speed: 121,00jobs / min. Real case tests

20. Test Drive

21. Graphic interface

22. Graphic interface

23. Conclusions • The collaborative computing not only can help the users to work together on the same project, but also can be extended efficiently to share the computational resources that remain often unused. • WarpEngine can collect the unused computational power and convey it to carry out large calculations, such as a virtual screening, without interfering with the normal user activities. • The setup phase of a virtual screening plays a pivotal role to obtain good performances in terms of results and calculation speed.

24. Acknowledgements • Giulio Vistoli • Matteo Lo Monte • Angelica Mazzolari www.vegazz.net