1. Alessandro Pedretti Virtual screening and collaborative computing: a new frontier in drug discovery UNIVERSITÁ DEGLI STUDI DI MILANO Facoltà di Scienze del Farmaco 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. 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. What is the collaborative computing 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 Firewall Servers PCs Network devices Ethernet infrastructure 100-1000 Mbit/s 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.

5. 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. Main features

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

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

8. Project manager - server Parser module Server script Script compiler XML project Server module Event handler Actions Initialization Client connection Data dispatch Data receive Resource cleanup Validation Job managerClient manager Tcc – HyperDrive powered

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

10. Project manager - client XML parser Client script Script compiler XML project Client module Multithreaded worker Actions Download / check of files HTTP client Tcc – HyperDrive powered Project manager To the server Job end check GET / POST methods

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

12. 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: Drug discovery and virtual screening Structure-based It 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. 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 library.

13. 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. Database Virtual screening Hit compounds

14. For test purposes, we choose three well known and free docking software: Choice of docking software for virtual screening AutoDock 4.2 http://autodock.scripps.edu AutoDock Vina http://vina.scripps.edu PLANTS http://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.

15. 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: Hit rate evaluation 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.

16. 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.

17. 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.

18. 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).

19. 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 37 cores 42 Gb ram > 3 Tb storage Operating systems: 6 Windows 7 Pro x64 3 Windows 7 Pro 1 Windows XP Pro Network connection: Ethernet 100 Mbs

20. Communication stress test: delivery of empty jobs to the clients and receive of the result from them. 79.651,78 jobs / min. Preliminary performance test Database stress test: extraction (by SQL query), decompression and delivery of molecules to the clients and answer. 41.115,00 molecules / min. Apache Bench 2.0.41 100 requests with concurrency level of 5. 3.205,13 pages / sec. 1,560 ms / request Microsoft IIS 6.0 1.066,67 pages / sec. 4,688 ms / request

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

22. 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: 339,10 jobs / min. Real case tests 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: 121,00 jobs / min.

23. Test Drive

24. Graphic interface

26. 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.

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