Alessandro Pedretti
Virtual screening and collaborative computing:a new frontier in drug discovery
UNIVERSITÁ DEGLI STUDI DI MILANOFacoltà di Scienze del Farmaco
XI Congreso Venezolano de QuímicaCaracas, June 18, 2013
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.
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.
Typical scenario in a lab
Internet
Firewall
Servers
PCs
Networkdevices
Ethernet infrastructure100-1000 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.
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
… 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
Server scheme
UDP server HTTP server
Client manager
Project manager
Jobmanager
VEGA ZZcore
Databaseengine
IP filterPowerNetplug-in
Main program
To clientsTCP/IP, HTTP,broadcast
Optional encrypted tunnelprovided by WarpGate
Client scheme
UDP client HTTP client
Project manager
Multithreadedworker
VEGA ZZcore
PowerNet plug-in Main program
To the serverTCP/IP, HTTP, broadcast
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
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-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.
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.
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
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.
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
100._
_moleculesAll
moleculesActiveHR
Considering the whole dataset:
%68.2100.3999107
RandomHR
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.
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:
Random
TopNTopN HR
HREF %
%
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.
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.
Enrichment factor Software Exhaustiveness Flexible
chains 1% 2% 5% Single
CPU time (hours)
AutoDock Screening No 4,67 3,27 1,68 44,96 AutoDock Full docking No 7,47 4,20 3,55 1344,00 Vina 7 No 1,87 2,34 2,06 342,00
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).
Enrichment factor Score Exhaustiveness Flexible
chains 1% 2% 5% Single
CPU time (hours)
ChemPLP Speed1 No 19,62 11,21 5,98 97,64 ChemPLP Speed2 No 18,69 10,74 5,23 66,64 PLP Speed1 No 19,62 10,28 5,23 44,08 PLP Speed2 No 19,62 10,28 5,23 30,28 PLP Speed2 Yes 20,56 10,28 5,05 350,80 PLP95 Speed1 No 17,75 10,28 4,86 37,04 PLP95 Speed2 No 16,82 9,81 4,48 34,44
Hardware for the test
1 PC configured as client and server:Quad-core
9 PC configured as client:1 six-core7 quad-core1 dual-core1 single-core
37 cores42 Gb ram
> 3 Tb storage
Operating systems:6 Windows 7 Pro x643 Windows 7 Pro1 Windows XP Pro
Network connection:Ethernet 100 Mbs
Software & data for the test
APBS – Adaptive Poisson-Boltzmann SolverCalculation of solvation energy.
PLANTS – Protein-Ligand ANT systemStructure-based virtual screening.
Database of drugs in .mdb format174.398 molecules, average MW 353,70.
Human M2 muscarinic receptorPDB ID: 3UON.
Both programsare single-threaded
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.
Test Drive
Graphic interface
Graphic interface
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.
Acknowledgements
www.vegazz.net
Giulio Vistoli
Matteo Lo Monte
Angelica Mazzolari
