G. Terstyanszky, T. Kukla, T. Kiss, S. Winter, J.: Centre for Parallel Computing School of Electronics and Computer Science, University of Westminster London, United Kingdom J. Kovacs, Z. Farkas, P. Kacsuk MTA-SZTAKI Budapest, Hungary, Combining Desktop and Service Grids to Support e-Scientists to Run Simulations European Desktop Grid Infrastructure = EDGI

2 2 Binding pocket Sugar (ligand) Protein (receptor) Docking and Molecular Dynamics Simulations

3 In-vitro (or wet lab) research It investigates components of an organism that have been isolated from their usual biological surroundings in order to permit a more detailed and convenient analysis than can be done with whole organisms. In-silico simulation It simulates components of an organism for example docking of ligands and proteins downloading them from public libraries, binding them and analysing the properties of the compound molecules. Aims of in-silico docking simulation Understanding how pathogens bind to cell surface proteins can lead to the design of carbohydrate-based drugs and diagnostic and therapeutic agents Highlighting potential novel inhibitors and drugs for in vitro and on-chip testing.

4 Advantages of in-silico methods: Reduced time and cost In vitro experiments are expensive Better focusing wet laboratory resources: Better planning of experiments by selecting best molecules to investigate Increased number of molecules screened Problems of in-silico experiments: Time consuming Weeks or months on a single computer Simulation tools are too complex for an average bio-scientist Linux command line interfaces Bio-molecular simulation tools are not widely tested and validated Are the results really useful and accurate? Docking and Molecular Dynamics Simulations

5 In-silico Simulation in Service Grids PDB file 1 (Receptor) PDB file 2 (Ligand) Energy Minimization (Gromacs) Validate (Molprobity) Check (Molprobity) Perform docking (AutoDock) Molecular Dynamics (Gromacs) Phase 1 Phase 2 Phase 3 Phase 4

6 phase 1 – pre-processing of protein phase 2 – pre-processing of sugar phase 3 – docking phase 4 – molecular dynamics simulation Executed on 5 different sites of the UK NGS Parameter sweeps in phase 3 and 4 MPI in phase 4 In-silico Simulation in Service Grids

EDGI Infrastructure

Usage Scenario in Desktop – Service Grids EDGI Portal SG Broker Compute Element(n) SG->DG Bridge Desktop Grid Server EDGI Application Repository Service Grid Desktop Grid Compute Element(2) Compute Element(1) Worker Node(m) Worker Node(2) Worker Node(1) search, select & download application’s implementation submit application’s implementation retrieve & deploy impl e-scientist DG admin query implementation

EDGI Application Repository: Actors, Entities and Operations user /group man platform man. upload appl. mark appl valid browse/ search appl. download appl. E-scientistsxx Application Developers xxxx Application Validators xx Desktop Grid Administrators xx Repository Administrators xxxxxx with registration without registration Repository Entities Application represents an application which implementations can be executed on the EDGI infrastructure. It describes the inputs and outputs and explains what the application does. Implementation is an application implementation. It contains references (via e.g. URLs) to all the files and data necessary to run the application on a given platform and metadata. Platform describes desktop Grid and/or service Grid environment where the implementation can be executed. Configuration contains the implementation files required to run the applications. Repository Actors and Operations

10 Main menu: select users & groups + applications (implementations) + platforms + validation pages Action menu: create/delete entities + upload/download applications & implementations add/edit/remove metadata Search: users & groups + applications & implementations + platforms EDGI Application Repository: User Interface

11 EDGI Application Repository: Application Metadata

12 EDGI Application Repository: Implementation Metadata

EDGI Application Repository in the EDGI Infrastructure EDGI Portal SG Broker Compute Element(n) SG->DG Bridge Desktop Grid Server EDGI Application Repository Service Grid Desktop Grid Compute Element(2) Compute Element(1) Worker Node(m) Worker Node(2) Worker Node(1) search, select & download application’s implementation submit application’s implementation retrieve & deploy impl e-scientist DG admin query implementation

DG clients: New Cavendish St 576 nodes Marylebone Campus 559 nodes Regent Street 395 nodes Wells Street 31 nodes Little Titchfield St 66 nodes Harrow Campus 254 nodes Lifecycle of a DG node: 1.PCs basically used by students/staff 2.If unused, switch to Desktop Grid mode 3.No more work from DG server -> shutdown (green solution) University of Westminster Local Desktop Grid

15 gpf file pdb file (ligand) pdb file (receptor) prepare_ligand4. py prepare_receptor 4.py pdbqt file AUTOGRID AUTODO CK map files Bio Scientist dpf file AUTODO CK dlg files SCRIPT1 SCRIPT2 best dlg files pdb file In Silico Docking User Scenario Research objectives: Constructing a library of tens of thousands of small molecule candidates available in databases (eg. DrugBank) and preparing PDBQT files To be screened against known targets using Autodock Vina Small molecule library will be made available to other researchers Promising candidates can be validated in vitro

16 In-Silico Docking Workflow receptor.pdb ligand.pdb Autogrid executables, Scripts (uploaded by the developer, don’t change it) gpf descriptor file dpf descriptor file output pdb file The Generator job creates specified numbered of AutoDock jobs. The AutoGrid job creates pdbqt files from the pdb files, runs the autogrid application and generates the map files. Zips them into an archive file. This archive will be the input of all AutoDock jobs. The AutoDock jobs are running on the Desktop Grid. As output they provide dlg files. The Collector job collects the dlg files. Takes the best results and concatenates them into a pdb file. dlg files number of work units

17 Free access to pre-deployed molecular docking “primitive” scenarios running on the EDGI infrastructure  Random blind docking and virtual screening DG versions of applications are coming from the EDGI AR Docking workflows are executed on the Desktop Grid EDGI Docking Portal

18 Docking the Protozoan Neuraminidase

19 Docking the Protozoan Neuraminidase

20 Computer Scientists They created the combined desktop grid and service grid infrastructure where e-scientists can run their application on They created the combined desktop grid and service grid infrastructure where e-scientists can run their application on The EDGI Application Repository and Portal is able to support application developers, e-scientists and application validators The EDGI Application Repository and Portal is able to support application developers, e-scientists and application validators Bio Scientists The EDGI infrastructure can provide potential for unlimited computational power to the biologists The EDGI infrastructure can provide potential for unlimited computational power to the biologists They can offer access to methodology (application porting) and tools (portal and repository) They can offer access to methodology (application porting) and tools (portal and repository) They have a library of small molecules available for screening and access to Chip based technology They have a library of small molecules available for screening and access to Chip based technology Conclusions