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BioSimGRID: A GRID Database of Biomolecular Simulations Mark S.P. Sansom

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Presentation on theme: "BioSimGRID: A GRID Database of Biomolecular Simulations Mark S.P. Sansom"— Presentation transcript:

1 BioSimGRID: A GRID Database of Biomolecular Simulations Mark S.P. Sansom

2 Overview u Introduction to biomolecular simulations u u Why? Case study – added value from comparisons u How? Progress towards a prototype of BioSimGRID u The future? Towards computational systems biology

3 MD Simulations: from Structure to Dynamics u Molecular simulations as a tool for protein structure analysis u MD – Newtonian simulation of molecular dynamics using an empirical forcefield u Why? - Proteins move X-ray structure: average structure at 100 K in crystal MD simulations: dynamics at 300 K in water (& membrane) u Challenge: to relate structural dynamics to biological function

4 Molecular Dynamics u Describe the forces on all atoms: bonded (bonds, angles, dihedrals) non-bonded (van der Waals, electrostatics) u Describe the initial atom positions: u Integrate: F = ma (a few million times…) u Result: positions and energies of all atoms during a few nanoseconds u Applications: liquids … peptides … proteins … membranes u Membrane + protein + water = ca. 50,000 atoms u Need for comparative analysis of simulations – GRID data and collaboration u Need for efficient parallelisation – clusters and/or HPC

5 Current Paradigm for MD Simulations u Target selection: literature based; interesting protein/problem u System preparation: highly interactive; slow; idiosyncratic u Simulation: diversity of protocols u Analysis: highly interactive; slow; idiosyncratic u Dissemination: traditional – papers, posters, talks u Archival: ‘archive’ data … and then mislay the tape!

6 Integrating Simulations and Structural Biology of Proteins Novel structure (RCSB) Sequence alignment Biomedically relevant homologue(s) Homology model(s) MD simulations Biomolecular simulation database Comparative analysis Evaluation/refinement of model Biological and pharmacological simulation & modelling e.g. drug discovery bacterial K channel mammalian K channel dynamics in membrane drug docking calculations Interaction site dynamics bioinformatics & structural biology BioSimGRID drug discovery

7 Comparative Simulations: Drug Receptors u Why? – increase significance of results u Sampling – long simulations and multiple simulations u Sampling via biology – exploiting evolution u Biology emerges from comparisons… u e.g. mammalian receptor vs. bacterial binding protein u Rat GluR2 EC fragment u Major receptor in mammalian brains – drug target u MD simulations with/without bound ligands u Analyse inter-domain motions glutamate S1 S2

8 GluR2 – Flexibility & Gating… u Flexibility depends on ligand occupancy & species u Gating mechanism – decrease in flexibility on channel activation u But … incomplete sampling u Need: longer simulations & comparative simulations empty Kainate Glutamate >> > “OFF” “ON” time (ns) RMSD (Å) 0 empty +Kai +Glu 2.0

9 GlnBP – A Bacterial Binding Protein u GlnBP – bacterial 2-domain periplasmic binding protein u Similar fold to mammalian GluR2 u X-ray shows ligand binding induces domain closure u MD shows ligand binding reduces inter-domain motions - cf. GluR2 simulations + Gln empty Gln bound X-ray structures MD Simulation empty Gln bound

10 Main Initial Tasks u To establish a distributed database environment u To develop Grid/Web services using GT3/OGSA infrastructure u To develop software tools for interrogation and data-mining u To develop generic analysis tools u Annotation of simulation data with biological and structural data from other databases York Nottingham Birmingham Oxford RAL Southampton London collaborating groups

11 Oxford –database management system (Bing Wu) –(meta)data curatorship & integration (Kaihsu Tai) Southampton –application programming interface & data retrieval (Muan Hong Ng) –generic analysis tools (Stuart Murdock) Dividing up the Tasks

12 table trajectory: one entry for each trajectory table coordinate: {x, y, z} one entry for each atom in each residue in each frame in each trajectory table atom: one entry for each atom in each residue in each trajectory table residue: one entry for each residue in each trajectory table frame: one entry for each frame in each trajectory dictionary tables metadata tables Database Design: Simplified

13 Database Design: A More Complete Version

14 Simulation Metadata u Difficult to extract from published literature u This is a prototype: a needs analysis with users/depositors must be conducted u Annotation/links to other biological databases essential id molecules author depositors affiliations publications method src_stru ref_stru prog ver hardware num_of_proc timestep num_of_frame ens_type thermostat solvent forcefield ele_stat equ_prot hyd_atom unit_shape … metadata

15 Database Editor & SQL Query Capability

16 BioSimGRID Prototype Target date for prototype: July 2003

17 Deliverables to Date… Database schema Sample database (with test trajectories) Prototype shared between 2 sites Analysis tools – preliminary versions Interface to database for data retrieval Python hosting environment

18 Roadmap u Dec 2002 – project started u July 2003 – (internal) prototype u September 2003 – working prototype (All Hands meeting) u November 2003 – test ‘real world’ applications u December 2003 – multi-site prototype u 2004 – multi-site deposition of data u 2005 – open up to additional groups for deposition/testing

19 Future Directions u HTMD – simulations coupled to structural genomics Diamond light source u Computational system biology – virtual outer membrane HPCx u Multiscale biomolecular simulations – from QM/MM to meso- scale modelling GRID-enabled simulations u Combine all of these with BioSimGRID…

20 Structural Genomics & HTMD u Overall vision – simulation as an integral component of structural genomics u Needs capacity computation – GRID? u MD database (distributed) – BioSimGRID synchrotron MD database novel biology… compute GRID

21 Towards a Virtual Outer Membrane (vOM) u First step towards computational systems biology – a suitable system u Bacterial OMs – 5 or 6 proteins = 90% of protein content u Structures or good homology models of proteins are available u Complex lipid – outer leaflet is lipopolysaccharide (LPS) u Minimum system size ca. 2.5x10 6 atoms; simulation times ca. 50 ns cf. current FhuA – 80,000 atoms & 10 ns – need HPCx

22 Multiscale Biomolecular Simulations u Membrane bound enzymes – major drug targets (cf. ibruprofen, anti-depressants, endocannabinoids) u Complex multi-scale problem: QM/MM; ligand binding; membrane/protein fluctuations; diffusive motion of substrates/drugs in multiple phases u Need for GRID-based integrated simulations

23 Oxford Dr Phil Biggin Dr Carmen Domene Dr Alessandro Grottesi Dr Andrew Hung Dr Daniele Bemporad Dr Shozeb Haider Dr Kaihsu Tai Dr Bing Wu George Patargias Oliver Beckstein Yalini Pathy Pete Bond Jonathan Cuthbertson Sundeep Deol Jeff Campbell Loredana Vaccaro Jennifer Johnston Katherine Cox Robert d’Rozario John Holyoake Andrew Pang BBSRCDTI The Wellcome TrustGSK EC (TMR) OeSC (EPSRC & DTI) EPSRC OSC (JIF) MRC BioSimGRID Leo Caves (York) Simon Cox (Southampton) Jon Essex (Southampton) Paul Jeffreys (Oxford) Charles Laughton (Nottingham) David Moss (Birkbeck) Oliver Smart (Birmingham) Southampton Dr Stuart Murdock Dr Muan Hong Ng Dr Richard Maurer Dr Hans Fangohr Steve Johnston


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