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Overview
Introduction to biomolecular simulations
www.biosimgrid.org
Why?
Case study – added value from comparisons
How?
Progress towards a prototype of BioSimGRID
The future?
Towards computational systems biology
MD Simulations: from Structure to Dynamics
Molecular simulations as a tool for protein structure analysis
MD – Newtonian simulation of molecular dynamics using an empirical forcefield
Why? - Proteins move
X-ray structure: average structure at 100 K in crystal
MD simulations: dynamics at 300 K in water (& membrane)
Challenge: to relate structural dynamics to biological function
Molecular Dynamics
Describe the forces on all atoms:
bonded (bonds, angles, dihedrals)non-bonded (van der Waals, electrostatics)
Describe the initial atom positions: Integrate: F = ma (a few million times…) Result: positions and energies of all
atoms during a few nanoseconds Applications: liquids … peptides …
proteins … membranes Membrane + protein + water = ca.
50,000 atoms
Need for comparative analysis of simulations – GRID data and collaboration
Need for efficient parallelisation – clusters and/or HPC
Current Paradigm for MD Simulations
Target selection: literature based; interesting protein/problem
System preparation: highly interactive; slow; idiosyncratic
Simulation: diversity of protocols
Analysis: highly interactive; slow; idiosyncratic
Dissemination: traditional – papers, posters, talks
Archival: ‘archive’ data … and then mislay the tape!
Integrating Simulations and Structural Biology of Proteins
Novel structure(RCSB)
Sequence alignmentBiomedically relevant homologue(s)
Homology model(s)
MD simulationsBiomolecular simulation database
Comparative analysis
Evaluation/refinement of model
Biological and pharmacological simulation & modellinge.g. drug discovery
bacterial K channel
mammalian K channel
dynamics in membrane
drug docking calculations
Interaction site dynamics
bioi
nfo
rmat
ics
& s
tru
ctur
al
biol
ogy
Bio
Sim
GR
IDdr
ug
disc
over
y
Comparative Simulations: Drug Receptors
Why? – increase significance of results
Sampling – long simulations and multiple simulations
Sampling via biology – exploiting evolution
Biology emerges from comparisons…
e.g. mammalian receptor vs. bacterial binding protein
Rat GluR2 EC fragment Major receptor in mammalian
brains – drug target MD simulations with/without
bound ligands Analyse inter-domain motions
glutamate
S1
S2
GluR2 – Flexibility & Gating…
Flexibility depends on ligand occupancy & species
Gating mechanism – decrease in flexibility on channel activation
But … incomplete sampling Need: longer simulations &
comparative simulations
empty Kainate Glutamate
>> >
“OFF” “ON”
0 1.0 1.50.5
1
2
3
4
time (ns)
RM
SD
(Å
)
0
empty
+Kai
+Glu
2.0
GlnBP – A Bacterial Binding Protein
GlnBP – bacterial 2-domain periplasmic binding protein
Similar fold to mammalian GluR2
X-ray shows ligand binding induces domain closure
MD shows ligand binding reduces inter-domain motions - cf. GluR2 simulations
+ Gln
empty Gln bound
X-ray structuresMD Simulation
empty
Gln bound
Main Initial Tasks
To establish a distributed database environment
To develop Grid/Web services using GT3/OGSA
infrastructure
To develop software tools for interrogation and
data-mining
To develop generic analysis tools
Annotation of simulation data with biological and
structural data from other databases
York
Nottingham
Birmingham
OxfordRAL
Southampton
London
collaborating groups
• 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
table trajectory:one entry foreach trajectory
table coordinate: {x, y, z}one entry foreach atom in each residue in each frame in each trajectory
table atom: one entry foreach atom in each residue ineach trajectory
table residue: one entry foreach residue in each trajectory
table frame: one entry foreach frame in each trajectory
dictionary tablesmetadata tables
Database Design: Simplified
Database Design: A More Complete Version
Simulation Metadata
Difficult to extract from published literature
This is a prototype: a needs analysis with users/depositors must be conducted
Annotation/links to other biological databases essential
idmoleculesauthordepositorsaffiliationspublicationsmethodsrc_struref_struprogverhardwarenum_of_proctimestepnum_of_frameens_typethermostatsolventforcefieldele_statequ_prothyd_atomunit_shape…
metadata
Database Editor & SQL Query Capability
BioSimGRID Prototype
Target date for prototype: July 2003
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
Roadmap
Dec 2002 – project started
July 2003 – (internal) prototype
September 2003 – working prototype (All Hands meeting)
November 2003 – test ‘real world’ applications
December 2003 – multi-site prototype
2004 – multi-site deposition of data
2005 – open up to additional groups for deposition/testing
Future Directions
HTMD – simulations coupled to structural genomics
Diamond light source
Computational system biology – virtual outer membrane
HPCx
Multiscale biomolecular simulations – from QM/MM to meso-scale modelling
GRID-enabled simulations
Combine all of these with BioSimGRID…
Structural Genomics & HTMD
Overall vision – simulation as an integral component of structural genomics
Needs capacity computation – GRID?
MD database (distributed) – BioSimGRID
synchrotron
MD database
novel biology…
compute GRID
Towards a Virtual Outer Membrane (vOM)
Om
pT
Om
pX
Om
pA
Om
pF
PhoE
FhuA
Pi
TolC
LamB
FhuDMalE
PiBP
OM
PLA
OpcA
- - - -+
Pi
TonB
First step towards computational systems biology – a suitable system
Bacterial OMs – 5 or 6 proteins = 90% of protein content
Structures or good homology models of proteins are available
Complex lipid – outer leaflet is lipopolysaccharide (LPS)
Minimum system size ca. 2.5x106 atoms; simulation times ca. 50 ns
cf. current FhuA – 80,000 atoms & 10 ns – need HPCx
Multiscale Biomolecular Simulations
Membrane bound enzymes – major drug targets (cf. ibruprofen, anti-depressants, endocannabinoids)
Complex multi-scale problem: QM/MM; ligand binding; membrane/protein fluctuations; diffusive motion of substrates/drugs in multiple phases
Need for GRID-based integrated simulations
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
BBSRC DTI
The Wellcome Trust GSK
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