Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
DAMMIF Update
Get the latest version of DAMMIF together with the latestrelease of ATSAS!
ATSAS 2.5.0 will be available soon!
http://www.embl-hamburg.de/biosaxs/download.html
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
Ab-Initio ModellingDAMMIN and DAMMIF
Daniel Franke
European Molecular Biology Laboratory
2012/10/19
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
The following slides describe the how,not the why!
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
Outline
1 Introduction
2 Ab-Initio Modelling
3 Obtaining Models
4 Postprocessing Models
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Basic Idea
Find a threedimensional objectwhose theoretical
scattering curve wouldresemble the
experimental databest.
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Results
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The Dummy Atom ModelMany little scatterers ...
A Dummy Atom Model (DAM) isbuild by a tightly packed group of
dummy atoms. The volumeoccupied by dummy atoms in any
state (particle, solvent) is alsoknown as search volume.
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The Dummy AtomOne little scatterer ...
Acts as a placeholder for, but does not resemble, areal atomOccupies a known position in spaceHas a known scattering patternMay either contribute to the solvent or the particle
Dummy atoms are also referred to as beads.
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Basic IdeaRevisited.
Find a three dimensional object whose theoreticalscattering curve would resemble the experimental
data best.
Find the set of dummy atoms within a search volumewhose accumulated scattering resembles the
experimental data best.
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
Basic IdeaRevisited.
Find a three dimensional object whose theoreticalscattering curve would resemble the experimental
data best.
Find the set of dummy atoms within a search volumewhose accumulated scattering resembles the
experimental data best.
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
Validity of InputGarbage In – Garbage Out
Validate input data; check for
aggregation at thebeginningnoise at higher angles
Remember: noise can bemodelled nicely
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Outline
1 Introduction
2 Ab-Initio Modelling
3 Obtaining Models
4 Postprocessing Models
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An estimate on the problem’s size.The Universe is not enough
A search volume of 2000 dummy atoms has
22000 ≈ 10600
possible conformations, i.e. scattering curves.
On 40.000.000 conformations per hour per CPU, 1000CPUs, 24 hours a day, 365 days a year one would spendthe next couple of universes’ time on enumerating allscattering curves!
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Imposing restrictions in solution space.
A valid conformation is ...connected: particle beads must beinterconnectedtightly packed: particle beads shallbe tightly packed, avoid loosestrandscentered: assemble the particlewithin the search volume, avoidboundary contactin right shape: oblate or prolateshapes can be enforced
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Advances And Differences In ProgramsSelection Scheme
DAMMIN DAMMIF
At the current iteration:dark blue particle, might become solventlight blue solvent, might become particlewhite solvent, won’t change
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DAMMIF Walkthrough
$> dammif shape.out
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DAMMIF OutputReading the output of DAMMIF
Step: 1, T: 0.130E-03, 42/1941,Succ: 1229, Eval: 20001, CPU: 00:00:03
Rf: 0.0875, Los: 0.17, Dis: 0.00, Rg: 0.15,Cen:22.57, Ani: 0.00, Fit: 0.0989
Step Step numberT Temperature, artificalp/a Number of particle beads of all beadsSucc Number of successfull iterations at current TEval Accumulated number of iterationsCPU Accumulated runtime
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DAMMIF OutputReading the output of DAMMIF (cont.)
Step: 1, T: 0.130E-03, 42/1941,Succ: 1229, Eval: 20001, CPU: 00:00:03
Rf: 0.0875, Los: 0.17, Dis: 0.00, Rg: 0.15,Cen:22.57, Ani: 0.00, Fit: 0.0989
Rf Goodness of Fit, data onlyLos Contribution of Looseness PenaltyDis Contribution of Disconnectivity PenaltyPer Contribution of Periphal PenaltyAni Contribution of Anisometry PenaltyFit Goodness of Fit, data and penalties
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Outline
1 Introduction
2 Ab-Initio Modelling
3 Obtaining Models
4 Postprocessing Models
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Sequentially on your local machineWindows; .bat files
dammif lyz.out --mode=slow --prefix FMRP1dammif lyz.out --mode=slow --prefix FMRP2dammif lyz.out --mode=slow --prefix FMRP3dammif lyz.out --mode=slow --prefix FMRP4dammif lyz.out --mode=slow --prefix FMRP5dammif lyz.out --mode=slow --prefix FMRP6dammif lyz.out --mode=slow --prefix FMRP7dammif lyz.out --mode=slow --prefix FMRP8dammif lyz.out --mode=slow --prefix FMRP9dammif lyz.out --mode=slow --prefix FMRP10
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Sequentially on your local machineLinux, MacOS; bash syntax
for i in ‘seq 1 10‘ ; dodammif --prefix=lyz-\$i --mode=slow lyz.out;
done
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In parallel on your local cluster
Please contact your system administrator for details ofyour cluster and how to submit jobs.
Important: as processes are being run in parallel, multiplemay be started at the same time – with the same randomseed – resulting in exactly the same model.
Make sure to redefine the random seed for each run!
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Input redirectionFine tuning parameters in scripts
1 Start dammif in slow mode once, abort2 Find the $prefix.in file3 Modify as needed4 Run dammif as
$> dammif --prefix=... --mode=i < modified.in
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In parallel using ATSAS-Onlinehttp://www.embl-hamburg.de/biosaxs/atsas-online/
Create an account (emailaddress only) and submityour dammin/dammif jobs tothe EMBL BioSAXS cluster.
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In parallel on the GRIDhttp://www.wenmr.org/wenmr/ab-initio-modelling
A worldwide e-Infrastructurefor NMR and structuralbiology.
In preparation and not yetavailable.
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Outline
1 Introduction
2 Ab-Initio Modelling
3 Obtaining Models
4 Postprocessing Models
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Postprocessing ModelsHow to proceed ...
With multiple models:find those that are most similar(uniqueness of reconstruction is not guaranteed) ORgroup models into clusterssuperimpose and average the selectionrestart fitting process using the averaged model
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Multiple modelsFunari et al. (2000) J. Biol. Chem. 275, 31283–31288.
5S RNA, multiple solutions with equally good fit.
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Selecting ModelsDAMSEL, DAMCLUST
Computes the similarities between all pairs of input files.
Measure of similarity of models:
Normalized Spatial Discrepancy (NSD)NSD < 1 implies similar models
DAMSEL selects similar models, rejects outliersDAMCLUST groups models to clusters, rejects nothing
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Superimposing ModelsSUPCOMB, DAMSUP
SUPCOMB: superimpose any two models(principle axis alignment, gradient minimization, localgrid search)DAMSUP: superimpose multiple models on areference using SUPCOMB.
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Superimposing models5S RNA continued ...
Solution spread region.
Most populated volume.
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Superimposing models5S RNA continued ...
Solution spread region. Most populated volume.
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Averaging ModelsDAMAVER, DAMFILT
DAMAVER: Creates a bead probability density mapwithin the search volume.DAMFILT: Generates the averaged model, using auser-defined probability threshold. Will give a validmodel, violating the threshold if necessary.
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Ab-Initio ModellingOptions at this point.
take the model(s) that have the least NSD to allothers – this fits the datatake the filtered model(s) – but this will not fit the datause averaged model(s) and restart DAMMIN to fit theexperimental data (via DAMSTART)
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
Ab-Initio ModellingOptions at this point.
take the model(s) that have the least NSD to allothers – this fits the datatake the filtered model(s) – but this will not fit the datause averaged model(s) and restart DAMMIN to fit theexperimental data (via DAMSTART)
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Introduction Ab-Initio Modelling Obtaining Models Postprocessing Models
Ab-Initio ModellingOptions at this point.
take the model(s) that have the least NSD to allothers – this fits the datatake the filtered model(s) – but this will not fit the datause averaged model(s) and restart DAMMIN to fit theexperimental data (via DAMSTART)
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Postprocessing ModelsSummary.
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Ab-Initio Modelling5S RNA continued ...
Finalized model, filtered by DAMSTART, refined byDAMMIN.
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That’s all folks.
Questions? Visithttp://www.saxier.org/forum
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