Transcript Towards high-throughput structure determination at SSRL Ashley Deacon Stanford Synchrotron Radiation Laboratory

Towards high-throughput structure
determination at SSRL
Ashley Deacon
Stanford Synchrotron Radiation Laboratory
Motivation for high-throughput structure
determination
SMB user program
Structural genomics
Five macromolecular
crystallography beamlines in
operation (including 11-1).
BL 11-1 Stanford/TSRI/SSRL
Monochromatic
BL 9-1
Monochromatic
BL 9-2
Multi-wavelength
BL 1-5
BL 7-1
Multi-wavelength
Monochromatic
SPEAR 3 upgrade of storage ring
to 3rd generation capabilities
by 2003
Solve hundreds of structure per
year without relying on many
crystallographers
High-throughput goals
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Automate the crystallography experiment
 New hardware (e.g. crystal mounting robot)
 Rapid crystal characterization
 Optimal data collection from best crystals
Paul Phizackerley P34,
Ana Gonzalez P30, Aina Cohen P27
Automate crystallographic computations
 Include latest crystallographic techniques
 Route data through an analysis pipeline
 Evaluate progress of structure determination
Integrate the experiment and the analysis
 Feedback to the diffraction experiment.
 Feedback to the other core groups.
Develop the Automated Structural
Analysis of Proteins (ASAP) system
 Integrate SDC with the SMB program.
 Staged delivery of useful components.
Paul Ellis P12
Aina Cohen P27, Hsiu-Ju Chiu P33
Thomas Eriksson P29, Scott McPhillips P32
The world of crystallography according to
Ashley (pre-JCSG) I
SSRL
Mosflm
SnB
Mlphare
warpNtrace
XPLOR
Data
Collection
Data
Processing
Locate
Heavy
Atoms
Solve
Structure
Model
Building
Model
Refinement
What do I do if this approach fails?
Re-run programs with modified parameters
Slow trial-and-error process
Not very systematic
What if that fails?
???
The world of crystallography according to
Ashley (pre-JCSG) II
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Consult the literature
 Discover the “Golden Bullet”.
 Learn new applications.
Energy
Barrier
Consult colleagues
 Borrow scripts.
 Try out suggestions.
Problems and bottlenecks…
 Slow learning process.
 Cannot systematically try all applications / possibilities.
 Rely on hearsay.
The world of crystallography according to
Ashley (pre-JCSG) III
SnB
Mosflm
Data
Processing
Locate
Heavy
Atoms
Mlphare
warpNtrace
XPLOR
SnB
Solve
Structure
Model
Building
Model
Refinement
Locate
Heavy
Atoms
SSRL
Data
Collection
•
DENZO
SHELX
SHARP
Data
Processing
Locate
Heavy
Atoms
Solve
Structure
Still have problems
 Limited experience
 Not systematic.
The world of crystallography according to
ASAP I
Frank
Ashley
Tassos
Duncan
Glen
Gerard
The world of crystallography according to
ASAP II
•
JCSG staff
Operation
Manager
Operations
The Operation Manager
allows
 Single-click execution of
Operations.
 Standardized file input and
output to all Operations.
 A common communication
protocol to Operations for
developers via an API and
Library.
The world of crystallography according to
ASAP III
JCSG Staff and
Scripted Operations
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Scheduler
Market-based resource allocation
Operatio
n
Manager
The Scheduler supports
 Multiple Operation
Managers.
 Distribution of resources
to multiple projects.
 Efficient use of all
resources.
Operatio
n
Manager
The world of crystallography according to
ASAP IV
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Static rules-based Solver
 An “if…then…else…”
approach.
 All decisions must be
preprogrammed.
 Hard to take all factors
into account.
 Nothing learnt from
past operations.
Operatio
n
Manager
Solver
Rules-based execution
of a project
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Scheduler
Market-based resource allocation
Dynamic rules-based Solver
 Modify rules on the fly to
reflect knowledge
accumulated from all
projects.
 Take all characteristics of
the current project into
account when interpreting
rules.
Operatio
n
Manager
The world of crystallography according to
ASAP V
•
Operations
 Can be connected together as
defined by the inputs they
require and the outputs they
produce.

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Can incorporate some internal
feedback and intelligence to
make them smart.
An ASAP Operation
Inputs
Outputs
Attributes
 Describe the inputs and
outputs of an operation.

Correlations between the
attributes can be used to
generate rules, which can
guide the Solvers.
Input
Attributes
Output
Attributes
The world of crystallography according to
ASAP VI
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Build a graph of Operations
Traverse the graph by the most
efficient route or try many routes
and choose the best results
The world of crystallography according to
ASAP VII
Feedback to Solver
Solver
Rules-based execution of a project
Data Miner
Derives rules for the Solver
Scheduler
Market-based resource allocation
Operatio
n
Manager
A production ASAP system
Solver
Rules-based execution of a project
Scheduler
Market-based resource allocation
Data Miner
Derives rules for the Solver
System State Database
Stores past operations
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Operatio
n
Manager
System State Database will
 Store file locations and all
Attributes derived from past
operations for Data Miner.
 Track progress of all crystals
relating to each target
protein.
ASAP – Summary
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The ASAP architecture is
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Capable of parallel operation on multiple samples within a project
Capable of parallel operation on multiple projects
Flexible and modular in design
Scalable in both hardware and software
Maintainable
Testable
The ASAP staged-delivery will
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
Provide a series of useful systems that gradually improve throughput.
Ultimately lead to a fully automated production system.
Acknowledgements
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The entire SMB group at SSRL
 Fred Bertsch
 Tim McPhillips
 Peter Kuhn
GNF
 Glen Spraggon
The Scripps Research Institute
 Frank von Delft
Syrrx
 Duncan McRee
SSRL is funded by:
Department of Energy, Office of Basic Energy
Sciences,
The Structural Molecular Biology Program is supported
by:
National Institutes of Health, National Center for
Research Resources, Biomedical Technology Program
and Department of Energy, Office of Biological and
Environmental Research.