Towards high-throughput structure determination at SSRL

1. Towards high-throughput structure determination at SSRL Ashley DeaconStanford Synchrotron Radiation Laboratory

2. 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 Multi-wavelength BL 7-1 Monochromatic SPEAR 3 upgrade of storage ring to 3rd generation capabilities by 2003 Solve hundreds of structure per year without relying on manycrystallographers

3. High-throughput goals • 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. Paul Ellis P12 • Develop the Automated Structural Analysis of Proteins (ASAP) system • Integrate SDC with the SMB program. • Staged delivery of useful components. Aina Cohen P27, Hsiu-Ju Chiu P33 Thomas Eriksson P29, Scott McPhillips P32

4. Mosflm SnB Mlphare warpNtrace XPLOR SSRL The world of crystallography according to Ashley (pre-JCSG) I ??? Data Collection DataProcessing Locate Heavy Atoms SolveStructure ModelBuilding 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?

5. The world of crystallography according to Ashley (pre-JCSG) II • 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.

6. The world of crystallography according to Ashley (pre-JCSG) III SnB Locate Heavy Atoms Mosflm Mlphare warpNtrace XPLOR DataProcessing SolveStructure ModelBuilding Model Refinement SnB Locate Heavy Atoms SSRL Data Collection • Still have problems • Limited experience • Not systematic. DENZO SHELX SHARP DataProcessing Locate Heavy Atoms SolveStructure

7. Frank Ashley Tassos Duncan Glen Gerard The world of crystallography according to ASAP I

8. The world of crystallography according to ASAP II JCSG staff • The Operation Managerallows • 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. OperationManager Operations

9. The world of crystallography according to ASAP III JCSG Staff and Scripted Operations • The Scheduler supports • Multiple Operation Managers. • Distribution of resources to multiple projects. • Efficient use of all resources. Scheduler Market-based resource allocation OperationManager OperationManager

10. The world of crystallography according to ASAP IV • 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. • 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. Solver Rules-based execution of a project Scheduler Market-based resource allocation OperationManager OperationManager

11. Inputs Outputs InputAttributes OutputAttributes 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. • Can incorporate some internal feedback and intelligence to make them smart. An ASAP Operation • Attributes • Describe the inputs and outputs of an operation. • Correlations between the attributes can be used to generate rules, which can guide the Solvers.

12. The world of crystallography according to ASAP VI • Build a graph of Operations • Traverse the graph by the most efficient route or try many routes and choose the best results

13. 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 OperationManager

14. A production ASAP system Solver Rules-based execution of a project Data Miner Derives rules for the Solver System State Database Stores past operations Scheduler Market-based resource allocation • 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. OperationManager

15. ASAP – Summary • The ASAP architecture is • 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 • Provide a series of useful systems that gradually improve throughput. • Ultimately lead to a fully automated production system.

16. Acknowledgements • 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.