1. Why are

2. we not

3. solving

4. more

5. struct

6. tures?

7. James Holton JMHolton@lbl.gov University of California San Francisco and Advanced Light Source Lawrence Berkeley National Laboratory Berkeley, CA 94720 USA This work was supported by contributions from the ALS 8.3.1 participating research team, a University of California Campus-Laboratory Collaboration Grant and grants from the National Institutes of Health: GM74929 and GM24485. Beamline 8.3.1 was funded by the National Science Foundation, the University of California, Berkeley, the University of California, San Francisco and Henry Wheeler. The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, Materials Sciences Division, of the US Department of Energy under contract No. DE-AC02-05CH11231 at Lawrence Berkeley National Laboratory.

8. About 50 data sets (MAD,SAD or native) are collected for every PDB deposition Only one in 12 MAD/SAD datasets can be solved Failures are generally due to: Overlaps – run strategy! Site-specific damage - stay under ~5 MGy Insufficient signal-to-noise - need ΔF ano > σ(ΔF ano ) Summary

9. SecondsDescriptionPercent 104490 Assigned and available91% 42093 Shutter open40% 52684 Collecting (3026 images)50% 51806 Something else50% Beamline operation efficiency “representative” 8.3.1 user

10. SecondsDescriptionPercent 51806 Something else 100% 247s  45 Mounting11% 229s  37 Centering8% 179s  109 Strategizing19% 309s  37 Prepping12%

11. NumberDescriptionPercent 446028 Images (~7 TB)33% of light 2346 Data sets47% of light 449 MAD/SAD (1:2)19% of data sets 48 Published2% of data sets ALS 8.3.1 in 2003 Structure Productivity

12. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s x 25% beamline operation efficiency ≈ 100,000 datasets/year ÷ 1324 str in 2003 ≈ 2% efficient USA Structure Productivity Henderson (1990) Jiang & R.M.Sweet (2004) biosync.sdsc.edu

13. Investigated with Elves Automation Elves examine images and set-up data processing Elves run… mosflm scala solve mlphare dm arp/warp

14. Apr 6 – 24 2003 at ALS 8.3.1 Investigated with Elves Automation 27,686images collected 31investigators 56unique cells 5 KDa – 23 MDaasymmetric unit 0.94 – 32 Åresolution (3.2 Å)

15. 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures Why do structures fail?

16. Overlaps Signal to Noise Radiation Damage Why do structures fail?

17. Avoiding Overlaps c c

18. Is it real, or is it MLFSOM ? Simulate diffraction experiment to test hypotheses

19. MAD phasing simulations Anomalous signal to noise ratio Correlation coefficient to correct model mlphare results Threshold of interpretability

20. Reduce Noise: minimize background scattering Resolution (Ǻ) Photons/s/pixel Se edge with detector at 100 mm  7.5 3.8 2.5 1.9 1.5 1.2 1.1

21. Increase Signal: use multiple crystals and incremental strategy incremental_strategy.com merged.mtz auto.mat

22. e-e- e-e- + Interactions of x-rays with matter + e-e- e-e- + e-e- elastic scattering inelastic scattering Photoelectron emission fluorescence Secondary ionization

23. Radiation Damage Lattice Damage Site-specific Damage

24. Where do photons go? beamstop elastic scattering (6%) Transmitted (98%) useful/absorbed energy: 7.3% inelastic scattering (7%)Photoelectric (87%) Protein 1A x-rays Re-emitted (~0%)Absorbed (99%) Re-emitted (99%)Absorbed (~0%)

25. Protein crystal in oil x-rays cause sample expansion before after 20% glycerol

26. Data quality vs phasing quality dose (MGy) Correlation coefficient 0 12 24 36 48 60

27. Individual atoms decay at different rates dose (MGy) Correlation coefficient to observed data 0 12 24 36 48 60

28. 1.0 0.0 fraction unconverted peak valley

29. 660%