1. Center for Structures of Membrane Proteins © 2006 Optimizing x-ray structure determination James Holton LBNL/UCSF April 6, 2006

2. Beamline 8.3.1 staff Acknowledgments George Meigs Jane Tanamachi

3. UCSF UC Berkeley Plexxikon MD Anderson Alberta Synchrotron Institute 8.3.1 PRT Members Funding

4. Optimizing structure determination

5. How many are we solving?

6. Optimizing structure determination How many are we solving? What is the limit?

7. Optimizing structure determination How many are we solving? What is the limit? Are we there yet?

8. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not?

9. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

10. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

11. How many are we solving?

12. http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALL.html

13. How many are we solving? http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALL.html Jiang & R.M. Sweet (2004)

14. How many are we solving? http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALS.html

15. How many are we solving? http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALS.html

16. How many are we solving? http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALS.html

17. Breaking it down

18. $$ → photons Breaking it down

19. $$ → photons photons → data Breaking it down

20. $$ → photons photons → data data → models Breaking it down

21. $$ → photons photons → data data → models models → results Breaking it down

22. $$ → photons photons → data data → models models → results results → $$ Breaking it down

23. $$ → photons photons → data data → models models → results results → $$ Breaking it down

24. $$ → photons 2x10 11 photons/s ÷ $600,000/year 6x10 12 photons/dollar Breaking it down

25. $$ → photons photons → data data → models models → results results → $$ Breaking it down

26. $$ → photons photons → data data → models models → results results → $$ Breaking it down

27. Operational Efficiency “representative” 8.3.1 user

28. SecondsDescriptionPercent 115200 Assigned to user- Operational Efficiency “representative” 8.3.1 user

29. SecondsDescriptionPercent 115200 Assigned to user- 104490 Light available Operational Efficiency “representative” 8.3.1 user

30. SecondsDescriptionPercent 115200 Assigned to user- 104490 Light available91% Operational Efficiency “representative” 8.3.1 user

31. SecondsDescriptionPercent 104490 Assigned and available91% Operational Efficiency “representative” 8.3.1 user

32. SecondsDescriptionPercent 104490 Assigned and available91% Shutter open Operational Efficiency “representative” 8.3.1 user

33. SecondsDescriptionPercent 104490 Assigned and available91% 42093 Shutter open40% Operational Efficiency “representative” 8.3.1 user

34. SecondsDescriptionPercent 104490 Assigned and available91% 42093 Shutter open40% Collecting (3026 images) Operational Efficiency “representative” 8.3.1 user

35. SecondsDescriptionPercent 104490 Assigned and available91% 42093 Shutter open40% 52684 Collecting (3026 images)50% Operational Efficiency “representative” 8.3.1 user

36. SecondsDescriptionPercent 104490 Assigned and available91% 42093 Shutter open40% 52684 Collecting (3026 images)50% Something else Operational Efficiency “representative” 8.3.1 user

37. SecondsDescriptionPercent 104490 Assigned and available91% 42093 Shutter open40% 52684 Collecting (3026 images)50% 51806 Something else50% Operational Efficiency “representative” 8.3.1 user

38. SecondsDescriptionPercent 51806 Something else50% Operational Efficiency “representative” 8.3.1 user

39. SecondsDescriptionPercent 51806 Something else100%  45 Mounting Operational Efficiency “representative” 8.3.1 user

40. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% Operational Efficiency “representative” 8.3.1 user

41. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22%  37 Centering Operational Efficiency “representative” 8.3.1 user

42. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% 229s  37 Centering16% Operational Efficiency “representative” 8.3.1 user

43. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% 229s  37 Centering16%  109 Strategizing Operational Efficiency “representative” 8.3.1 user

44. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38% Operational Efficiency “representative” 8.3.1 user

45. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38%  37 Prepping Operational Efficiency “representative” 8.3.1 user

46. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38% 309s  37 Prepping24% Operational Efficiency “representative” 8.3.1 user

47. SecondsDescriptionPercent 51806 Something else32% 10s  45 Mounting1% 30s  37 Centering2% 140s  109 Strategizing29% 0s  37 Prepping0% Operational Efficiency “expert” user

48. SecondsDescriptionPercent 16820 Something else100% 10s  45 Mounting3% 30s  37 Centering7% 140s  109 Strategizing90% 0s  37 Prepping0% Operational Efficiency “expert” user

49. $$ → photons photons → data data → models models → results results → $$ Breaking it down

50. $$ → photons photons → data data → models models → results results → $$ Breaking it down

51. Turning data into models

52. NumberDescriptionPercent Images 8.3.1 in 2003 Turning data into models

53. NumberDescriptionPercent 446028 Images (~7 TB)33% 8.3.1 in 2003 Turning data into models

54. NumberDescriptionPercent 446028 Images (~7 TB)33% Data sets 8.3.1 in 2003 Turning data into models

55. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% 8.3.1 in 2003 Turning data into models

56. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% MAD/SAD 8.3.1 in 2003 Turning data into models

57. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 8.3.1 in 2003 Turning data into models

58. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% Published 8.3.1 in 2003 Turning data into models

59. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 48 Published2% 8.3.1 in 2003 Turning data into models

60. Top producing beamlines of the world http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALL.html Structures credited

61. Top producing beamlines of the world http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALL.html x10 6 unique HKLs

62. Top producing beamlines of the world http://asdp.bnl.gov/asda/Libraries/pdb_statis/latest/bml/ALL.html http://biosync.sdsc.edu/ Structures/10 20 photons

63. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

64. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

65. What is the limit?

66. 28 operating US beamlines What is the limit?

67. 28 operating US beamlines 2x10 13 ph/s http://biosync.sdsc.edu/ What is the limit?

68. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit 2x10 13 ph/s Henderson et al (1990) What is the limit?

69. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit 2x10 9 ph/μm 2 /s http://biosync.sdsc.edu/ What is the limit?

70. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s = 400,000 datasets/year What is the limit?

71. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 200,000 datasets/year What is the limit?

72. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 100,000 datasets/year What is the limit?

73. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 100,000 datasets/year ÷ 1324 str in 2003 Jiang & R.M. Sweet (2004) What is the limit?

74. 28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 100,000 datasets/year ÷ 1324 str in 2003 ~ 2% efficient What is the limit?

75. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 48 Published2% 8.3.1 in 2003 Turning data into models

76. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

77. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

78. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

79. DVD data archive

87. Breaking it down $$ → photons photons → data data → models models → results results → $$

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

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

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

91. How often does it really work? Elven Automation

92. Apr 6 – 24 at ALS 8.3.1 Elven Automation How often does it really work?

93. Apr 6 – 24 at ALS 8.3.1 Elven Automation 27,686images collected

94. Apr 6 – 24 at ALS 8.3.1 Elven Automation 27,686images collected 148datasets (15 MAD)

95. Apr 6 – 24 at ALS 8.3.1 Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators

96. Apr 6 – 24 at ALS 8.3.1 Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators 56unique cells

97. Apr 6 – 24 at ALS 8.3.1 Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators 56unique cells 5 KDa – 23 MDaasymmetric unit

98. Apr 6 – 24 at ALS 8.3.1 Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators 56unique cells 5 KDa – 23 MDaasymmetric unit 0.94 – 32 Åresolution (3.2 Å)

99. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets

100. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded

101. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours

102. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed

103. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours

104. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

105. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

106. NumberDescriptionPercent 446028 Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 48 Published2% 8.3.1 in 2003 Turning data into models

107. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

108. Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

109. Why do structures fail?

110. Overlaps Why do structures fail?

111. Overlaps Signal to noise Why do structures fail?

112. Overlaps Signal to noise Radiation Damage Why do structures fail?

113. Overlaps Signal to noise Radiation Damage Why do structures fail?

114. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

115. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31 failed ~61 (0-231)hours 2 / 15MAD structures

116. unavoidable overlaps

117. detector

118. unavoidable overlaps phi detector

119. unavoidable overlaps mosaicity phi detector

120. unavoidable overlaps mosaicity phi detector c*

121. unavoidable overlaps mosaicity phi detector c* Ewald sphere

122. unavoidable overlaps mosaicity phi detector c* Ewald sphere

123. unavoidable overlaps mosaicity phi detector c* Ewald sphere

124. unavoidable overlaps mosaicity phi detector c* Ewald sphere

125. unavoidable overlaps mosaicity phi detector c* Ewald sphere

126. unavoidable overlaps mosaicity phi detector c* Ewald sphere

127. unavoidable overlaps mosaicity phi detector c* Ewald sphere

128. unavoidable overlaps mosaicity phi detector c* b c a

129. unavoidable overlaps mosaicity phi detector c* b c a

130. unavoidable overlaps mosaicity phi detector c* b c a

131. unavoidable overlaps mosaicity phi detector c* b c a

132. unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

133. unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

134. unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

135. unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

136. unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

137. Overlaps Signal to noise Radiation Damage Why do structures fail?

138. Overlaps Signal to noise Radiation Damage Why do structures fail?

139. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

140. Apr 6 – 24 at ALS 8.3.1 Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

141. “What is a good exposure time?”

142. “How much signal do I need?”

143. MAD phasing simulation Anomalous signal to noise ratio Correlation coefficient to correct model mlphare results

144. SAD phasing simulation Anomalous signal to noise ratio Correlation coefficient to correct model mlphare results

145. Minimum required signal (MAD/SAD)

146. SAD phasing experiment Anomalous signal to noise ratio Correlation coefficient to published model

147. MR simulation Signal to noise ratio Correlation coefficient to correct density corrupted data

148. MR simulation Signal to noise ratio Correlation coefficient to correct density corrupted data

149. MR simulation Rmsd from perfect search model ( Å ) Correlation coefficient to correct density corrupted model

150. MR simulation Fraction of full search model Correlation coefficient to correct density trimmed model

151. Is it real, or is it MLFSOM ?

152. Background scattering Resolution (Ǻ) Electron equivalents The form-factor of the cryostream 20 10 5 4 3 2.5 2 1.8 1.6 1.4 1.2 1 0 2 4 6 8 10 12 14 16 measured theoretical

153. 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

154. “We really need those high-resolution spots”

155. Incremental strategy incremental_strategy.com merged.mtz auto.mat

156. Incremental strategy incremental_strategy.com merged.mtz auto.mat

157. “We have a problem with non-isomorphism”

158. Proteins move

159. Overlaps Signal to noise Radiation Damage Why do structures fail?

160. Overlaps Signal to noise Radiation Damage Why do structures fail?

161. thaw Radiation Damage

162. Distention of cryo with dose

163. before

164. Distention of cryo with dose after

165. Water ring shift saturated sucrose in 250mM WO4 0 MGy

166. Water ring shift saturated sucrose in 250mM WO4 37 MGy

167. Water ring shift saturated sucrose in 250mM WO4 80 MGy

168. Water ring shift saturated sucrose in 250mM WO4 184 MGy

169. Water ring shift Resolution (Ǻ) Photons/s/pixel  7.5 3.8 2.5 1.9 1.5 saturated sucrose in 250mM WO4

170. Water ring shift Resolution (Ǻ) Photons/s/pixel  7.5 3.8 2.5 1.9 1.5 saturated sucrose in 250mM WO4

171. Water ring shift Resolution (Ǻ) Photons/s/pixel  7.5 3.8 2.5 1.9 1.5 saturated sucrose in 250mM WO4

172. Water ring shift Resolution (Ǻ) Photons/s/pixel  7.5 3.8 2.5 1.9 1.5 saturated sucrose in 250mM WO4

173. Water ring shift Resolution (Ǻ) Photons/s/pixel  7.5 3.8 2.5 1.9 1.5 saturated sucrose in 250mM WO4

174. Water ring shift Absorbed dose (MGy) Water ring position (Ǻ) saturated sucrose in 250mM WO4

175. Protein crystal background

177. Water ring shift Absorbed dose (MGy) Water ring position (Ǻ) GCN4-p1-N16A trigonal crystal

178. Water ring shift Absorbed dose (MGy) Water ring position (Ǻ) GCN4-p1-N16A trigonal crystal crystal background saturated sucrose

179. Water ring shift http://www.lsbu.ac.uk/water/amorph.html

180. Water ring shift http://www.lsbu.ac.uk/water/amorph.html

181. Water ring shift bubbles? Richard D. Leapman, Songquan Sun, Ultramicroscopy (1995)

182. Water ring shift Hydrogen bubbles? Richard D. Leapman, Songquan Sun, Ultramicroscopy (1995)

183. Water ring shift Hydrogen bubbles? http://www.rcdc.nd.edu/compilations/Rxn.pdf “The hydrogen atom reacts with organic compounds by abstracting H from saturated molecules and by adding to centers of unsaturation, for example,

184. Water ring shift Hydrogen bubbles? http://www.rcdc.nd.edu/compilations/Rxn.pdf “The hydrogen atom reacts with organic compounds by abstracting H from saturated molecules and by adding to centers of unsaturation, for example,

185. Damage model system

186. 67 consecutive data sets

188. Data quality vs exposure Exposure time (min) Correlation coefficient

189. Data quality vs exposure Exposure time (min)  

190. Data quality vs exposure Exposure time (min)  

191. Data quality vs exposure Exposure time (min) Resolution limit

192. Data quality vs exposure Exposure time (min) R sym

193. Experimentally-phased map

195. Data quality vs phasing quality Exposure time (min) Correlation coefficient

196. Specific Radiolysis of Selenomethionine

198. 67 consecutive data sets

200. Individual atoms decay at different rates Exposure time (min) Correlation coefficient to observed data

201. Damage changes fluorescence spectrum Photon energy (eV) counts

202. Damage changes fluorescence spectrum Photon energy (eV) counts

203. Damage changes fluorescence spectrum Photon energy (eV) counts

204. Damage changes fluorescence spectrum fluence (10 3 photons/mm 2 ) Fraction unconverted 25mM SeMet in 25% glycerol 0.0 0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 100 120 Exposing at 12680 eV

205. Damage changes fluorescence spectrum fluence (10 3 photons/mm 2 ) Fraction unconverted 25mM SeMet in 25% glycerol 0.0 0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 100 120 Exposing at 12680 eV Se cross-section at 12680 eV

206. Damage changes fluorescence spectrum Absorbed dose (MGy) Fraction unconverted 25mM SeMet in 25% glycerol 0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 30 40 50 60 Half-dose = 10.6 MGy Exposing at 12680 eV

207. fluorescence probe for damage Absorbed Dose (MGy) Fraction unconverted Wide range of decay rates seen 0.0 0.2 0.4 0.6 0.8 1.0 0 50 100 150 200 Half-dose = 41.7 ± 4 MGy “GCN4” in crystal Half-dose = 5.5 ± 0.6 MGy 8 mM SeMet in NaOH Protection factor: 660% ± 94%

208. “Can we do more with what we’ve got?”

209. SecondsDescriptionPercent 51806 Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38% 309s  37 Prepping24% Beamline Efficiency “representative” 8.3.1 user

210. SecondsDescriptionPercent 51806 Something else32% 10s  45 Mounting1% 30s  37 Centering2% 140s  109 Strategizing29% 0s  37 Prepping0% Beamline Efficiency “expert” user

211. SecondsDescriptionPercent 16820 Something else100% 10s  45 Mounting3% 30s  37 Centering7% 140s  109 Strategizing90% 0s  37 Prepping0% Beamline Efficiency “expert” user

212. Interleaved Scheduling experiment queuebeamline Minor 30s Choe 120s Alberta 60s Choe 30s Minor 30s

213. cool hand luke

214. Hampton Pin

215. Syrrx Pin

216. plastic Pin

217. Yale Pin

218. what we have here is… failure to communicate

219. SuperPin

220. SuperTong

221. Hampton PinSuper Tong

222. Syrrx PinSuper Tong

223. plastic PinSuper Tong

224. Yale PinSuper Tong

226. “infinite capacity” sample carousel

227. 6-foot conveyor

228. Carousel open

229. Carousel cold

230. CHL idlepos

231. Beamline 8.3.1 staff Acknowledgments George Meigs Jane Tanamachi

232. Is it real, or is it MLFSOM ?

233. http://ucxray.berkeley.edu/~jamesh/elves Download Elves from: