1 / 25

Shotgun crystallization of the Thermotoga maritima proteome

Shotgun crystallization of the Thermotoga maritima proteome. Protein properties and crystallization conditions that correlate with crystallization success. Rebecca Page The Scripps Research Institute 3.30.2004 – PSI, NIH. Data mining for faster structure determination. Crystallization

jamal-taylor
Download Presentation

Shotgun crystallization of the Thermotoga maritima proteome

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Shotgun crystallization of the Thermotoga maritima proteome Protein properties and crystallization conditions that correlate with crystallization success Rebecca Page The Scripps Research Institute 3.30.2004 – PSI, NIH

  2. Data mining for faster structure determination Crystallization Conditions Protein Properties

  3. Data mining for faster structure determination Crystallization Conditions Protein Properties

  4. Data mining for faster structure determination Crystallization Conditions Protein Properties

  5. Data mining for faster structure determination • Minimize initial crystallization screens Crystallization Conditions Protein Properties

  6. Data mining for faster structure determination • Minimize initial crystallization screens • Improve target selection Crystallization Conditions Protein Properties

  7. Experimental design • Process all T. maritima proteins through the JCSG structure determination pipeline • Targets are not prefiltered • Targets are processed using identical experimental methods Thermotoga maritima 1877 ORFs Lesley, et al. (PNAS, 2002)

  8. Experimental design A more complete, less biased crystallization dataset for data mining Thermotoga maritima 1877 ORFs Lesley, et al. (PNAS, 2002)

  9. The Numbers • 258720 crystallization experiments • 465 of 539 (86%) proteins crystallized • 472 of 480 (98%) conditions produced crystals • 5546 total crystal hits Targets 1791 1376 539 539

  10. Data mining crystallization conditions Minimize initial crystallization screens

  11. Data mining crystallization conditions Minimize initial crystallization screens

  12. 0 1-5 6-10 11-15 16-20 21-25 26-50 51 or more Many proteins crystallized in 5 or more of the original 480 conditions 21; 3.9% 32; 5.9% 73; 13.5% 19; 3.5% 24; 4.5% 47; 8.7% 73; 13.5% 249; 46.2%

  13. Identify minimalcrystallization screens MINCOV Iterative selection algorithm that identifies minimal screens, subsets of the original 480 conditions that would have crystallized all 465 proteins • 472 minimal screens • Each contained 108-116 conditions • Intersection = Core Screen Repeat 472 times (each condition) Slawomir Grzechnik

  14. Core ScreenBest 96 conditions crystallize 448 proteins • Core Screen • 67 conditions (14%) • All precipitants • 392 proteins crystallized (84%) • Expanded Core Screen • 96 conditions (20%) • 448 proteins crystallized (96%) 180 Original Screen Core Screen 140 100 60 20 High MW PEG Low MW PEG Salts Poly- alcohols Organics Page, et al. (Acta Cryst D, 2003)

  15. Data mining protein properties Improve target selection

  16. Data mining protein properties Improve target selection

  17. Better target selection for JCSG pipeline 20 • Gravy Index • - hydrophilic • + hydrophobic 15 Frequency 10 Identify upper and lower bounds of crystallized proteins and use these limits in future target selection 5 1.0 -1.0 0.0 Gravy Index

  18. Proteins with 40 or more SEG residues rarely crystallize • SEG: Filtering to identify low complexity segments • Long SEG segments can be unstructured Low-complexity segments TPPTMPPPPTT GGGSSSSHS PNGLPHPTPPPP QQQGRQQQQQLK

  19. 30 20 % crystallized 10 0 0 20 40 60 80 100 Number of SEG residues Proteins with 40 or more SEG residues rarely crystallize • SEG: Filtering to identify low complexity segments • Long SEG segments can be unstructured

  20. New target selection

  21. Goal: more structures! Crystallization Conditions Protein Properties

  22. Goal: more structures! Crystallization Conditions Protein Properties

  23. Goal: more structures! Crystallization Conditions Protein Properties

  24. Goal: more structures! Crystallization Conditions Protein Properties

  25. GNF / TSRI - CC Ray Stevens Scott Lesley Rebecca Page Carina Grittini Jeff Velasquez Kin Moy Eric Sims Bernard Collins Tom Clayton Angela WalkerHeath Klock Eric Koesema Eric Hampton Jamison Campbell Mike Hornsby Tanya Biorac Dan McMullan Kevin Rodrigues Mike DiDonato Andreas Kreusch Glen Spraggon Marianne Patch Xiaoping Dai Terry Cross Kevin Rodrigues Polat Abdubek Eileen Ambing SSRL - SDC Keith Hodgson Ashley Deacon Mitchell Miller Henry van den Bedem Guenter Wolf S. Michael Soltis R. Paul Phizackerley Irimpan Mathews Qingping Xu Amanda Prado John Kovarik Hsiu-Ju Chiu Ross Floyd Inna Levin Ronald Reyes Fred Rezazadeh UCSD - BIC John Wooley Adam Godzik Susan Taylor Slawomir Grzechnik Bill West Andrew Morse Jie Quyang Xianhong Wang Jaume Canaves Lukasz Jaroszewski Robert Schwarzenbacher Ray Bean, Josie Alaoen Exploratory Projects Kurt Wüthrich, TSRI Linda Columbus Touraj Etezady Margaret Johnson Wolfgang Peti Virgil Wood, UCSD Phillip Bourne Barbara Cottrell Raymond Deems Jack Kim Dennis Pantazatos Geoffrey Chang, TSRI TSRI - AC Ian Wilson Peter Kuhn Marc Elsliger Frank von Delft Vandana Sridhar Dan Taillac

More Related