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Shotgun crystallization of the Thermotoga maritima proteome Protein properties and crystallization conditions that correlate with crystallization success.

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Presentation on theme: "Shotgun crystallization of the Thermotoga maritima proteome Protein properties and crystallization conditions that correlate with crystallization success."— 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 Crystallization Conditions Protein Properties Minimize initial crystallization screensMinimize initial crystallization screens

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

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

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

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

10 Data mining crystallization conditions Minimize initial crystallization screens

11 Data mining crystallization conditions Minimize initial crystallization screens

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

13 MINCOV Iterative selection algorithm that identifies minimal screens, subsets of the original 480 conditions that would have crystallized all 465 proteins Repeat 472 times (each condition) Slawomir Grzechnik 472 minimal screens472 minimal screens Each contained 108- 116 conditionsEach contained 108- 116 conditions Intersection = Core ScreenIntersection = Core Screen Identify minimal crystallization screens

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

15 Data mining protein properties Improve target selection

16 Data mining protein properties Improve target selection

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

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

20 New target selection CharacteristicProteins Eliminated Crystals Eliminated Protein Total (1877) Crystal Total (465) Length 12011757464 Charged AA 18801602464 Gravy 20401562464 pI 5701538464 TMHMM; SignalP 538151245448 Coiled-Coil7241213445 SEG1446118763%43994%

21 Goal: more structures! Crystallization Conditions Protein Properties

22 Goal: more structures! Crystallization Conditions Protein Properties

23 Crystallization Conditions Protein Properties Goal: more structures!

24 Crystallization Conditions Protein Properties Goal: more structures!

25 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 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 GNF / TSRI - CC Ray Stevens Scott Lesley Rebecca Page Carina Grittini Jeff Velasquez Kin Moy Eric Sims Bernard Collins Tom Clayton Heath Klock Angela Walker Heath 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 TSRI - AC Ian Wilson Peter Kuhn Marc Elsliger Frank von Delft Vandana Sridhar Dan Taillac 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


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