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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

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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
Data mining for faster structure determination

Crystallization

Conditions

Protein

Properties

data mining for faster structure determination1
Data mining for faster structure determination

Crystallization

Conditions

Protein

Properties

data mining for faster structure determination2
Data mining for faster structure determination

Crystallization

Conditions

Protein

Properties

data mining for faster structure determination3
Data mining for faster structure determination
  • Minimize initial crystallization screens

Crystallization

Conditions

Protein

Properties

data mining for faster structure determination4
Data mining for faster structure determination
  • Minimize initial crystallization screens
  • Improve target selection

Crystallization

Conditions

Protein

Properties

experimental design
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)

experimental design1
Experimental design

A more complete, less biased crystallization dataset for data mining

Thermotoga maritima

1877 ORFs

Lesley, et al. (PNAS, 2002)

the numbers
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

data mining crystallization conditions
Data mining crystallization conditions

Minimize initial crystallization screens

data mining crystallization conditions1
Data mining crystallization conditions

Minimize initial crystallization screens

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

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%

identify minimal crystallization screens
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

core screen best 96 conditions crystallize 448 proteins
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)

data mining protein properties
Data mining protein properties

Improve target selection

data mining protein properties1
Data mining protein properties

Improve target selection

better target selection for jcsg pipeline
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

proteins with 40 or more seg residues rarely crystallize
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

proteins with 40 or more seg residues rarely crystallize1

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
goal more structures
Goal: more structures!

Crystallization

Conditions

Protein

Properties

goal more structures1
Goal: more structures!

Crystallization

Conditions

Protein

Properties

goal more structures2
Goal: more structures!

Crystallization

Conditions

Protein

Properties

goal more structures3
Goal: more structures!

Crystallization

Conditions

Protein

Properties

slide25

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