Transcript Surface Entropy Reduction: Methodology and

Surface Entropy Reduction
Methodology and Application
David Cooper for
Zygmunt Derewenda
Crystallization by
Surface Entropy Reduction
Systematically altering the protein surface to
facilitate crystallization
Lysines and Glutamates on the protein’s
surface create an “entropy shield” that
can prevent crystallization.
Lysine
Rotamers
“SER structures” usually have crystal
contacts involving the engineered
residues.
Candidate Proteins:
•Soluble and purify well
•Difficult to crystallize or diffract poorly
•Contain a cluster of highly-entropic residues
Glutamate
Rotamers
Our Model Protein -- RhoGDI
 Meets all SER criteria
 Rich in lysines (10.1%) and glutamates (7.9%)
(average incidence of 7.2% and 3.7%, respectively)
 It took years to get a poorly-diffracting wild-type crystal.
(Longenecker, et al Acta Cryst. D57:679-688. 2001)
(Mateja, et al Acta Cryst. D58:1983-91. 2002)
Our SER Structures










The RGSL domain of PDZRhoGEF
Longenecker KL, et al. & Derewenda Z.S. Structure (2001) 9:559-69
The LcrV antigen of the plague-causing bacterium Yersinia pestis
Derewenda, U. et al. & Waugh, D.S. Structure (2001) 9:559-69
Product of the YkoF B. subtilis gene
Devedjiev, Y. et al. & Derewenda, Z.S. J Mol Biol (2004) 343:395-406
Product of the YdeN B. subtilis gene
Janda, I. et al. & Derewenda, Z.S. Acta Cryst (2004) D60: 1101-1107
Product of the Hsp33 B. subtilis gene
Janda, I. et al. & Derewenda, Z.S. Structure (2004) 12:1901-1907
The product of the YkuD B. subtilis gene
Bielnicki, J. et al. & Derewenda, Z.S. Proteins (2006) 1:144-51
Human Doublecortin N-terminal domain
Cierpicki, T. et al, & Derewenda, Z.S. Proteins (2006) 1:874-82
The Ohr protein of B. subtilis
Cooper, D. et al. & Derewenda, Z.S. in preparation
Human NudC C-terminal domain
Zheng, M. et al. & Derewenda, Z.S. in preparation
APC1446 -- Crystals diffracting to 3.0 Å, but unsolved.
**MCSG Targets**
Publications by other labs using SER
Novel proteins (black) or
higher quality crystal forms (green)
The CUE:ubiquitin complex
Prag G et al., & Hurley JH, Cell (2003) 113:609-20
Unactivated insulin-like growth factor-1 receptor kinase
Munshi, S. et al. & Kuo, L.C. Acta Cryst (2003) D59:1725-1730
Human choline acetyltransferase
Kim, A-R., et al. & Shilton, B. H. Acta Cryst (2005) D61, 1306-1310
Activated factor XI in complex with benzamidine
Jin, L., et al. & Strickler, J.E. Acta Cryst (2005) D61:1418-1425
Axon guidance protein MICAL
Nadella, M., et al. & Amzel, M.L. PNAS (2005) 102:16830-16835
Functionally intact Hsc70 chaperone
Jiang, J., et al. & Sousa, R. Molecular Cell (2005) 20:513-524
L-rhamnulose kinase from E. coli
Grueninger D, & Schultz, G.E. J Mol Biol (2006) 359:787-797
T4 vertex gp24 protein
Boeshans, K.M., et al. & Ahvazi, B. Protein Expr Purif (2006) 49:23543
Borrelia burgdorferi outer surface protein A
Makabe, K., et al. & Koide, S. Protein Science, (2006) 15:1907-1914
SH2 domain from the SH2-B murine adapter protein
Hu, J., & Hubbard, S.R J Mol Biol, (2006) 361:69-79
Mycoplasma arthriditis-derived mitogen
Guo, Y., et al., & Li, H. J., Acta Cryst (2006) F62:238-241
Ongoing Work and Progress
 SER method development
 Which target residues are best?
 What is the most effective screening
method?
 How should mutation sites be selected?
 Method Application and Validation.
 Incorporating Bioinformatics into
Target Selection.
 Development of the UVA pipeline.
 Structures and crystals.
Optimizing SER
 Evaluated the use of other amino acids at crystal
forming interfaces:
Alanine, Histidine, Serine, Threonine, Tyrosine
E
A
B
C
D
F
G
H
I
Optimizing SER
 Evaluated the use of other amino acids at crystal forming interfaces:
Alanine, Histidine, Serine, Threonine, Tyrosine
 Optimized the screening protocols.
Target Residue Evaluation
Overall approach:
Replace 8 high entropy clusters with Ala, His, Ser, Thr and Tyr
Our Screening Process
Standard Screen
 Drops of Super Screen reagent + protein
Our Super Screen is very similar to JCSG+
We now use JCSG+

Reservoir is 100 l of Super Screen reagent
“Salt” Screen
 Drops of Super Screen reagent + protein
 Reservoir is 100 l of 1.5 M NaCl
Wild-Type RhoGDI


Failed to crystallize in the Standard Screen
1 hit in the Salt screen
The Most successful Mutant
K138Y, K141Y (also known as DY)
•34 hits in the traditional screen
•35 hits in the salt screen
Wild Type
No hits in the traditional screen
1 hit in the salt screen
Observations:




Alanine, tyrosine and threonine can be effectively
used as crystal-contact mediating residues.
The salt screens produced almost 33% more hits –
242 vs. 183.
Performing traditional and alternative reservoir
screening greatly increases the chances of getting a
hit and greatly increases the number of conditions
that give hits.
At certain surface locations some amino acids seem to
nucleate crystal contacts better than others. Thus,
different amino acids may be tried at each selected
site to increase chances of success.
Optimizing SER (reprise)
 Evaluated the use of other amino acids at crystal forming interfaces:
Alanine, Histidine, Serine, Threonine, Tyrosine
 Optimized the screening protocols.
 Incorporating bioinformatics
into surface engineering.



We now routinely use the SERp server to design mutants.
We compared the output of the SERp Server to all SER
Structures, with a good correlation between hand picked
sites and server suggestions.
We are now vetting the server by mutating the top three
predictions for each target we work with.
Progress on MCSG Targets
Selection Criteria
No homologues with > 30 identity.
Easy to express, purify, and
concentrate.
Failed at Crystallization stage.
High SERp Score.
Of the 10 clones
 2 code for proteins with very similar
homologues in the PDB.
 3 can be easily predicted bases on
PDB-Blast
 At least 2 are multidomain proteins.
 At least three require co-factors:
Two Zn and one Co-A
 One is part of a trans-membrane
transport system.
 Several have regions of disorder
predicted.
Some successes
Apc22734
(K347A-E349A-K350A)
Apc22720
(K90A-E91A-K92A)
Apc1126
(K18A, E20A, Q21A)
DinB --Apc36150
WT crystallized in Salt Screen
Optimizing SER (reprise reprise)

Evaluated the use of other amino acids at crystal forming interfaces:
Alanine, Histidine, Serine, Threonine, Tyrosine

Optimized the screening protocols.
 Incorporating bioinformatics – part 2!
Target selection
The “Local Page” allows us to
•record our comments
•post primers that need to be ordered
•upload files
•link to the most pertinent information for each target.
Streamlining the UVA Pipeline
Goal:
Reduce the time, expense, and effort it takes to screen mutants
Overall
Standardized protocols, stocks and buffers
Using G-mail Calendar to schedule equipment
Using internal web pages to track target progress
Will be linked to ISFI website and TargetDB
Streamlining the UVA Pipeline
Goal:
reduce the time, expense, and effort it takes to screen mutants
Overall
Standardized protocols
Stock and common buffers
Using Google Calendar to schedule equipment
Protein Expression Highlights
Using 2-Liter Bottles doubles shaker space
(Now 9 proteins a day capacity)
Lining centrifuge bottles with zipper bags
(Dramatically reduces harvesting time)
Growth and harvesting are done by a 2 person team
(Reduces demand on 1 individual.)
Streamlining the UVA Pipeline
Goal:
reduce the time, expense, and effort it takes to screen mutants
Overall
Standardized protocols, stocks and buffers
Using Google Calendar to schedule equipment
Protein Expression Highlights
Using 2-Liter Bottles doubles shaker space (Now 9 proteins a day)
Lining centrifuge bottles with zipper bags (Dramatically reduces harvesting time)
Protein Purification Highlights
Streamlined Purification Protocol
HisTrap  Phenyl Sepharose  Desalt  Screen
Custom web interface for AKTA Prime Systems
Streamlining the UVA Pipeline
Goal:
reduce the time, expense, and effort it takes to screen mutants
Overall
Standardizing things and using computers efficiently
Protein Expression Highlights
Using Pepsi Bottles and Ziplocs
Protein Purification Highlights
Custom web interface for AKTA Prime Systems
Streamlined Purification Protocol (HisTrap  Phenyl Sepharose  Desalt  Screen)
Crystallization
Alternate reservoir and standard screening.
Mosquito Crystallization Robot for screening.
Custom BioRobot3000 application with web interface:
Crystallization Grid Screen Generator
Will incorporate CLIMS for data maintenance
Experiments to do
 SER vs Reductive methylation of lysines
 Computational SERp Server validations


Compare SERp Server predictions with
surface accessibility of structures already
in the PDB (Outreach to UCLA).
Look for correlations between SERp Server
predictions and regions of protein-protein
interactions. (Outreach to UCLA).
Areas that still need addressing
 Target evaluation -- still time consuming, even with the
collection of links on our “Local Target Page”
 Protein production
We should be using the BioRobot for mutagenesis.
We would like to better utilize the C&PP Facility
(Perhaps even share BioRobot training).
 Crystallography
We would like some training on Phenix.
We need help setting up our own CLIMS
We need help linking our web pages with the ISFI website
and TargetDB
 Sequencing -- We need a new resource for sequencing.
Could reduce costs by sequencing 96 reactions at once instead
of by mutant series.
Conclusions
At UVA we have




Further Developed the SER method.
“Seen the light” about the importance of
bioinformatics in target selection and
choosing mutations.
Developed tools for internal use, ISFI use,
and use by the structural community.
Made progress toward our current “metrics”
while laying the groundwork for more
structures in the future.
Our Wish List
Less redundancy. SG needs common tools.

Bioinformatics gathering for target selection and
protocol matching –the meta-server




Why should we gather or build these tools when the JCSG already has what
appears to be an excellent system.
The Bioinformatics site should be a meta-server that automatically
suggests the most applicable technology.
The public should have access to a “target this please” button or form.
For data management (CLIMS, PHENIX)
Utilize data exchange technologies – share
resources
Remote desktop sharing for training or installations, Skype, Google Calendar
Need better access to Large Center data, especially on targets we select.
Acknowledgements
University of Virginia
Zygmunt Derewenda
David Cooper
Tomek Boczek
WonChan Choi
Urszula Derewenda
Kasia Grelewska
Natalya Olekhnovich
Gosia Pinkowska
Michal Zawadzki
Meiying Zheng
UCLA
David Eisenberg
Daniel Anderson
Sum Chan
Luki Goldschmidt
Celia Goulding
Tom Holton
Markus Kaufmann
Arturo Medrano-Soto
Maxim Pashkov
Teng Poh Kheng
Michael Strong
Poh Teng
Los Alamos National
Laboratory
Tom Terwilliger
Geoffrey Waldo
Chang Yub Kim
Emily Alipio
Carolyn Bell
Stephanie Cabantous
Natalia Friedland
Pawel Listwan
Jin Ho Moon
Jean-Denis Pedelacq
Theresa Woodruff
Lawrence Berkeley
National Laboratory
Li-Wei Hung
Evan Bursey
Thiru Radhakannan
Jim Wells
Minmin Yu
University of Chicago
Anthony Kossiakoff
Shohei Koide
Magdalena Bukowska
Vince Cancasci
Sanjib Dutta
Kaori Esaki
James Horn
Akiko Koide
Valya Terechko
Serdar Uysal
Jingdong Ye
Lawrence Livermore
National Laboratory
Brent Segelke
Dominique Toppani
Marianne Kavanagh
Timothy Lekin
Supplemental slides follow.
Target Residue Evaluation
RhoGDI Crystal Forms