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Surface Entropy Reduction Methodology and Application. David Cooper for Zygmunt Derewenda. Lysine Glutamate Rotamers Rotamers. Crystallization by Surface Entropy Reduction. Systematically altering the protein surface to facilitate crystallization.

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crystallization by surface entropy reduction

Lysine Glutamate

Rotamers Rotamers

Crystallization bySurface 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.

“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
our model protein rhogdi

The E2A series

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.

The K2A series

(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 YkoFB. subtilis gene

Devedjiev, Y. et al. & Derewenda, Z.S. J Mol Biol (2004) 343:395-406

Product of the YdeNB. subtilis gene

Janda, I. et al. & Derewenda, Z.S. Acta Cryst (2004) D60: 1101-1107

Product of the Hsp33B. subtilis gene

Janda, I. et al. & Derewenda, Z.S. Structure (2004) 12:1901-1907

The product of the YkuDB. 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**

Our SER Structures
publications by other labs using ser novel proteins black or higher quality crystal forms green
Publications by other labs using SERNovel 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) 49:235-43

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
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

A B C D

E F G H I

Optimizing SER
  • Evaluated the use of other amino acids at crystal forming interfaces:

Alanine, Histidine, Serine, Threonine, Tyrosine

optimizing ser8
Optimizing SER
  • Evaluated the use of other amino acids at crystal forming interfaces:

Alanine, Histidine, Serine, Threonine, Tyrosine

  • Optimized the screening protocols.
slide9
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

Target Residue Evaluation

slide10

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
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.
slide12

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
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
Some successes

Apc22734

(K347A-E349A-K350A)

Apc22720

(K90A-E91A-K92A)

Apc1126

(K18A, E20A, Q21A)

slide16

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
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 pipeline18
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 pipeline19
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 pipeline20
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
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
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
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
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.

slide25

Acknowledgements

University of Virginia

Zygmunt Derewenda

David Cooper

Tomek Boczek

WonChan Choi

Urszula Derewenda

Kasia Grelewska

Natalya Olekhnovich

Gosia Pinkowska

Michal Zawadzki

Meiying Zheng

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

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

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

Lawrence Berkeley National Laboratory

Li-Wei Hung

Evan Bursey

Thiru Radhakannan

Jim Wells

Minmin Yu

Lawrence Livermore National Laboratory

Brent Segelke

Dominique Toppani

Marianne Kavanagh

Timothy Lekin

Supplemental slides follow.