Surface Entropy Reduction Methodology and Application

1. Surface Entropy ReductionMethodology and Application David Cooper for Zygmunt Derewenda

2. 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

3. 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)

4. 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

5. 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

6. 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.

7. 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

8. Optimizing SER • Evaluated the use of other amino acids at crystal forming interfaces: Alanine, Histidine, Serine, Threonine, Tyrosine • Optimized the screening protocols.

9. 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

10. 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

11. 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.

12. 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.

13. 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.

14. Some successes Apc22734 (K347A-E349A-K350A) Apc22720 (K90A-E91A-K92A) Apc1126 (K18A, E20A, Q21A)

15. DinB --Apc36150WT crystallized in Salt Screen

16. 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.

17. 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

18. 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.)

19. 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

20. 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

21. 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).

22. 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.

23. 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.

24. 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.

25. 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.

26. Target Residue Evaluation

27. RhoGDI Crystal Forms