Surface Entropy Reduction Methodology and Application

Surface Entropy Reduction

Methodology and Application

David Cooper for

Zygmunt Derewenda

Crystallization bySurface Entropy

ReductionSystematically altering the protein surface to facilitate crystallization

Lysine GlutamateRotamers Rotamers

Candidate Proteins:•Soluble and purify well•Difficult to crystallize or diffract poorly•Contain a cluster of highly-entropic residues

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.

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)

The RGSL domain of PDZRhoGEFLongenecker 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 domainCierpicki, T. et al, & Derewenda, Z.S. Proteins (2006) 1:874-82

The Ohr protein of B. subtilisCooper, D. et al. & Derewenda, Z.S. in preparation

Human NudC C-terminal domainZheng, 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)The CUE:ubiquitin complex

Prag G et al., & Hurley JH, Cell (2003) 113:609-20Unactivated insulin-like growth factor-1 receptor kinase

Munshi, S. et al. & Kuo, L.C. Acta Cryst (2003) D59:1725-1730Human choline acetyltransferase

Kim, A-R., et al. & Shilton, B. H. Acta Cryst (2005) D61, 1306-1310

Activated factor XI in complex with benzamidineJin, L., et al. & Strickler, J.E. Acta Cryst (2005) D61:1418-1425

Axon guidance protein MICALNadella, M., et al. & Amzel, M.L. PNAS (2005) 102:16830-16835

Functionally intact Hsc70 chaperoneJiang, J., et al. & Sousa, R. Molecular Cell (2005) 20:513-524

L-rhamnulose kinase from E. coliGrueninger 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 AMakabe, K., et al. & Koide, S. Protein Science, (2006) 15:1907-1914

SH2 domain from the SH2-B murine adapter proteinHu, J., & Hubbard, S.R J Mol Biol, (2006) 361:69-79

Mycoplasma arthriditis-derived mitogenGuo, 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.

Evaluated the use of other amino acids at crystal forming interfaces: Alanine, Histidine, Serine, Threonine, Tyrosine

A B C D

E F G H I

Optimizing SER

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

Alanine, Histidine, Serine, Threonine, Tyrosine

Optimized the screening protocols.

Overall approach: Replace 8 high entropy clusters with Ala, His, Ser, Thr and Tyr

Our Screening ProcessStandard 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

The Most successful MutantK138Y, K141Y (also known as DY)

•34 hits in the traditional screen•35 hits in the salt screen

Wild TypeNo hits in the traditional screen1 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

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.

Selection CriteriaNo homologues with > 30 identity.Easy to express, purify, and concentrate.Failed at Crystallization stage.High SERp Score.

Some successes

Apc22734 (K347A-E349A-K350A)

Apc22720 (K90A-E91A-K92A)

Apc1126(K18A, E20A, Q21A)

DinB --Apc36150WT 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

OverallStandardized protocols, stocks and buffersUsing G-mail Calendar to schedule equipmentUsing internal web pages to track target progress

Will be linked to ISFI website and TargetDB

Goal: Reduce the time, expense, and effort it takes to screen mutants

Streamlining the UVA Pipeline

OverallStandardized protocolsStock and common buffersUsing Google Calendar to schedule equipment

Protein Expression HighlightsUsing 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.)

Goal: reduce the time, expense, and effort it takes to screen mutants

Streamlining the UVA Pipeline

OverallStandardized protocols, stocks and buffersUsing Google Calendar to schedule equipment

Protein Expression HighlightsUsing 2-Liter Bottles doubles shaker space (Now 9 proteins a day)Lining centrifuge bottles with zipper bags (Dramatically reduces harvesting time)

Protein Purification HighlightsStreamlined Purification Protocol

HisTrap Phenyl Sepharose Desalt Screen

Custom web interface for AKTA Prime Systems

Goal: reduce the time, expense, and effort it takes to screen mutants

Streamlining the UVA Pipeline

OverallStandardizing things and using computers efficiently

Protein Expression HighlightsUsing Pepsi Bottles and Ziplocs

Protein Purification HighlightsCustom web interface for AKTA Prime SystemsStreamlined Purification Protocol (HisTrap Phenyl Sepharose Desalt Screen)

CrystallizationAlternate reservoir and standard screening.

Mosquito Crystallization Robot for screening.Custom BioRobot3000 application with web interface:

Crystallization Grid Screen GeneratorWill incorporate CLIMS for data maintenance

Goal: reduce the time, expense, and effort it takes to screen mutants

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 productionWe 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 CLIMSWe 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 ListLess 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.

University of VirginiaZygmunt DerewendaDavid CooperTomek BoczekWonChan ChoiUrszula DerewendaKasia GrelewskaNatalya OlekhnovichGosia PinkowskaMichal ZawadzkiMeiying Zheng

Lawrence Livermore National LaboratoryBrent Segelke Dominique ToppaniMarianne KavanaghTimothy Lekin

Lawrence Berkeley National LaboratoryLi-Wei Hung Evan BurseyThiru RadhakannanJim WellsMinmin Yu

University of ChicagoAnthony Kossiakoff Shohei Koide Magdalena BukowskaVince CancasciSanjib DuttaKaori EsakiJames HornAkiko KoideValya TerechkoSerdar UysalJingdong Ye

Los Alamos National LaboratoryTom Terwilliger Geoffrey WaldoChang Yub KimEmily AlipioCarolyn BellStephanie

CabantousNatalia FriedlandPawel ListwanJin Ho MoonJean-Denis PedelacqTheresa Woodruff

UCLADavid Eisenberg Daniel AndersonSum ChanLuki GoldschmidtCelia GouldingTom HoltonMarkus KaufmannArturo Medrano-

SotoMaxim PashkovTeng Poh KhengMichael StrongPoh Teng

Acknowledgements

Supplemental slides follow.

Target Residue Evaluation

RhoGDI Crystal Forms