Experimental design for high throughput protein crystallization Patrick Shaw Stewart

1. Experimental design for high throughput protein crystallization Patrick Shaw Stewart Douglas Instruments Limited (near Oxford, UK): ( A copy of this file can be found at http://www.douglas.co.uk/resrep.htm )

2. Experimental design for high throughput protein crystallization • Largely the same as for low throughput experimental design, but: • Good design is more important. • Don’t waste time thinking – do the thinking first

3. Degree of automation • Crystallization methods (with phase diagrams) • Experimental design – steps of protein crystallization projects

4. Degree of automation • Crystallization methods (with phase diagrams) • Experimental design – steps of protein crystallization projects

5. Automation: don’t over-automate!

6. Automation: don’t over-automate! • Recovery from errors can be very time-consuming • Avoid long chains of automatic systems • Use human buffer zones e.g. move plates by hand

7. Degree of automation • Crystallization methods (with phase diagrams) • Experimental design – steps of protein crystallization projects

8. Vapor diffusion

9. Phase diagram of a protein precipitation nucleation [Protein] metastable zone clear [Precipitant]

10. Thermodynamic processes which develop so slowly as to allow each intermediate step to be an equilibrium state are said to be reversible processes.

11. Phase diagram of a protein p n [Protein] m.z. Vapor diffusion [Precipitant]

12. Vapor diffusion • Works well • Gentle – drop is concentrated AFTER mixing • Doesn’t suit all proteins

13. Dialysis

14. Phase diagram of a protein p n Dialysis [Protein] m.z. V.D. [Precipitant]

15. Dialysis • Gives a lot of control • You have to be patient • Not easy to automate

16. Microbatch

17. Phase diagram of a protein p n Dialysis [Protein] M.B (paraffin) m.z. V.D. [Precipitant]

18. Phase diagram of a protein p n Dialysis [Protein] M.B (paraffin) m.z. V.D. M.B. (Si / paraffin) [Precipitant]

19. Phase diagram of a protein p n Dialysis M.B (p) OPTIMIZATION [Protein] m.z. V.D. M.B. (Si/p) SCREENING [Precipitant]

20. Microbatch • Simple and cheap • Versatile – screening / optimization, different oils, additives, volatile reagents (ethanol, iso-propanol etc.) • Suits some proteins very well

21. Counter-diffusion

22. Phase diagram of a protein p Counter-diffusion n Dialysis [Protein] M.B (paraffin) m.z. V.D. M.B. (Si/paraffin) [Precipitant]

23. Counter-diffusion • Arguably the BEST physical method of crystallization • Gives “self-selection” of crystallization conditions • Not easy to automate, but quite easy to set up by hand • 18 examples in the PDB

24. Counter-diffusion • Arguably the BEST physical method of crystallization • Gives “self-selection” of crystallization conditions • Not easy to automate, but quite easy to set up by hand • 18 8 examples in the PDB

25. What % volume of protein should you use? • 100 nl + 100 nl ? • 200 nl + 100 nl ? • 1 µl + 1 µl ? • 2 µl + 1 µl ?

26. What % of protein should you use? Microbatch with Si. / Par.: n [Protein] m.z. Precipitant saturated [Precipitant]

27. What % of protein should you use? Microbatch with Si. / Par.: n [Protein] Protein stock m.z. 50% Precipitant saturated Precipitant stock [Precipitant]

28. What % of protein should you use? Microbatch with Si. / Par.: n [Protein] Protein stock m.z. 66% 50% Precipitant saturated Precipitant stock [Precipitant]

29. What % volume of protein should you use? • Increasing the proportion of protein in the drop: • Reduces the chance of salt crystals • Facilitates scaling up from nanodrops (personal communication, Heather Ringrose, Pfizer) • Use e.g. 0.2 µl (protein) + 0.1 µl (reservoir soln.) • This scales up to 1 + 1 µl (protein may be lost by denaturation in small samples, and small samples equilibrate faster) • Generally, data mining suggests that you should increase the salt in larger drops

30. Degree of automation • Crystallization methods (with phase diagrams) • Experimental design – steps of protein crystallization projects

31. Conventional Approach • 1. Screening – get first crystals • 2. Optimization – improve crystals

32. Experimental Design Steps Step 1. “Primary Screen.”Approx. 60-dimensional search. Step 2. “Targeted Screen”Approx. 12-dimensional search. Step 3. “Multidimensional Grid”Approx. 5-dimensional search.

33. Experimental Design Steps Step 0. “Prescreen” to find precipitation points 1-dimensional search. Step 1. “Primary Screen.”Approx. 60-dimensional search. Step 2. “Targeted Screen”Approx. 12-dimensional search. Step 3. “Multidimensional Grid”Approx. 5-dimensional search. Step 4. “2-D Grid”2-dimensional search.

34. Experimental Design Steps • Step 0. “Prescreen” to find precipitation points • 1-dimensional search. • E.g. Pre-crystallization assay, • Pre Screening Assay, • Footprint Screen • Use to adjust protein concentration • Automation is available

35. Experimental Design Steps • Step 1. “Primary Screen.”Approx. 60-dimensional search. E.g. Sparse Matrix • Many robotic systems are available • Use pre-mixed solutions

36. Experimental Design Steps Step 2. “Targeted Screen”Approx. 12-dimensional search. 1. Additive approach 2. De novo approach

37. Step 3: “Targeted Screen” 1. Additive approach e.g. You get a hit in Jancarik and Kim screen = 0.2M Mg formate You make a targeted screen by adding 10 % of a second screen to the successful condition:

38. Step 3: “Targeted Screen” • 1. Additive approach • e.g. You get a hit in Jancarik and Kim screen = 0.2M Mg formate • You make a targeted screen by adding a second screen to the • successful condition: • 2.1 0.18M Mg formate + 0.1M Na acetate pH 4.6 • 2.2 0.18M Mg formate + 0.1M Na citrate pH 6.5 • 2.3 0.18M Mg formate + 4% w/v PEG 8000 • 2.4 0.18M Mg formate + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc.

39. Step 3: “Targeted Screen” • 1. Additive approach • e.g. You get a hit in Jancarik and Kim screen = 0.2M Mg formate • You make a targeted screen by adding a second screen to the • successful condition: • 2.1 0.18M Mg formate + 0.1M Na acetate pH 4.6 • 2.2 0.18M Mg formate + 0.1M Na citrate pH 6.5 • 2.3 0.18M Mg formate + 4% w/v PEG 8000 • 2.4 0.18M Mg formate + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc.

40. Step 3: “Targeted Screen” • 1. Additive approach • e.g. You get a hit in Jancarik and Kim screen = 0.2M Mg formate • You make a targeted screen by adding a second screen to the • successful condition: • 2.1 0.18M Mg formate + 0.1M Na acetate pH 4.6 • 2.2 0.18M Mg formate + 0.1M Na citrate pH 6.5 • 2.3 0.18M Mg formate + 4% w/v PEG 8000 • 2.4 0.18M Mg formate + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc. • 2. De novo approach • e.g. You get a hit in Jancarik and Kim screen = 30% w/v PEG 1500 • You mix up a targeted screen by adding a second screen to the • successful condition:

41. Step 3: “Targeted Screen” • 1. Additive approach • e.g. You get a hit in Jancarik and Kim screen = 0.2M Mg formate • You make a targeted screen by adding a second screen to the • successful condition: • 2.1 0.18M Mg formate + 0.1M Na acetate pH 4.6 • 2.2 0.18M Mg formate + 0.1M Na citrate pH 6.5 • 2.3 0.18M Mg formate + 4% w/v PEG 8000 • 2.4 0.18M Mg formate + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc. • 2. De novo approach • e.g. You get a hit in Jancarik and Kim screen = 30% w/v PEG 1500 • You mix up a targeted screen by adding a second screen to the • successful condition: • 3.1 30% v/v PEG 600 • 3.2 20% w/v PEG 4000 • 3.3 25% w/v PEG 1500 + 0.1M Na acetate pH 4.6 • 3.4 20% w/v PEG 4000 + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc.

42. Step 3: “Targeted Screen” • 1. Additive approach • e.g. You get a hit in Jancarik and Kim screen = 0.2M Mg formate • You make a targeted screen by adding a second screen to the • successful condition: • 2.1 0.18M Mg formate + 0.1M Na acetate pH 4.6 • 2.2 0.18M Mg formate + 0.1M Na citrate pH 6.5 • 2.3 0.18M Mg formate + 4% w/v PEG 8000 • 2.4 0.18M Mg formate + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc. • 2. De novo approach • e.g. You get a hit in Jancarik and Kim screen = 30% w/v PEG 1500 • You mix up a targeted screen by adding a second screen to the • successful condition: • 3.1 30% v/v PEG 600 • 3.2 20% w/v PEG 4000 • 3.3 25% w/v PEG 1500 + 0.1M Na acetate pH 4.6 • 3.4 20% w/v PEG 4000 + 4% w/v 2-methyl-2,4-pentanediol • ……………. etc.

43. Step 3: “Targeted Screen” • 1. Additive approach • Easy to set up / automate • Some limits on where you can go • Doesn’t greatly reduce the number of variables that you have to deal with • E.g. Nextal’s Optimizer • De novo approach • Difficult and slow to automate • All areas of crystallization space are accessible • Contributes to the reduction of the number of variables • E.g. Matrix Maker, Pick & Mix software • Allows “reshuffling” of ingredients in separate hits

44. Experimental Design Steps Step 3. “Multidimensional Grid”Approx. 5-dimensional search. E.g. Central Composite, Box Behnken, XSTEP Autodesign

45. Multivariate experimental design • Almost all protein crystallization experiments have at least 4 parameters: • Protein concentration • Precipitant concentration • pH • Temperature • Additive ? …………….

46. Central Composite design

47. Box-Behnken design

48. The Autodesign function of XSTEP ….

49. …. automatically fills a “spreadsheet” …

50. …. and XSTEP executes it.