experimental design for high throughput protein crystallization patrick shaw stewart

Microbatch seminar- slide 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 )

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

1. Degree of automation

2. Crystallization methods (with phase diagrams)

3. Experimental design – steps of protein crystallization projects

Automation: don’t over-automate!Per year

Per week

Per day

WorthAutomating? Assumptions

Structures 70 1.4 Target: structures per year 70

Purified proteins 9.8 2Purified proteins/structure

(SPINE) 7Screening:

Plates 38 8 Screens (96 wells) per protein 4(Reservoirs to be dispensed) /

12 64 NO(Drops to be dispensed) * 2 1536 YES

Optimization: Optimizations per structure 1Plates 4.2 0.8 Plates per optimization 3

Reservoirs to be dispensed 81

YES, but it’s not easy (use

microbatch?)(Drops to be dispensed) * 2 161 Yes

Crystal observation:Plates to move to imager 44 9 NO

Images to collect and view 6789 YES Images collected per drop 4Crystals to mount 17 3 NO Crystals per structure (UGA) 12

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

Vapor diffusion

Phase diagram of a protein

[Protein]

[Precipitant]

precipitation

nucleation

metastable zone

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

[Protein]

[Precipitant]

m.z. Vapor diffusion

Vapor diffusion

• Works well

• Gentle – drop is concentrated AFTER mixing

• Doesn’t suit all proteins

Dialysis

[Protein]

[Precipitant]

Dialysis

• Gives a lot of control

• You have to be patient

• Not easy to automate

Microbatch

[Protein]

[Precipitant]

M.B (paraffin)

Dialysis

[Protein]

[Precipitant]

M.B (paraffin)

M.B. (Si / paraffin)

Dialysis

[Protein]

[Precipitant]

M.B (p) OPTIMIZATION

M.B. (Si/p) SCREENING

Dialysis

Microbatch

• Simple and cheap

• Versatile – screening / optimization, different oils, additives, volatile reagents (ethanol, iso-propanol etc.)

• Suits some proteins very well

Counter-diffusion

[Protein]

[Precipitant]

M.B (paraffin)

M.B. (Si/paraffin)

Dialysis

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

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

Counter-diffusion

What % volume of protein should you use?

100 nl + 100 nl ?

200 nl + 100 nl ?

1 µl + 1 µl ?

2 µl + 1 µl ?

What % of protein should you use?

[Protein]

[Precipitant]

Microbatch with Si. / Par.:

Precipitant saturated

[Protein]

[Precipitant]

Protein stock

Precipitant stock

[Protein]

[Precipitant]

Protein stock

Precipitant stock

50%66%

What % volume of protein should you use?

Increasing the proportion of protein in the drop:

1. Reduces the chance of salt crystals

2. 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

Conventional Approach

1. Screening – get first crystals

2. Optimization – improve crystals

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.

Experimental Design StepsStep 0. “Prescreen” to find precipitation points1-dimensional search.

Step 4. “2-D Grid” 2-dimensional search.

Experimental Design StepsStep 0. “Prescreen” to find precipitation points1-dimensional search. E.g. Pre-crystallization assay,

Pre Screening Assay, Footprint Screen

• Use to adjust protein concentration• Automation is available

Step 1. “Primary Screen.” Approx. 60-dimensional search. E.g. Sparse Matrix

• Many robotic systems are available• Use pre-mixed solutions

Step 2. “Targeted Screen” Approx. 12-dimensional search. 1. Additive approach2. De novo approach

Step 3: “Targeted Screen”1. Additive approache.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:

Step 3: “Targeted Screen”1. Additive approache.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 4.62.2 0.18M Mg formate + 0.1M Na citrate pH 6.52.3 0.18M Mg formate + 4% w/v PEG 80002.4 0.18M Mg formate + 4% w/v 2-methyl-2,4-

pentanediol ……………. etc.

2. De novo approache.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 40003.3 25% w/v PEG 1500 + 0.1M Na acetate pH 4.63.4 20% w/v PEG 4000 + 4% w/v 2-methyl-2,4-

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

2.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

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

Multivariate experimental design

Almost all protein crystallization experiments have at least 4 parameters:

1. Protein concentration2. Precipitant concentration3. pH4. Temperature5. Additive ? …………….

Central Composite design

Box-Behnken design

The Autodesign function of XSTEP ….

…. automatically fills a “spreadsheet” …

…. and XSTEP executes it.

Step 4. “2-D Grid” Approx. 2-dimensional search. E.g. XSTEP grids, manual experiments

Xstep Optimization 2-d grid

2-Dimensional grids

• Probably not really needed• Arguably it is good to make very small

changes for production plates (to make crystals for data collection)

• [Precipitant] vs [protein] or [precipitant] vs pH

• Easy to set up and interpret

Experimental Design StepsStep 0. “Prescreen” to find precipitation points1-dimensional search.

Step 4. “2-D Grid” 2-dimensional search.

Reducing the dimensions that must be considered60

Number of dim

ensions that you are thinking

60 dimensions (ingredients)

Primary screen

Number of dim

Primary screen

Number of dim

Thinking ……

Primary screen

Number of dim

Primary screen

Additve-type targeted screen

Number of dim

Primary screen

De novo targeted

screen

Number of dim

Primary screen

De novo targeted

screen Multi-d screen

Number of dim

Primary screen

De novo targeted

screen Multi-d screen

2-d grid

Number of dim

Finally ….

Oryx (arabian)

The Biblical Zoo in Jerusalem

experimental design for high throughput protein crystallization patrick shaw stewart

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