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18th Jan, 2017
Protein crystallization breakthrough: the power of rMMS microseeding
Harness the power of seeding.
Roughly double the number of structures your lab can produce using random Microseed Matrix-Screening 'rMMS' for:
More crystal hits. [1, 2]
Better diffracting crystals. [1, 2]
Greater control of the number of crystals. [3]
Protein crystallization success with Seeding: the rMMS method
The technique of adding crystal seed-stock to random screens (rMMS) is a significant breakthrough in protein crystallization. Many groups find that routine rMMS can roughly double the number of structures that a typical group can produce. rMMS not only produces more hits, it also typically generates better-diffracting crystals [1, 2] – because crystals are more likely to grow in the metastable zone of the protein’s phase diagram. (Below: example of a dilution experiment with crystals growing in a metastable condition.)
Note also that in cases where only one or a few crystals are obtained in screening experiments, the seed stock that can be made is very valuable – often more valuable than the protein sample. It is therefore a great advantage to be able to use the smallest possible sample of seed stock. Using any robot from the Oryx range, seeding can be performed to a whole 96-well plate using only 1.5 µl of seed stock.
Free rMMS toolkit
Successful rMMS experiments require the correct tools. Douglas Instruments have produced the 'rMMS toolkit' containing everything you need to do an rMMS experiment:
Glass probe for breaking crystals
Crystal crushing bead and tube
A notepad for recording crystal hits
Useful references
Instructions for preparing the seedstock and setting up an rMMS experiment
rMMS toolkits are available free of charge for anyone interested in the method. To request a free rMMS toolkit please click here or on the button below.
A brief introduction to rMMS
The video below explains the basic theory of rMMS microseeding. It also shows a case study of cross seeding rMMS [4]
Please contact Douglas Instruments for more information
References
[1] D'Arcy, Allan, Terese Bergfors, Sandra W. Cowan-Jacob, and May Marsh.
"Microseed matrix screening for optimization in protein crystallization: what have we learned?."Acta Crystallographica Section F: Structural Biology Communications 70.9 (2014): 1117-1126.
[2] Obmolova, Galina, Thomas J. Malia, Alexey Teplyakov, Raymond W. Sweet, and Gary L. Gilliland.
"Protein crystallization with microseed matrix screening: application to human germline antibody Fabs." Acta Crystallographica Section F: Structural Biology Communications 70.8 (2014): 1107-1115.
[3] Shaw Stewart, Patrick D., et al.
"Random microseeding: a theoretical and practical exploration of seed stability and seeding techniques for successful protein crystallization." Crystal Growth & Design 11.8 (2011): 3432-3441.
[4] Obmolova, Galina, Thomas J. Malia, Alexey Teplyakov, Raymond Sweet, and Gary L. Gilliland. "Promoting crystallization of antibody–antigen complexes via microseed matrix screening." Acta Crystallographica Section D: Biological Crystallography 66.8 (2010): 927-933.
Roughly double the number of structures your lab can produce using random Microseed Matrix-Screening 'rMMS' for:
More crystal hits. [1, 2]
Better diffracting crystals. [1, 2]
Greater control of the number of crystals. [3]
Protein crystallization success with Seeding: the rMMS method
The technique of adding crystal seed-stock to random screens (rMMS) is a significant breakthrough in protein crystallization. Many groups find that routine rMMS can roughly double the number of structures that a typical group can produce. rMMS not only produces more hits, it also typically generates better-diffracting crystals [1, 2] – because crystals are more likely to grow in the metastable zone of the protein’s phase diagram. (Below: example of a dilution experiment with crystals growing in a metastable condition.)
Note also that in cases where only one or a few crystals are obtained in screening experiments, the seed stock that can be made is very valuable – often more valuable than the protein sample. It is therefore a great advantage to be able to use the smallest possible sample of seed stock. Using any robot from the Oryx range, seeding can be performed to a whole 96-well plate using only 1.5 µl of seed stock.
Free rMMS toolkit
Successful rMMS experiments require the correct tools. Douglas Instruments have produced the 'rMMS toolkit' containing everything you need to do an rMMS experiment:
Glass probe for breaking crystals
Crystal crushing bead and tube
A notepad for recording crystal hits
Useful references
Instructions for preparing the seedstock and setting up an rMMS experiment
rMMS toolkits are available free of charge for anyone interested in the method. To request a free rMMS toolkit please click here or on the button below.
A brief introduction to rMMS
The video below explains the basic theory of rMMS microseeding. It also shows a case study of cross seeding rMMS [4]
Please contact Douglas Instruments for more information
References
[1] D'Arcy, Allan, Terese Bergfors, Sandra W. Cowan-Jacob, and May Marsh.
"Microseed matrix screening for optimization in protein crystallization: what have we learned?."Acta Crystallographica Section F: Structural Biology Communications 70.9 (2014): 1117-1126.
[2] Obmolova, Galina, Thomas J. Malia, Alexey Teplyakov, Raymond W. Sweet, and Gary L. Gilliland.
"Protein crystallization with microseed matrix screening: application to human germline antibody Fabs." Acta Crystallographica Section F: Structural Biology Communications 70.8 (2014): 1107-1115.
[3] Shaw Stewart, Patrick D., et al.
"Random microseeding: a theoretical and practical exploration of seed stability and seeding techniques for successful protein crystallization." Crystal Growth & Design 11.8 (2011): 3432-3441.
[4] Obmolova, Galina, Thomas J. Malia, Alexey Teplyakov, Raymond Sweet, and Gary L. Gilliland. "Promoting crystallization of antibody–antigen complexes via microseed matrix screening." Acta Crystallographica Section D: Biological Crystallography 66.8 (2010): 927-933.
13th Jan, 2017
Protein Crystallization using Microbatch-Under-Oil
Microbatch-Under-Oil is great for:
Membrane proteins
Preventing protein 'skin' forming
Reproducability - no drop equilibration when using paraffin
Getting different results to vapor diffusion
Proteins in higher conc. buffers
Microbatch-under-oil is a very simple approach to crystallization. Small samples of protein (100 nl to 5 µl) are mixed with stock solutions in small drops, and covered with oil to prevent evaporation. For screening experiments it helps to use a 50:50 mixture of paraffin oil and silicone oil. The silicone allows slow evaporation over about a month, which gives a scanning effect across the phase diagram of the protein. For optimization, pure paraffin oil can be used, which reduces evaporation to a minimum. Studies have shown that microbatch finds as many or slightly more hits that vapor diffusion, but the main advantage is that (for reasons that may not be well-understood) certain proteins crystallize much better in microbatch than other methods. Microbatch can help to protect sensitive proteins such as membrane proteins and anaerobically-produced proteins because it reduces the oxidation and gives thinner skins on the surfaces of drops.
Using the Combined MB-VD experiment on Douglas Instruments Oryx robots it is possible to dispense screening experiments for both microbatch-under-oil and vapor diffusion methods at the same time.
The microbatch-under-oil method, and the IMPAX crystallization system, were invented by Imperial College and Douglas Instruments in collaboration (Chayen et al. J. App. Crystallography. 23(1990) 297-302). Microbatch-under-oil has become a well-established and convenient crystallization method, and it is a true batch method when drops are covered with paraffin oil. Because the experiment is covered with a layer of oil the concentration of the drop remains constant. This means the condition can be scaled up without adjusting the concentration.
Under Oil 2D gradients - Phase diagram experiments
Oryx robots now have a range of dedicated experiments for microbatch-under-oil. The robots are designed to dispense the aqueous drop into a 'dry' well and then automatically cover the aqueous drop with a volume of paraffin, silicon or Al's oil. This ensures high accuracy, dispensing drops volumes as small as 100 nL protein + 100 nL precipitant.
For more information about under-oil crystallization please visit our website or contact us.
Membrane proteins
Preventing protein 'skin' forming
Reproducability - no drop equilibration when using paraffin
Getting different results to vapor diffusion
Proteins in higher conc. buffers
Microbatch-under-oil is a very simple approach to crystallization. Small samples of protein (100 nl to 5 µl) are mixed with stock solutions in small drops, and covered with oil to prevent evaporation. For screening experiments it helps to use a 50:50 mixture of paraffin oil and silicone oil. The silicone allows slow evaporation over about a month, which gives a scanning effect across the phase diagram of the protein. For optimization, pure paraffin oil can be used, which reduces evaporation to a minimum. Studies have shown that microbatch finds as many or slightly more hits that vapor diffusion, but the main advantage is that (for reasons that may not be well-understood) certain proteins crystallize much better in microbatch than other methods. Microbatch can help to protect sensitive proteins such as membrane proteins and anaerobically-produced proteins because it reduces the oxidation and gives thinner skins on the surfaces of drops.
Using the Combined MB-VD experiment on Douglas Instruments Oryx robots it is possible to dispense screening experiments for both microbatch-under-oil and vapor diffusion methods at the same time.
The microbatch-under-oil method, and the IMPAX crystallization system, were invented by Imperial College and Douglas Instruments in collaboration (Chayen et al. J. App. Crystallography. 23(1990) 297-302). Microbatch-under-oil has become a well-established and convenient crystallization method, and it is a true batch method when drops are covered with paraffin oil. Because the experiment is covered with a layer of oil the concentration of the drop remains constant. This means the condition can be scaled up without adjusting the concentration.
Under Oil 2D gradients - Phase diagram experiments
Oryx robots now have a range of dedicated experiments for microbatch-under-oil. The robots are designed to dispense the aqueous drop into a 'dry' well and then automatically cover the aqueous drop with a volume of paraffin, silicon or Al's oil. This ensures high accuracy, dispensing drops volumes as small as 100 nL protein + 100 nL precipitant.
For more information about under-oil crystallization please visit our website or contact us.
13th Jan, 2017
Data mining of the Protein Data Bank
www.douglas.co.uk
Data mining of the PDB
Introduction
The following data were mined from remark 280 of the PDB, which gives details of crystallization conditions. This involved downloading gigabytes of data, and analysing it with Perl and Python scripts. Further details are given in the Methods section, below.
1. The most popular organic and salt precipitants
Ammonium sulfate was the most popular precipitant with 900 entries, followed by PEG 4K and PEG 8K. However, if you combine the medium and high molecular-weight PEGs (1968 entries) they easily outnumber ammonium sulfate. Salts are generally less popular than organic materials. For more information see http://www.douglas.co.uk/top14.htm
2. The temperature used in crystallization experiments
Room temperature is the most popular temperature, followed by 4-8°C. Prior to 1998, 4°C was the most popular temperature. Since then, the range 24-28°C has increased in popularity, which may reflect the higher proportion of proteins that were crystallized from thermophilic organisms.
3. The protein concentration used in crystallization experiments
Proteins have been crystallized at concentrations as low as 0.75 mg/ml and as high as 300 mg/ml. Five, 10 and 20 mg/ml are over-represented because these concentrations are often selected at the start of the crystallization procedure and not adjusted during optimization. Without this bias, the most successful concentration would probably be around15 mg/ml .
4. The protein concentration used for crystallization, plotted against the number of amino acids in all chains; also.
The number of aminoacids was extracted from the PDB file. In oligomeric complexes, the total number of amino acids in all chains was counted (this is the same as the number of amino acids in each chain multiplied by the number of monomers in the complex). Small correlations were seen in both cases, with smaller proteins being crystallized with higher protein and ammonium sulfate concentrations on average, compared to larger proteins. The average ammonium sulfate concentration used for proteins with fewer than 250 amino acids was 1.80M, while for those with over 1000 it was 2.01M. Similarly, the average protein concentration used for proteins with fewer than 250 amino acids was 16.14 mg/ml, while for those with over 1000 it was 13.18 mg/ml. Peat et al. performed a similar analysis using a standard z-test (Acta Cryst. (2005). D61, 1662-1669). They found that the relationship between molecular weight of a protein and the concentration of ammonium sulfate used for crystallization was "highly statistically significant" (p1666).
5. The acid-base character of proteins crystallized and the pH used for crystallization
It is noticeable that basic proteins are under-represented in the PDB, possibly because of the tendency for lysine side-chains to be disordered. The four charged residues Asp, Glu, Arg and Lys are present in approximately the same frequencies in humans (in E. coli the basic residues are slightly more abundant) This may suggest the use of surface entropy reduction to crystallize basic proteins (Protein Science. 16:1569-1576 (2007 Aug).) However, there is no evidence that manipulating the pH can provide extra help in crystallizing basic proteins.
Methods
A complete set of PDB files was downloaded, and analysed using PERL and Python scripts. As well as the crystallization conditions from REMARK 280, the date of the structure, the description, the sequence and the atomic co-ordinates were also pulled out. After this, the crystallization conditions were normalized by hundreds of substitutions using regular expressions in a PERL script. For example ammonium sulphate was listed as AS, A.S., AMM.S., A-S, AMM. SULF., AMONIUM SULPH. etc. All of these listings were normalized and replaced by AM_SULF. Also, the concentrations were converted to molarities, and the concentration were placed in front of the name of the chemical (“AM_SULF, 0.1 M” became “0.1 M AM_SULF”). This was converted into a CSV file that could be loaded into Excel. The ingredients were further sorted into columns, with the most common precipitants (ammonium sulphate, sodium chloride, PEG 4K and PEG8K) assigned to separate columns. Temperature, date, the number of amino acids in the sequence and pH were also included. Temperatures in Fahrenheit and Kelvin were converted to centigrade.
Many entries in the PDB were either completely missing, or could not be parsed. A total of 3939 entries could be parsed. Where two or more different pHs were mentioned in REMARK 280, the pHs were ignored. The data covers the period up to October 2004. If anyone is interested in using the scripts to acquire an up-to-date data set, please contact Patrick Shaw Stewart.
The last plot was generated by a more complex analysis where the exposed areas of residues on the surfaces of proteins was calculated using the CCP4 program AREAIMOL.
Data was extracted by Peter J. Leicester and Patrick D. Shaw Stewart. Analysis was carried out by Patrick Shaw Stewart.
Data mining of the PDB
Introduction
The following data were mined from remark 280 of the PDB, which gives details of crystallization conditions. This involved downloading gigabytes of data, and analysing it with Perl and Python scripts. Further details are given in the Methods section, below.
1. The most popular organic and salt precipitants
Ammonium sulfate was the most popular precipitant with 900 entries, followed by PEG 4K and PEG 8K. However, if you combine the medium and high molecular-weight PEGs (1968 entries) they easily outnumber ammonium sulfate. Salts are generally less popular than organic materials. For more information see http://www.douglas.co.uk/top14.htm
2. The temperature used in crystallization experiments
Room temperature is the most popular temperature, followed by 4-8°C. Prior to 1998, 4°C was the most popular temperature. Since then, the range 24-28°C has increased in popularity, which may reflect the higher proportion of proteins that were crystallized from thermophilic organisms.
3. The protein concentration used in crystallization experiments
Proteins have been crystallized at concentrations as low as 0.75 mg/ml and as high as 300 mg/ml. Five, 10 and 20 mg/ml are over-represented because these concentrations are often selected at the start of the crystallization procedure and not adjusted during optimization. Without this bias, the most successful concentration would probably be around15 mg/ml .
4. The protein concentration used for crystallization, plotted against the number of amino acids in all chains; also.
The number of aminoacids was extracted from the PDB file. In oligomeric complexes, the total number of amino acids in all chains was counted (this is the same as the number of amino acids in each chain multiplied by the number of monomers in the complex). Small correlations were seen in both cases, with smaller proteins being crystallized with higher protein and ammonium sulfate concentrations on average, compared to larger proteins. The average ammonium sulfate concentration used for proteins with fewer than 250 amino acids was 1.80M, while for those with over 1000 it was 2.01M. Similarly, the average protein concentration used for proteins with fewer than 250 amino acids was 16.14 mg/ml, while for those with over 1000 it was 13.18 mg/ml. Peat et al. performed a similar analysis using a standard z-test (Acta Cryst. (2005). D61, 1662-1669). They found that the relationship between molecular weight of a protein and the concentration of ammonium sulfate used for crystallization was "highly statistically significant" (p1666).
5. The acid-base character of proteins crystallized and the pH used for crystallization
It is noticeable that basic proteins are under-represented in the PDB, possibly because of the tendency for lysine side-chains to be disordered. The four charged residues Asp, Glu, Arg and Lys are present in approximately the same frequencies in humans (in E. coli the basic residues are slightly more abundant) This may suggest the use of surface entropy reduction to crystallize basic proteins (Protein Science. 16:1569-1576 (2007 Aug).) However, there is no evidence that manipulating the pH can provide extra help in crystallizing basic proteins.
Methods
A complete set of PDB files was downloaded, and analysed using PERL and Python scripts. As well as the crystallization conditions from REMARK 280, the date of the structure, the description, the sequence and the atomic co-ordinates were also pulled out. After this, the crystallization conditions were normalized by hundreds of substitutions using regular expressions in a PERL script. For example ammonium sulphate was listed as AS, A.S., AMM.S., A-S, AMM. SULF., AMONIUM SULPH. etc. All of these listings were normalized and replaced by AM_SULF. Also, the concentrations were converted to molarities, and the concentration were placed in front of the name of the chemical (“AM_SULF, 0.1 M” became “0.1 M AM_SULF”). This was converted into a CSV file that could be loaded into Excel. The ingredients were further sorted into columns, with the most common precipitants (ammonium sulphate, sodium chloride, PEG 4K and PEG8K) assigned to separate columns. Temperature, date, the number of amino acids in the sequence and pH were also included. Temperatures in Fahrenheit and Kelvin were converted to centigrade.
Many entries in the PDB were either completely missing, or could not be parsed. A total of 3939 entries could be parsed. Where two or more different pHs were mentioned in REMARK 280, the pHs were ignored. The data covers the period up to October 2004. If anyone is interested in using the scripts to acquire an up-to-date data set, please contact Patrick Shaw Stewart.
The last plot was generated by a more complex analysis where the exposed areas of residues on the surfaces of proteins was calculated using the CCP4 program AREAIMOL.
Data was extracted by Peter J. Leicester and Patrick D. Shaw Stewart. Analysis was carried out by Patrick Shaw Stewart.
