advances in protein expression: high throughput tools for ......high throughput tools for improving...
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Advances in Protein Expression: High Throughput Tools for Improving Analysis
Webinar 17 April 2013
[0:00:00] Sean Sanders: Hello and welcome to this Science/AAAS audio webinar. I'm Sean
Sanders, editor for custom publishing at Science. Slide 1 The correct expression of proteins in vitro can be a challenging endeavor.
The characterization of protein expression systems, particularly for biotherapeutics and structural biology, requires the testing of many variables to obtain the optimal clones.
Assessing these variables is costly and time‐intensive if done individually.
However, high throughput analytical technologies and multiuse automation platforms can accommodate the high sample throughput necessary to provide fast, accurate and efficient optimization of protein expression and purification conditions. This ultimately improves quality, reduces risk, and accelerates the time to produce proteins for both research and therapeutic applications.
In today's webinar, our exceptional panel of experts will be discussing the
factors to consider when optimizing the conditions for protein expression and characterization experiments, and we'll provide pointers on adapting your workflow to incorporate high throughput analysis.
Slide 2 It gives me great pleasure to introduce our speakers today. They are
Dr. Brandan Hillerich from Albert Einstein College of Medicine in New York and Mr. Jason Payne from Pfenex in San Diego, California. I'm so pleased you could both join us today.
Before we get started, I have information that our audience might find
helpful. Note that you can resize or hide any of the windows in your viewing console. The widgets at the bottom of the console control what you see. Click on these to see the speaker bios or additional information
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about technologies related to today's discussion or to download a PDF of the slides.
Each of our panelists will give a short presentation followed by a Q&A
session, during which our guests will address the questions submitted by our live online viewers. So if you're joining us live, start thinking about some questions now and submit them at any time by typing them into the box on the bottom left of your viewing console and clicking the submit button. If you can see this box, just click the red Q&A widget at the bottom of the screen.
Please remember to keep your questions short and concise as this will
give them the best chance of being put to our panel. You can also log in to your Facebook, Twitter or LinkedIn accounts during the webinar to post updates or send tweets about the event. Just click the relevant widgets at the bottom of the screen. For tweets you can add the hash tag #sciencewebinar.
Finally, thank you to PerkinElmer for their sponsorship of today's
webinar. Slide 3 Now I'd like to introduce our first speaker for today, Dr. Brandan Hillerich.
Dr. Hillerich received his Ph.D. in Molecular Genetics from the University of Georgia, after which he joined the New York Consortium on Membrane Protein Structure where he developed high throughput techniques for the expression and purification of integral membrane proteins. He is now the managing director of High Throughput Protein Production at the Albert Einstein College of Medicine where he oversees a production pipeline that services the New York Structural Genomics Research Consortium and the Enzyme Function Initiative. In the past two years, the facility has produced over 24,000 clones and purified over 4000 proteins, including over 500 expressed in insect and mammalian systems.
A very welcome to you, Dr. Hillerich. Dr. Brandan Hillerich: Thank you, Sean. So I am going to talk a little bit today about our multiplatform expression
evaluation as part of the High Throughput Protein Production facility here at Albert Einstein College of Medicine.
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Slide 4 Our main project and our main source of funding is the Protein Structure
Initiative. For those of you that don't know, the Protein Structure Initiative is an ongoing effort that began in 2000 to accelerate discovery in structural genomics and to contribute to understanding of biological function through atomic structure.
The center based at Albert Einstein is named the New York Structural
Genomics Research Consortium or NYSGRC, and it is one of four PSI: Biology Large‐Scale Centers. NYSGRC has three partnership centers and it acts as the protein production and structured determination facilities for those centers. The one I'm going to talk about today is the Immune Function Network or IFN.
Here at NYSGRC, we rely on the use of automation and high throughput
small‐scale expression screening to rapidly determine which domains, expression systems, cell lines and purification conditions yield enough pure protein for structural studies. We are also using these methods to screen production conditions for full‐length proteins which we'll use for functional characterization.
[0:05:00] Slide 5 So just a little bit of background on the Immune Function Network, it
targets all of the Ig superfamily proteins in the human genome. There are about 500 of these. Some of these members include co‐stimulatory molecules; CD28 and B7‐1 are examples; inhibitory molecules like PD‐1, PD‐L1 and butyrophilins. There's also antigen and antigen receptors, cell adhesion molecules and cytoskeletal regulators.
As far as structure goes, these are a fairly good target set as every one of
these members contains at least one Ig‐like domain which tends to behave fairly well and crystallize fairly well. Many of these IgSF members are involved in human disease and they interact with each other.
And as a future direction of the Immune Function Network, we're going
to be working out ways to identify new interactions of these that are relevant to human physiology and disease and work on elucidating the mechanisms of some known therapeutic targets, and then we will use this information to hopefully design novel reagents which could be
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potential therapeutics and to look at these complexes crystallographically.
Slide 6 So the pipeline that we have built here at Albert Einstein is actually
several pipelines but they run in parallel. Everything starts in our Molecular Biology Core. We can clone several hundred constructs a week and this is the beginning of the funnel that allows us to test mini conditions in cell lines in a high throughput manner.
For the purposes of the talk today, I'm going to just talk a little bit about
our bacterial system but mainly only the refolding part. And then I'll move on to our pIEx based vector system for insect expression and then talk briefly about our mammalian systems.
Slide 7 As I said, these Ig superfamily targets can contain at least one Ig fold, and
over the past several years we've shown that refolding these proteins, making them in E. coli and inclusion by reagent refolding them is a good way to lead the structure.
Here are a few examples. The problem that we encountered was that if
you chose blindly what targets to refold, the success rate was much below 20%, which means that we are wasting at least 80% of our effort. So we decided that we were to use our high throughput cloning core and start testing different domains and trying them and refolding. However, we had to develop a high throughput assay to do this.
Slide 8 So in order to do that, we looked at our process and tried to match it on a
small scale. What we've done now is we've made a screen that you can grow 1 ml cultures, take these, you spin them down and you isolate the inclusion bodies. We can take the inclusion bodies, resolubilize them and do rapid dilution refolding all in a 1 ml reaction volume. This allows us to do many of these things quickly.
Slide 9 Once we finish with the refolding, we take what we have and we load
them onto our PerkinElmer GX II in 384‐well format, and we screen for things that have bands that made it through the refolding process. And
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on the top here there's a representative gel if you will of the ones that are refolded.
But that's not the end of the story because things maybe have refolded
and be soluble, but they may be soluble aggregates. So we take everything that gave a band and we load that onto an HPLC and we look for things that are monomeric and about the size that we want. So as you can see below, the red line is a protein that happens to be an aggregate, and the blue trace is what we're looking for.
And this method has worked very well for us. We've got it down now
where we can sort of flip it, and now 80% of our effort is translated into protein whereas before it was only 20%, and that makes us very happy. But that doesn't give us all the targets we want. As our list is finite, we need to move on to other efforts.
[0:10:11] Slide 10 And so for this, we move to our insect production pipeline. We wanted to
match this, have some sort of small‐scale effort for the insect pipeline as it can be very expensive to scale up from any failures.
Slide 11 So when we moved to this, we decided we needed some automation
help, and what we did was we contracted PerkinElmer to build us a robot, a cell::explorer. Here's a layout. It's all built around a Janus Liquid Handling Robot and has a six‐axis arm. It has a plate reader, a fax analyzer, some incubators.
Slide 12 And here are some pictures of the arm because I just think it's kind of
neat. And what that allows us to do is it allows us to screen hundreds of viral constructs in baculovirus, and we also use this now to be make lentivirus and some of our other mammalian cultures. And so this allows us to screen mini conditions on the small scale and it saves us time and money.
Slide 13
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So because of this we have taken two screening multiple cell lines. In this case I'm showing you two, Sf9 and Hi5, where we grow 3 ml cultures. We do a nickel pull‐down and we look at what's expressed. In this case, you will notice that there's not much difference in what can be expressed in Hi5 and Sf9, but often what we see is the Hi5 gives us better protein yields, so that's our go‐to with Sf9 being a rescue.
Slide 14 And similarly as with the refolding, just seeing a band doesn't tell you the
whole story. Slide 15 So what we have to do again is go back to our HPLC and see if what we're
looking at is a nice monomer or dimer instead of an aggregate. And as you can see here, there's another example of the void, a void peak or an aggregate peak and a monomer. This takes a lot of effort, and it's still expensive to do this on a small scale so we don't want to waste these samples.
Slide 14 So in an effort to rescue them, we have trained our HPLCs to be able to
do buffer screening, and so what you can see on this slide here is in the middle you can see that there are some peaks. Some of those are retained and some of those are void. If we apply a buffer screen which has varying pHs and high and low salt and some additives, urea or L‐arginine, what you can see on C is that we can move more of those proteins from aggregate into retained peaks. This is great and this has allowed us to rescue about 20% of our targets.
Slide 16 This has come online in the last year and I'm happy to report some
successes. We have several crystal structures from our baculoviral expression system. There are three here. We have several more that are being refined now.
So as I said before, the Immune Function Network is mostly about
structure but it's not all about structure. So we want to make these high‐value proteins to be able to use as reagents for structural studies or as antigens to generate antibodies or aptamers or something.
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Slide 17 So to get more at the full‐length proteins we have a mammalian
expression system. We do this two ways. One is we do a sort of traditional select and integrate stable system. We use the cell::explorer again to do our transfections here on the small scale, and we can do a couple of hundred of these a week. Again, we let them grow out for a little while and we then do a nickel pull‐down and score them for expression.
The problem with this is that it takes a lot of time. It takes several weeks
to get these things integrated and to see expression, and then the expression can be unstable or variable. So we've also incorporated a lentiviral system; and yet again, the cell::exploration allows us to do many of these at a time.
[0:14:54] Slide 18 So just a quick example of what our lentiviral system has been able to do,
not only is it fast but we've seen, as you can see in this slide, that we've been able to increase the amount of protein per liter, in this case from 15 milligrams a liter up to 90 milligrams per liter. And on the bottom which is very difficult for you to see, I'm sure, but what we've found is that with the GFP marker, if you let the cells grow out and you can then sort them. And as you do, you can see that you retain many, many more high‐expressing cells, and so this adds to the production level of the protein.
Slide 19 When we first started using this, this was not high throughput. When we
started out, it was using adherent cells; it required serum, purification of the virus. We had to do ultracentrifugation overnight. We use ion exchange and that just slowed the whole process down.
We've changed this around now and we're adapting it to use suspension
cells. We've incorporated tubespin bioreactors which are 50 ml Falcon tubes with a little corkscrew in there, and we've switched over to direct PEI mediated transfection method which is faster and cheaper.
Slide 20
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And the result is that now we have a system, a lentiviral system where we can look at small‐scale screening rapidly for mini constructs. And so just little highlights of the new system, we can produce 30 ml of virus at titers of 1x107. We can easily do 48 of these a week. And that leads to scale‐up production, and this goes from clone to protein in less than two weeks, which is great for us.
Slide 21 And just to show some of what we've been doing, this is a slide of several
important Ig superfamily members made as both Fc fusions for functional studies. And then on the end, we've actually made some of our domains that have been fed into our crystallography pipeline. And what we've found is that this is an excellent complement to our other systems and we're being able to mine more targets out of our target list.
Slide 22 So just to close, I'd like to tell you some of our keys to success. The first
thing to take away is that there are ways to overcome the difficulties associated with production of these proteins. We've attacked this problem by designing multiple domains, trying multiple cell lines and expression systems, and screening multiple buffer conditions whenever we can get the protein but maybe it's not as soluble or as in the proper form that we want.
The major takeaway from our experience is that you should do as much
as you can on the small scale. We prefer to fail quickly at the small scale than slowly at the large scale. Doing this and being able to test constructs on a small scale allows you to save quite a bit of time and money. It's a good idea to try to figure out any way you can to incorporate automation into this process because that's only going to increase your throughput and your reproducibility.
Slide 23 So with that, I'll close and give some acknowledgements here to some of
the people that helped us create this pipeline in this center over the last year. Thank you.
Sean Sanders: Fantastic! Thanks so much, Dr. Hillerich. Slide 24
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We're going to move right on to our second speaker today. That's Mr. Jason Payne.
Originally from the wheat fields of Washington State, Mr. Payne received
his bachelor's and master's degrees from the School of Molecular Biosciences at Washington State University. In 1999, he started his career with Dow Chemical, which had just begun a biotech effort in the San Diego area. It was there that Mr. Payne and his colleagues developed the Pfenex Expression System, using Pseudomonas fluorescens strains. In late 2009, Dow spun out the company Pfenex based on this technology.
Mr. Payne is an analytical scientist with a broad range of experience in a
number of technologies, including laboratory automation, SDS‐capillary gel electrophoresis, high‐pressure liquid chromatography, and liquid chromatography‐mass spectrometry.
[0:20:05] Welcome and thanks for being with us, Mr. Payne. Jason Payne: Thank you, Sean. Slide 25 So today I'm going to tell you a little bit about Pfenex. As Sean said, we
produce proteins and we're located in San Diego. It's based on Pseudomonas fluorescens, which is where the Pf in Pfenex comes from. We have process development capabilities going from molecular biology all the way to purified protein. We're a non‐GMP site, but we do have partners that we can produce GMP quality proteins. We have about 35 employees.
Slide 26 We have several different business segments based on the Pfenex
Expression System. We have lead proteins that we work with other biopharmaceutical companies to produce, vaccines, fusion proteins, scaffold proteins.
We also have partnerships with government agencies to produce malaria
antigens and our anthrax rPA antigen. We have a reagent proteins business where you can go and buy reagent
grade proteins at reagentproteins.com.
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And we also are getting into producing several different biosimilar
products such as GCSF and interferon products. So we have a lot going on for a small company.
Slide 27 So why do we need high throughput technologies? Well, we have a lot of
strains based on the number of promoters, different ribosome binding strains, many different secretion leaders to target the periplasm for correctly folded and disulfide bond formation. And we also have strains that have on the second plasmid chaperone overexpression and disulfide bond isomerase overexpression in order again to try to get soluble, quickly folded proteins. We also have many protease deletion strains for obvious reasons to keep our proteins intact.
And if you do the math there, there are thousands and thousands of
combinations you could try for every project. So we end up with hundreds of thousands of samples and so we need some automated way to prepare those samples and analyze the protein expression.
Slide 28 So we have some robotics and we basically do everything we can in 96‐
well format from transformation of our plasmid ‐‐ well, first of all, we have the gene of interest synthesized and then we put it into the plasmid of choice, usually screened several different secretion leaders and transform those into various host strains, start a seed culture and eventually inoculate and grow about 0.5 ml cultures in 96‐well format.
We usually evaluate about 1000 strains when we get a new protein and
grow those up. Slide 29 So I'll talk a little bit about why we screen so many strains. The thing is
you can't really predict what combination of strain and secretion leader for example or plasmid will produce the best results or the highest expression or the most soluble expression. So you can see in this cartoon that on the left you have plasmid, the different plasmids, and then going up the expression on the right you have the different strains and then an expression going up, and you can see there's a variety of plasmids that work and a variety of strains that work, but it's the combinations that give you the highest expression that we're looking for.
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Slide 30 So we kind of take a two‐tiered approach similar to Brandan's group. We
use CGE on the first tier often, or sometimes if we have a binding assay we'll use Bio‐Layer Interferometry. With CGE you can run a 96‐well plate in about an hour and a half; and the BLI is a little bit faster, about a half an hour per plate. But it takes a little bit more development to get a good protein interaction assay going.
So then after we downselect maybe 50 or 100 samples, we'll do a second
tier analysis using HPLC, even good old Western blots, LC‐MS, and then sometimes go to BLI at that point to look at proper folding and binding if we have that assay.
Slide 31 So we have robotically enabled sample preparation. For some reason I'm
not allowed to show you our robot, but it also looks pretty cool. We have multiple work stations where we use a liquid handler to harvest and dilute the cells and then sonicate. The cells are lysed using a 24‐pin sonicator and then centrifuged to separate our soluble and insoluble fractions, and then we can also use the automation to prepare our CGE plates.
Slide 32 So I just want to say we run CGE every day for strain screening obviously.
We also perform design of experiments on fermentation samples after we downselect strains; purification fractions which would be either based on concentration or whatever the high levels of salt can need dilution; stability study samples and so on. And then last year we ran about 100,000 samples on our GX II instrument.
Slide 33 So I'll talk a little bit about fermentation. Once we do our two‐tiered
screen and select about five strains, five of our best strains, it looked they have high expression, soluble expression. We'll take those into our mini bioreactors which are 5 milliliter disposable cassettes that we can look at different fermentation conditions. And then once we find the best conditions there, we can go to 1 liter or up to 20 liter fermentation scale based on how much material we need for purification or to further optimize the fermentations.
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Slide 34 And so we also, as I mentioned, use CGE for downstream processing.
We'll often look at the capturability, if you will, of the proteins using PhyTips, which are our little resin plugs in pipette tips to bind and loop the proteins from the small‐scale expression. Or we can use filter plates if we want a little bit more resin to work with.
So as usual, with the plates you can bind your protein sample, wash,
elute, and then pick your parameters to go to bench‐scale chromatography, and also we have pilot‐scale chromatography for production of several grams of material.
Slide 35 Here's an example of our resin screening using 96‐well format filter plate,
the Sciclone liquid handler and a centrifuge. So with the CGE, growing samples isn't a problem, so we can easily run 96‐well plates, run the load, run the flow‐through, the wash, the elution fractions. This is an affinity resin and you can see different levels of expression and the amount was able to be purified with the affinity resin.
Slide 36 So some examples of proteins we've produced. We've produced
messenger proteins such as interferon, antibody derivatives such as Fab, antibody fragments, enzymes, and recently Protein G is the product we have.
[0:30:05] Vaccine manufacturing for components such as TcdB, cholera toxin,
tetanus toxin and CRM197 carrier protein, which we have GMP grade material, and then also rPA, the anthrax antigen.
Slide 37 Here's an example of the C. diff (TcdB) expression. It's a very large protein
so we had to use the high molecular weight assay on the GX, but it worked well. And the nice thing is it's away from the main expressed protein below.
Slide 38
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So then we picked our best strains. Okay, here's another example of
tetanus toxin C from Clostridium and you can see different levels of expression, and we decided to purify that for our reagent proteins business.
Slide 39 So here is an example of chromatography of the tetanus toxin. First of all,
you've got anion exchange; and again, with the CGE you can run every fraction and it's not a problem.
Slide 40 The red box indicates the fractions we've proceeded to run on the second
column, which was a mixed mode column, and we're able to get over 95% purity with the two‐column process here.
Slide 41 And then, of course, we do other analytical such as binding activity and
LC‐MS to confirm that we've actually produced the protein that we're trying to, and it's of high quality.
Slide 42 CRM197 is another protein we produce. It's a nontoxic mutant of the
diphtheria toxin. It's a carrier protein of a number of approved vaccines, so it has a lot of possibilities.
We also transferred this to a GMP manufacturing partner site and it's
currently for sale. Slide 43 Okay. So we're able to scale up the CRM197 production, and I just want
to point out that it's an animal‐free based process, and it's currently in phase one trials.
Slide 44 The other thing we use CGE for is the protein stability studies. You can
easily run many samples incubated at different temperatures.
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We have an example of here of a wild type and a mutant protein incubated for one year in the refrigerator and also at 25 degrees, and you can see quite a bit of degradation going on for the wild type with the mutant folding up pretty well.
On the top right is the electropherogram from the CGE. You could see
quite a bit of detail there of what's going on, even more so perhaps than reverse‐phase HPLC on the bottom right.
Slide 45 So one more thing quickly that we use the GX II instrument for is strain
genotyping once we've selected production strains. We use the PCR and the CGE analysis to look at the fragment sizes and verify that we indeed have the strain that we started out with in the 96‐well plate.
Slide 46 So in summary, we can produce many different types of proteins in
Pfenex Expression Technology with high levels of expression and high quality. We use 96‐well format for expression screening and that allows us to screen a lot of strains and keep track of everything.
We also use CGE daily for pretty much everything again. [0:34:57] We purify a variety of vaccine antigens and we intend to develop our
biosimilar programs and vaccine programs continually. Slide 47 I want to thank a few people. Jeff Allen is our Director of Protein
Sciences. Diane is our Head of Molecular Biology. Greg is our Head of Analytical. And Nicole does a lot of our automation.
So thank you. Sean Sanders: Great. Thank you so much, Mr. Payne, and many thanks to both of our
speakers for the great presentations. We're going to move right on to questions submitted by our online viewers.
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Just a reminder to those watching us live that you can still submit questions. Just type them into the textbox and click the submit button. If you don't see the box, click the red Q&A icon and it should appear.
So I'm going to go I guess right back to the beginning of the process with
our first step. A viewer asks, "When you're growing up your cultures," and I believe this is particularly for mammalian cultures, "How many parameters do you need in place to optimize your clones and are there any standard conditions that can be used which will work most of the time or do you need to optimize for each new protein?" We'll start with you, Dr. Hillerich, if you could address that.
Dr. Brandan Hillerich: So in our case, most of the proteins that we're working with are of the
same sort of class and so when we start out, we start out with just a standard normal off‐the‐shelf media and just standard expression conditions. If that doesn't work, we generally start changing either the construct or we start changing the ‐‐ look at how we can put it in a different vector. At the very end is when we would try to work on media conditions and expression conditions.
But in our case, which is different than Jason, we have a large target list
and we are allowed to lose some of those targets. But in Jason's case, if a client comes and wants one protein, he has to throw everything he can at it. So ours is a little different.
Sean Sanders: Mr. Payne, could you talk to that as well? Jason Payne: Yeah. We try to start from the beginning and, as Brandan said, throw
everything at it. Our media, even at the 96‐well scale, mimics as closely as possible our fermentation media. So we try to start off giving ourselves the best chance.
We don't really change much. We sometimes induce at different
temperatures, sometimes different amounts of IPTG, but there are not a lot of parameters we change in the screening.
Sean Sanders: Excellent. The next question is regarding the lysis step. Can you talk a
little bit about some of the hurdles that you encountered at this point in the process? There was one specific question asking if either of you use histone deacetylases in your lysis buffers. Mr. Payne, maybe you could start us off.
Jason Payne: No, we basically dilute usually in the PBS and sonicate with our
automated 24‐pin sonicator, and that breaks them right open.
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Sean Sanders: Great. Dr. Hillerich? Dr. Brandan Hillerich: So with our E. coli system, we do similar to what Jason does where you
just ‐‐ we have an automated sonicator. We add some protease inhibitors. We use a standard buffer. It keeps pH at 7.5 with half molar salt and a little bit of glycerol and that just works.
For our insect or mammalian systems, both of these targets that we're
expressing are secreted. So we actually just spin the cells out and then we purify the protein from the media which has its own challenges. But for the ones that we do, the mammalian and insect proteins that are cytosolic, we've found that we can still use similar buffers through our E. coli pipeline and we can do either just sort of a pressure or osmotic system to break the cells up and/or we use sonication.
[0:40:02] Sean Sanders: Excellent. I'm going to stay with you Dr. Hillerich. We have a question
about the recovery rate of refolding proteins from inclusion bodies. Can you talk about that?
Dr. Brandan Hillerich: Yes. As I said, one of our biggest problems was that when we decided we
wanted to try to refold all these proteins, we would get them to be expressed, but when we try to refold them they wouldn't refold. In our hands, even with domains of these Ig superfamily proteins we were looking at success rates of sub 20%. So it's a big challenge, and we've been able to rescue that by changing buffers and changing some conditions, but our real leap was trying to do this on a small scale.
Sean Sanders: So Mr. Payne, I just go this question a few minutes ago so I thought I'd
shoot it out to you quickly. The viewer asked that he was under the impression that one of the chief advantages of the Pfenex system was that the proteins are secreted. So he was asking why you are sonicating the cells.
Jason Payne: They're actually not normally secreted. They're secreted through the
periplasm with our different secretion leaders and that allows for proper folding and disulfide bonds formation. But normally we have to either lyse or do an osmotic shock step to release our targets.
Sean Sanders: Excellent! I'm going to stay with you Mr. Payne. A question asks, "Could
you talk a bit more about the SDS‐CGE system that you use to screen colonies?"
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Jason Payne: Sure. It's the PerkinElmer GX II. It uses a microfluidics chip that you prime
with reagents that include a fluorescent dye. And then it's separated with electrophoresis on a very small scale, and each sample only takes about 40 seconds. So after the other steps involved, it only takes about an hour and a half to run the 96‐well plate. And you can run it reduced with DTT for example and non‐reduced. And as I mentioned, you can also do the different reagent kits and ship for DNA analysis on the same instrument.
Sean Sanders: Great. And just to clarify, we did get a question in about what CGE stands
for. That's capillary gel electrophoresis, correct? Jason Payne: Right, right. And the gel‐like images that both Brandan and I were
showing, they're not actually gels. Those are images that are put together by the software. So each sample has an electropherogram and then that can be translated into what looks like a gel because that's what a lot of us like to look at. It's kind of a nice way to look at a lot of samples at once. But all those images that look like gels, they're not gels.
Sean Sanders: Okay, excellent. I'm going to come to you, Dr. Hillerich, for a question on
scaling. A viewer asks, "How well do small‐scale optimized conditions translate to production‐scale work, and is there a difference between bacteria and mammalian systems when you do this?
Dr. Brandan Hillerich: Yes. So as Jason said, what we try to do to make the scalability translate
as best as it can is we match our small‐scale conditions to our large‐scale conditions, the same media, the same resin, the same way of lysing the cells, the same buffers. What we've found is when we do that for our E. coli system and even the refolded proteins we're looking at about 80% scale‐up success. Now, you can probably rescue some of those by tweaking your large‐scale conditions.
And then for the insect and mammalian systems, we're looking at roughly
about the same thing. It depends on the size of the protein. Often we find if it's a really large protein the scale‐up isn't quite as good as if it's a smaller protein, but I guess that's not surprising. But even for our insect and mammalian systems we're looking at I would say greater than 70% scale‐up rate just going straight through without much extra optimization.
Sean Sanders: Mr. Payne, anything to add? Jason Payne: No.
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Sean Sanders: Okay, excellent. The next question is about glycosylation, and maybe we'll start with you, Dr. Hillerich, and then Mr. Payne you can talk about the Pfenex system. So what about proteins that require glycosylation, how do you deal with this in your workflow?
[0:45:12] Dr. Brandan Hillerich: So all of the Ig superfamily targets are potentially glycosylated because
they're secreted proteins. And from our standpoint as a structural lab, that's sort of a good thing and a bad thing. The glycosylations, when they're made in insect cells or mammalian cells, they tend to make the protein more stable, which allows us to get the protein and get more of the protein. However, when we move to crystallography, often the heterogeneous nature of the glycosylations makes it difficult to get the proteins to crystallize or to get higher resolution structures. We often use NaOH or something of that nature to cleave off the glycosylations.
And then for the bacterial, as I said, there's no glycosylation on those,
and those are only used in crystallographic studies. Sean Sanders: Mr. Payne? Jason Payne: Pfenex is for gram‐negative bacteria, so you're not going to have
glycosylations in that case. Sean Sanders: Excellent. A question for you, Mr. Payne. This is about the PhyTips that
you're using. How predictive do you find these in scale‐up purification? Jason Payne: The PhyTips? Sean Sanders: Yes. Jason Payne: It kind of depends on the protein. We've found that in some cases the
filter plates work better. It's a little more work to create them. But we've had success with both, but it just kind of depends on the project and the expression level for which one we go with.
Sean Sanders: Great. Dr. Hillerich, a question for you. Can you talk more about the how
the lentiviral system expression system works and some of its advantages and drawbacks relative to the other systems that you're familiar with?
Dr. Brandan Hillerich: Yes. So we're using a system called the Daedalus system, and that was
developed by Ashok that was at Seattle, but then we hired them to come over here and bring it to us.
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So in his system he has added some enhancement. He has a UCOE which
allows for the chromatin to stay open. He has also changed out our promoter for another promoter to reduce silencing.
And also with the UCOE, it's a minimal UCOE which allows you to produce
larger proteins. One of the drawbacks of lentivirus is, of course, the packaging size, but
what we've found is that it works very well for making high yield stables in a very rapid manner because it takes only a couple of weeks to generate the virus and then the virus, once you get the cells infected, the virus goes and sticks 100, 200 copies of your gene in there and you have the chance of instantly having a high yield stable. And if you add some sorting into that, you can increase that even more.
Sean Sanders: Excellent. Let me grab the next question. So just coming back quickly to
refolding, and I'll give it to you, Dr. Hillerich, first, this viewer asks about the best conditions and perhaps some tricks for refolding of cytosolic proteins.
Dr. Brandan Hillerich: Yeah. There are a few published papers about sort of the best buffers to
use. But one of the things that we have found that if you're going after a target that you really want, it definitely pays to change pHs and change sort of your denaturant and your additives, so whether you're using arginine or DSB or something like that, if you can do a nice little screen where you vary pH and additives and reduction potentials, then you're going to have a really good shot at getting the protein.
Sean Sanders: Great! Mr. Payne? Jason Payne: Well, normally our goal is to produce protein in the soluble fraction, but
we have had a couple of projects where we've gone after the insoluble. [0:50:05] Trying a variety of detergents, ionic detergents are sometimes helpful in
that they can be gentle enough that it may not even require a refold per se. You can solubilize the insoluble pellet that way.
Sean Sanders: Great. I'm going to come quickly back to you, Dr. Hillerich, about the
Daedalus system. There was a question that came in asking how large the changes in expression levels are with the UCOE elements. What sort of difference do you see?
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Dr. Brandan Hillerich: So we actually have never used a lentiviral vector that doesn't have a
UCOE element, but I believe that if you read into some of the literature that supports at least a twofold increase if not closer to a fivefold depending on your protein.
Sean Sanders: Excellent. I'm going to stay with you for a second. This question is about
the cell::explorer setup. This viewer asks whether this kind of setup is within reach of most academic labs and what would it take in terms of time to set up and cost.
Dr. Brandan Hillerich: Yeah. So unfortunately, my answer is that it's probably not within the
reach of most academic labs. We are fortunate with the PSI grants to have the funding to do it. From design or concept to installation and getting it up and running, it probably took close to a year, which is actually really fast if you ask me. But the price tag is large. It's a high six‐figure to seven‐figure instrument, which sort of does put it out of the reach of a lot of people.
Sean Sanders: Excellent. Mr. Payne, the system that you have set up, could you talk a
little bit about the setup time for that? Jason Payne: Yeah, I agree, about a year to get all the bugs worked out and the
methods written and get it running. Yes, that's about right. Sean Sanders: Okay, excellent. Mr. Payne, a question that just came in for you. Do you
use LC‐NMR, and if not, why not? Jason Payne: We don't. We don't have one I guess is the short answer. But we do have
LC‐MS and many other analytical techniques, light scattering, CD, fluorescence, things like that.
Sean Sanders: Great. Another question for you Mr. Payne about the biosimilar protein
production, and they ask how you guarantee the highest level of similarity with the innovator protein and where do you get the original and control.
Jason Payne: Well, I mean it's obviously very important to have the similarity
extremely high. There are various ways of getting the innovator protein, and I'm not sure ‐‐ I haven't done it myself personally so I can't really comment on that.
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Sean Sanders: Okay. So the next question is for you Dr. Hillerich. How does the lentivirus expression in HEK cells compare with transient transfection in these cells?
Dr. Brandan Hillerich: Yeah. So before I answer that question, I'd like to go back to the question
before about the cell::explorer, and I would like to tell people that you don't necessarily have to have a million‐dollar robot to do high throughput. If you set your mind to it, you will be very surprised what you could do with a nice set of multichannel pipettes.
And on to the lentiviral question, so what we see is a definite increase, in
some cases fivefold or more, from a normal transient system. But what we actually have been able to do is show that if we could do a small‐scale transient expression and check that by either Coomassie, CGE or Western. And if we see it there we're relatively sure probably 80 plus percent of the time that if we go to a lentiviral system, we will also be able to produce that protein and increase the yield that we get.
[0:55:04] Sean Sanders: Great. Thanks for that addition on the robot system. I think that's
important information. Quick question, let me go back to Mr. Payne on this one. What about
expression of membrane proteins? Are there specific factors that need to be considered and do you have experience in this area?
Jason Payne: We have a very limited experience with production of membrane
proteins, primarily just expression of the nonmembrane regions of proteins. I think with the bacterial system, you may only have luck with the bacterial type membrane proteins, but usually our vaccine antigens for example are subdomains of the larger proteins.
Sean Sanders: Great. Dr. Hillerich? Dr. Brandan Hillerich: Yes. So here, we don't do much work with membrane proteins, but my
previous experience, I've spent three years in a high throughput membrane protein production lab, and we've found that some of the tricks that you use for soluble proteins can work for expression of membrane proteins. You can change temperatures, change strains, change constructs. Instead of doing our solubility screen like we do now, you can do screening with different detergents and you often find that you're able to get soluble protein by just switching the detergent. So a lot
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of these techniques I think that we talked about today can be taken over to look at membrane proteins.
Sean Sanders: Fantastic. So we're almost at the end of our broadcast so I'm going to put
just one more question to both of you and ask maybe a crystal ball type of question as to where you see this research moving in the next five to ten years and also what you would like or what technologies you would like to see that might drive this research and really help you with the work that you're doing.
So Dr. Hillerich, we can start with you. Dr. Brandan Hillerich: So for us, what I see coming in the next few years out of our work is that
we're going to transfer some of our knowledge from just making protein to being able to screen things for functional studies, be it protein‐protein interactions or receptor‐ligand discovery. We're working towards that now, and I think in the next couple of years we're going to make some really exciting developments in that.
Sean Sanders: Excellent! Mr. Payne? Jason Payne: Yeah, I think looking forward to advances in the software to have it more
automated so you have your data and then be able to generate a report from that and generate your list of top picks for example. And also we're developing a LIMS system to be able to track everything and mine our data a little more efficiently.
Sean Sanders: Fantastic. Well, unfortunately, we are out of time for this broadcast. It
just remains for me to thank our speakers for providing such great talks and very interesting discussion, Dr. Brandan Hillerich from Albert Einstein College of Medicine and Mr. Jason Payne from Pfenex.
Many thanks to our online audience for the great questions you
submitted. I'm sorry we didn't have time to get to all of them. Please go to the URL that I'll be putting up in your slide viewer right now to learn more about resources related to today's discussion and look out for more webinars from Science available at webinar.sciencemag.org.
This webinar will be made available to view again as an on‐demand
presentation within about 48 hours from now. We'd love to hear what you've thought of the webinar. Just send us an email at the address up in your slide viewer, [email protected].
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Again, thank you to our panel and to PerkinElmer for their kind sponsorship of today's educational seminar. Goodbye.
[1:00:27] End of Audio