Engineering Life's CO2 Engine with Ahmed Badran

What role does biotechnology play in solving the climate crisis? Join us as we spotlight Ahmed Badran, assistant professor at the Scripps Research Institute and a leader in climate biotech innovation. Ahmed is a recipient of Homeworld Collective's Garden Grants for Protein Engineering. 

Ahmed shares his journey, from growing up in Egypt in a family of scientists to becoming a pioneer in engineering enzymes for climate solutions. 

We dive into the fascinating intersection of machine learning, synthetic biology, and climate innovation, spotlighting Rubisco, a key enzyme in photosynthesis with untapped potential. Ahmed unpacks his research on enhancing Rubisco’s efficiency and its revolutionary implications for carbon capture and climate sustainability.

Ahmed also offers insights for aspiring biotechnologists, sharing advice on bridging computational and chemical expertise and the value of tackling bold, ambitious projects. Tune in for a closer look at the work happening at Scripps Research and the future of climate biotech.

(00:00) Introduction to the Climate Biotech Podcast
(00:50) Meet Ahmed Bajran: A Rising Star in Climate Biotech
(01:49) Ahmed's Unique Background and Early Influences
(03:29) Academic Journey and Early Research
(05:33) Synthetic Biology and Bioengineering Insights
(07:30) David Liu's Lab and Complex Problem Solving
(11:54) Garden Grant Proposal: Tackling Climate Change
(16:04) Rubisco and Carbon Capture Innovations
(22:51) Advanced Genetic Engineering Techniques
(31:08) Future of Synthetic Biology and Final Thoughts
(34:04) Rapid Fire Questions and Closing Remarks

Transcript

[00:00:00] Ahmed Badran: So that was the foundation of my garden grants proposal.

Can we think of ways of manipulating the rate determining step in photosynthesis to improve the way that it captures carbon dioxide and then use the fact that photosynthesis is a very impressive feat of biology to deploy that mechanism to potentially address climate change long term. 

[00:00:24] Daniel Goodwin: Welcome to the climate biotech podcast, where we explore the most important problems at the intersection of climate and biology, and most importantly, how we can solve them. I'm Dan Goodwin, a technologist who spent years transitioning from software and neuroscience to a career in climate biotechnology.

As your host, I will interview our sector's most creative voices from scientists and entrepreneurs to policymakers and investors. 

[00:00:50] Dan Goodwin: Hey everybody we're thrilled to welcome Ahmed Bajran ahmed is a wizard of evolution based bioengineering, and in general, a rising star in climate biotech. He did his PhD with Professor David Liu at Harvard. You can see an example paper on continuous evolution. And completed a double major at University of Arizona.

Today, Ahmed is an assistant professor in the Departments of Chemistry and of Integrative Structural and Computational Biology at the Scripps Research Institute in San Diego. Bajon's research leverages the programmability of biological systems and uncovers next generation strategies to address pressing global problems.

Ahmed's research includes engineering and deployment of carbon capturing enzymes. Discovery and optimization of pathogen specific antimicrobials development of new to nature catalysts that bridge the scope of synthetic chemistry transformations with the programmability of biology.

We just want this to be a natural conversation for the whole world to get to meet Ahmed. So I'm gonna toss it right over to you, who are you? Where did you grow up? 

[00:01:49] Ahmed Badran: Thanks first for the introduction. It's a pleasure to have this conversation with you. The answer to that question is pretty hard, my, my upbringing was very unconventional. I'm an immigrant to the U. S. I've lived on three separate continents for, Considerable part of my life. And originally I'm from Egypt. So I was actually born in Germany. I've lived in Egypt in the US for about half my life in each of those.

My parents are both scientists and they really instilled in me this idea of exploring the phenomena of the natural world and then, as I went through my career trying to integrate engineering and chemistry into that discipline.

[00:02:27] Dan Goodwin: Wow. And with hopping around like that, did you ever imagine that you'd be making cutting edge biotechnology tools that get spread all around the world? 

[00:02:36] Ahmed Badran: It's funny that you say that. A large part of why I moved around a lot was because my parents as both of them are scientists, they for reasons that are really difficult to explain, had to travel back and forth from Egypt to come to the U.

S. to pursue their science. And. The way that they did that was that they were supported by the Egyptian government to come to the U. S., but there's a clock, and then whatever time they spend in the U. S., they have to replicate it in Egypt and really, the story the lesson that I learned from that is, here are two individuals that are pursuing their passion, and it's hard and I think, What I took away from that is it's a privilege to do the stuff that we do.

And, where I am today and what I do is a by product of the lessons that they've taught me.

[00:03:19] Dan Goodwin: Wow. That's super beautiful. I get pretty emotional sometimes when I think I'm inspired and continuing some of the work of my parents. So I didn't know the family connection to your work.

That's super beautiful. And so thinking about you growing up, you go to a university of Arizona and they see that you did a double major. Was this pretty linear? Did you know what you were doing or was there an exploratory period? 

[00:03:39] Ahmed Badran: So it actually, I got started on that path much earlier. So when I was in high school, I was fortunate enough for my high school to be across the street from the University of Arizona.

And I actually transferred the first 2 years of high school from Egypt to the U. S. I got started in high school as a junior in Arizona, and the curriculum in Egypt is intense, and so I'd finished a lot of this stuff that people would have finish in their third or fourth year in high school.

So I had a lot of free time, and I took this class that was called Research Methods. It was a very interesting class where the teacher set us up with professors at the University of Arizona, and we would go and do research as high school students. So every day, Whatever, 1 p. m. I would finish all my classes go across the street.

and work in the lab of Indranil Ghosh. And that was the beginning of my entire career because what he did was one of the most incredible things I'd ever seen. I remember meeting him for the first time and he'd opened a binder that had a bunch of fluorescent proteins in them. This was very shortly after the the engineerability of GFP becoming quite widespread.

And so what he was telling me is that there's all of these interesting proteins, they fluoresce, you can see them, and What his lab was doing was going a step further to create biosensors around these fluorescent proteins where they could detect certain metabolites or nucleic acids or proteins. That foundation was really the inspiration of the degrees that I ended up getting as an undergrad, because I knew that I wanted to do that work, which fundamentally is bioengineering.

But undergraduate degrees, especially at that time, didn't really have bioengineering. They often came from kind of biochemistry or molecular biology tracks. And so those are the degrees that I ended up getting, which really solidified the foundation of how to manipulate biological processes and think at the molecular level that is the foundation of bioengineering.

[00:05:33] Dan Goodwin: That's a fascinating side point. I didn't actually consider how much bioengineering education has changed. Do you think it's a post CRISPR thing? Or do you think it's a computation thing? 

[00:05:42] Ahmed Badran: Maybe this is a potentially different conversation about kind of synthetic biology as a paradigm.

I actually think that there's two different fields, both of which are called synthetic biology. There's the synthetic biology that you might associate with folks like Mike Elowitz or Jim Collins, that comes from a fundamentally more physics based or mathematics based background where you treat biological components as being modular.

And you try to program that modularity to create higher order. Circuitry, but then there's this other side, which is, I would say a lot more molecular and comes from a historically chemistry driven discipline. These are the people that you might associate like David Lu or, Francis Arnold.

That really approach synthetic biology as a chemist would, that the biological parts are really just parts and what they care about is what the side chains are doing and how they're interacting with solvent or substrates or things like that. For me I was really trained in the latter.

The lab, Indranil Ghosh is fundamentally a chemist turned biologist and the way that he thought about problems was through the lens of chemistry. David Liu is very much the same thing. And for me, I think the molecular foundation, molecular biology, even as a field, was started by chemists people like Linus Pauling really set the foundation for this field.

And that's where I operate. I like to think about molecules, and I like to think about the programmability of biology through the lens of chemistry. And at the time, all of those people were coming through with chemistry degrees, or at best, biochemistry degrees. So that's the track that I went through.

[00:07:16] Dan Goodwin: I totally agree. It makes me think of back in my PhD, I came in with a computer science background, and my favorite people to engage with were the chemists, because they just had such a ferocious sense of rigor. And intuition that I just didn't have. And they were always my favorite people to interact with.

And so let's talk about David Lou a little bit before we get to your work, right? So David Lou's lab is famous for being extremely productive over billion dollars of, downstream companies that have been formed from there, mostly in the medical applications of biotech. I'm so curious kind of what, what you learned in that lab, like if there's a lesson and then also, there's a lot of To you being there to stay in quote unquote, just medical, right.

And so I'm really curious about your growth throughout that PhD program. 

[00:07:59] Ahmed Badran: Yeah, I guess so, so first an undergrad so high school through undergrad, I spent about six years in the Gershwan and the way that we approach problems was at the level of single proteins. So creating a biosensor, this may be a fusion protein between a GFP and something else.

And really, the, For the problems we were interested in that was sufficiently complex to address the need when I got to David's lab difficulty of the problems grew. And so 1 of the key takeaways that I had from that experience. Even just beyond the kind of biomedical interests that I think he's had for a very long time is, how do you think about solving increasingly complex problems?

And the solutions as a result became increasingly complex. And I think that's one of the things that I really took away from that experience is how do you develop biology as a tool to answer problems for you? And then scale the problem difficulty. And then what happens on the kind of the technique side that allows you to now explore that increasingly difficult problem. So that was really foundational for me. And from a practical or technical standpoint, what that looks like is gene circuits. Like, how do you take the idea of studying a ball at biomolecule in vitro, which is in a test tube, and it's very easy to manipulate and you can do whatever you want.

How do you convey that information through circuitry? Inside of a living cell. And that has really, for over a decade now been the way that I think about problem solving is effectively doing biochemistry inside of a living cell with, The most hands off approach that I can build in.

[00:09:39] Dan Goodwin: Don't mean to put words in your mouth, but it's like a protein first viewpoint versus a cell based viewpoint. Is that the right way to play it back? 

[00:09:46] Ahmed Badran: I think that's right. I think, if I look back, and I've tried doing this analysis multiple times, and I come to the same conclusion, and Basically, our ability to do interesting things in biology, at some level, relies on the idea of orthogonality.

And if you think about early molecular biology, fundamentally, all of the capabilities are we bring something from one system and we put it in another, and it just, it only does exactly what we want in that system because it can't interact with the rest of the system. And that idea, I think, is so powerful that It allows you to operate within the confines of a really complicated, biological setting, but because it's orthogonal, it adheres closer to the way that you think about manipulating a single protein in a test tube.

[00:10:34] Dan Goodwin: Yeah. I think to, to land orthogonality, I'd be curious if there's like a sentence or two that we can talk about I look at you as one of the leaders on the frontier of doing really wild synthetic biology, the chemical viewpoint. What was that about? And then we can go into the garden grants, but I think it shows that the breadth of the work you do.

[00:10:51] Ahmed Badran: Yeah, maybe to elaborate on the idea of orthogonality, I think it's just about control. I think it's about trying to test the idea of whether or not you understand what's happening in a cell. And that's the first part to orthogonalize something is to enforce your viewpoint on the circuitry in the cell.

And that has to come from a place where you understand the thing that you put in and you understand the way that it interacts with the rest of the system. But then the other Kind of thing that's really important. There is that imagine that you're building a computer and you have all of these sort of signals that are doing many different things and talking to many different parts of your computer.

They have to be able to interface at some point, and they have to be able to be controlled so that they don't talk to each other at other points. And sometimes in kind of orthogonalization, that can be quite tricky. And maybe we can get into this a little bit later. And 1 of the studies that was supported by the garden grant, we had to do that in a way that was.

Incredibly unconventional, but actually ended up being very powerful. 

[00:11:53] Dan Goodwin: Cool. Yeah. So let's set up the garden grant and then get to the solution. And so the big thing that we did, the garden grants is we really are trying to build a culture of problem centricity. And the whole spirit there is some fields.

It's very obvious what the problems are and machine learning, I think was the best at that. If you're starting all the way back from the image net challenge in 2011, everyone knew what the benchmark to beat was. Everyone built their algorithms. And then a year after image net, we had the deep learning revolution.

And then in bio. I think some things have been clear. I think when you were doing your PhD, there's a race to getting the CRISPR systems to span the whole genome, right? And I think that was like a very kind of obvious, like that's, that is a clear problem, everyone in genetic engineering is trying to do that.

But more broadly in kind of climate problems, we really try to get people to be super explicit with what the problem is before they talk about the solution. And so I would love obviously we were super excited to read your proposal and and we're very proud to support. And so I'd be curious just to hear what is the problem that you were trying to solve currently?

And then what are some of the how have you been solving that? And then we can go into the outcomes. 

[00:12:56] Ahmed Badran: Yeah, I think, and this was one of the lessons that I got from David during my PhD, is that, we're in a very privileged situation. We are at some level given money to do the thing that we want, and other human beings, directly or indirectly, give us that money.

And, we want to solve problems. And It's important to, to remember, I think the, that weight on our shoulders when we're trying to decide what problems we want to explore. And this was, a lesson that I both learned from David and also from Neil, which is we spend so much time, like in our training and we are armed with all of these capabilities.

You might as well pick really hard problems to be able to solve them. So that was the foundation of what I started to do when I came to Scripps. I noted that Synthetic biology as a field had matured very rapidly over the course of my training to go from as Ron Weiss once called it like a set of kind of tools, or sorry, of toys, to, a really hierarchical ordered set of machines and capabilities that you could bring to bear on increasingly complicated problems.

And so I saw that maturity as being a really enabling. Kind of resource to solve a problem, but I had also noted that biomedical problems are things that many people are interested in, and people are very creative at trying to solve those problems. But on the other hand, there are these arenas of sustainability or climate change that are very much lacking in the same depth of resources and the same sort of focused problem solving.

And that's really what I decided I wanted my career to be about. And so for the Garden Grant in particular it was focused on climate change as a very long term kind of Area that I wanted to operate and recognizing that there were two kind of major caveats to addressing this problem.

The first is that you just needed ways of pulling greenhouse gases from the atmosphere that are really efficient and you needed really good ways of being able to deploy that resource once you were able to create it. And what we've learned about biology across many examples is that it's very malleable, very easy to deploy.

And our understanding of how to achieve those things has also grown tremendously over time. So that was the starting point. Trying to solve climate change in the very long term, but thinking about it through the lens of biology. And fortunately, biology has made a huge dent in this problem.

And it's photosynthesis. It's the major means by which carbon dioxide is captured. From the atmosphere and for reasons that I would say no one really completely appreciates. It has problems. It's not very good at doing what it does. It's sometimes not very specific to carbon dioxide. So that was the foundation of my garden grants proposal.

Can we think of ways. Of manipulating the rate determining step in photosynthesis to improve the way that it captures carbon dioxide and then use the fact that photosynthesis is a very impressive feat of biology to deploy that mechanism to potentially address climate change long term. 

[00:16:03] Dan Goodwin: Yeah. And when you work in climate biotech, very quickly, people with the protein engineering viewpoint first, they're like, oh, the protein touching the biggest greenhouse gas flux is rubisco.

So we're just going to go just engineer rubisco. You always have to tell people, hold up, this is a very complex thing. The rational viewpoint of pick one subunit and just, optimize it hasn't really worked. Please correct me if I'm wrong, but I'm curious how you're beginning to solve that of, proteins involved with carbon flux.

[00:16:31] Ahmed Badran: Yeah from a biochemical standpoint, we know that it's rate determining, and so it stands to reason if you make it faster, you will see improvements in the efficiency of carbon capture. , it's 

[00:16:41] Dan Goodwin: worth just saying Robisco really sucks. It does suck, that's right. Depending on like the, depending how you measure suck, the turnover rate is what, a few times a second.

It falls over really easy. That's right. It's really easy to look at this thing and be like, come on nature. 

[00:16:55] Ahmed Badran: That's right. But on the other side, right? Like nature is quite good at solving other problems. And so one also has to be quite conservative in saying is it actually bad because, nature messed up or is there something that we fundamentally don't understand about.

This specific reaction with this specific scaffold of an enzyme. 

[00:17:13] Dan Goodwin: So The rational approach, like a single protein approach, is one way and maybe there's going to be success, but you're taking an orthogonal approach to that, right? 

[00:17:21] Ahmed Badran: That's right.

Yeah, so maybe to walk you through how we thought about this problem, and again, going through the lens of trying to study a protein, the key requirement for us to be able to do anything was that we had to be able to explore rubiscus function very quickly. To put this into context, the gold standard way of understanding rubiscus function is you purify this protein, and then you do in vitro sorts of like assays to measure its carbon capture efficiency or speed.

The most sensitive versions of these assays require Radiolabeled carbon dioxide gas, which you can imagine is maybe not the easiest sort of experiment to do in addition, if you were to think about, the most sensitive experiments like those being used as a framework to engineer rubisco's that are increasingly robust.

Then you're just using radio labeled CO2 all the time, and also how do you engineer that kind of one step at a time, iteratively, until you find something that's better. So that was the first step. How do you create systems that allow you to quantitatively measure Rudisko function in very high throughput?

And to cut a long story short, we went back to this idea of orthogonalization and considered that If you are able to orthogonalize Rubisco, then you could very effectively measure what it's doing inside of a living cell, say through biosensors or some sort of other quantitative capability. But that introduces a very difficult problem that one has to solve, and namely rubisco is really important in photosynthesis because the metabolite that it creates after fixing carbon dioxide fits directly into glycolysis, which is one of the most important and highly conserved pathways in biology.

So to orthogonalize rubisco means to ablate glycolysis. That should be. a very tricky thing to try to address if you've been trained as a biochemist because what you've learned in biochemistry is this pathway is key it creates the ATP that an organism like a microbe for example or even a higher order eukaryote needs to be able to survive and so that was a challenge we approached it first from a chemistry standpoint to say How do you make something orthogonal and the easiest kind of brute force approach to that is I'm just going to delete all the genes that make that metabolite.

And so when you do that, that organism that lacks glycolysis is very unhappy, we're doing all of this in E. coli and that's, It's really just a practical constraint, right? Bacteria grow very quickly and they're very easily engineered.

But the ideas that we've developed, I think can cross the barrier between prokaryotes to eukaryotes. 

But fundamentally what we did is we deleted glycolysis and all of a sudden, rubisco becomes much more insulated in that cell, but the cell is very unhappy. And so one of the things that I've learned throughout my career is that.

If you don't know what the answer is, just let evolution tell you what the answer might be. So we take these organisms that are very unhappy and we ask them, can you reestablish glycolysis? But now you've forgotten basically every step in the pathway. Can you find alternative pathways? And This, I think, is a testament, and there's many studies that are like this, where you delete something fundamental, and you ask if the cell has enough malleability to solve that same problem, and the answer is almost always yes.

And so what our microbe figures out is that it actually capitalizes, we think, it capitalizes on a very toxic pathway that makes something called methylglyoxal. This is a metabolite that will modify proteins, and bacteria can't handle it very well. And so what we think actually happens is they use this pathway to catabolize glucose, in effect, doing glycolysis, but they go through this metabolite where they re control the concentration of it in the cells such that it's no longer toxic.

And then through that pathway, they relink up all of the metabolism. And so the bacteria now eat glucose where they couldn't before because they can't do glycolysis, but they don't make any of the metabolites that you've ever seen in glycolysis.

 And so Rubisco now, when you put it in the setting, it creates its product, but product can't go anywhere. And that was a foundational point for us because now we can start to build biosensing capabilities around this metabolite.

And we know that the metabolite can exclusively come from Rubisco. Therefore its concentration can be used to monitor the activity of Rubisco or the kinetics of Rubisco or the specificity of Rubisco. 

[00:21:42] Dan Goodwin: Yeah, I would love to go towards the biosensing challenge. I know you've published two papers on this work, so it'd be good to walk through them, but I have to just remark, it's another case where my, it's, I have those moments of, I know absolutely nothing about synthetic biology.

I like having these humbling moments and it was, it is counterintuitive to me that the way to optimize the Rubisco pathway is to completely sabotage the metabolism of innocent E. coli. Yeah, but not a thought before I go after glycolysis to go after Rubisco. 

[00:22:10] Ahmed Badran: But I think there's something like at some level what we're doing is we're like hollowing out the test tube to create a space just for Rubisco to exist, right?

Like we're just trying to create, we're trying to turn the cell into a test tube where we can very effectively manipulate Rubisco and what it's doing.

[00:22:30] Dan Goodwin: That this would be the end point. Yeah. So is E. coli now an obligate phototroph? 

[00:22:33] Ahmed Badran: No, in our case, it's not. So it has its own metabolism where it builds its biomass. And then it has the carbon capture kind of cycle where it creates that metabolite, which we then monitor and we can convert that metabolite into other downstream stuff.

But those two pathways are now they're linked upstream, but not downstream. 

[00:22:50] Dan Goodwin: Fascinating. Fascinating. Okay. So two papers, I think you guys have published. One was the genetically encoded system to quantify and evolve Robisco catalyzed carbon fixation and the other one is efficient genetic code expansion without host genome modifications.

first one is 

[00:23:06] Ahmed Badran: really meant to create the platform to study and evolve Robisco. And so I think we've had a lot of success with that. We've, of course, been pushing the system to really capitalize on its capabilities. But on, on the other side of this, we've been trying to think creatively about the possible solution space to make Rubisco better.

And there's many different ways to think about this. So if you start from why is Rubisco bad? many answers that are described in the literature. One is nature has made mistakes due to its evolutionary history. And we can delve into that a little bit more if you'd like. Another is that the chemistry that Rubisco is doing is fundamentally thresholded.

It can't be any better given the building blocks that Rubisco has to function on. And there's some other arguments, but those are really my favorite too. So for the first one, you can start to ask questions. What if you have really effective ways of replaying that tape of life and asking what happens if you start with a really old rubisco and you push it through evolution?

Does it now have much better sorts of activities? And we're starting to explore these types of ideas. But the second question is really important. What do you do if Rubisco cannot fundamentally be made better because of how it's built? And this is a chemistry problem, right? Proteins are built with 20 canonical amino acids, plus or minus one or two, and then there's post modifications that can be added onto that, or cofactors to supplement enzymes with new chemistries.

If those are available to nature all the time and they're not used for this, it stands to reason that there is a possibility that Rubisco is at the best possible framework that it can be for the activity that it does. But what if now we're no longer limited to those building blocks? What if we can start to integrate building blocks that provide Rubisco with new chemistry, that allow it to better select for carbon dioxide, or distinguish it from carbon monoxide?

Oxygen, or to enhance the catalytic rate of that reaction. And so this is predominantly called genetic code expansion as a field, where you can trick the cell into incorporating very unique building blocks that are beyond those 20. And you can put them at very specific places in a protein and ask, how do they influence the protein's function?

And so that was that second study is how do you create resources around doing that, that are really efficient, can be very multi flexible. And importantly used potentially to evolve or engineer enzymes. 

[00:25:32] Dan Goodwin: Yeah, I love this. And so implicit in this is the kind of, we're stepping over the idea that working with Rubisco so far has been really hard, because if you're working on it in a plant cell, one, they're hard to use, two, they're normally really sick.

There's many copies of Rubisco. And so what we're, Taking for granted in this conversation is just how cool it is to have it in E. coli. All right. And now it's just working. And so now you're dealing with this E. coli and then now you're using the, like the frontier wild stuff in Symbio to engineer new mutually orthogonal TRNA synthetase pairs.

So I'd love to just to geek out a little bit and like dive in exactly what were you doing in that paper? And I think the word orthogonal, like means I think it means a lot, but it's nice to just like really go into the bio, like biochemistry a little bit, like what do we mean by, developing these tRNAs?

[00:26:17] Ahmed Badran: Yeah. So if we take a step back, just a quick primer on translation. So there are these enzymes that bind to cognate tRNAs and attach amino acids to their ends. Those aminoacylated tRNAs are then used by the ribosome to polymerize proteins. That process is fundamentally the basis of all of life.

It is universally conserved, and there are some tweaks across the tree of life as to, what sequence in the glycolic acid corresponds to what amino acid. But for, by and large part, they're all the same. And so what scientists have really recognized, and this is work that's been pioneered by Pete Schultz many decades ago, who's the president of Scripps is the idea that If you understand that process, there is a way for you to potentially engineer additional building blocks to be incorporated just by engineering additional tRNA synthetase pairs.

And that field has exploded over the past 20 or so years where the number of building blocks that can be incorporated into a given protein and site selectively is on the order of 400 plus. So that's a 20x multiplier of nature's capabilities that we can put into a given protein. 

[00:27:32] Dan Goodwin: If you ever want to turn a totally civil Hangout with Biologists into a total melee, is you mentioned non canonical amino acids.

Because I don't know if it's a generational thing or what, but some people absolutely hate it. And then other people think this is the only future of symbiology. Oh, that's 

[00:27:47] Ahmed Badran: so interesting. Maybe. I actually, I think I fit somewhere in between. I think there's all of these capabilities are meaningful and useful, and I don't think that any one of them is going to be the strategy to solve any given problem.

But together, they will probably arrive at interesting solutions. 

[00:28:05] Dan Goodwin: Very good. It depends answer from a scientist. That's right. 

[00:28:08] Ahmed Badran: Yeah. So from a chemistry standpoint, We've gotten really good at this process. We can put in these interesting building blocks, but there's one major limitation, and it actually has to do with the genetic code itself.

So if you think about, you go out into the world and you pick up any given organism, it comes pre programmed with a genetic code. And that genetic code means something to the cell. Every three base sequence identifies as a given amino acid when it's translated. So for you to then incorporate new amino acids, Something about that code has to change.

You have to reprogram it to add new building blocks into its genetic code. And that has been probably the biggest barrier to kind of efficiency and multiplexibility in this field because the genetic code has to change fundamentally at an organismal level. for you to access many different amino acids at the same time.

Now there's an asterisk next to that statement, which is this is an observation that was made many decades ago. I think it goes back as early as the sixties, which is that you can actually create like a second layer of information on top of the genetic code. If you do something very clever, and this was also discovered accidentally in certain situations, you can create tRNAs.

that rather than read the so called universal triplet based genetic code, these tRNAs can actually read four bases at a time. So these are called quadruplet decoding tRNAs. And what they offer is a potentially vast kind of sequence resource that you can assign to new amino acids where at each one of the four base sequences that you introduce into an mRNA, you can incorporate a new to nature amino acid.

However, biology doesn't like these sequences. You might imagine that over billions of years, organisms have evolved to be really good at reading, three bases at a time. And so when you put in something that reads four bases at a time, the efficiency plummet. And that's true, it drops by roughly three to four orders of magnitude.

So we got interested in this as a paradigm to potentially incorporate these new building blocks, but from a basic biology standpoint to understand what limits that decoding process? How can you create systems that are better equipped to read four bases at a time? And we started first by looking at the tRNA.

We've also done some work on the ribosome itself. And when I got to scripts, we just took a step back and asked what part of protein translation has not been explored as a parameter for this? Sort of bioactivity and very quickly. We realized that despite it's important for the acts of importance in the act of translation No one had really looked at the contribution of the mRNA And that's what that paper is about, trying to understand how you can tweak the sequence of an mRNA to allow the ribosome to better read a four base sequence.

And when you find these rules, you can actually generalize them and recapitulate that three to four order of magnitude. Drop in decoding efficiency for a quadruplet codon where now that efficiency goes all the way up to 100 percent compared to normal translation 

[00:31:07] Dan Goodwin: I just get so excited. This is cutting edge while it's in bio and it's being done in the context of the global challenge of climate change. So it just gets me really excited. And I'd love to just try to land this with one sentence and we go to just like rapid fire to wrap up, which is that I, read the abstract of this paper and it says we evolve and optimize five tRNA synthetase pairs to incorporate a broad repertoire of canonical amino acids.

 I think that is speaking for the frontier of the field. And so would love just to get your sense of where that frontier is. What does it mean? What's coming next? 

[00:31:39] Ahmed Badran: Yeah, one of the things that I've been really excited about is if these resources truly work, how far away can you get from nature and what sorts of bio activities can you start to develop?

So just to put this into context at a given position for a unique quadruplet codon, we can put an average of 20 different amino acids. And so even for those five tRNA synthetase pairs, the number of possible things we can create is the natural 20 plus a new 20 or 30 per position. So you have 50 ish amino acids to the power of 5, right?

So it's an immense sequence landscape. And so the question that has to be asked what can you do if you're able to access such. wild chemistries. And the short answer is, of course, we have no idea. But we're approaching this from two standpoints. The first is, can you start to create molecules that might have very privileged activities?

say, as like macrocycles, which is what we do in that study, that might become potentially starting points for small molecule discovery. And these could potentially turn into binders or drugs in some way. But the other side of that coin is, can you start to really manipulate enzyme active sites such that you're not just incorporating one unique chemical handle, but you're building an entire active site?

That is as chemically defined as you could possibly want it to be, with halogens in there, or, alkenes, or whatever sorts of structures you might want to integrate, that you know from a chemical standpoint can influence catalysis. And that to me is really the frontier can you really bridge the gap between how a synthetic organic chemist thinks about methodology development, how they pick their solvents, and how they design their ligands to a protein that can integrate additional levels of control with stereoselectivity and enantioselectivity and potentially even have conformational changes that create, catalytic efficiencies well beyond what we see in synthetic organic chemistry.

And for me, that's really the frontier is bridging that gap to do chemistry that life has not been able historically to do. 

[00:33:44] Dan Goodwin: Oh, I love it. That's a good point to press pause on for now. And what you're talking about just feels like the vision that I think we all have in synthetic biology from the chemical perspective, which is that we can build these, infinitely scalable programmable machines, and these programmable machines are precise at the atomic level.

So count me in the group that's excited about non canonical amino acids. And this is awesome. So to wrap up, we're going to do just a few rapid fire questions. Thanks. What is a single book or paper or art piece or just idea that blew your mind and shaped your development as a scientist? 

[00:34:17] Ahmed Badran: So it's hard for me to distill it down into one thing, but maybe I'll just pick books.

My, very early kind of attachment to science came from reading a lot of books by Michael Crichton, who you might remember as an author that, wrote, Things like Jurassic Park of course many other books. And he's actually a trained physician. He also developed ER, the show, which was maybe more in our kind of generation.

But one of the things that, that I was really blown back by him reading his books was that he had this incredible attention to detail. At a, like a, such a nuanced molecular level, and he would build these stories around that molecular complexity all the way where their impacts were at the level of an individual or a country or kind of the globe.

And that, I think, framework has colored a lot of how I think about the world that, Rubisco is just one enzyme and it has an active site that has a couple of atoms in just the right orientation to do that chemistry. But if you unlock it, you can have global level impact. So for me, that, that kind of, Science across scales has been really foundational and I, I would ascribe much of that to his writing.

[00:35:35] Dan Goodwin: Oh, that's such a good answer. I also read a lot of his books growing up. What I say jokingly now is the only thing fake in Jurassic Park was the business model. 

[00:35:45] Ahmed Badran: It failed from the very beginning, that's right. 

[00:35:47] Dan Goodwin: But man, the science is great. And also his book Prey does really good. Exploration of exactly what you're saying.

It's an awesome answer. What is the best advice line that a mentor gave you?

[00:35:57] Ahmed Badran: That would probably be a point that I mentioned earlier, which is that like we train for so long and I think it's even more, it's even longer nowadays where you have students that start like in their first year of high school.

And then through college, and then as, graduate students, and then as postdocs, you have this immense amount of effort and time that you've put into training and becoming the best in a given field. And I think in academia, especially, there's this notion that zeal for pushing the limits disappears the moment you start your own lab.

because there's this sense that it's now scary and we have to be able to deliver on a very specific timeline. So the advice that I got from both my undergrad and graduate mentors in different ways is that heart problems and, simple problems are going to take the same amount of effort at the bench.

They're going to take the same amount of effort to write that paper. But at the end, you'll probably be a lot more satisfied with solving a very hard problem than you will be solving a simple problem. And so the advice was, pick hard problems. They're worth it. Even if you fail, you'll still learn a lot.

And so I apply that very heavily in my lab and try to convey that same sort of advice to my students. 

[00:37:13] Dan Goodwin: I love it. Two more. And then which is if you had a magic wand to get more attention and resources into one part of bio what would it be?

[00:37:20] Ahmed Badran: I think my answer for this comes from the impact of very basic biology on synthetic biology.

Like our ability to create these orthogonal circuits or to engineer cells comes from a very fundamental understanding of like how these proteins work naturally and how they responded to their metabolites and how they did the chemistry inside of a cell. I think synthetic biology is potentially At a point where we might over commit to what we know and stop pursuing the basic biology that's necessary to build really complicated levels of circuitry or biological phenomena.

And so I think 1 of the most important elements that I would love to see have more attention or resources is. That really weird symbio where you're really pushing the frontier of something that maybe doesn't have a natural counterpart, but you're doing it just to see like how far you can press down on biology and get it to still be okay with what you're doing.

At some level it's closer to kind of fundamental basic science because you don't know what the answer is. And it's not really applied either. But it's an arena where I think historically there's been a non commensurate amount of support. And I think that something like that would be really exciting.

[00:38:35] Dan Goodwin: Wow. I'm throttling myself to not riff on this too much. I think you are real hero, frankly the last question is, lots of developing biotechnologists listen to this PhD students, community labs. People like all that. And so what is something that you'd want to say to them that they might overlook, say a trend or a foundational skill that you think a more junior biotech researcher might overlook.

So what's an overlooked thing that you think developing biotechnologists should invest in? 

[00:39:04] Ahmed Badran: I think going back to probably the first thing now that we've discussed is like, where does a synthetic biology receive their training? Is it from someone that's a little bit more chemistry minded or someone that's a little bit more kind of computational or mathy or physicsy?

And I think both are very strong and have made huge contributions to their respective fields, but both also have weaknesses. At the chemistry side, I think over focuses on the molecular level details and When engineering, it's like a singular protein at a time. It's not really like hierarchical like engineering paradigms that are manipulated at the same time, whereas the kind of more mathematical or physics based synthetic biologists.

I think over commit to this idea of like hierarchical complexity, but forget about the chemical nuance at the molecular level. And so the, I think this. Advice that I would give would be if some, if I were starting out today, I would try to master both. I would try to develop my kind of computational chops to understand how to develop increasingly complicated gene circuits that are really hierarchical and operate across multiple modes of biomolecules.

But I would also not forget about, how an amino acid side chain influences catalysis and how ph or pka is relevant to a catalysis that an enzyme does. So being really an expert across. really the scales of science going from the much more quantitative to the much more chemical, I think is probably the best advice that anyone could receive today.

[00:40:35] Dan Goodwin: Fantastic. So Professor Ahmed Bajran, so grateful to have you here. Is there anything that you want to share with the audience about where to find you or things you'd want to share before we wrap? 

[00:40:46] Ahmed Badran: Yeah first of all, thanks again for the invitation. This is a real pleasure to have this chat. So yeah I'm at Scripps Research.

It's a great place to live and do science. The sun is basically always here, it doesn't rain, it doesn't snow and it's mid January and I'm wearing a t shirt right now. So if anyone listening to this is interested in doing some cool science or potentially coming to visit to see what we do please feel free to shoot me an email.

[00:41:10] Dan Goodwin: Awesome. Thank you everybody for listening.

[00:41:12] Daniel Goodwin: Thank you so much for tuning into this episode of the climate biotech podcast. We hope this has been educational, inspirational, and fun for you as you navigate your own journey and bring the best of biotech into planetary scale solutions, we'll be back with another one soon.

And in the meantime, stay in touch with homeworld on LinkedIn, Twitter, or blue sky. Links are all in the show notes. Huge thanks to our producer, Dave Clark, and operations lead Paul Himmelstein for making these episodes happen.