July 14, 2025 • 14 mins
🎙️ Episode 75: Metabolic Shaping of Enzyme Structures Over 400 Million Years
🧬 In this episode of PaperCast Base by Base, we explore a large-scale study published in Nature by Lemke et al. that leverages AlphaFold2-predicted and experimentally determined enzyme structures across 27 yeast species to reveal how metabolic properties have constrained and shaped enzyme structural evolution over 400 million years in the Saccharomycotina subphylum.
🔍 Study Highlights:
The authors analyzed over 11,000 enzyme structures across diverse yeast species to link sequence divergence in conserved regions to metabolic flux, enzyme abundance, and pathway membership. They found that enzymes in central metabolism, oxidoreductases, and metal-binding proteins exhibit high structural conservation, whereas hydrolases and peripheral pathway enzymes are more divergent. Variability in metabolic flux and catalytic rates, rather than absolute values, was more predictive of structural divergence across species. The study also uncovered a hierarchy of cost optimization, showing that surface residues are preferentially optimized for biosynthetic cost while binding sites remain highly constrained. Finally, clusters of fully conserved residues were found to correspond to small-molecule and protein–protein interaction sites, highlighting functional hotspots.
🧠 Conclusion:
This integrative structural genomics approach reveals how catalytic function and metabolic context govern enzyme evolution and offers new avenues for enzyme annotation and metabolic engineering.
📖 Reference:
Lemke O, Heineike BM, Viknander S, Cohen N, Li F, Steenwyk JL, Spranger L, Agostini F, Lee CT, Aulakh SK, Berman J, Rokas A, Nielsen J, Gossmann TI, Zelezniak A & Ralser M. The role of metabolism in shaping enzyme structures over 400 million years. Nature. 2025. doi:10.1038/s41586-025-09205-6
📜 License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/
On PaperCast Base by Base you’ll discover the latest in genomics, functional genomics, structural genomics, and proteomics.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:14):
Welcome to Base by Base, the paper cast that brings genomics
to you wherever you are. Have you ever wondered about
life's incredible ability to adapt and thrive across, well,
vast stretches of time? I mean, imagine single celled
organisms like yeasts, right? Surviving and evolving for over
400 million years through huge shifts in their environments.
(00:35):
What's their secret? Well, at the heart of it all are
enzymes. These tiny sort of unsung
molecular machines that run pretty much every biological
process. Think digestion, copying DNA
basically. But here's the thing, they
aren't static, they evolve. So the mystery we're digging
into today is what forces guy the evolution of these tiny
(00:55):
machines? Is it just random chance or
there's something you know deeper going on?
It's a fantastic question, and arecent research really points to
a perhaps surprising answer. It's all about how organisms
process energy and nutrients, their metabolism.
And for the first time, really, thanks to breakthroughs in AI
like Alpha Fold 2, we can actually map this evolution in,
well, incredible detail. Right.
(01:15):
So that's what we're exploring in this deep dive.
It's a scientific journey spanning 400 million years,
revealing how the chemistry of life itself shapes its molecular
machinery, enzyme by enzyme. It's a really fundamental to
look at adaptation. So today we want to celebrate
the groundbreaking work of Oliver Lemke, Benjamin Murray
Heineke, Marcus Ralzer and theirmany collaborators, folks from
(01:38):
institutions like Sheratte and Berlin, the University of
Oxford, Chalmers University, a big team effort.
Absolutely. Their approach, integrating
structural biology with evolutionary genomics, it's
really pushed our understanding forward, seeing how metabolism
shapes enzyme structure over these huge time stales.
It's a great example of what happens when different fields
come together. OK, so let's unpack this a bit.
(01:59):
Enzymes. They're fundamental to pretty
much everything biological. Everything.
Digestion, DNA, replication, energy, you name it.
And because they're so critical,they're targets for, well, all
sorts of things. Drug development, finding Z's,
markers, even bioengineering right.
Definitely. But despite how important they
are, our grasp on the sort of global biochemical constraints,
(02:23):
the big rule shaping how they function and evolve has been
surprisingly incomplete. It's a big gap.
We've known for a while about certain factors, of course, like
the properties of the amino acids, they're built from
matter, and even the cost of making them plays a role.
You know, sometimes cells preferless energy efficient pathways
like fermentation simply becausemaking the enzymes for it is
(02:45):
cheaper resource wise. And you see that high abundance
enzymes, the one cells make a lot of, tend to use less
energetically costly amino acids.
That makes sense. Sort of like cellular economics,
Yeah. And building on that, there were
these two main ideas about how whole metabolic networks
evolved, right? Exactly.
Did the network structure come about because of how enzymes
evolved? Or did simpler non enzymatic
(03:06):
chemical reactions happen first like a basic scaffold, and then
enzymes evolved later to make those reactions better, more
efficient? And this new research leans
towards the second idea, the scaffolding first.
It provides some pretty compelling evidence for that,
yeah. And this is where the new tech
like AI really comes in. The central idea, the hypothesis
of this paper was basically thatbeing able to predict protein
(03:29):
structures on mass with alpha fold two.
Well, that would let scientists finally link up high res
structural data with the evolutionary genomics.
So connecting the shape to the history basically.
Precisely the hope was this integration could unlock a much
deeper understanding of how metabolism and protein evolution
are, you know, fundamentally linked.
And the way they did it was really innovative.
(03:52):
They leverage the huge, huge evolutionary history of the
Saccharomycatina yeasts. It's an amazing group. 400
million years of evolution rightthere.
Includes species we know well like Saccharomyces, Sarah
Vizier, Baker's yeast, and Candida albicans.
And the scale the data set sounds enormous.
Oh, it is. They analyzed, get this, 11,269
(04:13):
enzyme structures. Yeah, and over 9900 of those
were newly predicted using alphafull 2.
That just shows you the power plus over 1300 existing
structures. And this covered enzymes from
361 metabolic reactions across 224 pathways.
It's like building an evolutionary Atlas for these
molecular machines. OK, so how did they actually
(04:33):
compare them across all those species and structures?
26 diverse yeast species plus anout group species for
comparison. They used Baker's yeast S
cervesier as the reference pointand they calculated something
called a conservation ratio or CR CR OK right.
It basically measures the percentage of amino acids that
are identical in the same structural spot between enzymes
(04:55):
and different species. So a low CR it means lots of
change, High divergent. High CR means it stayed very
similar. Highly conserved.
Got it. Structure conservation.
What else did they look? At they pulled in loads of other
data, phenotypic data like how well the yeasts grew on
different sugars, 21 different ones, actual protein abundance
measurements from proteomics, and predictions of metabolic
(05:17):
flux from genome scale models. They even calculated the cost of
making amino acids and looked atdifferent parts of the enzyme
structure, surface versus core binding sites, helices versus
sheets. And crucially, they did
evolutionary selection analysis and looked for these clusters of
highly conserved amino acids. They even trained a machine
learning model on these clustersto find potential new binding
(05:39):
sites. OK, that's a massive multi
layered analysis. So pulling it all together,
what's the headline? What did they find?
Headline is really clear. Metabolism profoundly shapes
enzyme structural evolution, andit does it at multiple levels,
from the whole species down to specific pathways and even right
down to the molecular details inside the enzyme.
So metabolic environment is likea sculptor for these proteins.
(06:02):
That's a great way to put it, yeah.
For instance, at the species level, enzymes from yeast that
specialize in fermentation showed higher structural
conservation compared to the relatives in Esser Vizier.
And the biggest structural differences they saw, those were
in enzymes involved in central carbon metabolism and the
electron transport chain core energy processes.
(06:23):
Can you give an example of that specialization?
Sure. So they looked at species that
can use xylose, which is anothertype of sugar.
In those species, enzymes related to actually using xylose
showed shifts in conservation, but interestingly, so did parts
of the electron transport chain and even different versions of
the same enzyme, like acetylcoa synthase.
There's an aerobic and an anaerobic version behave
(06:44):
differently depending on whetherthe yeast could use xylose or
not. It really highlights how the
specific metabolic niche drives these evolutionary patterns.
OK, so special. What about general trends across
different metabolic pathways? Yeah, they saw clear trends
there too. Some were pretty intuitive
enzymes and really fundamental processes making purines for
(07:06):
DNA, certain amino acids, central metabolism like
glycolysis, the TCA cycle. The real core stuff.
Exactly, Those were highly conserved.
Makes sense, right? They're essential, but then
enzymes involved in things like lipid metabolism, breaking down
fatty acids and also protein glycosylation.
They were among the most divergent, the most changed.
(07:28):
Why Lipids? The researcher suggests that
maybe lipidomes, the collection of lipids in a cell, have more
flexibility to adapt compared toother core systems.
And looking at enzyme classes, oxidoriductuses, which handle
energy transfer, very conserved,especially because they're key
players in central metabolism. But here's a bit of a puzzle.
They found hydrolysis, the ones that use water to break bonds.
(07:48):
They were generally more diverse.
More diverse? That seems counterintuitive
somehow. It was puzzling.
We can circle back to why that might be and then thinking about
molecular interactions. Enzymes that bind metal ions,
more conserved. Enzymes that are inhibited by
lots of different molecules inside the cell also more
conserved. It shows how these specific
(08:10):
chemical interactions act as strong constraints.
Right. Those interactions are like
fixed points in the evolution. OK, let's talk about the the
economics again. Abundance cost?
How did that connect to conservation?
Right, so they found a strong link.
Low abundance enzymes were more diverse, high abundance ones
were more conserved. Makes sense.
(08:30):
Maybe you need the common ones to be reliable.
But here's the twist. It wasn't just the amount of
metabolic stuff flowing through a pathway, the flux, that
correlated most strongly with conservation.
It was the variability of that. Flux the variability.
How so? Enzymes and pathways where the
flux changes a lot goes up and down depending on conditions.
Those enzymes were actually morediverse.
It seems like needing to cope with those fluctuations requires
(08:53):
more evolutionary flexibility. Maybe.
Interesting. So stability breeds
conservation, but variability allows for more divergent and
the cost. And the cost ties in those lower
abundance, more diverse enzymes,they tended to be made of
costlier amino acids and that cost optimization, saving energy
on building blocks happened mostly on the enzyme surfaces,
(09:16):
not in the critical binding sites or the core.
The conserved high abundance enzymes tended to use cheaper
amino acids like glycine, glutamate, alanine, especially
on their surfaces. So protect the core function at
all costs, then economize on theoutside.
That's basically the picture, yeah.
Function first, then optimize cost where you can without
breaking it. OK, so we've got species level,
(09:37):
pathway level, economics. Let's go really micro now.
What about the different parts of a single enzyme?
The structure itself? Yeah, digging into the structure
confirmed this hierarchy. The enzyme score was more
conserved than its surface. That fits the cost idea too.
But even more conserved than thecore were the binding sites
where the substrates or other molecules actually interact.
(09:57):
They have the highest conservation ratio overall.
Makes sense. Change the binding site you
lose. The function exactly.
Mess that up and the enzyme is useless.
And here's a subtle detail that was quite surprising.
Helical structures. Alpha helices.
They were more variable than beta sheets.
More variable than sheets. OK.
Yeah, and maybe even more surprising, they were even more
(10:19):
variable than the random coil regions.
Random coils, shouldn't they be the most flexible and variable?
You'd think so, but the hypothesis is that specific
turns within those seemingly random coil regions are actually
highly conserved. They provide important
structure, so the random bits aren't entirely random,
evolutionarily speaking. Wow.
OK. That really shows the detail
(10:41):
here. And you mentioned the clusters
of conserved residues earlier. Yes, these highly conserved
amino acids weren't just scattered randomly.
They often formed distinct clusters on the enzyme
structure. They used pyruvate kinase as an
example, a key enzyme. The conserved cluster is mapped
perfectly onto the known sites for substrate binding, for
allosteric activation, where regulator molecules bind and
(11:03):
even where it interacts with other proteins.
So these. Clusters are like functional
hotspots. Pretty much, and they found
these clusters contain about twice as many known binding
sites compared to other regions.They even built a machine
learning model that could predict known binding sites just
by looking for these conserved clusters.
Which means you could potentially use this to find new
(11:24):
binding sites, ones we don't know about yet.
Exactly. It suggests these clusters are a
powerful tool for discovery, forannotating enzyme function.
So tying it all together, these findings really bolster that
idea of the ancient metabolic network acting as a scaffold.
The chemistry was there first, perhaps, and enzymes evolved
upon it, optimizing catalysis and the parts essential for that
(11:46):
core job, the catalysis, they changed the least over hundreds
of millions of years. Right.
It paints a picture of a clear hierarchy of evolution.
It's not random core functions, catalytic sites, absolutely
protected, highly conserved, butthe surfaces, other bits, they
adapt more freely to the cells environment, metabolic demands
and driven by that cost factor we talked about.
(12:09):
Which makes me wonder, does thismean if you want to engineer an
enzyme, you should focus mostly on tweaking the surface?
Well, that's certainly where a lot of the natural variation
happens. So it suggests it's more
malleable. Yes, it offers potential handles
for engineering and it also helps explain that hydrolase
paradox we mentioned. Oh yes, the hydrolysis being
more diverse. Why?
The thinking is, because hydrolysis often work without
(12:32):
needing complex cofactors, theirbasic reaction mechanism might
just be less constrained. There are maybe more ways to
achieve hydrolysis allowing for more evolutionary divergent,
whereas something like an oxid or ductase deeply tied into
central metabolism and often needing cofactors is much more
locked down. This level of detail is amazing,
and it has real practical implications, doesn't it?
(12:52):
For you listening, this isn't just abstract theory.
You mentioned enzyme annotation using these conserved clusters
to find new binding sites That could speed up figuring out what
thousands of enzymes actually do.
Absolutely, there are many enzymes where we know the
sequence, maybe the structure, but the precise function or
regulation is still unclear. This offers a new angle.
And beyond just understanding, what about metabolic network
(13:15):
engineering? That's a huge area.
If you want to design an enzyme for biotech, say making biofuels
or a specific chemical, knowing which parts are flexible and
which are critical is invaluable.
Or for medicine, understanding how enzymes evolve in pathogens
or how human enzyme variants affect health.
This framework gives us rules for thinking about enzyme
structure and function that we didn't have before.
(13:35):
It could help predict drug resistance or design better
therapies. So really the take home message
is that enzyme evolution isn't random at all.
It's tightly governed by the enzyme's job, it's catalytic
function, and significantly shaped by its metabolic context,
its environment. We see this clear hierarchy,
core functions, binding sites super conserved.
Other parts adapt, optimizing for cost, for flux variability.
(13:57):
It's a complex interplay. It provides such clarity on the
fundamental rules of how molecules adapt.
So the big question remains, what does this detailed
evolutionary map mean for our future ability to engineer
biology or to tackle big challenges like drug resistance?
Where we go from here? This episode was based on an
Open Access article under the CCBY 4 Point O license.
(14:20):
You can find a direct link to the paper and the license in our
episode description. If you enjoyed this analysis,
the best way to support Base by Base is to subscribe or follow
in your favorite podcast app andleave us a five star rating.
It only takes a few seconds but makes a huge difference in
helping others discover the show.
Thanks for listening and join usnext time as we explore more
science Base by Base.
Welcome to Base by Base, the paper cast that brings genomics
to you wherever you are. Have you ever wondered about
life's incredible ability to adapt and thrive across, well,
vast stretches of time? I mean, imagine single celled
organisms like yeasts, right? Surviving and evolving for over
400 million years through huge shifts in their environments.
(00:35):
What's their secret? Well, at the heart of it all are
enzymes. These tiny sort of unsung
molecular machines that run pretty much every biological
process. Think digestion, copying DNA
basically. But here's the thing, they
aren't static, they evolve. So the mystery we're digging
into today is what forces guy the evolution of these tiny
(00:55):
machines? Is it just random chance or
there's something you know deeper going on?
It's a fantastic question, and arecent research really points to
a perhaps surprising answer. It's all about how organisms
process energy and nutrients, their metabolism.
And for the first time, really, thanks to breakthroughs in AI
like Alpha Fold 2, we can actually map this evolution in,
well, incredible detail. Right.
(01:15):
So that's what we're exploring in this deep dive.
It's a scientific journey spanning 400 million years,
revealing how the chemistry of life itself shapes its molecular
machinery, enzyme by enzyme. It's a really fundamental to
look at adaptation. So today we want to celebrate
the groundbreaking work of Oliver Lemke, Benjamin Murray
Heineke, Marcus Ralzer and theirmany collaborators, folks from
(01:38):
institutions like Sheratte and Berlin, the University of
Oxford, Chalmers University, a big team effort.
Absolutely. Their approach, integrating
structural biology with evolutionary genomics, it's
really pushed our understanding forward, seeing how metabolism
shapes enzyme structure over these huge time stales.
It's a great example of what happens when different fields
come together. OK, so let's unpack this a bit.
(01:59):
Enzymes. They're fundamental to pretty
much everything biological. Everything.
Digestion, DNA, replication, energy, you name it.
And because they're so critical,they're targets for, well, all
sorts of things. Drug development, finding Z's,
markers, even bioengineering right.
Definitely. But despite how important they
are, our grasp on the sort of global biochemical constraints,
(02:23):
the big rule shaping how they function and evolve has been
surprisingly incomplete. It's a big gap.
We've known for a while about certain factors, of course, like
the properties of the amino acids, they're built from
matter, and even the cost of making them plays a role.
You know, sometimes cells preferless energy efficient pathways
like fermentation simply becausemaking the enzymes for it is
(02:45):
cheaper resource wise. And you see that high abundance
enzymes, the one cells make a lot of, tend to use less
energetically costly amino acids.
That makes sense. Sort of like cellular economics,
Yeah. And building on that, there were
these two main ideas about how whole metabolic networks
evolved, right? Exactly.
Did the network structure come about because of how enzymes
evolved? Or did simpler non enzymatic
(03:06):
chemical reactions happen first like a basic scaffold, and then
enzymes evolved later to make those reactions better, more
efficient? And this new research leans
towards the second idea, the scaffolding first.
It provides some pretty compelling evidence for that,
yeah. And this is where the new tech
like AI really comes in. The central idea, the hypothesis
of this paper was basically thatbeing able to predict protein
(03:29):
structures on mass with alpha fold two.
Well, that would let scientists finally link up high res
structural data with the evolutionary genomics.
So connecting the shape to the history basically.
Precisely the hope was this integration could unlock a much
deeper understanding of how metabolism and protein evolution
are, you know, fundamentally linked.
And the way they did it was really innovative.
(03:52):
They leverage the huge, huge evolutionary history of the
Saccharomycatina yeasts. It's an amazing group. 400
million years of evolution rightthere.
Includes species we know well like Saccharomyces, Sarah
Vizier, Baker's yeast, and Candida albicans.
And the scale the data set sounds enormous.
Oh, it is. They analyzed, get this, 11,269
(04:13):
enzyme structures. Yeah, and over 9900 of those
were newly predicted using alphafull 2.
That just shows you the power plus over 1300 existing
structures. And this covered enzymes from
361 metabolic reactions across 224 pathways.
It's like building an evolutionary Atlas for these
molecular machines. OK, so how did they actually
(04:33):
compare them across all those species and structures?
26 diverse yeast species plus anout group species for
comparison. They used Baker's yeast S
cervesier as the reference pointand they calculated something
called a conservation ratio or CR CR OK right.
It basically measures the percentage of amino acids that
are identical in the same structural spot between enzymes
(04:55):
and different species. So a low CR it means lots of
change, High divergent. High CR means it stayed very
similar. Highly conserved.
Got it. Structure conservation.
What else did they look? At they pulled in loads of other
data, phenotypic data like how well the yeasts grew on
different sugars, 21 different ones, actual protein abundance
measurements from proteomics, and predictions of metabolic
(05:17):
flux from genome scale models. They even calculated the cost of
making amino acids and looked atdifferent parts of the enzyme
structure, surface versus core binding sites, helices versus
sheets. And crucially, they did
evolutionary selection analysis and looked for these clusters of
highly conserved amino acids. They even trained a machine
learning model on these clustersto find potential new binding
(05:39):
sites. OK, that's a massive multi
layered analysis. So pulling it all together,
what's the headline? What did they find?
Headline is really clear. Metabolism profoundly shapes
enzyme structural evolution, andit does it at multiple levels,
from the whole species down to specific pathways and even right
down to the molecular details inside the enzyme.
So metabolic environment is likea sculptor for these proteins.
(06:02):
That's a great way to put it, yeah.
For instance, at the species level, enzymes from yeast that
specialize in fermentation showed higher structural
conservation compared to the relatives in Esser Vizier.
And the biggest structural differences they saw, those were
in enzymes involved in central carbon metabolism and the
electron transport chain core energy processes.
(06:23):
Can you give an example of that specialization?
Sure. So they looked at species that
can use xylose, which is anothertype of sugar.
In those species, enzymes related to actually using xylose
showed shifts in conservation, but interestingly, so did parts
of the electron transport chain and even different versions of
the same enzyme, like acetylcoa synthase.
There's an aerobic and an anaerobic version behave
(06:44):
differently depending on whetherthe yeast could use xylose or
not. It really highlights how the
specific metabolic niche drives these evolutionary patterns.
OK, so special. What about general trends across
different metabolic pathways? Yeah, they saw clear trends
there too. Some were pretty intuitive
enzymes and really fundamental processes making purines for
(07:06):
DNA, certain amino acids, central metabolism like
glycolysis, the TCA cycle. The real core stuff.
Exactly, Those were highly conserved.
Makes sense, right? They're essential, but then
enzymes involved in things like lipid metabolism, breaking down
fatty acids and also protein glycosylation.
They were among the most divergent, the most changed.
(07:28):
Why Lipids? The researcher suggests that
maybe lipidomes, the collection of lipids in a cell, have more
flexibility to adapt compared toother core systems.
And looking at enzyme classes, oxidoriductuses, which handle
energy transfer, very conserved,especially because they're key
players in central metabolism. But here's a bit of a puzzle.
They found hydrolysis, the ones that use water to break bonds.
(07:48):
They were generally more diverse.
More diverse? That seems counterintuitive
somehow. It was puzzling.
We can circle back to why that might be and then thinking about
molecular interactions. Enzymes that bind metal ions,
more conserved. Enzymes that are inhibited by
lots of different molecules inside the cell also more
conserved. It shows how these specific
(08:10):
chemical interactions act as strong constraints.
Right. Those interactions are like
fixed points in the evolution. OK, let's talk about the the
economics again. Abundance cost?
How did that connect to conservation?
Right, so they found a strong link.
Low abundance enzymes were more diverse, high abundance ones
were more conserved. Makes sense.
(08:30):
Maybe you need the common ones to be reliable.
But here's the twist. It wasn't just the amount of
metabolic stuff flowing through a pathway, the flux, that
correlated most strongly with conservation.
It was the variability of that. Flux the variability.
How so? Enzymes and pathways where the
flux changes a lot goes up and down depending on conditions.
Those enzymes were actually morediverse.
It seems like needing to cope with those fluctuations requires
(08:53):
more evolutionary flexibility. Maybe.
Interesting. So stability breeds
conservation, but variability allows for more divergent and
the cost. And the cost ties in those lower
abundance, more diverse enzymes,they tended to be made of
costlier amino acids and that cost optimization, saving energy
on building blocks happened mostly on the enzyme surfaces,
(09:16):
not in the critical binding sites or the core.
The conserved high abundance enzymes tended to use cheaper
amino acids like glycine, glutamate, alanine, especially
on their surfaces. So protect the core function at
all costs, then economize on theoutside.
That's basically the picture, yeah.
Function first, then optimize cost where you can without
breaking it. OK, so we've got species level,
(09:37):
pathway level, economics. Let's go really micro now.
What about the different parts of a single enzyme?
The structure itself? Yeah, digging into the structure
confirmed this hierarchy. The enzyme score was more
conserved than its surface. That fits the cost idea too.
But even more conserved than thecore were the binding sites
where the substrates or other molecules actually interact.
(09:57):
They have the highest conservation ratio overall.
Makes sense. Change the binding site you
lose. The function exactly.
Mess that up and the enzyme is useless.
And here's a subtle detail that was quite surprising.
Helical structures. Alpha helices.
They were more variable than beta sheets.
More variable than sheets. OK.
Yeah, and maybe even more surprising, they were even more
(10:19):
variable than the random coil regions.
Random coils, shouldn't they be the most flexible and variable?
You'd think so, but the hypothesis is that specific
turns within those seemingly random coil regions are actually
highly conserved. They provide important
structure, so the random bits aren't entirely random,
evolutionarily speaking. Wow.
OK. That really shows the detail
(10:41):
here. And you mentioned the clusters
of conserved residues earlier. Yes, these highly conserved
amino acids weren't just scattered randomly.
They often formed distinct clusters on the enzyme
structure. They used pyruvate kinase as an
example, a key enzyme. The conserved cluster is mapped
perfectly onto the known sites for substrate binding, for
allosteric activation, where regulator molecules bind and
(11:03):
even where it interacts with other proteins.
So these. Clusters are like functional
hotspots. Pretty much, and they found
these clusters contain about twice as many known binding
sites compared to other regions.They even built a machine
learning model that could predict known binding sites just
by looking for these conserved clusters.
Which means you could potentially use this to find new
(11:24):
binding sites, ones we don't know about yet.
Exactly. It suggests these clusters are a
powerful tool for discovery, forannotating enzyme function.
So tying it all together, these findings really bolster that
idea of the ancient metabolic network acting as a scaffold.
The chemistry was there first, perhaps, and enzymes evolved
upon it, optimizing catalysis and the parts essential for that
(11:46):
core job, the catalysis, they changed the least over hundreds
of millions of years. Right.
It paints a picture of a clear hierarchy of evolution.
It's not random core functions, catalytic sites, absolutely
protected, highly conserved, butthe surfaces, other bits, they
adapt more freely to the cells environment, metabolic demands
and driven by that cost factor we talked about.
(12:09):
Which makes me wonder, does thismean if you want to engineer an
enzyme, you should focus mostly on tweaking the surface?
Well, that's certainly where a lot of the natural variation
happens. So it suggests it's more
malleable. Yes, it offers potential handles
for engineering and it also helps explain that hydrolase
paradox we mentioned. Oh yes, the hydrolysis being
more diverse. Why?
The thinking is, because hydrolysis often work without
(12:32):
needing complex cofactors, theirbasic reaction mechanism might
just be less constrained. There are maybe more ways to
achieve hydrolysis allowing for more evolutionary divergent,
whereas something like an oxid or ductase deeply tied into
central metabolism and often needing cofactors is much more
locked down. This level of detail is amazing,
and it has real practical implications, doesn't it?
(12:52):
For you listening, this isn't just abstract theory.
You mentioned enzyme annotation using these conserved clusters
to find new binding sites That could speed up figuring out what
thousands of enzymes actually do.
Absolutely, there are many enzymes where we know the
sequence, maybe the structure, but the precise function or
regulation is still unclear. This offers a new angle.
And beyond just understanding, what about metabolic network
(13:15):
engineering? That's a huge area.
If you want to design an enzyme for biotech, say making biofuels
or a specific chemical, knowing which parts are flexible and
which are critical is invaluable.
Or for medicine, understanding how enzymes evolve in pathogens
or how human enzyme variants affect health.
This framework gives us rules for thinking about enzyme
structure and function that we didn't have before.
(13:35):
It could help predict drug resistance or design better
therapies. So really the take home message
is that enzyme evolution isn't random at all.
It's tightly governed by the enzyme's job, it's catalytic
function, and significantly shaped by its metabolic context,
its environment. We see this clear hierarchy,
core functions, binding sites super conserved.
Other parts adapt, optimizing for cost, for flux variability.
(13:57):
It's a complex interplay. It provides such clarity on the
fundamental rules of how molecules adapt.
So the big question remains, what does this detailed
evolutionary map mean for our future ability to engineer
biology or to tackle big challenges like drug resistance?
Where we go from here? This episode was based on an
Open Access article under the CCBY 4 Point O license.
(14:20):
You can find a direct link to the paper and the license in our
episode description. If you enjoyed this analysis,
the best way to support Base by Base is to subscribe or follow
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Thanks for listening and join usnext time as we explore more
science Base by Base.
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