Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: I want to start today with a number, and not
Speaker 2: just any number. It's a number so incomprehensibly massive that
Speaker 2: honestly the human brain just shorts out trying to visualize it.
Speaker 3: Oh, I have a sneaking suspicion. I know exactly where
Speaker 3: you're going with this. You're looking at the combinatorial math
Speaker 3: for biology right now, the protein problem. Absolutely am I
Speaker 3: was reading through the research for this deep dive and
Speaker 3: I hit this comparison that just stopped me cold. So
Speaker 3: for everyone listening, I want you to picture the entire
Speaker 3: observable universe right, every star, every planet, every cloud of
Speaker 3: interstellar gas made a number of atoms in all of
Speaker 3: that is roughly ten to the power of eighty. That
Speaker 3: is a one followed by eighty zero.
Speaker 2: It's the literal definition of astronomical exactly.
Speaker 3: Now, take a single average size protein, just a basic
Speaker 3: biological machine made of amino acids. If you wanted to
Speaker 3: test every possible combination of amino acids to find the
Speaker 3: absolute best version of that protein.
Speaker 2: The number of variants you'd have to build is larger
Speaker 2: than the number of atoms in the universe.
Speaker 3: It is significantly larger, actually, Yeah, and that creates what
Speaker 3: we call the fundamental bottleneck of bioengineering. We know that
Speaker 3: somewhere in that infinite haystack of combinations, there is a needle,
Speaker 3: a perfect needle, right, there is a version of that
Speaker 3: protein that cures a specific cancer, or eats plastic waste,
Speaker 3: or generates clean energy with perfect efficiency. But we physically
Speaker 3: cannot search the whole haystack.
Speaker 2: We don't have enough time.
Speaker 3: They don't enough time, we don't have enough resources, and frankly,
Speaker 3: we don't even have enough atoms on Earth to build
Speaker 3: all the test tubes you would need to run that
Speaker 3: extras which brings.
Speaker 2: Us to today's topic, because if you can't search the haystag,
Speaker 2: you're usually stuck just I don't know, picking at the
Speaker 2: edges right, hoping you get lucky.
Speaker 3: That's historically been the case.
Speaker 2: Yeah, But we are looking at a new paper published
Speaker 2: just recently in Science February nineteenth, twenty six to be exact,
Speaker 2: and it claims to have found a way to cheat
Speaker 2: the math.
Speaker 3: Well, cheat might be a strong word, but they have
Speaker 3: certainly found a massive shortcut. We're talking about a framework
Speaker 3: called multi evolve. It's coming out of Patrick's U's Lab
Speaker 3: at UC Berkeley and the Ark Institute, and what they've
Speaker 3: proposed is the way to use machine learning to navigate
Speaker 3: that impossible search space, that universe of options, exactly navigating
Speaker 3: it without having to physically test every single one.
Speaker 2: It's basically the difference between walking blindfolded through a mountain
Speaker 2: range trying to find the highest peak and having a
Speaker 2: satellite map that just drops you right at the summit.
Speaker 3: That is precisely the shift we are seeing here. It's
Speaker 3: a move from discovery by accident to discovery by design.
Speaker 2: So we're going to unpack how this works, why previous
Speaker 2: methods have failed us, and what this actually means for
Speaker 2: things like medicine and green energy. But before we get
Speaker 2: into the AI wizardry.
Speaker 3: We need to define the hardware.
Speaker 2: Yes, what are we actually trying to build here? Because
Speaker 2: I'll admit when I hear protein engineering, my mind instantly
Speaker 2: goes to bodybuilders drinking shakes at the gym.
Speaker 3: Huh, yeah, that is the most common association. But in
Speaker 3: this context, when we say protein, we are talking about
Speaker 3: the molecular machines that literally run the world.
Speaker 2: They do everything, they really do.
Speaker 3: Proteins are the hardware of biology. Dn I is just
Speaker 3: the code, the blueprint, right, But the protein is the
Speaker 3: physical thing that actually does the work.
Speaker 2: It catalyzes reactions, it.
Speaker 3: Fights off viruses, it transports oxygen, it digests your food.
Speaker 2: And we aren't just talking about human biology either. The
Speaker 2: research highlights a surprisingly huge range of applications for engineered proteins.
Speaker 3: Oh absolutely, and this is crucial to understand if we
Speaker 3: want to grasp why the SEE labs work is such
Speaker 3: a big deal. The demand for smart, custom built proteins
Speaker 3: is exploding across almost every sector of the economy.
Speaker 2: Take consumer goods for instance. The paper specifically mentions laundry detergent, right,
Speaker 2: which feels incredibly mundane compared to curing disease. But stick
Speaker 2: with us here, why on Earth do we need high
Speaker 2: tech machine learning design proteins just to wash our socks.
Speaker 3: It comes down to energy efficiency and environmental impact. In
Speaker 3: the old days, to get a heavy grease stain or
Speaker 3: grass stain out of clothes. You needed boiling hot water
Speaker 3: and really harsh chemicals.
Speaker 2: Which ruins the clothes.
Speaker 3: Eventually, it's bad for the fabric, yes, but it's also
Speaker 3: terrible for the energy grid. Heating all that water takes
Speaker 3: a massive amount of electricity. So manufacturers started adding enzymes
Speaker 3: to the detergent in enzymes of proteins exactly. Enzymes are
Speaker 3: just proteins that speed up chemical reactions in detergent. They
Speaker 3: act like little molecular scissors that cut up the lipids
Speaker 3: in grease or the proteins in a grip as stains,
Speaker 3: so they just wash away in the water.
Speaker 2: But there's a catch, right, because a washing machine isn't
Speaker 2: exactly a natural habitat.
Speaker 3: It is a totally hostile environment for a biological molecule.
Speaker 3: It's soapy, the pH is highly alkaline, it's spinning violently,
Speaker 3: and even if we use cold water, it's not the stable,
Speaker 3: cozy temperature of a living cell.
Speaker 2: Right.
Speaker 3: Nature never designed a protein to live inside.
Speaker 2: A tide cod so we have to design one.
Speaker 3: We have to engineer a protein that is tough enough
Speaker 3: to survive the chemical assault of the wash cycle, but
Speaker 3: remains active enough to actually eat the stain. That is
Speaker 3: a massive, multi variable engineering challenge.
Speaker 2: And that's just consumer goods. The paper also goes deep
Speaker 2: into the energy sector, specifically biofuels.
Speaker 3: This is a huge one for the climate crisis. We
Speaker 3: desperately want to be able to take biomass things like cornstalks,
Speaker 3: wood chips, switch grass and turn it into clean liquid fuel.
Speaker 2: But plants are stubborn.
Speaker 3: Very They are made of cellulose, and cellulose is designed
Speaker 3: by nature to be incredibly tough. It's literally the structural
Speaker 3: support that makes wood hard. It actively resists breaking down.
Speaker 2: So we need a protein that can chew through wood.
Speaker 3: We need engineered enzymes called celluluses. If we can build
Speaker 3: a cellulus that is say, ten times more efficient at
Speaker 3: breaking down plant fiber into simple sugars, the production cost
Speaker 3: of biofuel drops through the floor.
Speaker 2: And suddenly it can actually compete with fossil fuels precisely.
Speaker 3: But again, natural celluluses aren't optimized for giant, hot, acidic
Speaker 3: industrial vats. They're optimized for a fungus slowly rotting a
Speaker 3: log on a forest floor over five years. We need
Speaker 3: to upgrade them for industrial speed.
Speaker 2: And then, of course there's the big one, therapeutics medicine.
Speaker 3: This is where the precision requirement goes entirely off the charts.
Speaker 3: The researchers focus heavily on antibodies.
Speaker 2: And an antibody is essentially a protein that acts like
Speaker 2: a honing beacon.
Speaker 3: Exactly. It binds to a specific target, like a viral
Speaker 3: spike protein, or a cancer cell, or a signaling molecule
Speaker 3: in your body. That's an autoimmune flare up.
Speaker 2: But it has to be impossibly specific.
Speaker 3: It has to be perfect. If you engineer an antibody
Speaker 3: to kill a cancer cell, but it has even a
Speaker 3: slight affinity for your healthy.
Speaker 2: Liver cells, that's not a drug anymore. That's a poison. Right.
Speaker 3: You need absolute high affinity for the target and zero
Speaker 3: affinity for everything else. Plus it has to be structurally
Speaker 3: stable so it can survive in the bloodstream without falling apart.
Speaker 2: It can't clump up in the vial while sitting on
Speaker 2: a pharmacy shelf.
Speaker 3: It can't trigger a separate allergic reaction.
Speaker 2: It sounds like we're asking for a miracle molecule. We
Speaker 2: wanted to do five different, extremely difficult things perfectly, all
Speaker 2: at the exact same time.
Speaker 3: And that right there is exactly why this is so hard.
Speaker 3: That is the high dimensional search problem that Patrick Sue
Speaker 3: talks about.
Speaker 2: In the paper high dimensional meeting. Lots of variables.
Speaker 3: Yes, to get a protein to do all those things,
Speaker 3: be stable, be active, be specific. Usually can't just change
Speaker 3: one single thing about it. You have to change multiple
Speaker 3: parts of the proteins simultaneously.
Speaker 2: Okay, so let's get into the how. Let's talk about
Speaker 2: the mechanics of changing approaching. A protein is basically a
Speaker 2: long spring of amino acids folded up right correct, and
Speaker 2: there are twenty different amino acids to choose from. It's
Speaker 2: like an alphabet. So if I want to make a
Speaker 2: better protein, I just swap out some letters.
Speaker 3: In theory, yes, you perform mutations. You swap an alanine
Speaker 3: for a tripborn or a glycine for a lysine.
Speaker 2: But you mentioned earlier that we can't test all the combinations.
Speaker 2: So historically, before this new AI framework came along, if
Speaker 2: I wanted to make that better laundry enzyme, what was
Speaker 2: the actual process in the lab.
Speaker 3: For the last couple of decades, the gold standard has
Speaker 3: been a process called directed evolution.
Speaker 2: Which won Francis Arnold the Nobel Price.
Speaker 3: It did in twenty eighteen. And it's a brilliant technique,
Speaker 3: but it fundamentally relies on iteration. It's an educated guess
Speaker 3: and check method.
Speaker 2: Walk me through that cycle. How does directed evolution work?
Speaker 3: You start with your natural baseline protein. You introduce a
Speaker 3: bunch of random mutations into the d that codes for it,
Speaker 3: usually just changing one amino acid at a time across
Speaker 3: a whole batch.
Speaker 2: So you make a library of slight variation exactly.
Speaker 3: Then you screen those thousands of slightly tweaked variants in
Speaker 3: the lab. Now, most of them will be completely broken
Speaker 3: by the mutation.
Speaker 2: Because random mutations usually break things right.
Speaker 3: Some will act exactly the same as the original, but
Speaker 3: maybe one or two out of ten thousand are slightly
Speaker 3: better at surviving the hot, soapy water.
Speaker 2: Okay, so I find a winner. It's let's say five
Speaker 2: percent better.
Speaker 3: You take that specific winner, and that becomes the parent
Speaker 3: for the next generation. You mutate that one, You screen
Speaker 3: the new batch, find the best of those, and you
Speaker 3: keep climbing that ladder step by step.
Speaker 2: That sounds incredibly logical. I mean, it's essentially how natural
Speaker 2: evolution works. Survival of the fittest round after round, selecting
Speaker 2: for the trait you want.
Speaker 3: It is entirely logical, but it has a fatal flaw
Speaker 3: when it comes to advanced engineering. It gets stuck stuck out. Okay.
Speaker 3: Imagine you're climbing a mountain, but you're in a thick
Speaker 3: pea soup fog. You can only I see exactly one
Speaker 3: step ahead of you. Okay, And the strict rule of
Speaker 3: this climb is you must always take a step up.
Speaker 3: If a step goes down in elevation, you are not
Speaker 3: allowed to take it.
Speaker 2: Because taking a step down means the protein got worse.
Speaker 3: Exactly, you only keep the mutations that improve the function,
Speaker 3: So you keep stepping up and up until eventually every
Speaker 3: step around you goes down. You are standing on a peak.
Speaker 2: I've reached the top.
Speaker 3: You think you've reached the top, but because of the fog,
Speaker 3: you don't realize that you are just standing on a
Speaker 3: small foothill. The real mountain, the one that's ten times higher,
Speaker 3: the truly optimal protein, is a mile away. But to
Speaker 3: get there you would have had to walk down into
Speaker 3: a valley first, and directed evolution doesn't let you go.
Speaker 2: Down, so you're trapped on what they call a local optimum.
Speaker 2: You have a decent protein, but mathematically you are nowhere
Speaker 2: near the best possible one.
Speaker 3: Exactly, And the biological reason you get trapped in that
Speaker 3: valley is because of a concept called epistosis.
Speaker 2: Okay, yes, I love this word. It sounds incredibly intimidating,
Speaker 2: but it is actually the key to the entire puzzle
Speaker 2: of why mlt I evolve was created. Let's unpack episticis.
Speaker 3: It really is the concept that breaks the old step
Speaker 3: wise way of doing things. Epistosis simply means that the
Speaker 3: effect of one mutation heavily depends on the context of
Speaker 3: the other mutations around it.
Speaker 2: So in biology, one plus one does not always equal
Speaker 2: to far from it.
Speaker 3: Give me a food analogy.
Speaker 2: Oh, okay, if you add strawberries to a bowl, that's good.
Speaker 2: You add garlic to a pan, that's good.
Speaker 3: But if you combine strawberries and.
Speaker 2: Garlict an absolute disaster.
Speaker 1: Right.
Speaker 3: That is negative episticis the combination of the two elements
Speaker 3: is vastly worse than the sum of their individual parts.
Speaker 3: But epistosis works the other way too. Positive episticis like
Speaker 3: a lock and key. Yes, think of a physical key.
Speaker 3: If you change one single groove on a key, it
Speaker 3: doesn't open the door anymore. It's useless. Right, If you
Speaker 3: change a different single groove, it's also useless. But if
Speaker 3: you change both grooves at the exact same time to
Speaker 3: match a brand new lock, suddenly it works perfectly.
Speaker 2: I see. So if you were doing this step by
Speaker 2: step directed evolution method, you would have tested the first
Speaker 2: groove change. So I didn't open the door and thrown
Speaker 2: it in.
Speaker 3: The track exactly, you would have said, this mutation is broken.
Speaker 3: It's a step down the mountain.
Speaker 2: You never would have found the brilliant combination because you
Speaker 2: never tried them together.
Speaker 3: Precisely, the step wise approach blindly discards mutations that look
Speaker 3: detrimental on their own but are actually essential ingredients for
Speaker 3: a future breakthrough.
Speaker 2: Because they need a partner mutation to make sense.
Speaker 3: Structurally, yes, and because we are trying to optimize for
Speaker 3: these highly complex, high dimensional traits like stability and activity
Speaker 3: and specificity all at once. The true solution almost always
Speaker 3: requires a combination of mutations that have this complex epistatic relationship.
Speaker 2: So we fundamentally need a way to see the garlic
Speaker 2: and strawberries problem before we taste it. You need to
Speaker 2: predict these hidden interactions, and.
Speaker 3: That is exactly where a MULTII evolve enters the picture.
Speaker 3: The goal of Patrick Sue and his team was to
Speaker 3: stop walking step by step in the fog.
Speaker 2: They wanted the satellite map.
Speaker 3: They wanted to build a machine learning system that could
Speaker 3: predict the view from the top of the highest mountain
Speaker 3: without having to physically climb every single hill to get there.
Speaker 2: So let's break down the actual EMUALTI evolve architecture because
Speaker 2: reading the paper, it's very clear this isn't just a
Speaker 2: situation where they dumped a bunch of raw data into
Speaker 2: a black box AI and asked it to do magic.
Speaker 3: No, not at all.
Speaker 2: Three specific three step workflow correct.
Speaker 3: And the structure of this workflow is incredibly deliberate. It's
Speaker 3: designed specifically to capture and learn that epistatic interaction data
Speaker 3: we just talked about.
Speaker 2: Okay, Step one, walk us through it.
Speaker 3: Step one is establishing the baseline prediction. This is where
Speaker 3: they look at single mutations.
Speaker 2: Just the individual alphabet swaps, right.
Speaker 3: They use existing historical data, or they run a quick
Speaker 3: initial round of lab experiments to see what happens to
Speaker 3: the protein when you change just one single amino acid
Speaker 3: at a time across the board.
Speaker 2: So this established the floor. It tells the system what
Speaker 2: each individual piece does in total isolation.
Speaker 3: Exactly. It gives the AI the additive model if there
Speaker 3: were no epistosis, if there were no interactions, this is
Speaker 3: literally all the data you would need. You'd just pick
Speaker 3: the five best single mutations and mash them together.
Speaker 2: But we know that doesn't work the garlic and the.
Speaker 3: Strawberries, right, So that limitation leads directly to step two,
Speaker 3: which is arguably the most innovative part of the entire protocol,
Speaker 3: the epistatic step.
Speaker 2: And this is where they actually go back into the
Speaker 2: wet lab. Right. They don't just simulate this on a supercomputer.
Speaker 3: Yet, No, And that is a critical point. AI and
Speaker 3: biology is completely useless without high quality physical data. So
Speaker 3: in step two they physically synthesize and chest a library
Speaker 3: of proteins that have exactly two mutations.
Speaker 2: Wait, why just two? If the goal is a protein
Speaker 2: with ten mutations, why not generate data on groups of
Speaker 2: three or four?
Speaker 3: Because pairs are the fundamental building block of interaction. If
Speaker 3: you can deeply understand how mutation A affects mutation B,
Speaker 3: and how B effects C exactly, you start to build
Speaker 3: a web of logic you don't actually need to physically
Speaker 3: test every trio or quartet if the computer has a
Speaker 3: really robust grasp of the pairwise physics. It's about data efficiency.
Speaker 2: Oh, that makes sense. So they are generating this massive,
Speaker 2: incredibly clean data set of just how these two specific
Speaker 2: things get along in a given protein structure.
Speaker 3: Yes, they are essentially teaching the machine learning model the biochemical.
Speaker 2: Rules of the road, things like spatial reasoning.
Speaker 3: Spatial reasoning charge polarity. The model learns, for example, when
Speaker 3: a positively charged amino acid is placed directly next to
Speaker 3: a negative one, they stabilize each other.
Speaker 2: Right, they attract.
Speaker 3: But when two incredibly bulky amino acids are mutated next
Speaker 3: to each other, they physically clash and the protein falls apart.
Speaker 3: The model learns the underlying grammar of the protein's physical structure.
Speaker 2: And once the model has internalized that grammar from the
Speaker 2: double mutation data, that.
Speaker 3: Brings us to step three. High order. Now that the
Speaker 3: model truly understands the interactions, you ask it too.
Speaker 2: Extrapoly, You take off the training wheels.
Speaker 3: You do you say, okay, computer, you know exactly how
Speaker 3: these pieces interact in pairs. Now mathematically predict what happens
Speaker 3: if I put five of them together or seven or
Speaker 3: then this is the massive leap.
Speaker 2: This is what the paper refers to when it talks
Speaker 2: about condensing iterative cycles into a single round.
Speaker 3: It is instead of doing five years of mutate test, select, repeat,
Speaker 3: over and over, you do one single round of baseline
Speaker 3: and pairwise data gathering. You train the model on that
Speaker 3: specific protein's rules, and then the model just spits out
Speaker 3: a list of highly complex, multi mutated designs that it
Speaker 3: confidently predicts will be successful.
Speaker 2: And then you just go build those specific designs.
Speaker 3: Yes, instead of physically testing a million random variants in
Speaker 3: the lab to find a marginal improvement, you synthesize maybe
Speaker 3: the top twenty or fifty designs. The computer suggests.
Speaker 2: It sounds incredibly efficient on paper, but the proof is
Speaker 2: in the pudding, or I guess in this case, the
Speaker 2: proof is in the protein. Right, did it actually work?
Speaker 2: Because computer theory is one thing, but actual physical biology
Speaker 2: is notoriously messy and unpredictable.
Speaker 3: It did work brilliantly, And to prove it wasn't a fluke,
Speaker 3: the huslab didn't just pick one easy, well understood target.
Speaker 3: They tested the MLTI evolve framework on three completely different
Speaker 3: classes of proteins to prove it's a universal solution.
Speaker 2: Okay, let's go through these case studies because this is
Speaker 2: where rubber meets the road case steady a the autoimmune antibody.
Speaker 3: Right, So they took an existing antibody that is clinically
Speaker 3: relevant for treating autoimmune diseases. The engineering goal here was
Speaker 3: affinity maturation.
Speaker 2: Meaning they wanted it to bind tighter to its target.
Speaker 3: Exactly. If the antibody binds tighter, it's more effective. And
Speaker 3: if it's more effective, you can give the patient a lower.
Speaker 2: Dose, which means fewer side effects.
Speaker 3: Fewer side effects, lower manufacturing costs, better patient outcomes. So
Speaker 3: they ran THEMLTI evolve workflow. They gathered the pairwise data,
Speaker 3: trained the model, and the model predicted a set of
Speaker 3: variants with multiple simultaneous mutations, and.
Speaker 2: When they actually built them in the lab.
Speaker 3: They found variants that significantly outperformed the natural version. But
Speaker 3: here is the absolute picker. The individual mutations that made
Speaker 3: up that winning combination.
Speaker 2: Some of them, if you looked at them completely on
Speaker 2: their own, were neutral or even slightly detrimental.
Speaker 3: Wow. So the old step by step directed evolution method
Speaker 3: would have entirely missed them.
Speaker 2: Guaranteed, the traditional approach would have screened those single mutations
Speaker 2: in round one, seeing that they didn't improve binding, and
Speaker 2: thrown them in the trash.
Speaker 3: But maltii evolve saw the hidden connection.
Speaker 2: It saw that when you combined those specific detrimental mutations together,
Speaker 2: they created a completely new structural feature that locked onto
Speaker 2: the autoimmune target like a vice. It successfully navigated the epistosis.
Speaker 3: That is a massive validation of the whole concept. Okay,
Speaker 3: case study B. This is a buzzword pretty much everyone
Speaker 3: knows by now.
Speaker 2: Crisper, yes the genetic engineering tool. Specifically, the researchers were
Speaker 2: looking at the CAS proteins.
Speaker 3: These are the actual molecular scissors that cut the DNA
Speaker 3: strand right exactly.
Speaker 2: Christ is the guidance system and CAST nine or CAST
Speaker 2: twelve is the blade.
Speaker 3: Why do we need to engineer those?
Speaker 1: Though?
Speaker 3: I thought Crisper was already this miraculous, perfect tool. It's miraculous,
Speaker 3: but it is definitely not perfect. The natural CAST proteins
Speaker 3: were evolved by bacteria to fight off viruses. They weren't
Speaker 3: evolved to do precision gene therapy in human cells. Oh
Speaker 3: that makes sense, so they can be a bit sluggish,
Speaker 3: or they can be physically too bulky to easily deliver
Speaker 3: into a human cell, or most dangerously, they can have
Speaker 3: off target.
Speaker 2: Effects, meaning they cut the wrong piece of DNA right.
Speaker 3: And if you're editing a tomato plant, an off target
Speaker 3: cut might not be a huge deal. But if you
Speaker 3: are trying to cure a genetic disease in a living
Speaker 3: human being, oops, I cut the wrong gene is absolutely
Speaker 3: not an acceptable outcome.
Speaker 2: No, definitely not. That could cause cancer exactly.
Speaker 3: So the goal here was to engineer a CAST protein
Speaker 3: that was dramatically more efficient and precise. They applied the
Speaker 3: MLTI evolve framework, generated the pair wise interaction data, and
Speaker 3: asked the model for solutions, and did it find one?
Speaker 3: It identified multiple combinations of mutations involving five or more
Speaker 3: simultaneous amino acid swaps that created what you could basically
Speaker 3: call a supercast protein, highly active, highly precise.
Speaker 2: And again, this was a one shot success. They didn't
Speaker 2: have to evolve it iteratively over months and months.
Speaker 3: One shot from gathering the baseline data to synthesizing the
Speaker 3: final optimized protein in a single computational design cycle.
Speaker 2: That has to be incredibly exciting for the whole gene
Speaker 2: therapy field.
Speaker 3: It's completely transformative. If you can rapidly prototype and optimize
Speaker 3: the actual delivery and editing tools, you dramatically accelerate the
Speaker 3: entire timeline for developing cures for genetic disorders.
Speaker 2: Amazing. And they had a third case study right, just
Speaker 2: something evolving cellular tracking.
Speaker 3: Yes, this was more of a research tool application to
Speaker 3: show breadth. They looked at proteins used to track cellular movement.
Speaker 3: Usually these are fluorescent proteins like the.
Speaker 2: Green glowing stuff they originally found in.
Speaker 3: Jellyfish exactly, green fluorescent protein or GFP. Scientists routinely attach
Speaker 3: these glowing tags to other proteins inside a cell so
Speaker 3: they can physically watch them move under a microscope.
Speaker 2: Osy, like putting a GPS tracker on a car.
Speaker 3: Right, where's the virus going? How is the cell dividing?
Speaker 3: But to get good data, you need the glowing tag
Speaker 3: to be extremely bright, physically stable, and it can't be
Speaker 3: so bulky that it interferes with the cell's normal function.
Speaker 2: And I assume l multii evolve built a better flashlight.
Speaker 3: It did it identified complex variants that were significantly brighter
Speaker 3: and more stable than the baseline. And the reason highlighting
Speaker 3: this matter is is that it proves the versatility of
Speaker 3: the machine learning framework.
Speaker 2: It's not just a trick for antibodies exactly.
Speaker 3: It's not just for drugs, it's not just for cutting DNA.
Speaker 3: It works for literally any protein where the biological function
Speaker 3: is tied to its physical structure. It's all of them,
Speaker 3: which is all of them.
Speaker 2: So we basically have a unified tool that works on
Speaker 2: laundry detergents, cancer drugs, and DNA scissors that covers a
Speaker 2: massive chunk of the entire bioeconomy.
Speaker 3: It really does. It's a platform technology.
Speaker 2: I want to pivot to the future implications here because
Speaker 2: the researchers Patrick Sue and his colleagues, they didn't just
Speaker 2: drop this methodology paper and walk away. They laid out
Speaker 2: a really compelling vision for where this goes next. And
Speaker 2: one of the things they highlighted that really struck me
Speaker 2: was enzyme replacement therapy.
Speaker 3: Yes, this is a really poignant medical application. There are
Speaker 3: thousands of rare genetic diseases where a child is born
Speaker 3: missing the ability to manufacture one single specific enzyme.
Speaker 2: Like Gaucher's disease or POMP disease exactly.
Speaker 3: And because they lack that one specific enzyme, metabolic toxins
Speaker 3: just continuously build up in their cells. It can be
Speaker 3: fatal or it can cause severe, lifelong discip.
Speaker 2: And right now, the primary treatment is to just artificially
Speaker 2: inject the missing enzyme into the patient.
Speaker 3: Right, yes, recombinant enzyme replacement, But think about the biology
Speaker 3: of what's happening there. You are injecting a naked foreign
Speaker 3: protein directly into the bloodstream.
Speaker 2: The body's immune system probably doesn't like that.
Speaker 3: The immune system attacks it, the liver tries to clear it,
Speaker 3: and the protein naturally degrades just from the thermal energy
Speaker 3: of the body being warm. So the half life of
Speaker 3: that incredibly expensive drug might be measured in just minutes
Speaker 3: or hours.
Speaker 2: Which means the patient, who is often a very young child,
Speaker 2: has to be hooked up to an IV constantly.
Speaker 3: It's a massive physical and emotional burden, and it's astronomically expensive.
Speaker 3: But with a tool like MLTI evolve, we could intentionally
Speaker 3: design what you might call a hardened version of that enzyme.
Speaker 2: What does a hardened enzyme look like?
Speaker 3: We could use the AI to find the highly specific
Speaker 3: combination of five or ten or fifteen mutations that make
Speaker 3: that exact enzyme rock solid, stable at human body time, temperature,
Speaker 3: or we new take the surface so it becomes invisible
Speaker 3: to the immune system.
Speaker 2: But it still digests the toxin perfectly exactly.
Speaker 3: It retains perfect catalytic function, but its durability is engineered
Speaker 3: to the absolute maximum. So instead of a hospital infusion
Speaker 3: every single day, maybe it's an injection once a.
Speaker 2: Month or once every six months.
Speaker 3: Right. That completely changes the fundamental quality of life for
Speaker 3: the patient and their family, and it makes the therapy
Speaker 3: significantly cheaper to produce because you simply don't need to
Speaker 3: manufacture as much of it.
Speaker 2: That's the real human side of this insane math problem.
Speaker 2: It's so easy to get lost in the ten to
Speaker 2: the power of eighty stuff and the computational physics, But
Speaker 2: at the end of the day, we are talking about
Speaker 2: a kid spending less time tethered to a hospital.
Speaker 1: Bit.
Speaker 3: That is exactly the goal, and it perfectly encapsulates the
Speaker 3: broader shift in science that Patrick Chu mentioned in his commentary.
Speaker 3: He talked about the tremendous interest right now in fundamentally
Speaker 3: changing the practice of science itself.
Speaker 2: Yeah, this is the philosophical shift. We are essentially moving
Speaker 2: from being observers of bioology to being actual architects.
Speaker 3: That is the perfect way to frame it. Think about it.
Speaker 3: For centuries, biology has been a purely descriptive science. We
Speaker 3: find a bird in the jungle, we describe the bird.
Speaker 3: We isolate a protein from a fungus, we see what
Speaker 3: it does.
Speaker 2: Even with directed evolution, we were essentially just blindly nudging
Speaker 2: nature and watching what happened.
Speaker 3: We were playing the lottery. We were buying a million
Speaker 3: mutation tickets and hoping one of them won the jackpot.
Speaker 2: Right.
Speaker 3: But now, with machine learning frameworks being fed high quality
Speaker 3: interaction data, we are moving toward a true predictive engineering discipline.
Speaker 3: We are starting to design biological machines the exact same
Speaker 3: way we design suspension bridges or commercial airplanes.
Speaker 2: You don't build a bridge by throwing random steel beams
Speaker 2: in a pile and seeing which ones hold.
Speaker 3: Up a truck exactly. You calculate the loads, you simulate
Speaker 3: the physical stresses. You design the entire structure in silico
Speaker 3: on a computer before you ever pour the first yard
Speaker 3: of concrete.
Speaker 2: And MLTI evolve is essentially the structural engineering software.
Speaker 3: For it is the very beginning of it. Yes, it
Speaker 3: strongly suggests that the future of the biological lab isn't
Speaker 3: just technicians pipetting thousands of random liquids. The role of
Speaker 3: the physical lab is shifting to generating highly specific, high
Speaker 3: quality data to feed algorithms that do the heavy computational lifting.
Speaker 2: The fundamental nature of the experiment changes.
Speaker 3: It changes from let's randomly test this and see if
Speaker 3: it works, to let's physically gather the exact epistatic data
Speaker 3: the AI needs to make a perfect prediction.
Speaker 2: It effectively merges the wet lab and the dry lab
Speaker 2: into one closed loop. You really can't have one without
Speaker 2: the other anymore.
Speaker 3: No, you can't. And that is a key takeaway from
Speaker 3: the science paper. This wasn't just computer scientists writing cod
Speaker 3: in a vacuum, and it wasn't just traditional biologists running essays.
Speaker 3: It was a completely seamlessly integrated workflow. The wet lab
Speaker 3: was specifically designed to feed the AI, and the AI
Speaker 3: was designed to direct the next step in the wet lab.
Speaker 2: That deep synergy is just the und deniable future of biotech.
Speaker 3: It absolutely is. So.
Speaker 2: If I'm a listener trying to wrap my head around
Speaker 2: the big picture of this whole deep dive. We have
Speaker 2: basically broken the speed limit of biological evolution.
Speaker 3: We have removed the blinders. We can now actually see
Speaker 3: the entire fitness landscape. We can see those optimal peaks
Speaker 3: that were completely hidden behind the fog before.
Speaker 2: And practically that means faster drug development, vastly better industrial fuels,
Speaker 2: and maybe entirely new classes of synthetic materials we haven't
Speaker 2: even conceived of yet.
Speaker 3: The constraints on what we can physically build are no
Speaker 3: longer defined by what nature accidentally evolved over four billion years.
Speaker 3: They are entirely defined by what we can imagine and compute.
Speaker 2: That is just a staggering thought. I want to leave
Speaker 2: the listener with one final provocation. Actually, okay, we started
Speaker 2: the discussion with the universe analogy more possible protein variants
Speaker 2: than atoms in space. We've talked extensively about finding the
Speaker 2: best version of an existing protein for a specific job.
Speaker 2: But if the search space is truly that unfathomably big,
Speaker 2: go on, if the landscape is infinite, aren't we just
Speaker 2: barely scratching the surface of what proteins can functionally do?
Speaker 2: I mean, we are optimizing them to be better laundry
Speaker 2: detergents or sharper crisp or scissors. But what about functions
Speaker 2: that simply do not exist in nature at all.
Speaker 3: Ah, That is the ultimate question of synthetic biology. You
Speaker 3: have to remember Nature only explores the specific biological solutions
Speaker 3: that help a given organisms survive and reproduce.
Speaker 2: Right. Evolution is lazy. It stops when it's good enough.
Speaker 3: Exactly, it never had an evolutionary reason to invent a
Speaker 3: protein that conducts electricity perfectly like a copper wire, or
Speaker 3: a protein that acts like a microscopic logic gait in
Speaker 3: a quantum computer, or a protein that harvests raw solar
Speaker 3: energy with one hundred percent thermodynamic efficiency.
Speaker 2: But mathematically, those exact solutions might be out there, hidden
Speaker 2: somewhere in that ten to the eightieth power haystack.
Speaker 3: They almost certainly are, And for the very first time
Speaker 3: in human history, we have a computational flash light that
Speaker 3: can actually shine into those dark, unexplored corners of the
Speaker 3: sequence library. We aren't just optimizing what already exists anymore.
Speaker 3: We are standing on the absolute verge of pure bottom
Speaker 3: up invention.
Speaker 2: The map is really just being drawn, and I, for
Speaker 2: one absolutely cannot wait to see what we find on
Speaker 2: the other side of the mountain.
Speaker 3: It is going to be a fascinating climb.
Speaker 2: Thanks for diving deep with us today. This one really
Speaker 2: stretched the brain cells, but in the best way possible.
Speaker 3: Always a pleasure.
Speaker 2: We'll catch you on the next one.