Welcome back to the deep dive, everybody. Today,
we're diving into a pretty complex topic. Multiscale
modeling. Yeah. You know, it's kind of like trying
to understand, let's say, a hurricane, right?
OK. You could look at the swirling clouds from
a satellite. But to really predict its path and
get its power, you also have to understand like
those. tiny little air currents and pressure
systems. That's kind of what multi -skill modeling
is all about. It's about using all these different
lenses, right? Right. To try and understand this
complex system at different levels of detail.
And this is important for, well, tackling a lot
of different things like biological systems and
even designing materials. Yeah. And especially
biological systems are so hierarchical, right?
You have from the quantum mechanics of the atom
and then the molecules and the cells and the
tissues and the organs. And to really understand
how life works, you have to connect those dots
between all those different levels. Yeah, absolutely.
And that's what multiscale modeling tries to
do. And this is helpful for all kinds of applications
in biology, from enzymes, how they work, to ecosystems.
It's really amazing. It's like, if you're trying
to appreciate a painting and you only look at
one brushstroke, you'd miss the big picture.
Totally. And this isn't just limited to biology.
Right. I mean, chemical engineers use this a
lot to design catalysts or to optimize industrial
processes. So it really is this powerful tool,
this interdisciplinary approach that is really
fascinating. And a lot of the most important
processes in biology hinge on these complex interactions
between proteins, lipids, nucleic acids. These
interactions drive everything from how our cells
communicate to how genes are processed. And modeling
these is a huge challenge because it occurs at
such a huge range of scales. I mean, we're talking
about events that occur at fractions of a nanometer
in femtoseconds all the way to micrometer -sized
structures change over seconds or even longer.
OK, so let's unpack that a little bit. Sure.
You're saying that these traditional computer
simulations really have a hard time capturing
those tiny little interactions and these slow
changes simultaneously. It's kind of like trying
to photograph a bullet train and a snail with
the same camera settings. Exactly. You'd probably
miss one or the other. Totally. But this is where
machine learning comes in. Yeah, exactly. This
is where things get really, really interesting.
That's right. So that leads us to what we're
talking about today, this new infrastructure
called MumMI, Multiscale Machine -Verned Modeling
Infrastructure. And our mission, really, in this
deep dive is to explore how MumMI is tackling
these challenges, specifically how proteins interact
with the membranes of cells using the Rostov
pathway as a key example. And this pathway, of
course, is really important because it regulates
cell growth. And when things go wrong with it,
it due to mutations or other things, it can lead
to cancer. Right. So let's start with the challenges
then of bridging these incredible scales, these
time scales, these size scales. You mentioned
molecular dynamic simulations. And these are
crucial, but they have limitations. They do.
They do. Molecular dynamic simulations are really
amazing. They allow us to see these movements
of atoms and molecules. But the problem is to
accurately represent the forces and interactions
across those spatial scales and across those
time scales from femtoseconds to seconds is computationally
a huge, huge challenge. Yeah. I mean, it's like
trying to map every single ant in a colony, but
also track how the whole colony is moving all
at the same time. That's a great analogy, yeah.
It's kind of mind boggling. It is. And many important
processes, like when proteins assemble or the
rearrangements of membranes, involve these huge
structures that span many orders of magnitude.
And traditional MD simulations struggle to capture
both the rapid little vibrations and the slower
movements. At the same time. At the same time.
And then it just becomes computationally way
too expensive. So you're forced to choose exactly
between this incredible level of detail and this
broader perspective. Yeah, that's right. And
what's missing is a truly integrated framework
that can handle these different levels at the
same time. OK, right. So you can have an atomistic
simulation that tells you how two molecules bind,
but it won't tell you how often that happens
in a cell, which is what you need for those larger
scale models. Right. And then there's this time
scale difference. Huge. Yeah. I mean the vibrations
in a molecule happen so fast compared to a membrane
changing shape. Exactly. So it's not just running
simulations at different scales separately. Right.
You have to somehow transfer that information
from one level to another. Yes. So it's like
translating an engineering blueprint into a simple
sketch. Yeah. Yeah. But you can't lose that structural
information. Exactly. That's right. And even
within methods that simplify like coarse graining
methods, bridging different time scales is still
a big, big problem. To create a truly robust
workflow, we need to not just overcome the computational
limitations, but also figure out how to transfer
this information, integrate it. Okay, so these
are some pretty big hurdles, but you mentioned
machine learning could help. Yes. How so? Yeah,
so this is where machine learning really comes
in and helps us bridge those gaps. And machine
learning is great at learning complex patterns
from data. So it can really help connect different
scales in this multi -scale framework. OK, so
how does that work? Yeah, so think of it as like
feedback loops between these different levels,
okay? So machine learning can help with something
called forward coupling. This is where you use
information from a larger scale to improve things
at the smaller scale. So for example, imagine
a model that shows specific regions of the cell
membrane changing a lot. That information can
then be used to focus a smaller, more detailed
simulation on that exact region. So this is like
the bigger simulations guiding the smaller one.
Exactly, exactly. backward coupling where the
detailed simulations give feedback to improve
the larger scale model. So for example, a detailed
simulation might reveal some very specific atomic
interaction. Then machine learning can use that
to adjust the parameters of the larger model
so it becomes more accurate. So you have this
back and forth and things get more accurate.
So it's this iterative process. Exactly. Can
you give me some concrete examples of how ML
is being used? Yeah, yeah, sure. So one example
is in these QMMM methods, where you combine quantum
mechanics with molecular mechanics. Machine learning
can be used to handle these very complex interactions
and reduce the computational cost. Another example
is in material science, where you can train machine
learning on simulations to predict how material
might fail or classify defects. And then you
can use that in these larger continuum models
to go from very small to very large. really connecting
those scales. Exactly. And then you also have
things like ML potentials. These are ways to
predict energy and forces between atoms with
almost the accuracy of quantum mechanics, but
much, much faster. Wow. And then ML is also used
to go from these simplified representations back
to the full atomic detail, like enhancing a blurry
picture. That's a great analogy. Yeah. And it's
not just simulations. It's also integrating it
with experiments, with biophysical methods. So
ML is helping us overcome those computational
limitations, making things faster, focusing on
the important parts. And these deep learning
advancements are really making this accessible
to more researchers. That's really exciting.
So where does Mamma fit into this? Yeah, so Mamma
Mai is a platform designed specifically to leverage
machine learning for multi -scale modeling. And
it focuses on those intricate biomolecular systems.
And it does this by really tightly coupling the
different scales using machine learning. OK,
so what makes Mamma Mai stand out? Yeah, so one
key thing is its ability to create these huge
ensembles of micro simulations. We're talking
tens of thousands even, all guided by machine
learning. It also uses this thing called a machine
learned latent space, which is a way to simplify
complex data. So it's like taking all this complex
information and boiling it down to its essence.
And that allows us to see patterns and connections
we couldn't see otherwise. And it focuses initially
on these interactions between RAS -RAF proteins
and the cell membrane. And as we said, this pathway
is crucial for cell growth. And when it goes
wrong, it can cause cancer. So Mamamai is trying
to understand how these proteins interact with
the membrane at a molecular level. And it can
identify these lipid protein fingerprints, which
are these patterns of lipid molecules. that can
affect how these proteins work. Interesting.
OK, so tell me more about this three -scale architecture
that Mamai uses. Yeah, so Mamai has these three
levels to bridge these gaps in space and time.
The first level is the macro scale. OK. And it
uses something called dynamic density functional
theory, or DDFT. OK. And this allows us to simulate
milliseconds of time over a fairly large area,
like one square micrometer of the membrane. So
it's the broad view, right? Exactly. Exactly.
It doesn't show every atom, but it can capture
the general movement of the membrane and how
proteins like RAS and RAF generally prefer to
sit on the membrane. OK, so that's the big picture.
Right. And then we zoom in. Right, so then you
have the coarse -grained or CG scale. OK. And
this uses the martini bead model. And in this
model, groups of atoms are represented as single
beads, so we can simulate larger systems for
longer. And this level focuses on smaller patches
of the membrane, maybe 30 by 30 nanometers, areas
that the macro scale has identified as interesting.
OK, so the action spots. Exactly, yeah. And these
patches often have proteins like RAS or the the
RAS -RB -DCRD complex, which is part of RAF.
And we use the martini force field, which are
the rules for how these beads interact. And we
can run these on GPUs, and we can get about a
microsecond per day on a single GPU. And this
gives us a good view of how proteins change their
shape and interact with each other. OK, so we've
got the big picture. We've got some more local
details. What about the really, really fine details?
Right, so then you go to the all -atom, or AA,
level, and now we're back. to every single atom
being represented. We use the CharM M36 force
field, which is very detailed. And this allows
us to see those very specific interactions between
lipids and the amino acids in the protein and
how those lipids might affect the protein structure.
But this must be very computationally expensive.
It is. It is. So we take snapshots from the CG
simulations and we convert them back to all atom,
a process called backmapping. And then we use
energy minimization to to kind of relax the structure.
And we run these simulations using things like
AMBER or GROMACS. And these are much slower.
We're talking maybe 14 ms per day on a GPU. But
it gives us that really crucial atomic detail
that we can't get otherwise. And so these transitions
between scales, are there tools that help you
with that? Yes. Yes. MUMMI has tools to help
move between these different levels. OK. For
example, there's a tool called Create Sims that
converts the macro scale to the coarse grain
scale. And then for going from CG to AA, there's
a backmapping protocol to make sure we get an
accurate atomic structure. OK. So all these tools
help us move between these different levels consistently.
OK. So. We've got these three scales, each with
their own methods. How does machine learning
tie into all of this? Yeah, so machine learning
is not just an add -on here. It's really essential
to Mamamai's ability to connect these scales
to manage all this data. And it does this by
coupling these scales, both macro to CG and also
sort of indirectly CG to AA through choosing
those starting configurations. OK. So it's really
the glue that holds everything together. So let's
talk about that coupling in more detail. How
does ML do this forward and backward flow? Yeah.
So in the forward direction, the ML models are
trained on the macro scale data. And they're
trained to pick up the interesting parts, the
parts we want to look at in more detail. OK.
So let's say protein starts to cluster or it
really likes a specific lipid or the membrane
changes shape in a specific way. The ML model
will flag that and MMMI will then say okay let's
look at that region with the CG model. So it's
like a smart filter. Yeah, yeah, like a scout
that says, OK, look here. This is where the action
is. And then in the backward direction, machine
learning helps refine those larger models based
on what we learn from the smaller simulations.
OK. So as the CG simulations run, we analyze
in real time how those proteins and lipids are
interacting. OK. And that information can then
be used to adjust the macro model, make it more
accurate. Similarly, the information from the
all -atom simulations can be used to improve
the CG. model. So it's this constant learning
and refining. Exactly, exactly. It's fascinating.
And beyond that, machine learning also helps
with how MomMI manages all these simulations,
how it decides which simulations to run. Okay.
It uses something called dynamic importance sampling.
Okay, so what does that mean? What makes a simulation
important? So it really depends on what the scientist
is looking for. OK. Right. So maybe it's a protein
binding to a specific lipid, or proteins clustering
together, or maybe it's the membrane changing
shape. The machine learning model will look at
all these configurations from the macro scale
and say, OK, this one's interesting. This one's
not so interesting. OK. And then MOMA -I uses
that to decide which regions to simulate in more
detail. So it's like a priority system. Exactly,
yeah. And this is really important because it
helps us use those computational resources efficiently.
So we're not wasting time on things that aren't
important. So this must require huge amounts
of computing power. It does. It does. High resolution,
long time scale models need a lot of power. Yeah.
And MamaEye is designed to run on all kinds of
high performance computing systems, from smaller
clusters to super computers. Wow. It's been run
on systems like Sierra at Lawrence Livermore,
which has hundreds of thousands of CPU cores
and thousands of GPUs. Wow. It's hard to even
imagine that much power. How do you manage that?
Yeah, so Mamamai is designed to use both CPUs
and GPUs. OK. The macro scale model with those
equations on a grid, it's really good for CPUs.
And we can get almost a millisecond per day with
thousands of cores. Wow, that's fast. And then
the CG and AA simulations with all those particles,
they're better for GPUs. OK. So CG. can get a
microsecond per day on one GPU, AA, about 14
nanoseconds per day. So by using both, we can
handle all those different scales. Right. And
with thousands of simulations running, how do
you keep track of everything? Yeah. So we have
the Workful Manager. or WM, and this is like
the control center. It monitors resources, it
starts jobs, it tracks progress, it restarts
things that fail, and it uses these tools like
Flux or Maestro to handle all the communication.
So it's like the conductor of the orchestra.
Exactly, yeah. And all these simulations must
generate a ton of data. Oh, they do, hundreds
of terabytes, sometimes even petabytes. How do
you manage that? So we need special storage solutions,
special analysis tools, and a lot of this data
is made public through server. in a repository
so other researchers can use it. That's great.
Yeah. So how has MUMI actually been used to study
this RAS -RAF system? Yeah, so studying RAS -RAF
has really been a key focus for MUMI. OK. This
pathway is so important for cell growth. And
when it goes wrong, it causes cancer. Right.
So we really need to understand how these proteins
work and how they interact with the membrane.
OK. And MUMI is perfect for this because it can
handle those atomic details and those long time
scales. OK, so what have you learned? Well, one
of the biggest things is that we've been able
to identify those lipid protein fingerprints.
These are those patterns of lipids that surround
the protein, and they can affect how it's oriented,
how it binds to other molecules. OK. So by simulating
both RES on its own and the RES -REF complex
on the membrane, we've learned a lot about how
they interact with the lipids. OK. Can you give
me some more specific examples? Sure. So for
example, we've looked at how a specific region
of REF, cysteine -rich domain, or CRD, moves
when it binds to RAS. This is important for understanding
how RAF gets activated. We've also looked at
how the orientation of the RAS rave complex affects
how it interacts with lipids. And we've even
looked at specific RAS variants like KRS4B, which
is known to be really difficult to target with
drugs. And we've been able to simulate all this
on realistic membranes. So it's really relevant
to what's happening. in a cell. Okay, so really
connecting those details to the bigger picture.
Exactly, exactly. And that's the power of this
multi -scale approach to get that full picture.
So what are some of the key findings from all
this? Yeah. So one surprising thing is that RAS
proteins are not just sitting there passively
on the membrane. OK. They can actually change
the lipids around them. Interesting. So they're
shaping their environment. Exactly. Yeah. And
we've also seen that RAS proteins like to cluster
together. And the way they cluster is influenced
by those lipid fingerprints. So the lipids might
be controlling how RAS signals. So the membrane's
not just a backdrop. It's an active participant.
Yeah, absolutely. And we've also seen how RAS
changes its orientation when it binds to REF,
which then affects how it interacts with lipids.
OK. And it turns out that RAS has different lipid
fingerprints, depending on whether it's bound
to REF or not. So it's like the lipids are telling
us what state RAS is in. Yeah, exactly. And we've
also learned a lot about how the race rough complex
forms and how it moves. OK. The Continuum model,
when we use data from the CG simulations to refine
it, it can actually accurately show how RAF binds
and unbinds to RAS. And the all -atom simulations
have shown us very specific lipid -dependent
changes in the complex, which we can then use
to improve the CG model. So again, that back
and forth. And the CG simulations have even hinted
that you can get these larger clusters of RAS
and RAS. So it's really a complex and dynamic
picture. And this all gives us a much more accurate
understanding of these interactions. Yes, absolutely.
And by using both the continuum and CG models
together, we can actually make the simulations
run faster. Oh, really? Yeah, so the CG simulations
reach equilibrium faster. Okay, so it's more
efficient. More efficient, yeah. So all of this,
I think, shows how powerful MUMi is and how it
can be used to generate these hypotheses that
we can then test in the lab. This has been a
fascinating deep dive, so... What's the key takeaway?
Well, I think the key takeaway is that mummini
is a major step forward in computational biology.
OK. Its design and how it uses machine learning
lets us study these complex systems across these
huge ranges of time and size. And what it's told
us about Rostroff is really exciting, right?
It is. It's giving us this really detailed understanding
of how these proteins work, which is important
for understanding cancer and potentially for
developing new drugs. So looking ahead, what's
next? for MummyMai? Well, I think we can expand
it to study all kinds of other systems, other
proteins, other signaling pathways. We can use
even more advanced machine learning, like deep
learning or reinforcement learning. And I think
this could really change how we develop drugs,
especially for targets like RASROF that are so
difficult. It sounds like these machine learning
-driven simulations are going to be essential
tools going forward. Oh, I think so, yeah. I
think with the way computing power is increasing
and how these methods are getting better, this
is going to be the standard way to study complex
molecular systems. And maybe we'll even see fully
automated simulations in the future. Oh, I think
that's very likely. Thinking about all this complex
dance of proteins and their environment makes
you wonder, how will this change how we treat
diseases? Maybe we'll go beyond targeting just
the proteins themselves and start thinking about
the whole context. It's really fascinating. Thank
you so much for joining us. This has been a great
deep dive. My pleasure. And for everyone listening,
keep thinking about these questions. And we'll
see you next time.