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[Music]
our next speaker Yan is a structural
biologist he has more than 14 years of
experience in structural biology and a
PhD in Biochemistry from the University
of con why structural biology well it's
the most concrete way to look at the
mechanisms of how living things actually
work
work the talk how machine learning is
changed structural biology forever or
not let's hear a warm welcome for
Yan thank you thank
you so I'm quite amazed how many people
showed up so thanks for that already I
hope I can present you something which
is interesting to all of you and I tried
to adjust this talk so that you can
learn something if you are a biologist
even if you're a structural biologist I
hope you learn something but even if you
have no idea about structural biology
proteins or all of these kind of things
I hope that I break it down so much that
you can actually learn at the end what
Alpha does how important structure
biology is and why I'm dealing this
dealing with these topics for for years
now and I'm still pretty Amed about it
so the talk goes about Alpha fold and
let me go back a little bit a couple of
years it was
2001 um when the winner of the 13th Casp
award was announced and the Casp award
is a particular award in the field of
structural protein prediction and I come
to that what that mean what is Exel
structur protein and what is structural
protein prediction we come to that a
little bit later but there is an there
is a test there is a kind of
championship in this field and this is
called kasp and every two years people
submitting their best predictions
whatever they are the best guesses to
this kind of uh of this uh to this kind
of championship ship and then at the end
them there were the guesses were
compared with the wheel solution and
then the winner is cured and at this
particular case in 2001 it was quite an
amazing thing because For the First Time
a company actually entered this field
and this company was Google and
especially it was Google Deep Mind and
here we have a graph which is very easy
to see you can just see here on the
bottom is how complicated the
predictions are so you have to give
multiple predictions and here on the top
how good you are in terms of how well
you actually guessed how what the right
answer is and these are all the other
contestants and the blue line here and I
think that's easy to see they're just
above all of the others so they actually
were for the first time they showed up
on this contest and they obliviated all
the other contestants which was a little
bit shock for the scientific Community
because we are doing this stuff for
years I mean this thing is cup 13 so
it's 25 years old so we all did it for
years and they just
wed in and took us all by surprise um
then the second thing which I was
actually I mean you shouldn't self
reference but I do it here once only I
was actually pretty um wored that this
is now a company doing science but there
was actually no mention that they
publish what they actually found and
science means you do something you find
something and you share it with all the
others this is what science is all about
and in 2018 when they came out they said
well
nothing came out and it took them more
than two years in 2020 to actually
publish a paper how they potentially did
it it was a little bit vague and nobody
really knew and in 2020 um there was Al
already actually the next CP which was
the CP 14 and they again entered this
this time with a new machine learning
algorithm and you don't really need to
understand this graph but what you can
see again is these are all the other CPS
so this is easy targets difficult
targets and moderately different guess
so riddles you can see an easy riddle
riddle riddle difficult riddle and how
good are all these people solving that
and the black line more or less is what
Google did so they solved all the
riddles perfectly from the get-go and
this was also very amazing and this BR
and to see how good they are you have
this a little bit difficult graph but it
brings the point over I guess here on
the bottom one you see all the
contestants so 400 whatever groups or
algorithms which submitted their guesses
to this Casp championship and on the
left side here this is Alpha fold and
you see it's just way above all the
others um and just for those of you were
a little bit in the field the second one
this one here s let me get my pointer
going this one here this is a baker lab
those of you who were in the field know
they are from The Institute for protein
design and University of Washington they
are they were the leader of the field
for ages now and they were actually also
pretty much abolished so this was Major
news um in at least science news outlets
so um some people said well this problem
is now solved nature published that deep
Minds I I make a giant leap in solving
protein structures um it was even going
to the New York Times which say that the
uh AI has now solved the protein stru
problem which we were working for 50
years 25 years we tried in this kind of
test but before that we already
tried and the spegel which is a German
news Outlet so to say if you want to say
that um they actually asked should this
win a machine the Nobel prizes machine
not sure if they actually ever read the
rules for the Nobel Prize but
nevertheless um and it also um I again
the German one it's saying about anyone
can now Pro fold proteins and I will
show you that this is true everyone here
in your field can in this room can at
the end of this talk fold a protein I'm
not sure if you can do anything with it
but at least you are able to do it um
and then the spiegle again is always
good for some new new stuff they say
that 2020 will be the year which will be
known for future generation as a year
where machines began to overtake Us in
research this is of course a bleak
outlook for me because I'm a researcher
that's my bread and butter job and if
now an II can do that better not sure if
I have a job next year um and this was
not only the Spiegel I have to admit
also the guardian said are we witnessing
the dawn of post Theory science so this
is quite a bit of rockers for just a
scientific paper in nature normally you
don't hear that in the name in the main
newspapers um so let's now have a look
why is it so dramatic what is the big
deal about Alpha fold what is the big
deal about structural biology in general
and how can we understand what's
happening here so to understand this we
first have to step a couple of steps
back so I now take you through a quick
tour during biochemistry studies so you
now start as an undergrad in in our
research lab here and we first have to
discuss what are actually proteins and I
started this by asking chat GTP to draw
a more or less accurate picture of my
office and this is how it more or less
look like um it is some quite similar to
a to a nerd office I would say or the
hacker office which is because we have
computers and we have quite a bit of
coffee we also use quite a bit of energy
drinks and but when you look now at the
ingredients there actually nearly no
proteins in there those the protein here
is 0.1 G and actually in your Club Mar
there's actually no protein in it but in
the pizza it's always a good track this
is now a pizza for American friends
because I never tasted this is from
Walmart so um I have no idea if it
tastes very well but it has a lot of
protein 56% of your daily average usage
8 gr of protein so and we can already
see here that proteins are one of the
four major micromolecules in your body
so it's normally proteins nucleic acid
sugars or and fats or lipids but we
don't need the other three we only talk
about proteins so just this just this
one um so what are proteins in principle
so proteins you all know that we are
coming from cells and if we now take a
little bit simpler we can say our cell
is more or less like a house and every
cell in your body is like one one room
of your of your house that we could
imagine and the proteins in this allergy
would be then all the tools which you
have in your room for in your kitchen
you have all the kitchen util utilities
and meaning all these would be different
proteins and proteins in your cell are
actually what's actually doing stuff so
if you smell your neighbor because he
has hopefully followed the 6 to1 rule
that's a protein which detects the smell
then it's corn then it's connected to
your brain so that's the protein which
actually fires this whatever electric
signal to your brain if you eat the
pizza outside then there's a protein
which actually cuts down this protein
this pizza into some kind of small
molecules which you can actually use so
proteins are the building blocks of your
body and are those who are actually
doing the work yeah they are those which
actually do stuff measure stuff signal
stuff whatever whatever you can think of
it's normally a protein with very rare
exceptions which we have to ignore here
so obviously just by looking at this
image we can already see what these kind
of proteins are doing so this here is
probably a microwave this is probably
digesting some pizza and this is cooking
whatever doing some new stuff but in
general if you really want to understand
what they really need to do we have to
normally look very closely and to make
this point a little bit clearer I show
you here three proteins yeah in a rela
low resolution meaning we look from Rel
ly far away we can't really see what's
going on so we see here three different
stuff and just by looking at that and
this we could do for years already just
by looking at these like say low
resolution images we can say well
there's something here on the top this
might be a motor motor domain yeah for
those of you are struct biologist atpa
domain potentially yeah nobody knows
then there's some long stuff which we
don't know what it's doing so probably
it's a Linker that's called a Linker and
then the bottom we have an effector
domain something which is doing
something yeah it's
somehow driven by this motor and then it
might spin or whatever it do and we can
just by this very rough rough estimation
we can say well this is probably a
protein which do some mixing or blending
action and we can all agree but if you
now go very closely and really want to
go into the F that's what structural
biology is normally about going to
really and looking very very closely and
by very closely I mean we go from this
eight angstrom to this let's say two
angstrom resolution yeah and an angstrom
is just how well you can Define pin from
a f we see that in the minute then you
see that this guy over here this is
probably a blender which you probably
all have in your kitchen household and
this is very good for making smoothies
yeah so this is very nice the other one
it's also a blender but it's a
scientific equipment yeah from Pro
scientific I just named the names
because there their images I don't care
I don't have never used any of those
yeah um this is actually doing for
making cannaboid oils so you probably
don't want to mix them up yeah if you
make your smoothie for your for your kid
don't use the wrong blender because it
probably makes you the wrong product and
the last one yeah it's obviously
something which makes mortars and glues
and you shouldn't eat it at all yeah the
other one we can discuss if you should
or not so you see it's very important to
look in detail and not only have an
overview but look very finely what is
actually
happening so what we learn already if we
know the structure so if you have it in
a very detailed structure we often and
this often is now because I'm a
biologist in biology never ever
everything is 100% true is always often
or mostly but for you you can say we can
always INF fear the function of the
protein yeah and this is one of the
relationships you can say paradigm of
structure biology structure actually
leads to function so if you have a
structure in a protein so if you have a
blender it will blend if you have a
cooking pot it will cook and if you have
a microwave it will heat up stuff that's
the idea of structure leads to function
and as soon as we know the structure we
can actually derive the function so but
proteins obviously are not blenders they
are not made of mole of electronics so
we have to think a little bit of of
chemistry and this is the most chemical
stuff we see today so don't be afraid
this uh is an amino acid which builds up
our Amino our proteins and the only
thing I want to tell you here proteins
are multimar so you are all built up of
these parts of things the blue one
always connects to the red one and then
another one like a Shain of multiple
little things and the only difference
here is where there is an R standing
here so this is a so-called side chain
and we have 21 of them that's the
natural side chains and you have seen
them now and you see all the little
Wiggly arms here go down here and you
can forget about them we don't need them
for this talk but at least I want to
show you how they look in principle for
this talk we can just simplify down a
little bit and say we have only six
amino acids and they look so
suspiciously like the pearls from my uh
daughter's uh from my daughter's
playroom but however so I just luse
these six amino acids and if you now use
the six amino acids and make proteins
out of them we can make different
different versions so this for example
is one protein of different lengths of
amino acids in a different organization
and this might be for example your
protein which is cleaving your pizza in
your stomach and there's a second
protein and this might be for example
electric conductor in your brain so this
tells your brain if the pizza was nice
or not and the important part is now the
difference is not the amount of pearls
in this particular case the difference
is the ordering of the pearls in this
orientation so there's first in Violet
then in yellow and so on this is also
Violet yellow but then it changes and
this kind of primary sequence as we call
it or primary structure then leads to
different forms so this protein this
protein string will automatically fold
itself up into these kind of structure
and this for example is now an alpha
Helix it's an Helix which we call Alpha
Helix because we are fancy and the
second one here like this one this forms
something else this forms this sheet and
this we would call the beta sheet this
is also very typical
component of of our proteins and what we
know by this is it depends not so much
it depends about how you order your purs
on your string what kind of structure
you get and we already know that
structure defines function so proteins
are strings of perls we have 20
different ones or 21 different types of
perls and they complicated knots and
they are specific for this particular
Pearl so whenever you have this chain it
always will link to that so this is the
easy introduction now we go a little bit
more into what it really looks like
because if we want to look at Alpha fold
and what it produces we have to at least
have an idea how protein really looks
like so this is just our simple one and
in structure biology we normally like to
show this as a cartoon structure as we
call it it's not very cartoonish but it
just simplifies how we look at those so
this is cartoon of what we've seen below
and we can now have a look at this
cartoon in various different ways and I
just want to show you that this because
this you see sometimes in the news is
not really what is a protein so this is
now a cartoon where we see the side
chain so here we can also distinguish
which pearl color we have at which
position yeah you probably remember some
of those you have seen in the in this
big slide we can also have a look at the
whole atom so here we see all the atoms
so here we have now get rid of this
cartoonish Helix stuff and see now at
every edge of these borders we see an
atom and the colors actually tell tells
me which atoms they are blue is for
nitrogen red is for oxygen and this is a
sulfur and the rest is carbon if you are
interested if not you can forget it um
but this is of course also not the same
the real picture because atoms have
masses and if you now look as atom the
masses we see this is how it would be a
more appropriate depiction of this
little protein fragment yeah so this is
how it looks like you see you can't
really see a not a lot of things anymore
because it's all hidden behind it yeah
and the last thing which I want to show
you is you can also have a look at the
surface so this is how it would look
like if you could look at it yeah if
you're Antman or whatever and go very
deep and then you can run around on this
little protein stuff but most of the
times we actually look at this because
this is the one which we see the general
structure that it's in Helix that it's
turning around and we can only show
those residues which are interested to
us interestingly or sadly this is not
the real picture still because this is
just a very tiny fragment of a protein
so if you now have a look at real
program so this one is just this one and
this is structure which a colleague of
mine actually solved experimentally and
if we just have a look so this is where
we started so and this is the whole
protein now so this is now the first
protein you probably see hopefully or or
not this is one protein here and you see
it's all this little helixes around and
then we have this beta sheets here which
is the general General overview of how
these kind of things looks and if you're
now an experimental an experim sorry an
experienced structure biologist just by
the look at this crystal structure or at
this structure you could say well this
looks
like a blender doesn't but just de it is
what you can also see here there's a
second part there's a second molecule
here which is depicted in different way
but it's also protein and um what you
can then learn if you look very closely
you see that for whatever reason and I
don't want to go into detail but just
that you have an idea we see that these
protein this is the left one which is
our big domain and this is the smaller
ones um that it somehow recognizes this
so there are some interactions between
amino acids and I don't go into detail
how they are working because you need a
little bit of more of biochemistry to
understand that I guess but you can
appreciate some amino acids they like
each other and they like to stick
together and that's the way how you can
for example make complexes and how
proteins are folded okay so this now we
have so we know sequence leads to to
structure and weow that structure
defines function and this is now the
famous sequence structure function
relationship in structural biology which
leads all the efforts which we are doing
and this is also what um Alpha for uses
because he says he knows the sequence
the sequence defines the structure and
then you should be able to actually
Define what the function of the protein
is so how useful is it so now I told you
what protein structures are at least I
hope you have an idea so how useful is
structure biology why should we care
yeah and the thing is if you do
structure biology you actually
understand the life in a very particular
and very very defined way so this is a
picture from David
Godel um he's
an he's a structure biologist I was
nearly saying former but I think I don't
think that's fair he's a structure
biologist who now doing all this nice
very nice uh um water painted images and
they are accurate in in that regard that
he really use this structure biology
information to make up these pictures so
what you see here on the left is a cell
which are connected to an intricate
network of collagens which are just by
chance my research interest to this
outer membrane here so this is skin this
is a huge collagen fiber here and we
learned so much from here I can name you
most of the proteins which are going in
here and we know all the structures just
because we did structure biology and the
important part and we have this already
for example is that just because we did
structural biology we actually know that
collagens and that are those proteins we
which make up your bone which make up
your skin and which makes up your jelly
if you're are not a vegan um all these
kind of things they are all St collagens
and they have a very particular form
which is look like this and it just goes
shortly over you see this is triple
helix it looks a little bit like a DNA
but it's triplicated it's a little bit
more complicated and interesting enough
to form these kind of collagens this
triple helix you need a helper like you
would need if you make ropes if you make
a rope you also intertwine at least
three or four whatever of different
little ropes and here you need the same
helper you need a helper here to make
this rope and this is actually a protein
I just shown you a minute ago yeah so
this protein sits on the side of the
collagen and helps collagen to form and
these kind of of relationship we only
understand if we do structural biology
if we really look at how the atoms are
orientated and what the atoms are doing
with um this particular protein so it
helps us to understand basic physiology
and this is actually my main driving
force because I'm really curious about
how life is actually working but it has
also some approach some some real life
um let's say usage um so understanding
diseases is a second big thing and it of
course is linked to understanding the B
basic physiology I'm not sure if you all
know what this one is uh we have all
experience it for years now this is the
Corona virus um zarov 2 and all these
little Parts on the top are the spike
proteins we have all heard so much in
all these little podcasts um and the SP
of course immediately crystallized back
in the day sorry structurally solved
back in the day and only by that we
could actually understand what all this
mutations you have probably heard
actually are doing yeah and this is
again this little nice dis piction of
David Godel about it um this is the
Corona virus in a more artsy U the
depiction and how it actually in in
infects the cell in your in hopefully
not in your body so that's that's tape
and just to see because viewing is
understanding so this is an image which
I just show you a very very short part
of it so this is all based on structural
biology we have the spike proteins the
spike proteins are processed I haven't
shown you that then they elong elongate
parts of the amino sequence you see this
Alpha heles here down which I've shown
you yeah and then it actually fuses up
it goes open and then actually your
virus which is here on the top fuses
with the with your body cell and then it
ex then it excretes all its content into
your body cell and infects your body
cell if you want to see this movie in
full length you found it here at the
animation Lab at the Utah University
it's actually a very nice movie and
explains very well how this works I show
you the cakd at the end as again so
don't don't panic so how do we determine
this kind of structur so we already know
it is poly important you see that and we
need to know them and the thing is they
are very very very tiny yeah so they are
one billions so this link from one atom
to another one is one angstrom which is
0.1 nanometers so it's 0.1 of a
billionth of a nan of a meter so it's
quite quite quite small and the problem
with that is we cannot really look with
a microscope if we use the best
microscope so to say which is an
electron microscope um then we have
these kind of pictures this is luckily
the same picture as we see here we have
here collagen again and I just use
collagen because you know because I work
on them you can use any protein for that
so this is the collagen here this one
again on em grid and then this little
little white bulbs is actually our
protein here which is called hsp47 and
this would fit
there so you don't see much you see a
couple of pixels so this is not enough
so a microscope is not going to do it
those are you were experts they say Qui
am and so on yeah I get it we can talk
about that but this is now a traditional
microscope and it didn't work for that
so you have three different experimental
methods to actually determine protein
structures um so one is so-called cryoem
one is bio NMR and one is protein
crystallography um and I do protein
crystallography most of my days
sometimes I do a little bit of cry but
if you look how many of the structures
we actually solved up to now you see
that most structures we know up to now
are actually solved by protein
crystallography this will change in the
future cam is on the uprise but let's
let's ignore that we just have time to
look at one of these methods and it's
obvious which one we took which one we
take it's
crystallography and also there I will be
very shortly it's not the topic of the
talk but at least I want to convey how
much work it is to get the crystal
structures so that you actually
appreciate how easy it is now with Alpha
fold and how much this might change all
the stuff so if you want to crystalize
as a protein yeah and meaning
crystallization sorry this means getting
the structure at the end yeah so this is
this crystallography is just in the
middle so you first have to identify
which one you want to take so let's say
we take the spike protein so uh first of
all and I can you have to isolate the
spike protein from the virus because you
cannot use the virus completely for
various reasons you have to isolate the
spike protein on its own which means
cloning and here's one of my colleagues
actually doing some fancy cloning
technique it's just blue color um but
this is how you do it then you have to
purify the protein yeah and if you're
lucky enough then you mix your purified
protein with thousands and I mean really
thousands of different chemical
cocktails we have robots for that
pipeting a lot of stuff and you have
lots of different trays of combinations
and if you're lucky in one of these
5,000 combinations you get a protein
Crystal and this protein crystal is
actually what you think about it I mean
it's a little bit complicated to explain
but in principle it's a crystal it looks
like a salt crystal it is all matters
it's a crystal it looks like more or
less like this if it's a beautiful one
so you see it immediately it's a crystal
like a diamond yeah and then you have to
pick it because it's tiny it's so small
you can only look at them under a
microscope normally so you pick them and
then you go to a fancy synchroton source
for example so this is the SLS in
villing but we have one here in hurg um
the pet three ring that's the embl which
we also go regularly so and there you
actually find x-rays on them and this is
me doing it in our home source it's not
that big as this one but it yeah more or
less does the job so then you you you
shine x-rays on it and then you get this
very
nice pattern so you don't really see the
protein it's not like microscopy that
you enlarge your protein you see a
defraction pattern and this is this one
it looks like this and then you have to
back calculate there's some magic going
into that as well which I won't explain
today and then you have these kind of
electron
density so where are all your electrons
and then you can sit in the middle of El
you can put some proteins and I did this
this morning a little bit for you so um
at some point I will find some proteins
and will put some protein structure in
it so here here we start with building
up the model and this is what we have
seen more or less before yeah just this
wireframe model of where items are
connected and where they are sitting so
how long does it take so it is of course
an experiment so you never know but
Target selection and cloning poate days
protein purification depends how good
the proteins to purify weeks two months
crystallization can actually sometimes
never work so it might be that you even
after years have never found a crystal
but it takes at least weeks to months
well the collection is easy it takes
hours or minutes or seconds in our days
but you have to go there so let's say
hours and then um you have the data and
then you have to build it up and this
takes again weeks so in all of those we
have a half a year before you have a
single protein crystal structure and the
typical PhD in our research group takes
three years so if you're lucky you get
five Six Crystal structures if you're
unlucky you get none yeah so this is
this is a way of of have to think so
it's quite a big way so and now Alpha
for tells you well you can just skip all
this and go to the back in just a couple
of hours and this is this is the
difference we go from years to hours and
this is of course amazing if this would
be true and to some regard it is so how
does alpha for cut all of this all of
the stuff so first of all we have to
think what information can Alpha for use
and for that we have to go a couple of
steps back to protein Evolution so um if
you look about proteins proteins of
course we are now looking again at
collagens for example because you know
so collagens are happening in all
proteins in all animals which have bones
and in this animals you see they have
all some kind of bones but the sharks so
the sharks are out of the picture yeah
unfortunately for them well they have
some collagens but not the ones I'm
interested in so if you have coll in all
of these that means you have all
proteins you should have the same
proteins on all of them which are
helping to F the collagen the ones which
we just has this little one which helps
the triple helix of coll to form they
all have it but as they are very old so
the one which are down here the
predecessor animal which was living here
which all of them are descendants from
this animal also had the protein which
folds collagen and now over the time
this protein has changed and this is
called mutation and this is how
Evolution works and that means you have
a a version of this protein in all this
animals but they are all slightly
different and I can just show you an
image and it's just about the colors so
ignore what the letters are the amino
acids but it's just about the colors
yeah I I showed you here that we have uh
sorry we have the dog around here the
cat somewhere is the chicken here all
the amphibians and we have the fish down
here and you see most of the proteins
most of the sites are the same so for
example this k whatever that means is
more or less everywhere once here but
you sometimes see positions where you
have mutations so where the protein has
changed in other species okay so and how
is it and what can we learn from that
and for that we can have a look here so
what we have already known is that
proteins interact I showed you that in
the on where the collagen was recognized
by our helping protein there was this
little Dash lines and I told you some
Pro some amino acids just like to to uh
talk to each other and this is what I
depicted here so let's say this is our
protein it's a very short protein but we
can live with that and to form this KN
there is one particular interaction here
in the middle so there's a blue eror
which is recognized versus orange
whatever it is yeah and it links this
Loop together so is this now if this is
for whatever reason changing in
Evolution for example if this one gets
now the orange one then the other side
has to change as well otherwise you
don't get this inter action and if you
don't get this interaction the whole
protein will fall apart and you don't
have the same structure anymore yeah so
that means those parts which are
interacting with each
other like this ones they should
co-evolve so if if one side is changed
in an animal the other side has to
change as well otherwise it's not
functional if both changing whatever
they want willly vanil like here this
one changes to something and uh I don't
have another one thisle then they are
not doing anything particular important
but this is the thing to do and what we
now can do for we can now have a look at
our previous alignment with all of
different stuff and have a look which of
these
changes and if this changes which other
side changes and then we know that these
two are probably in close combination to
each other and it's not only these two
we have lots of combinations how they
can interact it's not only that we have
just two interactions yeah so this is
just simplified so that's the first
thing which we can see this is an old
idea actually it's not from alpha full
or deep mind but this co-evolving over
Evolution it's an old
idea um and then we need to understand
the second thing so dealing with
threedimensional structures is difficult
because you can rotate them and you can
move them around and then everything is
different so what Alpha normally uses
and it was also invented before they
used it is they don't use a
three-dimensional representation which I
don't use here either but which I used
before they use a two- dimensional
representation and this looks like this
so what they do they plot the amino acid
here on the top and on the left side and
then in these little boxes you just
enter how close are these prote these
amino acids to each other so obviously
the blue one to the blue one it's pretty
close because it's the same yeah and
then the next one so the blue one to
this blue one here they're also pretty
close because they're connected by the
main chain that's also very clear that's
nothing new so this middle line is
always dark and there's always
interactions but it gets interesting if
you look for example for this
interaction so this part this blue box
and this green star they are pretty far
away on their primary sequence yeah so 1
2 3 4 5 six amino acids away but they
are close in three dimensional space and
that's why we put a dark spot over there
oops sorry dark spot over here and the
same is true for this blue one and the
orange one they also 1 2 3 four apart
but they are pretty close in three
dimensional space e in my three
dimensional space which is only 2D but
and then you get the same over here
and obviously as this is catic you have
the same side on the other side so they
are always looking the same so in this
we also need to understand so that is
pretty simple that's an easy way
to represent a threedimensional
structure as 3D protein in this
two-dimensional table and this is how it
looks in real so this is a protein we
have now looked multiple times on this
is again this cin folding protein and
this is how this two-dimensional I say
would look like so in the middle you see
of course the name and then you see some
interactions somewhere and this is how
how it looks okay so this is information
Alpha can both use and this is just what
Alp always shows it in black and white
so I I just switched it here to a black
and white version so and the last one is
if you use about machine learning and I
think we have heard it in many many
talks over uh over this conference
already if you use machine learning we
need data to train our machine with and
what data can we use and they alha could
for example could luckily use
um the combined knowledge of the
structural biology Community uh which up
to date goes up to
25,000 experimentally determined protein
structures so you can just add say
215,000 years of of PhD labor at least
PhD student labor at least which go into
this database yeah probably more um so
you can all access them in the pdb
database uh the protein Data Bank is now
50 years old and we have this
215,000 and you see it was pretty
shallow at the beginning but actually it
rise experimentally over exponentially
over the time so this is what they could
use and what you have in this database
in a very nice and very neat way you
have the sequence and you have how it
should look like and this is exactly
what Alpha wanted to learn by Machine
learning so they had very very good data
set and they never said anything else so
they are very grateful for this
database and this is just one entry how
it looks like you see the structure then
you see the sequence somewhere and all
the stuff in there so just to see and
these are 25,000 of these entries yeah
so it's a very it's a database actually
created by structure Bist
themselves okay so now let's have a look
at Alpha fold itself so this is from the
paper from 2001 which is the Alpha
version which uh dealt with which what
the latest one and I'm not a machine
learn expert anyway um so some of you
probably understand this better than me
but I try to guide you through as best
as I can and I'm happy to get
Corrections at the end if there's
anything to say about it so the first
thing is here on the left
side um we first do the data Gathering
so you put some input sequence in and
then you look for this multiple sequence
alignment that's exactly what I told you
we look through all the species do we
have similar proteins so that we can
learn something about these co-evolving
proteins the second thing which they do
they make this two-dimensional plots
yeah which they don't know where the
positions are yet but they want to find
it out and the last one which they also
used but it's not necessary so we mostly
ignore it for this talk they look for
templates so they look for proteins
which are already solved but probably
looks the same or similar but this is
you can get rid of it you don't need it
so that's ignor it so first thing is
gathering the data so um I again use a
dog uh protein because I work with dogss
unfortunately or not unfortunately but
it just happened to be that case so what
you do you use a protein and you put it
into this databases and these databases
are huge so at least this one has 2.5
billion entries of proteins and we don't
even know where they're coming from so
they're just from soil sampled and you
just SE them through and there are some
species some soil bacteria just Express
proteins and we just have a look that
are proteins it's a huge database of
sometimes we know what it is sometimes
it's just randomly protein sequenc we
just lying around everywhere yeah uh and
the same is true for other dat so we
have a couple of billion databases and
we just a huge we get a huge um multiple
sequence alignment and this is how it
then looks if you really do it so on the
top ones are the very are the very good
ones here and on the bottom one you get
less good ones this is a quality score
from from from Blue to red and you get
even some where only parts of them align
so you just have fragments but it's
still helpful so it's not always the
same protein it's just looks so similar
probably has the same fault and the
second s is searching for the templates
so I did this here for the protein we
had a look at all the time so these are
now all proteins I could look in the
protein database which rather look
similar if you can see that yeah they
look so these can all be used by Alpha
to to guide so it should look roughly
like this yeah that's the templates and
we Mak this two- dimensional contact
Maps out of that and then we have this
part done as well so we have our
multiple sequence alignments our MSA we
have our this protein our amino acid
distant blots which is this
two-dimensional representation of our
threedimensional structure and then we
actually go into two neuronal red works
the first is called the evil former
which has 48 blocks and the second one
is the structure model with it eight
blocks and the first one is what they
try in this Evo form is actually to get
all the information they can from the
multiple sequence alignments and from
the templates to get this pair represent
a so the distances between all the
between all the amino acids as good as
possible so that's the idea try to
predict which amino aets are how close
to each other and then the last one is
doing the folding and I don't want to
talk so much about the folding so how
does it work so here we have our
multiple sequence alignment um it gets
into representation and we get our
distance block and the first thing they
do they analyze all the sequences and
all this coevolution variation based
with an attent with attention with
Buyers towards what they believe is
already the right distances between all
the amino assets and they know that
either by just guessing which is just
randomly guessing like diffusion model
more or less or if they have templates
they use the templates to guide the
first initial values for these parts
yeah so this is the first part so this
is first the multiple sequence alignment
which guides the re-evaluation of this
multiple sequence alignment of
representation and the second part is
then after they extracted all the
information of the multiple sequence
alignment they update now their um pair
representation so the distances between
all the amino acids and try to get a
better destination of that so this is
all pretty St and what they did then is
actually a little bit nice because they
did then a triangle equality check and
let me shortly explain that to that if
you look at a threedimensional structure
like this one and you have defined that
these lungs here for
example where is the information so
let's say this one here from four to
one you have found this is a very good
uh a very very close connection and you
see that also in this structure now then
you and you have also the determin that
3 to one which is this one no sorry four
this is all messed up a little bit the
number so this is 1 to five and this is
one to four so these two you know which
are or you believe they are very close
to each other and that's what you get
yes and then you immediately know that
the next one which is between four and
five needs to be also in some of close
proximity because the triangle needs to
be fulfilled with 180° in all corners
and this is Tri triangle equality so
whenever you change this one you have to
change this one and if you change both
of them you have to change this one as
well now this is built into the second
tower of the evil former and what
they're doing by this is to teach the
neural network that it it works in a
threedimensional space because otherwise
it would just change all of those
independently and it wouldn't fit up to
make a three-dimensional model later on
yeah that is the triangle equality check
which they build up in there and this
they do now 48
times and at the end they get a pair
representation out and a single um which
defines the distances between all amino
acids yeah and they get the first they
get the sequence and then they put it
into a structure model and I don't want
to explain the structure model too much
because first of time reason and second
it's it is interesting but it's not as
nice to explain how it works what
they're doing they're modeling their
restraints so all these pair restraints
they got like a like a protein gas and
we can have a look how this looks like
if we see this modeling so this is now
Alpha F modeling a
protein um normally you don't see that
this is from the homepage from from De M
and you see it it forms it up and gets
bigger and bigger and bigger and it
samples all these thing and at the end
it decides
that come
on this one now is the final
structure nearly 48 blocks so this is
the final structure
okay um so this is very very nice um and
what they did really really really well
as well is that they embedded into the
neural network also an error estimation
so they not only giving you this is our
solution they also tell you how good is
the solution and they do this with this
color code so this called the predicted
local distance local
PDT uh predicted local distance I now
miss the word PDT is the right
abbreviation and if it's blue for you
then it's a good prediction and if it's
red it's a bad prediction and that's the
way it is so here we see again a picture
and it's colored exactly in the way one
the same way so the bottom one is very
blue so this is very very nicely defined
Alpha fold and then we see this Loop
here which as this Dr I can tell you
this is [ __ ] and I see that
immediately but by the color Alpha knows
it as well so it doesn't say it's good
they say it's nobody knows what it is so
this is the red one okay uh and it gives
you also this pae blocks which are
similar to what we have already looked
um so what you can tell here if you look
at two positions that says this one and
this one it tells you 300 to 250 how
well it believes that these two are to
each other positioned to each other
that's the predicted alignment error so
it says this one is pretty nice but if
you compare this one to this one so that
300 to whatever 600 it tells you I'm not
sure if this is really oriented very
well so it gives you this
local local accuracy in this color code
blue to red and in this green color as I
callor it here it tells you how how sure
it is about the distant colors the
distant itself and if you now look you
just color this part and this is this
folding domain how we call that and this
one is the other one and what alha tells
you here is it knows both rigid bodies
very nicely this Globes but it doesn't
really know how they are linked to each
other can be any way yeah doesn't tell
you how it is but if you know need to
know for the function you should need
you you need to determine
that so this came out in 200
21 if I'm not mistaken and actually
after the second cast they was very fast
in publishing and also they gave us for
the first time the source code and all
the means to actually run the code
before that in 2020 they only gave us a
mega paper oh which is very bad thank
you um so this went viral on Twitter and
it was the first time where science and
Twitter really worked so um immediately
people found out that you can hack the
the network very nicely um to do things
which were not intended you not only can
predict individual domains you can also
predict domain complexes by just hacking
it I don't go into detail now but um
here M did some very simple programming
to just change how alphaa sees the
sequences and then could also comp
predict complexes yeah and the thing
next thing is Google didn't thought
about this but Ser
oov shikov thought about it then just
implement the whole thing in cab so you
don't need to download the stuff you can
do it all in collab by Google so uh
collab fold making proteins fold
accessible so you can go to GitHub scop
uh to get to this GitHub folder and just
make your own predictions and I actually
wanted to do that but I saw the time so
we just skip it now and you can do it on
your own it's very easy you just put in
your protein stens and it goes very fast
so how does it change how did it change
structure bology so mostly if I do now
structure biology I still solve protein
crystals experimentally because it is an
experiment it's a real world it's not a
prediction and if I then compare it
later on with my predictions with the
predictions this is one which I solved
relatively recently and if I then
compare the predictions with it I have
to admit very often it is pretty
accurate you see I mean the overlays up
up 100% I mean there's a little bit here
if this is your interesting part then
obviously it's it's nice but most of the
time it's not most of the time it's
might be might be not but sometimes it's
actually more wrong so this is another
structure I solved last year with a
colleague I didn't tell their names
because it's it's a little bit secret so
it's easier like this I don't have to to
all give you mdas to not tell anyone
about the structures so this is the
predicted structure and then I solved it
uh in a second and it looks it looks
quite a little bit different
um so this is the real structure so the
blue one um so it is it is recognizable
but it's different yeah and if you morph
it around
the videos are too small too
slow come on turn around you see it's
quite a bit of movement going on into
the structure yeah and sometimes so this
is my collagen which we talked about so
you've seen this so this is what alaa
think about the collag looks like it's a
little bit mean because I didn't tell
them all the stories but this is how Al
thinks this collagen looks like it
should look like this it makes it like
it knows it's [ __ ] it says it's red
so it tells you that it's not correct
but for me it looks like little bit my
probably some divine intervention into
this I don't know but it's not yet
there so how can it be helpful for
experimentalists it's very helpful
because I didn't tell you the whole
story for crystallography we have to use
old knowledge to actually solve our
structures and we can do that now with
using Alpha fold and this is also how I
solved this structure so this is the one
I just showed you and I solved it oh
sorry it's not working
I solved it by just using the red one
and the blue one indiv individually and
just put them differently together more
or less to make this long part symbol so
I can use it but I have to always think
about it what is really the problem then
we have integrative model building so
people have this very very low
resolution of huge structures so this is
a nuclear pore complex at seven angom
this is more or less the resolution we
saw earlier with my blenders which you
couldn't really say if it's now making
CBD oil or mortar this is the resolution
R yeah and if you now have those and you
have all this high resolution
predictions you can up and out fit those
because from the outside you could
probably see the
differences so this is how it looks in
real um so this is one of the major
outputs that we can now make huge
structures which we couldn't do before
so what can not do and this is the last
pple of slides so I'm probably going
three minutes over so but um I think we
can live with that hopefully so what it
can't do it can unfortunately not
predict the effects of single mutants
and this is very important because this
is now again our dog and we had this dog
not in our lab but we Veterinary veter
veterinarian came to us because this dog
always had broken bones and there was a
single mutation in this protein and this
I just indicated here where this little
mutation over here and this obviously
changed the protein quite a bit but if I
do it in Alpha fold obviously this one
mutation over this whole MSA so this one
change of it just it's just lost in the
in the neural network it just ignores it
so what it does just it just gives me
the same structure again and just
changes amino acid but obviously if I
would solve that that would look
completely different because it is a
delerious mutation in the moment so in
the moment we cannot predict effects of
single mutations of proteins then
normally what we wanted to do we want to
use this for docking so normally we have
proteins we want to change whatever they
do and then we would dock small
molecules it like viral stuff whatever
and this is also not good enough in the
moment so ala cannot use cannot be used
for that so we have to do still
experimental s
and finally you can never be sure and
this is my major problem I'm a structur
list because I want to know the truth
and the truth I only know when I have
experimental evidence for it and alphaa
it's very gu it's it's a very nice and
it's a major breakthrough but obviously
it is just a theory and it should be
dealt with as a theory and you have to
do the experiments anyway to test if
this theory is correct or not as you
always do in science and this is now the
major thing which goes through all the
stuff and I would just stop with this F
with this um uh with this quote from D
lover which is a columnist at nature Stu
has been greatly Advanced by these new
tools but it has not been outmoded
replaced or rendered IR relevant it's
more relevant than ever and now we C
down to even bigger questions with it
and with that I hope you enjoy the talk
and I'm open for
question
[Applause]
thank you so very much Yan we have
microphones in the room there's one
there there's one there I'm a little bit
blind but I think there's also one there
and one there of course our folded
protein signal angels are monitoring for
translations and mutations and all sorts
of questions on the internets in Matrix
in IRC and in the
fediverse we are in Zeus so the hashtag
is # 37 C3 Zeus or in the IRC room 37 C3
Hall Zeus so let's start here in the
front hi I'd like to thank you for your
wonderful talk and I'd like to ask um if
there is like any possibility to correct
the predictions of alha fold because you
said a lot of times F fa that's like
yeah like bu you you see on the first
look like that cannot be the truth so is
there any possibility to use our
knowledge of structural biology and
combine that with alha fault to have
like the ultra tool to use best of both
yeah yeah it is it is it's a good
question and I always need to be careful
Alpha for is not making [ __ ] in 99%
of the cases it is doing a very very
brilliant job just to make that clear
95% it's only couple of times which is
getting pretty bad so and probably I've
over over over brought this point
because it's so close to my heart that
um structure biology is not dead yet so
but yeah you can you can use multiple
other things you can use NMR structure
we can use restraints from other stuff
we can change the multiple sequence
alignment so there's a whole field I
mean I just recently over Nicola's day I
was actually on the conference for 4
days which just dealt with structureal
Biology so we are not at the end of this
computation structureal prediction yeah
this is just the beginning Alpha fold
made a huge step and since then we have
evolved quite a bit so the last cast for
example Al for didn't even go there but
many many other programs did there which
did it in a similar way so yeah yeah
definitely yes and it gets better every
day so I can see that the internet has
crystallized out a
question um the internet would like to
know um what your personal thoughts are
on um
where this progress uh could be utilized
in the field of medicon is this still
far off or is there already progress
being made no there's quite a bit of
progress being made I mean it depends a
little bit what you need what you need
your structures for I mean I told you
about the docking so this this this
typical idea that you for example you
have a Target like an insulin receptor
and you now generate a new insulin which
fits perfectly in this Bound in this in
this pocket this is
W
sorry so this is probably not not there
in the moment um but if you need more
more broad let's say more Rollo
resolution structure this is already uh
going and in the moment I mean I I
support or supervise many structur
biologist uh projects and also some
medical ones and none of them are not
using alha fault it's not that you're
not using it it's always there yeah and
sometimes you need to have more data and
then you have to do experimental work as
well so yeah I see microphone 3 has been
staring at the X-ray machine is there a
question yes um thank you for the
presentation so my question is if in the
future maybe Alpha F would be such good
that it would be possible like with omix
techn technology to combine it with
interatomic to simulate all organisms to
answer scientific questions for that
yeah very good question we we are on the
way already I mean there are groups um
there are this minimal minimal viable
cell which is an AR it's not artificial
it's a cell which is so boiled down that
it has very few proteins and they
already trying to simulate the whole
cell organism all the proteins on a
physical way and they use alphao to to
get this parameterization of their of
their model in that yeah so we are on
this way if we are ever
there how should I know I mean if you
asked me four years ago if or 5 years
ago if there will be a neural network
predicting protein structur I would say
no so I don't know probably but we are
we are doing that for years and it has
done now multiple multiple steps
forward yeah the collagens at microphone
one yeah uh so um most of the uh uh
proteins we know the structure of can be
crystallized um how's your estimate uh
regarding Alpha's uh performance to to
uh proteins which cannot be crystallized
because they don't turn up in the
training data as
much
um I'm not sure how far you aware about
crystalization I mean in theory all
proteins who have a defined
three-dimensional structure which are
not all but we forget about them in the
moment because this needs another talk
um all of them can be crystallized we
haven't found the real con real
conditions yet potentially that's at
least the idea behind it so so I guess
as long as there's a threedimensional
structure in this
protein a prediction method should be
able to some at some point predict it if
Alpha fold is doing the trick or some
other probably language models in the
future which also are there nobody knows
but y thank
you could it be that the internet has
distilled down the structure of another
question um the internet wants to know
whether the structure of a protein can
change during
crystallization uh this is a mean
question yeah um yes of course it can um
and some people say a crystal is so
artificial we shouldn't actually
consider it a protein structure but I I
I beg to differ yeah it can do that and
we see this Crystal artifacts as we call
them in the crystal structureal
Community but uh we have 200 whatever
thousand Crystal structures and most of
them are tested in many biochemical ways
that they are correct because you can
make hypothesis based on the structure
and you can test these in real life
scenarios like in cells or in Vivo or
Vito whatever so yes but most of the
time it's not a problem sometimes it is
as always in biology never 100% true so
there's time left in our PhD for two
small crystallizations or one big one
microphone 4 yeah thank you for the talk
um so I was wondering um how how do you
get to the initial amino acids that you
need to put into Alpha fold or need to
crystallize because if you not know
don't know what's there how do you know
how to PR
purify that or get yeah I get this
question it's a it's it's a good one but
in principle um you are normally you're
going the other way around so normally
you have a question what is your
scientific idea what do you want to do
so you have an hypothesis I want to deal
with cancer or with let's say I want to
deal with Corona virus and then you know
the Corona virus genome and you know all
the proteins in coron virus because we
have sequenced them so we know the DNA
and I didn't put it here sorry but we
know the DNA and the DNA is directly
translated to amino acids so as long as
you know the DNA you know all the
proteins I didn't put this link um sorry
forgot that should have put it
somewhere a very short question for
microphone too okay just again to
problematize this idea of a single um
folded structure I know it emerged from
the history of x-ray crystallography but
um like in human body sometimes a
protein can say Malou form mold or it
can have multiple metastable
configurations so could we imagine
alphao and other Technologies evolving
to be a more Dynamic thing where we're
seeing the evolution of the folding from
when it's initially synthesized the
protein and um could we imagine new
Optical techniques that allow us to just
double check that these Crystal proteins
are the same as these living proteins
this is this is a very important Point
uh and because first of all alphaa does
not know know how proteins are
folded just knows this is the sequence
and this is how it should end up but it
has no real way how to fold it so the
physics be the folding it's completely
unknown to Al which is my major concern
because it just makes the beginning and
the end so in Crystal graphic we have
very often different Crystal forms of
the same protein so we know this the vi
viability sometimes and my hope is and
now we go to the feature to the Outlook
that at some point we have probably like
a large language model of proteins which
actually understands how the protein
folding is kind of happen not like we
come from A to B but how what is the
different steps and meta for example the
Facebook company has done this esm fold
is a lang large language model but it's
much much less good than alha fold in
the moment and as as far as I know they
actually dropped the development so it
probably will never be better but
someone is probably working on a large
language model for proteins and then we
understand real the folding the
biophysics behind it and not only this
is how it looks like before and this at
the end in the middle is a black box you
perfect so I have a masteron and I have
a DCT you can find me yeah if you have
further questions I'm on the conference
tool and Yan thank you so much let's put
our hands together one final
[Music]
time