You know right now.

Yesterday.

Every day about another one of
these areas that I keep reading.

I'm telling you it sort
of fell over the you know

this already got more
than a hundred people

here only thank you Jennifer for Doc and

also for making the arrangements and
now with her first courteous and

as you're CARNEY I assume that
you're the educator aware about

what you know about protein Monica and
other areas but

just in case you're very brief
introduction about proteins so.

So there are many ways of approaching this
and a simple way to look at a protein

protein Monica is a part of your chain and
you can view it as prose.

So the purse Cami in

twenty different colors representing
the twenty of I mean I C's.

So this is technically
referred to what the quote

of the backbone and then the pros with
different cars are the side chains and

this represented a that I mean I
sees when a protein is a sized.

Typical media phone into a defined
structure and in this processors

the physical properties of the of
the side seams come into play.

So in particular on the.

And I.

Side chains stand have a positive
charge when negative charges and

the side change that are what's
referred to as a non-polar and

this is different physical
properties are you saying shill for

the structure formation
when approaching folds.

So the non-polar residues side chains.

Tend not to like water so when the protein
follows those slides genes are then buried

in the protein cheerier whereas the target
residues direct with the water so

they typically s'posed on the surface and
if they charge a side teens sometimes can

a range of such dead the interactive
favorably with each other.

Positive with a negative charge other
times they actually have you can have

a concentration of charges and
they saw will be important one.

Proteins way you interact with each other
so you would have a positive charge of

the protein directing ways which are the
protein for example and this will come

into play you some of the processes
that I would be describing so.

So that this discharges will play
an important role in the interactions.

So by now we know that proteins
function not an isolated isolation but

rather protein monikers already
very social beasts they form.

Complex and work together and so

protein Association the processors of
proteins coming together to form complexes

is a really key step function you know of

that underlie most Bon
article process sees so

I want to highlight it with just one
of those processes in the process so.

Approaching things is seize the senses.

Proteins itself and you may know that this
process happens through a big complex.

Consists of a protein monic
used in our name on it.

Use called the right.

So all the players
including the messenger.

This is where the genetic
information is a coded and

then trends are in a scene
new I mean I see these and

then these are stitched together
to form a new approach.

So this is a highly simplified view of
protein things that you may think this

is already complex complicated not but

if this is already kind of
complicated simplify it a little or.

Reduced to conversion has as many players.

I don't mean to go through this.

So it goes through this whole.

Different cycles that can take
would take the whole next year.

So rather than that I'm just going
to give you the impression that

this is a process that
involves a lot of association

disassociation many different
factors that are involved and the.

Properties of this association stamps.

Then I use a show for
controlling the accuracy and

efficiency of the censuses processors so.

So this is the point that protein
Association rates in America needs and

are essential for Under this is understood
in this particular one article process but

also for understanding.

When I can recognition the biological
processes in general I also.

Already mention about rates this is.

The speed at which
proteins as associate is

measured is measured by
the social rate constants and.

The magnetism based on refers to
the a a mechanical description

of the association curse.

So this is a problem that.

We have been studying for
for for quite some time.

So the focus is on the association rate.

Again the this is the speed at which
protein Association a curse and

is associated rates different
proteins span a very wide range over

ten the water is orders of magnitude and
the rate constants is mad in this

funny unit quote promotion or percent and
this is the bottom one I can oppressors.

So their concentration is involved.

That's why the promotion or
course is there so they were approaching

associate at a rate rate constant the high
incidence of pence Pomona per se.

Again and then there are also
proteins that associate at

around the ONE COME ON
percent in the lower.

So the span as I said ten orders of
magnitude and the message I want to.

A convey to you is that we've
made significant progress we

think that there will be
unable to do calculations for

about half of the spectrum so raise that
a higher than from the end of the first

Come on her second we can have a pretty
good a hen dong you making calculations.

So that's basically the the story I want
to tell you and I'm on the way I was so

long before the grad student audience
that make the pointed at you.

Will you will see that one
course is that your to

have taken while taking
will come into play.

So how about we were
ready then I mean to play

quantum mechanics to play where
you know Meghan coming to play and

the worst is just
a mechanics come into play.

So all those clues are used
in our in our research.

So how do we define the social.

Rate comes in and so
this basically describes the process

in which two protein molecules
come together to form a complex.

So those typically when you're
all involved in these two steps.

So the first step is
the coming together and

if uprooting monic use this is a
processors this process is essentially a.

Diffusion of step.

So basically proteins move together.

They swim together by.

Diffusion.

And then once they are nearly shot there.

Then there will also
have to make adjustments.

So that they form any specific
complexes in other words

the structure of the complex is such
that it's always one portion of one

protein come into contact with
another portion of a second approach

you need to always the same contact
at a form where you when protein is.

The comeback that would
form the same complex.

So so it's specific so

to model this processors we can use
different laugh always of descriptions.

So the simpler the model is the one which
would represent each protein as if it's

a circle particle and

this what it describes where
the first step the diffusion of step.

So we're basically described
this pair of monic used and

the function that we use for

here is what's called the pear dispersion
function this is basically it's a function

that depends on the separation of
the two spherical particles and

it's also a function of time
me to measures the relative TO

should be the city of despair as
a function of distance between then and

this is basically described
a by diffusion equation.

Except.

It's also could be influenced
by interactions between.

True proteins so the made one
protein may be positive charge

the other protein may be negative charge
and so they attract each other and

then combine the fusion that's what this.

This is both my factor and
need and it's reciprocal for.

So this space and model of the buyers
over the Internet can paint you to the.

Binding processors and so

that diffusion you question is
very similar to this equation so

this is you can basically see this is
real time a shred into creation and

you can't any trends of this equation
actually into a division equation by by.

Transmission word you relate
you to a different function so

basically this becomes a try to
equation with a transformed potential.

So if you will solve the diffusion
equation and then the rate constant.

Is then deter me to buy the phlox
of the pair distribute function and

calculate it at the end of
the context surface and.

And since this is a partial
difference equation then we need to

specify the initial condition the user
can he simply the Bozeman should be able.

And then the boundary condition then
there are there is out up on the can and

that's when that is a very far part.

That's basically it's.

Simply that the value of that if you
hear from one it's a reference and

then that you know about
a condition mottos the.

Their reaction this is basically when.

When the moderators have to make make
arrangements to form the complex and

then you can modify that as they say
you know boundary foundation So for

example the simpler the kind of
Obama inclination would be absorbing

going to condition which
mottoes the case were to.

Proteins form a complex as soon
as they touch each other and.

You can also use absorbing Barnett and

that would model the case where you
actually have to wait for a while for

them to form a complex In other
words they don't form a complex.

Every time when they see each other.

So that's that's that
mathematical problem and.

For the simple condom ato So whereas
protein monikers in our study not spheres.

So the problem can still be
described in the same framework.

So instead of just modern in the nation
at the fusion Now if a calm before

the shape of the proteins we have to then
worry about rotation of diffusion as well.

So instead of.

Only street space then now we
have sixteen years from three

fortunes in Asian history for diffusion so
fundamental Magic's is still the same.

It's just a slow motion
becomes more difficult.

So there are a few model systems a word
this problem in connection to solve and

economy and one that's simplest
is one which I just described

as soon as the two proteins see each
other and they form a complex and

this is the problem there is solved
about one hundred years ago that by.

And he was not certain not worrying about
Association of proteins rather he was

worried about the coordination of the Calo
oysters and the colorful Kahlo is.

The cognition happens nonspecific and so.

So this is a reasonable model.

And then.

Some years later.

Peter did by solving the problem.

Were.

You have interactions
between the two particles.

So for
example one would carry a positive charge.

There will carry and negative charge and

they say again the diffusion
problem can be solved and.

And this is the association rate
constant to get closer to prove

you weren't you need to worry about
the specificity of the complex form.

So a more realistic model will be
one that Barry carries a patch.

So that you assume that the complex form.

Only when the true patches are in contact
with each other and this is a problem.

This also been solved.

So I solved the.

Good by version of this model were
you have interactions between

the two patched queues and
this solving this problem is it's

very similar to solving the I'm going to
momentum problem in quantum mechanics and

so you see just this C L L one or
two of these.

Clamps cord and coefficients.

So it's the same problem that you solve
to get the you need to go results.

Now these solutions are complicated but

nevertheless they are limited to use
in the sense that that approach not.

And we need to worry about
the details shapes of a protein

molecules and for that.

I don't think you will be ever
be able to find and a and

in due course in the oceans.

So we have to then resort to some
in Asians in this kind of some

nations that we do.

Is referred to as
a Brownian dynamics minae

sions you're

right right.

But as long as you're.

Stay with the simple John the trees.

I mean there are there are solutions but
we want to move away from the simple

geometry is and work which is the actual
shapes of protein mining cues and

so as I said we do brawn and
I'm examinations by that basically if

the fusion of the true party goes and
we we base the front of the random

motion of the monic use in
trends in Asia and in rotation.

And then.

From the summit nations then we'll
get the rate constant so for

that we need an average.

So from the simulations that we so

that we can we can get
the rate constant So there is.

A algorithm and it's developed by and

I'm a chemist group in the one
nine hundred eighty S..

Just very briefly so what they did was
they start to fix one protein Monica in

the in the center and then start the other
proto monic you on a circle surface and

then for
each trajectory just started a final.

Until it either reaches the binding
site that's when you terminate

the trajectory or you to riches a surface.

Been absorbed there.

And so for you Are you basically.

Required was that one of
these final fate whether or

not it's absorbed at the binding
site was absorbed by the.

Outer surface.

And Kalkin and.

This this.

This kept your probe in eighty that it's
a fraction of a trajectory that I actually

captured about the by an inside as
opposed to reaching the outer surface and

from that you can get the rate constant So

there are a number of this is
the first math that develop and

you say why did he use and so
there are some limitations to.

This method so for example when
when the binding becomes more and

more stero specific then this capture
program it becomes the last and the last.

And so that you.

You get to a situation where you get
really really poor statistics so

if you kept it pretty is it's a one party
and one unit that means you need to

you need to sample many many millions
of trajectories to get good statistics.

So we develop a target of Edwards and

the idea is to minute
the detail is very different.

So instead of starting from a not official
as Franco service weapon start from

the site and with final the the victory.

As a function of time and
so when when the protein

is is is is absorbed at the binding
site and then with the trajectory.

But we don't have a alter serve
to capture this this trajectory.

Instead we have a time caught off
we simply cemented OP to a seven

second time and then we record when.

The top of that time whether or

not the victory has terminated if you
did it and we record the lifetime.

So for
you to tell you we have a life time either

at which the trickery is terminated
at what the cost of the time.

And from that we calculate

the fraction of trajectories that have
been terminated at gave a unit of.

And this is a was referred to
as they survive a probe in

the way of the sub I will probably
need to as a function of time.

And from that we can
find the rate constant.

So this it wasn't that
they had a way to use but

still we need to solve the problem.

When you find it and
this is basically the direction.

Due to charges on the proteins you
know steady interaction potential and

to do that we use a static motto.

So that's where you what
you learn from Jackson.

So so we have far protein monic you.

So and I will modify that as diametric And
if you can medium.

Which is a diet for
constant as appropriate for

protein molecules this value is the ocean.

About four and then we have
the charge of the protein Chargers.

That are humanity in this medium and
then on the outside.

It's a protein protein is a live
environment then we have.

On the other side we have a say going to
die of a medium with a different doctor

constant This is now about
eighty four four for water so

we have this space me problem with
two different types of media.

Four for the protein Monica an eighty for
the solvent and

the symphony we solve
the problem equation two.

Where pain the.

The static energy for protein molecule and

then we solve this for
one protein molecule for the for

protein A and it would do it for
protein B. and then will for solving for

the protein complex and then it would
take the difference to get the energy of

the interaction as a function of
the separation of the two mana cues.

Now this problem again to convert for
that detail to shave

the protein monikers away have to solve
numeric only and it typically we we use.

A finite a difference.

Edwards and C. and said.

This is a derivative So
we represent the second two or

three days as if I find a difference and
then we solve the made.

They result in measures matrix problem.

Suffice to say this is a very time
consuming problem and then you want to.

Solve the problem.

A few times as possible but if you want to
actually do the broader than examinations

then in principle have
to solve this problem.

Every step of your your your
simulation and that's actually not.

The time you just so you hate of
along that this actually happened

as has not been done and as a result
when you actually do the brunt and

I'm this nation you have to make
drastic simplifications and

that this indications you are poor
when the proteins are R R R R.

And that's when you actually
need it to be the highest So

you have this problem with dealing with.

The you know just added part.

Accurate and so we try to.

Overcome this problem from
a different perspective.

And what we're trying to do
was to find a way to capture

the fact of the potential in A and equal
way rather than through some innovations.

So we came up with this.

And it is good for me and

her that data captures the fact
of the internet campaign.

With a common for
you to do in the nation processors.

So this is in the form of approximate
a form in our for the rate constant So

this has a form of what we refer
to as a beta rate constant So

this is a rate constant when you turn off.

The next steady interaction.

And so that you basically in your
simulation will be full screen and

those from the nations
can be very efficient and

instead they forced the putting
is a captured a by a and for.

Both of my factor you and
over just the record region.

So now what you have to do is
basically the problem is and

I'm very confined to this
precise binding site.

So you can sample and emit a number of
conflagrations and if you calculate.

The range you.

And then you do a boy's mine for you to
get the fact of the interaction potential.

Now this is not something that we just
throw down there and in fact this is

true detailed analysis of the diffusion
equation I want to get into the details

but but suffice it to say it's now an
again I want time as an approximation and

the accuracy of this this approximate
a foreman or depends on two things first.

Depends on the the stairway Space Face
city of the protein complex.

So for example in our work for
the by a motto.

Were you have the case where the protein
molecules are uniformly reactive.

So this has to be have very specific.

Binding complex and

a segregated the interaction
potential has to be in the range and

that's exactly what we have when you
have a natural state interactions.

So the two requirements are then just
by chance happened to be exact and

what do we have and so.

So that we can use this.

Approximate a formula and

this is the basis of basing
our approach which were furred

to as they transcend complex theory and
yes

there is a surety about it
soon all arranged right.

So all we explain the trends and
converts part in a moment.

But the.

Clearly theories basis for me.

Enter this approximate it before
me enter and of course we want to.

Since it is just an important such
important part of our approach

you will want to test
its validity of any and.

And we've done.

Compared this form in a way that with
a broader that makes a nation results for

some four simplify tomatoes and

the in addition will also tested against
the results since I said I have this.

And it is before me and
so I can I can test.

The approximation against this result.

Now.

This depends on the shape of
the entire campaign show and

not the problem actually even though
Flamini can be solved in this way but

the problem can be solved only for
a limited number of potentials and

so I was able to solve for
two so one has a lot

of respect dependence on the distance the
other is a step function not that this.

Has any physical meaning just
a mathematical need to solve the problem.

And so.

The approximate for
me when I when I scale things

I get this about one point
seven times from the four and

then the I need a results
at the exact results then.

Listed here and

so you see that the four different
magnitudes of the interaction pertains to.

The units of K. T. one K. T. up to nine K.
T. they all have close agreement with the.

Approximated from the inner so.

So again this is just
an aspiration of the test.

Donna no one and no one said but
it was the accuracy of the medical form in

are now to give you a flavor of
this trends in a complex theory.

We can we can.

Cultured in the form of a kinetic
scheme as information that

the association processors
can be composed and

use them to two steps the first step is
the diffusion approach of the Toumani cues

and then the Sigma step is the
rearrangement of form the final complex.

So we can capture that two step
process by a kinetic scheme so

we say to protein monikers and
be they form this.

Transit in complex with great
constants of K. or D. and

it is trying to convert can disassociate
was written a K. of mind and

body this trying to complex and also
rearrange to form the final complex C.

So the transit complex is basically
the result of this is diffusion approach.

It has.

Said preachin in a war in Taishan
close to the final complex but

it has yet to form the specific short
range directions of the final complex.

And and.

So you few treat.

This trend the complex as a.

Comedian that does not.

Kim in eight and then you can you can find
the overall rate constant in terms of

the rate constant of the unit
the individual stamps and so

this has a property that that when
the same going to stampede to fast.

Then the first step becomes a rate
determined so the overall rate constant

becomes very close to the first step and
this is a diffusion of step and.

So that's what happens on the right
half of the rate spectrum.

So in this.

It's spectrum.

Ray said are higher than about a tenth
of the homeowner percentage and

isn't limited by diffusion whereas
it's rate constant that are smaller

than the four point two percent and
it will be limited by the second step

the the rearrangement will
commission a change of stamp and.

Within the diffusion limited regime
there is also a range and that

ranges than rate comes in the from ten to
four to ten to six point one per second

and the use of the rate constant that you
would have when the diffusion is on by is

to simply run that would be fusion whereas
a higher rate of constants in the ten

percent will be the case there were the
diffusion is biased by static attraction.

So in fact if you look at
the proteins that are associated

with rates beyond the six
promoted percent and

invariably you will see that they feature
high any company charge surfaces.

So you improve statics blue represents
positive and red representing negative so

usually they will have one protein that's
Heinie positive charge one protein there

will be negative charge and here I'm
going for examples of all of those cases.

So I just said being
able to solve the right

half of this problem and
if you some more details about

approach in particular how would
you find the trends and complex.

So the trends in complex.

Is based on are you since you're

describing the process of
the distribution approach.

So we can model the protein monitors
as basically as a true rigid

bodies and so the reality of.

The relative rotation
are already one Tasia and

we actually use a way in on
goes to represent the same.

Degrees of freedom.

So basically this is this what you can
conquer when you solve rigid body problems

in theoretical dynamics and so

the the energy in the trans

nation the rotation of space will
feature a deep well and that calms

about due to disobey civic interactions
between the two protein Monny cues and

that represents the the the the final
the bomb state.

And then when you're far away
then you're going to have.

Waken direction so that the and
its surface is relatively flat.

So the the transit complex is
basically the dividing line

between the divide is there
between the two regimes.

That is noted on the outer boundary
of the over the state of well so

that is a close to the banks that well
your terms overturns a nation no rotation

no degree but
it does not have the stability in

directions that gave rise
to the deep well and.

Most of the city.

When you are within this deep well within
your rotation is a significant restricted

so that there were patient angle that
in the sample has a very limited range.

Whereas once you are out of there.

Well then you can sample Wallace three
hundred sixty degrees in rotation so

the transit complex is basically
the transition region been

between those two regimes.

So just to recap shown at our approach.

So we decided we when we predicted
the rate constant based on this

approximate four manner.

And this depends on
the specification of the transit

complex as I have just explained and
then the beta rate constant we will

detain by force three Bryan Danielson
Asians and then the next step.

He directed energy we will paint
by solving the person equation.

So we've now.

There's a board room so

we because when you only need to solve
within the trans and complex and

it is trying to convert typically you
have separation between the morning.

Five on trumps right.

There's nothing that isn't a problem you

want to say ten you need to ten
twenty on some That's a long range.

So we have a number of them.

We all approach has a number of
advantages but I don't I'm not into this.

So highly technical I'm not
going to get into this but

just to show you one application
of our approach and this.

Is the prominence of a protein
binding to you and this is.

As you can imagine a larger system and

weighs more than fifty
thousand atoms involved and

we can handle this because just because
the the the the power of our approach.

So this is a picture of the right
goes on and this is the.

The protein that's bond to a particular
region of the right was and

this is a piece of the rebels Omar R.N.A.
and

this is a toxic enzyme that actually

binds to the to the our in a loop in
action to create seed so that that serves

to inactivate its rivals on a stops
protein synthesis So this is a poxy.

And this is so
he is referred to as a rival toxin.

And this is a you know large To view of.

The complex of the rabbit hogs and

with the particular robot.

In a piece.

And the.

Structure of the rubble talks in
ways just in the loop by itself and

has been determined by X.
or crystallography and

then we actually modeled it this structure
based on this and we simply superimposed

the darn loop to the to what's on there
our name to the ribozyme and then

this model for the complex and.

So the there is another interest in
this in this system and as that is that

demand the observed a rate Constantine
this case is actually set a record.

So the next week sees ten depends upon
approaches eleven Pomona per second.

So while quite interesting.

You know how such a high
rate constant was achieved.

So you can also study the.

To the association of the protein with
just of the piece over the rebels Omar and

they and that's sort of in the normal
range of the sevens Pomona percent and

so there is an additional for what is the
magnitude meant when the red was almost

loop present on the surface of the of
the ribosome So we were interested in our

region of the of this additional ten
ten ten thousand rate handsome and

there is no that's was a nice
approach there is no free parameters.

There are primitives about this.

Tape.

You know uniform way would only add it
just any parameters in doing this sort of

calculations are simply the by itself.

Yeah yeah that's unaided are in the loop.

And so there are two and
one of the advantages that.

That I skipped over was.

We can tease out the different
contributions to rather than

just calculating a single red cons and we
actually it contains are the contributions

to the rate constant and we found that.

So part of the contribution
to the ten thousand and four.

Additionally has in the cons and from the
additional charges on the rebels and so

the right was on these a large component
is the rebels in our name which

is a highly negative charge or
the rebel toxin is a positive charge.

So there is a narrow range nor range
attraction so that contributes to about

half of of the additional you handsome
in about one hundred fold and

then there is additional hundred for you.

Handsome and
that comes from short range fact.

And this comes.

By by reshaping the trends and complexes
this is a trend a complex of the eye of

the isolated right was Omar in A and then
in the context of the intact arrival zone.

What you see is that this
complex cannot is not

compatible with them for example you
run for caches you don't utilize this.

Class to well so.

So this in the neighboring rebels own
protein then modify the transit complex.

So as a result now the transit company is
a race shaped and this ratio of the rivals

in the transit complex and
allows the interactions between.

The protein and the end of Darnay loop to
be particularly strong so that So this is

through ratio in the transit complex and
that contributes the additional one

hundred fold over

all it's a rigid are treated as rigid

this isn't it correct
in the in the as long

as the flexibility does
not become limited and.

So that's why we stick to the higher
half of the rate spectrum and

this is the case the Reagan word
this sort of flexibility fast so

that they don't become
written in one thousand.

And so with now a made this already made a
prediction matter select it may appear at

a web server that you can actually submit
your protein complex and make predictions.

So so so this is you asked about
whether we're term promenaders so

we predict this automatic and then them in
the we're not in our prime interest and

just to give you an idea of
the of the performance so

with such an interest here and
we're following the forty nine.

Protein complex's word rate
constant have been measured and

will fit in those complex in outcomes.

To print the value is predicted
by the web server and

then this is sort of the comparison so
we get this sort of.

Agreement between this autumn

prediction matter and
observed rate Constance.

You go.
But what was wrong or

if you are right.

For those you're right.

So what is making the change
is the attraction.

So that can vary the rate comes in
by several orders of magnitude and

it's diffusion emitted it's diffusion
except the diffusion is buyers'

despise the diffusion as opposed
to unbiased diffusion so.

Speeding up the diffusion
through the traction.

All right so and
it is proving complex economy and

different shapes in the forms and so
there is so is high need diversified so.

So our methods don't really
care about what what

what is this protein what that protein so
so so so.

So we now we claim that we've now
solved the half of the social

problem that's the half that
is limited by diffusion.

All right so that.

OK That's OK go to is fine.

A good so
now the problem is out of work done.

It's is by little solutions
body in the in the cell.

It's really far from being diluted and
so this.

The inside of a cell is is a crowded
always all sorts of macromolecules and

in the aggregate a concentration
is extremely high.

So this is a straight shot
of the of the crowd and

is so full of the of the of the of
the sale is so you get a large and

small when the protein spill is
already did it is in the binds and

sometimes they aggregate into five groups.

So all of these happen you know
how many of crowded conditions and

the question is whether or
not the crowded conditions when you fact

the physical properties such as the by and
the by new rate constants and.

So we've done.

Studies see a model citizen and then they
show that indeed the crowded conditions

the cancer giving you fact the different.

Physical properties.

And so so so and so we are studying the.

The fact of sand in a crowded conditions.

So the first question that
we ask is when we would

have conditioned then when they
approached it based on a pair.

The.

Here's a question of
cost is that still valid.

And so to so interest this question.

Some time ago.

Ron and I'm exterminations So
here we have a box of hot fears and

then because fears it was something
label one of them as a and

others several others said to be and
then the rest as Crowder's and then.

So the bottom mockery of
this is to me is quite high.

About forty percent.

And so.

So we we as soon as the two particles

approach away assume that of the they
reacted so we calculated the survival.

Pretty as a function of time and
they will use that human out

how skip roach to predict and
that's given by the sign of the curve and

we actually get pretty good agreement
between the summation and the theory.

Now the theory is done by.

A commune for the fact of the Crowder's
and it is coming two ways.

And one is the there is
a huge fact if I traction.

Do the Crowder's between
the the attraction between the protein

monitors by the Crowder's and that has to
do with the fact that when two protein

Monny cues are close to each
other the volume exclusion.

OVER THE OVER THE LAST thing you if you
would have to to Monica to separate so

there is so crowded then your so
then space is at a premium so

you want to minimize the use of space and
that's when when

the proteins are associated So there is
a huge factor of the driving force or

attraction due to the Crowder's and

say going to the Crowder's serve
to reduce the diffusion constant.

Because these are becoming obstacles
to swinging from one place and

another position you have
to avoid those Crowder's so

that fission constant is reduced and
turns out that those to you.

Facts act.

We oppose each other so.

So the net effect is
actually quite modest and.

Worse yet.

This is not a you know this is excellent.

Vonnie type of track and this somebody
called me from expletive on me.

In fact it's not good for
words from you know static effects.

Depending on what kind of progress you
have so your progress on charge of it and

then so you're not going to have
any you know static effects.

Now you're you've got a charge and then
you walk you are going to yes I mean we're

art yourself.

Electricity is a dollar which
is where you work right.

Regardless.

Yeah.

So they know they were what effect on the
day I went back and looked at in the so

the fact of a battery constant now
becomes not true not eighty but

somewhere between about fifty and so the.

So the steady interaction is strange

because of the reduced
the screen in in that regard.

So so anyway so
we can convert those it to you.

Facts.

By by modifying our proximate a form in or
so now the base rate companies and

the modified because now
the diffusion content is reduced.

Now we have this.

Fact interaction due to crowding.

So this could be could involve and

could also be a modification of
the United standing interaction.

So by.

Accommodating for those the facts then
we can gain us our native formular and.

So we have ways of calculating
those you facts and

it just to skip over this and and get to.

One set of.

OK So this is we're collaborating
with Gideon Schreiber

where the wise men used to to do so
they've measured the rate constant of two

protein Pam and and various.

Crowding conditions and

they found that it basically the Crowder's
don't do much in and that's.

Somewhat surprising in the sense that if
you look at the diffusion constand of

proteins under those conditions the action
of the division on the slowed down by

about five full days it would expect
the rate constant to also be slow dong.

But then if you convert for the effect of
extraction to the Crowder's this to you.

Facts almost so that you get.

Essentially and now.

Net You fact so.

So this is for or calculations.

So to summarize.

Showing you of that here
we have the advantage and

approach the trends in conflict theory
then there were implemented through static

calculations and brought in dynamics and
Asians and this methodology for

automated would claim accurate
predictions of rate constants and

diffusion sets a limit on
association rate constants and

this in the range of ten to force to
twenty six point one percent again for

I'm biased diffusion But
you know static tracking combine POV this.

Limited by more than three What is the
magnitude and their wide range of social

rate comes and allows proteins to
perform different functions used in

different kinds to suit their particular
functional requirements and find any.

Association on the crowded condition can
also be modeled and when the facts of

interaction and diffusion are good for
and just funny.

People in the group that contributed
to this work and for fun.

Thank you for intention.

Thank you very much for that very

we have any more questions about this

so that decision is far more complicated.

And the raising is that to disassociate
you have to worry about the short range

interactions and
those are harder to model and

so we're just beginning to note
at this short range of facts

that we were right.

Some Monica dynamics and
that different representation

can be at a mistaken walking
most of the Course screen.

Yeah so.

So that's the kind of things
that we have to account for

the way to

reduce right road

were to well it was very
close to live very good.

There's you are the world we
remember we were the workers are we.

Your yes we were.

So you are right

the larger the whole of the world right.

This deep well yes computer.

You are right.

The second question was this road is

the wrong approach World War for
the right.

So right I mean it's a deficiency it's all
random and you can you can actually have

a complex a word the ramparts
of proteins are in contact.

So that's can happen.

Yes but
I want different kind of hold your target.

You can translate a complex
between protein a crowd or

so that can happen and if this happens
a lot of then our approach will not

work so so

the car will the factory become traps and
the question for some perspective.

I don't think you comment on this you have
this spectrum of really huge predictions.

If you had to sort of classify
the percentage of proteins

that would fall in one person and
I think I think you asked this question.

So I can.

Yeah so.

So we have not really done by the way did

do I limited a set of proteins a set
of I'm A name it is all relative.

I think it is one hundred eighty proteins.

So it's not so limited so
fought and fought.

A particular set.

About eighty percent of the proteins fall
on the high end of the range that we can

model about twenty percent of falls on the
ring in the range that we can't it's there

a way to glance at a protein structure and
it's going to go and one side or

the other because yeah yeah so
provided you have to

know know provide you have the structure
of both before and after having if you

have a structure of both the structures
of both the complex as well as state.

Because then you know if
you come from here and

there was a math that
actually tells us whether or

not if you saw automated method I
can actually tell when you to fail.

OK All right well thank you very much.