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PROFESSOR: So today we're going
to do an example of using some

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of the equilibrium concepts
that we've learned

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to drug design.
 

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And it's going to be an example
that's out in the literature.

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And potentially a very
big deal if you can do

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drug design this way.
 

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And it's an example of how,
remember how I told you that if

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you have the Gibbs free energy,
you have everything and how

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clueless I was as a graduate
student, and when I look back I

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think how silly it was for me
not to realize how

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important it was.
 

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Well, this is an example where
people calculate delta G's or

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Gibbs free energy changes
for binding of proteins to

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receptors on cells, or ligands.
 

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And from that, design drugs
that work much better

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than the real thing.
 

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And it's all about
calculating delta G.

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We're going to see how that
is, and how that comes

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in with equilibrium.
 

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So the paper that this is based
on was published in 2002.

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And since then there
have been larger-scale

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trials, animal trials.
 

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And I believe there have
been human trials of this

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concept, of this drug
that they have designed.

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You've got the reference
in the notes.

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And we're going to have to do a
little bit of review of biology

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first, to figure out
what's going on.

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And this particular process,
this particular drug, is called

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the, I have it right here,
granulocyte stimulating factor.

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Where was it in here?
 

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To give you the
right name for it.

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There it is.
 

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It's a protein
drug called GCSF.

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Granulocyte Colony
Stimulating Factor.

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And it's there's wild
tie for natural version

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of this protein.
 

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That is generated
by normal tissue.

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And this protein goes to the
blood, to the bone marrow.

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And it binds the receptors on
cells that are in the blood and

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the bone marrow, and stimulates
the growth of white blood

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cells, and stem cells, and also
acts to tell the bone marrow

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to pump out these stem
cells into the blood.

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And the problem is that if you
have a chemotherapy patient,

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their bone marrow cells are
largely destroyed through

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the chemotherapy.
 

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And so they have a problem with
the white blood cell counts,

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and with stem cell counts
and all that stuff.

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So one of the ways that you can
try to reverse this problem is

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by stimulating, artificially
stimulating the growth or the

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proliferation of these
white blood cells.

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And one of the ways you could
do that is by giving the

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patient a lot of this protein,
granulocyte colony stimulating

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factor, to stimulate the
output of white blood cells

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from the bone marrow.
 

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Whatever is left of
the bone marrow.

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To rebuild it up.
 

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And so that's done with
wild type, with protein.

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You can make the
wild type protein.

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But if you had something that
was basically the same thing

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but somehow mutated, where you
made a few changes to the amino

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acid sequence, so that it
worked a little bit better,

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then you might have a lot of
people, a lot of patients.

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And that's what the
basis of this paper is,

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that's referenced here.
 

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Is how to go about mutating
this protein here to make it

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a drug that would be more
effective then the

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natural protein.
 

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So that you can give
it to patients.

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So now let me go back and
tell you a little bit about

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this equilibrium that
we're talking about.

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So we've been talking about
equilibria of molecules

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coming together.
 

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And then creating new
molecules, products

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and reactants.
 

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Well, when you're talking about
ligands and receptors on cells,

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the same sort of ideas come in.
 

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The same equilibrium
concepts come in.

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So if you have a cell membrane,
the cell membrane could

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have a receptor in it.
 

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Which is a protein that extends
through the membrane that's got

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some sort of pocket
outside here.

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The membrane keeps on going.
 

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And you've got the blood on the
outside here, or some of the

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tissue of the cells, and you've
got the inside of

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the cell here.
 

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And this receptor is
waiting for some ligand.

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A ligand being another protein
that's floating around.

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That's specific for binding
to this receptor here.

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And there's the ligand here.
 

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There's the receptor here.
 

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And every now and then one of
these ligands will find a

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receptor and bind to it.
 

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So we write, then, in
equilibrium, ligand plus

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receptor goes to complex.
 

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This is happening all
the time on cells.

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There are millions of different
kinds of receptors, millions,

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billions of kinds of ligands.
 

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Wild types, fake ones,
et cetera et cetera.

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When you've got a binding
event, the cell knows that's

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something has bound, usually.
 

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And that usually triggers a
cascade of other things.

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It signals a cell to
do a lot of things.

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So, for instance, in the case
of the GCSF, you've got

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the receptor on the cell.
 

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And you've got this granulocyte
colony signaling factor, that

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would be the ligand here.
 

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It binds to the
receptor on the cell.

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That triggers the bone marrow
cells to produce more proteins,

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which signals more things to
happen, and eventually down the

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way, after a bunch of signaling
processes, out come a burst of

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stem cells or white
blood cells.

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It's a complicated process, and
every step of the way needs

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to be done correctly.
 

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And for this process at least,
this protein here, this ligand

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protein, triggers the cascade.
 

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And so this binding and
unbinding of this protein

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then triggers the whole
sequence of reactions.

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So this equilibrium becomes
super-important then.

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OK.
 

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What else should we review
about, I should have used a

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different board than this, but.
 

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This is going to
get covered up.

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What else can we review
about biology that

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would be interesting?
 

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So, other things that we will
need to remember, need to know,

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is that these receptors on the
surface of the cells

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aren't static.
 

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The cells recycle
their receptors.

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Things can happen.
 

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Things get degraded.
 

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And so the cell is constantly
taking these receptors,

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bringing them back inside the
cell, merging them with

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lysosomes, where the pH is
five, or 5.5 compared to the

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outside of the blood,
which is 7.4.

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It breaks down the proteins
into their amino acids, the

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cell can use the amino acids
again to make more proteins.

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Make better, make
new receptors.

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And that's how the cell
recycles the receptors.

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So basically, you end up with
the cell taking the receptor.

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Making a small sort of
cavity around the receptor.

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So there's the cell
sitting here.

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There's the inside of the cell.
 

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There's the receptor being
engulfed by the cell, now.

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And it could have the
ligand in on it, also.

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There's the ligand
sitting right here.

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It's called an endocytotic
event, or it's endocytosis

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of the receptor.
 

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Into the cell.
 

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And then once you're inside the
cell, let's put the nucleus in

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the middle here, you've got
things called lysosomes

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that are sitting nearby.
 

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There's a lysosome here.
 

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Lysosome.
 

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And there's your little vesicle
that contains your receptor.

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Merges towards the lysosome,
these two get together.

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Then you have your ligand, I'm
running out of colors here.

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So there's the receptor and
the ligand together in here.

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And then the pH here becomes
5.5, things break apart.

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And you've got to find another
receptor, another ligand,

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to continue the process.
 

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Everybody knows this biology,
or you know it well enough.

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OK, good.
 

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So let's go do this example.
 

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So this is the
process of binding.

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We can have a delta G
associated with this,

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delta G a, delta G0 is
minus RT log K sub a.

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This is the association
equilibrium.

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And you can have the reverse
process, where the complex

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gets broken up into
receptor plus a ligand.

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And then you have a delta
G for this process here.

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So this is delta
Ga, let's call it.

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This would be delta GD,
which is the negative

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minus RT log K sub D.
 

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This is the
dissociation process.

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And the dissociation process,
K sub D, is equal to R, the

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concentration of the receptors
times the concentration of

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the ligand, divided by the
concentration of the complex.

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And the lower K sub D is, the
smaller this number is, small

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means that you're mostly on
this side here, mostly in the

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complex, the tighter
the binding.

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So small KD means
tight binding.

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So if I want, in principle,
then, if I want to design a

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drug that's going to signal
this event here, I want K sub D

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for this ligand that I'm going
to design to be very small.

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To be small.
 

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Small enough so that it binds
strongly and does its job, so

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I don't need too much of it.
 

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Now, you need to do experiments
to figure out what's going on.

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So how do you do
these experiments?

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You need to be able to measure,
then these K sub D's,

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experimentally, to see whether
or not what you've designed on

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the computer, when you
calculate delta G's on the

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computer you've got to know
whether or not it's working.

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And this is still an
experimental science.

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How does it work?
 

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So, usually you do the
experiment with the ligand

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concentration very high.
 

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Very large.
 

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So that the concentration of
the ligand at any time is

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basically the same thing
as the concentration of

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the ligand you put in.
 

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So you overwhelm the
system with ligands.

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So that L is much bigger than C
or R, and so it doesn't matter

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which way the equilibrium is.
 

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L stays roughly the same.
 

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So you know, throughout
the experiment, what

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concentration is.
 

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Then you get a bunch of cells.
 

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In a well or something, or a 96
well plate with a bunch of

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different kinds of ligands.
 

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And you can take your ligand,
you can label it radioactively.

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So you can take your ligand
as some long protein.

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And you can take an iodine 125,
let's say, radioactive label on

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your ligand, on your protein.
 

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So you can keep track of it.
 

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The nice thing about
radioactive labels, and which

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is why people use them in
biology or bio-medicine.

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We use them to look at, for
instance, bio-distribution

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of things in animals.
 

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We want to know where things
are, and whether they

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all left the animal, or.
 

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Because you can use a
Geiger counter and you

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00:14:01 --> 00:14:06
can count the events.
 

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The radioactive events.
 

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And that gives you extremely
quantitative analysis

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of where things are.
 

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So you take those
radioactive ligand.

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And, L0, very large
concentration.

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00:14:20 --> 00:14:23
You expose this
concentration to the cells.

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The cells have some
receptors on the surface.

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There's a bunch of receptors
on the surface of the cell.

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And some fraction of the
receptors will have the

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ligand bound to them.
 

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So you incubate.
 

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You let it wait a while.
 

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Then you wash.
 

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So you get rid of all the
excess ligand that's

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00:14:47 --> 00:14:49
on top there.
 

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And then you take your petri
dish, or your 96 well plate.

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And you read the radioactivity
that's coming from the cells.

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And that signal, that
radioactive signals, tells

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you exactly how many ligand
you have on these cells.

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You knew what the cell
concentration was initially.

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So that tells you what the
concentration of ligands,

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00:15:13 --> 00:15:16
of complexes, was.
 

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So this experiment then gets
you, experimentally, gets

255
00:15:22 --> 00:15:27
you the concentration
of C, the complex.

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So now this is
something you know.

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You also know what L0 was,
because that's what you put in.

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And that's enough for you
to find out what KD is.

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And if you know what KD is,
then you know what delta G0 is.

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And so generally, then,
let's go ahead and do that.

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So we know what L is.
 

262
00:15:54 --> 00:16:01
We know what L0, C is, let me
just do it on this board here.

263
00:16:01 --> 00:16:07
So we start with rewriting KD
as equal to R times L divided

264
00:16:07 --> 00:16:09
by the complex concentration.
 

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And now L is basically L0, so
we can use that approximation.

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So we have L0 sitting here.
 

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We still have the complex
on the bottom, and that's

268
00:16:18 --> 00:16:20
something that we've
experimentally discovered.

269
00:16:20 --> 00:16:25
And the concentration of
receptors, this is the

270
00:16:25 --> 00:16:27
concentration of receptors
that don't have

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00:16:27 --> 00:16:28
anything bound to them.
 

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So it's these guys right here.
 

273
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That's equal to the
concentration of receptors, the

274
00:16:36 --> 00:16:41
total number of receptors,
minus those receptors that have

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a ligand bound to them.
 

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Complexes.
 

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Something we can experimentally
define, or discover.

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So we replace this R
here with RT minus C.

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And then we're all
at equilibrium.

280
00:16:57 --> 00:17:01
So we're going to put a little
equilibrium sign under these

281
00:17:01 --> 00:17:05
C's here. equilibrium.
 

282
00:17:05 --> 00:17:10
And then we can rearrange this
equation so that, people like

283
00:17:10 --> 00:17:13
to have plots that are linear,
in a way that is a linear plot,

284
00:17:13 --> 00:17:18
where the slope of the plot
is the inverse of the

285
00:17:18 --> 00:17:22
equilibrium constant.
 

286
00:17:22 --> 00:17:27
So you do some massaging
of this equation here.

287
00:17:27 --> 00:17:32
And you rewrite it in
terms of C over L0.

288
00:17:32 --> 00:17:37
There's the equilibrium
concentration of the complex.

289
00:17:37 --> 00:17:40
This is the total receptor
concentration divided by

290
00:17:40 --> 00:17:44
the equilibrium constant.
 

291
00:17:44 --> 00:17:50
And then you have the
equilibrium concentration of

292
00:17:50 --> 00:17:57
the complex divided by KD.
 

293
00:17:57 --> 00:18:01
And so you plot,
then, this ratio.

294
00:18:01 --> 00:18:04
Which is an experimental ratio.
 

295
00:18:04 --> 00:18:08
You've just measured this
concentration of complexes

296
00:18:08 --> 00:18:11
using this radioactivity,
radioactive labeling

297
00:18:11 --> 00:18:13
experiment.
 

298
00:18:13 --> 00:18:16
You know what this is, because
that's what you've put in.

299
00:18:16 --> 00:18:19
This is your x-axis
on your plot.

300
00:18:19 --> 00:18:20
This is what you've measured.
 

301
00:18:20 --> 00:18:23
And this is going
to be the slope.

302
00:18:23 --> 00:18:31
It's called a Scatchard plot.
 

303
00:18:31 --> 00:18:33
After Mr. Scatchard
 

304
00:18:33 --> 00:18:36
So you get a straight line.
 

305
00:18:36 --> 00:18:41
So we're plotting here
the equilibrium constant

306
00:18:41 --> 00:18:42
of the complex.
 

307
00:18:42 --> 00:18:45
And on this here we're
plotting the ratio of the

308
00:18:45 --> 00:18:48
complex divided by L0.
 

309
00:18:48 --> 00:18:52
And we get this straight
line like this, where the

310
00:18:52 --> 00:19:01
slope is one over KD.
 

311
00:19:01 --> 00:19:07
Minus one over KD.
 

312
00:19:07 --> 00:19:11
OK, now there's a couple of
things that you can look at

313
00:19:11 --> 00:19:15
that are sometimes useful.
 

314
00:19:15 --> 00:19:20
In this analysis here.
 

315
00:19:20 --> 00:19:24
You can also rewrite this
equation up here in terms of

316
00:19:24 --> 00:19:29
the ratio of C equilibrium
divided by RT.

317
00:19:29 --> 00:19:34
So that's basically the ratio
of receptors that have a ligand

318
00:19:34 --> 00:19:37
attached to them, divided by
the total concentration

319
00:19:37 --> 00:19:38
of receptors.
 

320
00:19:38 --> 00:19:45
So if most of the receptors
are empty, then this

321
00:19:45 --> 00:19:46
is a small number.
 

322
00:19:46 --> 00:19:48
Then most of the receptors
are taken up, this is

323
00:19:48 --> 00:19:49
a number close to one.
 

324
00:19:49 --> 00:19:53
It can't be anywhere, it can't
be ever bigger than one,

325
00:19:53 --> 00:19:55
because the biggest number of
complexes you can get is the

326
00:19:55 --> 00:19:56
total number of receptors.
 

327
00:19:56 --> 00:19:59
So if you take this equation
here and you massage it a

328
00:19:59 --> 00:20:10
little bit, one over one
plus KD over L0, and that,

329
00:20:10 --> 00:20:14
you can also plot that.
 

330
00:20:14 --> 00:20:19
As a function of L0.
 

331
00:20:19 --> 00:20:21
How much ligands you put in.
 

332
00:20:21 --> 00:20:25
And you find this is something
that saturates at one.

333
00:20:25 --> 00:20:27
So this ratio here is
going to saturate at one.

334
00:20:27 --> 00:20:34
This is C equilibrium divided
by total number of receptors.

335
00:20:34 --> 00:20:42
And so if L0 is small enough,
if L0 is small enough, meaning

336
00:20:42 --> 00:21:05
that it's smaller than KD, so
if L0 is much smaller then KD,

337
00:21:05 --> 00:21:14
then you can rearrange this
ratio here so that C

338
00:21:14 --> 00:21:24
equilibrium divided by RT
is roughly L0 over KD.

339
00:21:24 --> 00:21:27
So that's linear, with a
slope of one over KD.

340
00:21:27 --> 00:21:33
So this slope here is one
over KD is the slope.

341
00:21:33 --> 00:21:39
And as you get L0 to be quite
large, bigger than KD,

342
00:21:39 --> 00:21:40
then this saturate to one.
 

343
00:21:40 --> 00:21:42
And this one over something
very large become zero, and

344
00:21:42 --> 00:21:46
basically you end up with
something close to one.

345
00:21:46 --> 00:21:49
So you saturate at one.
 

346
00:21:49 --> 00:21:51
So that's another
way of doing this.

347
00:21:51 --> 00:21:54
But this is really what we
want to concentrate here.

348
00:21:54 --> 00:21:56
The fact that you can get
KD out of experimentally.

349
00:21:56 --> 00:22:00
If you have KD,
you have delta G.

350
00:22:00 --> 00:22:05
So now let's go back and think
about this whole process here.

351
00:22:05 --> 00:22:09
If we're going to design
this drug here, this

352
00:22:09 --> 00:22:11
protein, artificially.
 

353
00:22:11 --> 00:22:13
So what you want, then, is you
want something that's going to

354
00:22:13 --> 00:22:18
bind strongly enough to
receptor, to stimulate the

355
00:22:18 --> 00:22:24
growth of granulocytes, or the
colony of granulocytes.

356
00:22:24 --> 00:22:29
But when the cell decides to
recycle the receptor, and

357
00:22:29 --> 00:22:34
destroy it, chew it apart in
the lysosome here, you want

358
00:22:34 --> 00:22:38
this drug not to be degraded.
 

359
00:22:38 --> 00:22:41
Because you have to keep
injecting in the patient.

360
00:22:41 --> 00:22:45
So you want this drug to
release from the receptor,

361
00:22:45 --> 00:22:48
before the lysosome has
a chance to chew it up.

362
00:22:48 --> 00:22:53
So that means that you want the
equilibrium constant at pH 5.5,

363
00:22:53 --> 00:22:58
you want that KD, to be
much larger at 5.5 than

364
00:22:58 --> 00:23:00
you want it at 7.4.
 

365
00:23:00 --> 00:23:02
You want strong binding
at pH 7.4, and you want

366
00:23:02 --> 00:23:05
weak binding at pH 5.5.
 

367
00:23:05 --> 00:23:07
That way the drug
gets recycled.

368
00:23:07 --> 00:23:11
Can find, go back to the blood.
 

369
00:23:11 --> 00:23:14
Bind to a receptor again,
generate the signaling events,

370
00:23:14 --> 00:23:16
gets engulfed by the cell.
 

371
00:23:16 --> 00:23:19
Releases before it has a chance
to be chewed up, et cetera.

372
00:23:19 --> 00:23:22
The wild type will
get chewed up.

373
00:23:22 --> 00:23:25
The regular kind will get
chewed up, and so that's why

374
00:23:25 --> 00:23:27
you have to, with the patients
you have to keep giving them

375
00:23:27 --> 00:23:28
this drug over and over again.
 

376
00:23:28 --> 00:23:34
Because it gets chewed
up by the cells.

377
00:23:34 --> 00:23:38
So that's the design principle
that these authors had in mind

378
00:23:38 --> 00:23:39
when they started their study.
 

379
00:23:39 --> 00:23:42
And so they knew what the
sequence, what the amino acid

380
00:23:42 --> 00:23:46
sequence was, for this wild
type drug, and they started

381
00:23:46 --> 00:23:48
doing point mutations.
 

382
00:23:48 --> 00:23:50
Changing one amino
acid here and there.

383
00:23:50 --> 00:23:54
And cranking out the
calculation to calculate

384
00:23:54 --> 00:23:59
delta G for binding of this
protein to this receptor.

385
00:23:59 --> 00:24:03
At pH 7.4 and at pH 5.5,
and getting differences

386
00:24:03 --> 00:24:05
of delta G's.
 

387
00:24:05 --> 00:24:11
So let me then review again.
 

388
00:24:11 --> 00:24:19
What is the motivation here.
 

389
00:24:19 --> 00:24:27
The motivation is to try to get
the ratio, KD, at 5.5 divided

390
00:24:27 --> 00:24:33
by KD at 7.4, pH 7.4.
 

391
00:24:33 --> 00:24:35
And you want this to
be bigger than one.

392
00:24:35 --> 00:24:39
And really as large
as possible.

393
00:24:39 --> 00:24:42
As possible, of course
within limits.

394
00:24:42 --> 00:24:47
So we want this to
bind at pH 7.4.

395
00:24:47 --> 00:24:57
And the point of comparison
is the wild types.

396
00:24:57 --> 00:25:07
Which at 7.4 has a
KD of 270, roughly.

397
00:25:07 --> 00:25:18
And at the ratio of pH
5.5 to 7.4 is 1.7.

398
00:25:18 --> 00:25:23
So it's a little bit
weaker binding at 5.5.

399
00:25:23 --> 00:25:27
Remember, large KD
means weak binding.

400
00:25:27 --> 00:25:30
Small KD means tight binding.
 

401
00:25:30 --> 00:25:34
So the fact that KD at 5.5 is
bigger than KD at 7.5 means

402
00:25:34 --> 00:25:38
that it's slightly weaker
binding at 5.5 than 7.4.

403
00:25:38 --> 00:25:44
So this is sort of the baseline
that the protein designers

404
00:25:44 --> 00:25:46
had to deal with.
 

405
00:25:46 --> 00:25:53
So everything gets compared
to this guy here.

406
00:25:53 --> 00:25:58
So they don't actually, in
a calculation they don't

407
00:25:58 --> 00:26:00
actually measure this.
 

408
00:26:00 --> 00:26:03
What they do is, they
look at delta G's.

409
00:26:03 --> 00:26:09
And so they look at
differences of delta G's.

410
00:26:09 --> 00:26:16
They look at delta G0
for the binding of the

411
00:26:16 --> 00:26:18
ligand to the receptor.
 

412
00:26:18 --> 00:26:28
At 7.4 minus the delta G0
at pH 5.5, let's call

413
00:26:28 --> 00:26:34
this the delta delta G.
 

414
00:26:34 --> 00:26:39
Since you know delta G is minus
RT log K, this is equal to

415
00:26:39 --> 00:26:50
minus the delta of log KD,
which is log KD at 5.5

416
00:26:50 --> 00:26:54
divided by KD at 7.4.
 

417
00:26:54 --> 00:26:58
So then they calculate delta
delta G0, and it's completely

418
00:26:58 --> 00:27:04
related to this ratio that you
measured in the experiment.

419
00:27:04 --> 00:27:12
And so for the wild type, this
delta delta G is just basically

420
00:27:12 --> 00:27:15
the log of this number here.
 

421
00:27:15 --> 00:27:23
Is 0.53.
 

422
00:27:23 --> 00:27:26
So now they go ahead and
do their calculation.

423
00:27:26 --> 00:27:31
And they find a
couple of mutants.

424
00:27:31 --> 00:27:34
Where, and they focus
on delta delta G0.

425
00:27:34 --> 00:27:37
They want something
that's bigger than this.

426
00:27:37 --> 00:27:40
And then they'll worry about
whether there are more

427
00:27:40 --> 00:27:42
problems associated with it.
 

428
00:27:42 --> 00:27:47
So they found two
mutants, let's call

429
00:27:47 --> 00:27:58
them D110H and D113H.
 

430
00:27:58 --> 00:28:02
I think it's a histidine
mutation, point mutation at

431
00:28:02 --> 00:28:10
110 and 113, where this
ratio here was 8.3 and 17.

432
00:28:10 --> 00:28:14
So, huge differences here.
 

433
00:28:14 --> 00:28:19
More than a factor of 20, or
factor of 30 difference in

434
00:28:19 --> 00:28:22
the change in the binding
efficiency, at least according

435
00:28:22 --> 00:28:27
to these delta delta G,
between pH 5.5 and pH 7.4.

436
00:28:27 --> 00:28:31
That means that this protein
here with the point mutation,

437
00:28:31 --> 00:28:35
with this one point mutation,
if it binds as strongly to the

438
00:28:35 --> 00:28:40
receptor on the surface, as
soon as the lysosome comes in

439
00:28:40 --> 00:28:46
and begins to decrease pH
inside the cell, this ligand

440
00:28:46 --> 00:28:48
is going to come off.
 

441
00:28:48 --> 00:28:49
And it's going to be
able to float away.

442
00:28:49 --> 00:28:52
Hopefully, before
it gets recycled.

443
00:28:52 --> 00:28:53
Before it gets chewed up.
 

444
00:28:53 --> 00:28:57
So they can be used again.
 

445
00:28:57 --> 00:29:03
So that's good.
 

446
00:29:03 --> 00:29:07
Alright, so experiments
were done.

447
00:29:07 --> 00:29:17
And on the D110H and the D113H.
 

448
00:29:17 --> 00:29:19
And KD was measured
in the experiments.

449
00:29:19 --> 00:29:22
And remember, the
wild type is 270.

450
00:29:22 --> 00:29:24
The KD's were at 370.
 

451
00:29:24 --> 00:29:28
And 320, with some error bar.
 

452
00:29:28 --> 00:29:33
And the different, the ratios
of KD's were measured.

453
00:29:33 --> 00:29:37
There were 4.4 and 6.8.
 

454
00:29:37 --> 00:29:39
With some error bars.
 

455
00:29:39 --> 00:29:42
And they turned out to match
reasonably well, at least as

456
00:29:42 --> 00:29:46
far as comparison between
experiment and theory.

457
00:29:46 --> 00:29:49
Still not great for these sorts
of calculations, because

458
00:29:49 --> 00:29:50
pretty involved.
 

459
00:29:50 --> 00:29:52
A lot of approximations
go on in there.

460
00:29:52 --> 00:29:56
And it gives you a rough guide.
 

461
00:29:56 --> 00:29:59
So this 17 here is probably not
quite right, because it doesn't

462
00:29:59 --> 00:30:01
quite translate to 6.8 here.
 

463
00:30:01 --> 00:30:04
This difference between 4.4 and
6.8 doesn't quite match the

464
00:30:04 --> 00:30:06
difference that they saw
here in the calculation.

465
00:30:06 --> 00:30:07
But it's the right
direction, right?

466
00:30:07 --> 00:30:11
And that's why you still
need to do experiments.

467
00:30:11 --> 00:30:13
This number here is a
little bit bigger than

468
00:30:13 --> 00:30:13
this number here.
 

469
00:30:13 --> 00:30:19
Which means that these don't
bind quite as strongly.

470
00:30:19 --> 00:30:21
Because KD small means
strong binding.

471
00:30:21 --> 00:30:24
That means the initial binding
of the ligand to the receptor

472
00:30:24 --> 00:30:28
is not quite as good
as the wild type.

473
00:30:28 --> 00:30:29
That's often the case.
 

474
00:30:29 --> 00:30:31
It's often not so easy to find
something that binds as

475
00:30:31 --> 00:30:34
strongly as the wild version.
 

476
00:30:34 --> 00:30:36
But, it's good enough.
 

477
00:30:36 --> 00:30:39
And this ratio is really
what clamps the deal

478
00:30:39 --> 00:30:43
in this case here.
 

479
00:30:43 --> 00:30:59
And so, these mutants, in fact
these two mutants are the ones

480
00:30:59 --> 00:31:03
that have undergone
the animal trials.

481
00:31:03 --> 00:31:06
That are in the pipeline.
 

482
00:31:06 --> 00:31:07
This is a big deal.
 

483
00:31:07 --> 00:31:12
This is a big deal because
this is a huge, huge market.

484
00:31:12 --> 00:31:15
There's a very large number
of people that are affected

485
00:31:15 --> 00:31:16
with chemotherapy.
 

486
00:31:16 --> 00:31:21
And this is basic
thermodynamics here.

487
00:31:21 --> 00:31:24
It's basic thermodynamic
calculation of a complicated

488
00:31:24 --> 00:31:32
molecule with some fairly
simple equilibrium concepts.

489
00:31:32 --> 00:31:47
OK, any questions on this?
 

490
00:31:47 --> 00:31:53
OK, well, we're ended
really early today.

491
00:31:53 --> 00:31:57
I don't have the phase
transition ready, but Keith

492
00:31:57 --> 00:31:59
Nelson is going to start a
phase transition next time.

493
00:31:59 --> 00:32:00