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So, my name is
Anastasia [INAUDIBLE].

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I'd say that I'm
here in Mount Holyoke

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College during the summer
and I worked with professor

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[INAUDIBLE] in his lab.

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And before I came
to Mount Holyoke,

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I was assuming that
everybody who is [INAUDIBLE].

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But his lab actually uses
computational methods

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to study protein.

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So, just a little
bit of background--

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I'm sure that most of you
know how protein is, like,

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primary structure and it's
like [INAUDIBLE] assets.

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And you can see, like,
alpha carbon-- like,

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alpha carbon and [INAUDIBLE]
[? peptide ?] chain

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that's-- and this side
chain is very important

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in the [INAUDIBLE] played
a big role in the secondary

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structure.

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So this will be
alpha [INAUDIBLE]

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and then its tertiary
structure [INAUDIBLE] even more

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packed protein.

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So we used the coarse grain
[INAUDIBLE] of our [INAUDIBLE]

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and we would ask, why
wouldn't you use, like,

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[? quantum ?] or [? atom ?]
simulations which will be more

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precise and detailed and maybe
provide even more accurate

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information?

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But the thing is,
even though it's 2016,

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we still don't
have enough power,

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[? computational ?]
power, to do them.

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Or we-- I shouldn't say that.

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We can do that, but it will
take over a year, which

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we don't want to wait
that long to get results.

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So, many people use [INAUDIBLE]
model, which provides

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more smooth energy landscape.

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But you still can see all
[? the necessary ?] information

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needed, [? like minima. ?] It's
where [INAUDIBLE] protein will

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fold into more energy and
then perform alpha helices

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and [INAUDIBLE].

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So, we started the
summer with a new model.

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It was [INAUDIBLE]
tested this summer.

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And essentially,
the test involved--

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I was just putting the sequence
of the protein and running it.

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And we hope to see
the structure, how

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it's supposed to be compared
to the [INAUDIBLE] protein

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that-- PTB protein
that [INAUDIBLE].

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And then if it worked, we
were using this test protein

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and put it into the
crowded environment.

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And this picture, it's
not how we will do that.

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But it's just giving
you the idea of how,

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like, selection looks like.

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There's a lot of
proteins floating around

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and they all influence on the
[? stabilities ?] of protein.

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So we actually,
as a whole, for us

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is to do several
rounds of the testings.

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And first of all,
[INAUDIBLE] different sizes

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to see how they influence
on the product stability.

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And the second one
will be [INAUDIBLE]

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and small alpha helices.

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So I tested over 100
proteins this summer

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and unfortunately, very
few actually worked.

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That was, like, four of
them that actually worked

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and they [INAUDIBLE]
matching what they're

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supposed to [INAUDIBLE] like.

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But majority of
them didn't work.

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That was, like, how proteins
supposed to be looking like.

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And that's what we actually get.

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Like, one [INAUDIBLE] is kind of
like looking somewhat similar.

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Like, still [INAUDIBLE]
system didn't form where

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they're supposed to be forming.

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So we were not satisfied
with this model.

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So now, we're actually
using the different model

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that is more commonly
used in the other labs.

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So we can actually do
the further testing

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and be more confident about
interpreting the data from that

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rather than using models
that doesn't give us

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even the secondary
structures that proteins are

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supposed to be looking like.

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Yeah, and that's where
we are right now.

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And I want to say thank you
to Professor [INAUDIBLE],

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who took me into his lab and
taught me a lot of coding tips,

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a lot of stuff that did not
apply to my major as much.

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I don't know, like, now I'm
thinking-- taking some computer

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science courses as well.

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And thank you to my [INAUDIBLE]
and also [INAUDIBLE]

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science foundation [INAUDIBLE].

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