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[Music]

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hello everyone it's the last talk we're

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almost done I'll try to keep it fun I'm

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going to tell you about de novo proteins

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and how they bind transition metals and

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the way I'm going to do that is I'm

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going to talk to you about what makes

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all of us come together our common

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ancestry how proteins run can tell us a

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little bit about how we might be

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different get into the actual bulk of

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the science and conclude with some

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perspective for the future

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so I know we just met but I'd like you

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to meet my family these are my ancestors

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from a couple hundred well maybe a

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hundred years ago and what makes them my

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ancestors is that we share DNA

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and we share DNA that means we share a

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gym type and because the DNA makes us

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who we are we share a phenotype so you

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look into my eyes and their eyes see the

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same neuroses not only do I have

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ancestors but we all have ancestors and

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our ancestors as humans were related to

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other primates ancestors so we are all

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sort of connected and if we go a little

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further Charles Darwin says not only are

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all of us connected evolutionarily to

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other primates but you know all

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eukaryotes are connected to our ka are

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connected to bacteria because there was

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this one last Universal common ancestor

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we're all connected so that's common

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ancestry and where this might become a

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problem is when we try to look at the

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tips of the tree branches and understand

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how life works because all those three

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branches trace back to a common stem all

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right and we can imagine that if the

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Luca looked different then we would all

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look different all the bent branches of

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the tree would look different so

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anything that we conclude by looking at

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the recipe has the fingerprints of this

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common ancestry in it and that's not an

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easy thing to solve because

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in considering astrobiology we want to

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consider all possibilities of life and

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that's sort of hampering our

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understanding of all possibilities so

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how would we go about getting over a

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common ancestry well we've consider

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alternative ancestry and you know if we

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found another plant with life that

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that'd be it we'd be done but we haven't

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done that yet so the proposition is we

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can use a de novo genotype to find a de

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novo to create the de novo phenotype

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with new properties or maybe the same

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properties maybe life is sort of

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constrained in some ways either way

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would be informative

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that's where protein design comes in we

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want to create a de novo phenotype from

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a Genova genotype de novo meaning from

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scratch something completely new not

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related to what came before and when

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considering protein design we could

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consider a sequence of all amino acids

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you know every possibility and what

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you'd end up with is a bunch of

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insoluble aggregates and it's hard to

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study those so we're going to bias the

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system by asking first that it form

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secondary and tertiary structures so

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that it's sort of tractable to work with

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and the way we're going to accomplish

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that is with binary patterning so binary

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patterning is we break all our amino

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acids into those that are polar and

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those that are non poor those that are

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nonpolar want to come together in an

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aqueous environment so I've coated the

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nonpolar ones here and the hydrophobic

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effect drives all those how those

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hydrophobic residues into the core of a

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protein so if this binary pattern we

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haven't looked at the specific residues

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we can get this constant secondary and

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tertiary structure and because we've

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broken them down into you know generic

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properties of polarity we get enormous

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diversity because this could be you know

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any nonpolar amino acid so this could be

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any polar amino acid enormous diversity

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when we actually make it we're not going

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to achieve that enormous diversity it's

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something like more than all the

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molecules in the universe but a

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tractable number is a million may be

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nice to look at a million different

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genes that aren't affected by common

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ancestry so that's what we do we express

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it in e.coli and they have no

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small limitation because we broke it

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down into polar and nonpolar and we want

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to do this in genetically driven way eco

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lies make our proteins for us we limited

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our selection of amino acids to those

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that were kind of easy so if the middle

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position of the three nuclear bases that

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code for an amino acid is T it'll be

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nonpolar and if it's a it will be polar

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so NTN that triplet gives us any of five

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nonpolar amino acids and then and gives

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us any of six polar amino acids that's

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sort of an easy way to build this

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diversity so we build a library there's

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a million of them what can they do well

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I'm not going to be telling you about

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this my labs done this but I think we

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can replace essential genes I'll say

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that again we can replace the central

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genes we can take a binary pattern

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design protein with no common ancestry

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put it into an e coli that's going to

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die and it lives that's kind of weird

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we can also gene expression levels

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there's some interaction with RNA there

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and we can evolve this sort of early

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functional protein into something that's

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more functional and better this sort of

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system is based on amino acids alone and

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that's not the scope of all protein

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function that we find in nature and

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that's not the scope of all protein

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function that's interesting so the idea

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that I had was we're gonna add metals

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maybe it'll bind the metals maybe

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they'll give it an additional function

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and a side benefit of this is if we

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started with ten to the six a million

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and we added two metals we went you know

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two times ten to the six we're getting

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additional diversity and the reason

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those are interesting is because they

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perform a lot of key functions and life

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as we know it so some examples are

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oxidation and reduction small molecule

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binding sort of biologically important

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functions and it's not just biologically

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important functions that we find in

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modern organisms if we trace back that

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Luca the last Universal common ancestor

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that we all share iron was essential for

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some of the functions that it was doing

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so if we were

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operating medals and asking random or

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sort of semi random sequences to do

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those functions then those are important

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functions that we'd like to see

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recapitulated without that common

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ancestry bias so there's steps to get to

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that big answer and the big question we

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have to answer is can we take this

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binary pattern sequence add medals and

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then hopefully get function afterwards

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right but there's a prerequisite step

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that they stick to the medals so to get

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at that question we're just going to

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pick 52 of them express them and see if

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they bind some representative medals and

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if they do then we'd like to know how

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that works sort of characterization I'll

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mention at this point that this is all

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published it's under the same title as

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this talk so if you're interested you

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can read further but this is we'll start

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out with the qualitative screen of 52

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and again what we're looking for is does

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the protein stick to a metal very simple

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questions so we're going to immobilize

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the metal we're gonna add the protein

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and if they stick we should be able to

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detect it so this is the important part

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of a protein gel so if you see the

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protein there that means we were able to

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detected in some part of the sample so

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we had it hopefully it sticks and this

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is how much protein got added so there

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was a bunch of protein added the thicker

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the band the more protein we can wash

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away all the stuff that didn't stick you

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know all the endogenous proteins those

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sorts of things and it didn't come off

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didn't come off that's good because when

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we eventually wash it off I mean we rate

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it by the percent retained we can see

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that this protein that I added here in

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this example stuck very well to the

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beads so we washed off all the non

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binding stuff and this was specifically

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balanced there was some interaction of

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our protein with a metal and that's

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promising for looking for later metal

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dependent functionality so we did this

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with 52 different things and to our

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surprise a lot of them stuck in fact

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almost all of them stuck we wanted to do

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that

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sticking quantitatively though so it

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wasn't just you know

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it stick to a bead yes or no wanted that

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to be quantitative so we did this with

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equilibrium dialysis so these hexa our

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Pentagon's

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are the protein and there's some free

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metal floating in solution and there's a

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membrane in between the two that the

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metal can transfer through but the

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protein can't so we can take some from

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this side measure the free metal and add

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more metal and that will diffuse through

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and bind to the protein and if we keep

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iterating that over and over again and

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measuring the free metal we can

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interpolate how much was you know we

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could pull out because it wound up stuck

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to the protein so we can do this

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quantitatively and I'll just sort of

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summarize the results from all these

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studies so what we saw was many possible

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binding sites it wasn't just one metal

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per protein like you might find in

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evolved systems we saw many possible

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binding sites and many of them ended up

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being used and that comes down to a

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binary patterning approach there's two

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residues in the binary patterning

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approach that can bind metals they're

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histidine and carboxylic acid containing

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residues both of which combine those

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with different affinities and there is a

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bunch of ways that this could work so

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they could have it on one helix sort of

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over here and one he looks over here and

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between the two there's a binding site

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it could all be shared on long helix it

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could be on the turns there's a lot of

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different places this could bind and we

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ended up seeing a lot of different

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binding sites the screening results I

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said a bunch of them in fact almost all

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of them stuck so here's that on sort of

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a general look so what we want to look

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at is over here the protein of interest

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it's around ten kilotons so this is a

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protein it's called us at twenty four we

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add this much protein we wash away

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everything that doesn't bind and then we

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have see what bound to zinc and so

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there's something that bounces neck we

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cut that histidine the number of

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histidines honest at twenty four and a

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half and a lot of them stop binding to

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the metal so the hissings

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were as we expected important for the

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binding and if we get rid of all the

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histidines

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then not

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sticks um I said there was 52 so this is

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the 11th one sort of showing that it's

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representative many of them bind the

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20th one also sticks and an interesting

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comparison is these are all the protein

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of interest but you can take this bead

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experiment and look at the whole

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proteome that you can see on a gel of

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e.coli and so the whole all the coli

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proteins that we add are there you know

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there's thicker band's thinner bands but

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it's a lot of proteins if you do the

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same see what sticks to metal and see

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what comes off not much sticks to metal

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so there's something different between

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our proteins where it's abundant and

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evolved proteins where this sort of

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sticking isn't we did the equilibrium

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dialysis we quantify the affinity it was

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between 100 and animal or 3 micro molar

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if that means anything to you

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stoichiometry I said there's multiple

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possible binding sites in there are

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multiple actual binding sites so it's 1

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to 4 binding sites per protein so this

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one will bind you to equivalents of

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cobalt that will bind four equivalents

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00:12:17,070 --> 00:12:14,620
of zinc and more interesting is it's

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specific so a single protein might bind

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one equivalent of cobalt but more

302
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equivalents of zinc suggesting that some

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of these unselected undesigned binding

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sites are being are preferentially

305
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binding one metal and not another so

306
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there's some specificity going on here

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and in summary because we're building

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towards you know the function metal

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binding is ubiquitous it's weak it

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occurs in multiple locations in our

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hands in this in this library and

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because we don't see that same

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stickiness in modern proteins and might

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suggest that they evolved away from this

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sort of permissive binding but that's

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sort of speculative hard to prove so if

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natural systems great proteins that have

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functions that make cats and dogs

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the alternative seems less specific

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bindings everywhere and hard to say what

321
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it'll do and that's sort of what I'd

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like to segue into this is sort of

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tentative

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publish stuff but if there's an

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important function in life it might be

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the use of ATP right because ATP is the

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currency of the cell and we've been

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hearing RNAi talks ATP hydrolysis is

329
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important reaction and metalloproteins

330
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can be used to do this and tentatively

331
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very tentatively one of the

332
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metalloproteins

333
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seems to hydrolyze ATP and do it better

334
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in the presence of magnesium so

335
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tentatively this might be a result of

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the middle button future directions so

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can we create useful functional

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metalloproteins without ancestry can we

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evolve them to be comparable to the

340
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things we see today can we see how

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they're different from the things we see

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today because if they're different but

343
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to let's say the same function that

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would highlight this difference between

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common ancestry and alternative ancestry

346
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and sort of a question I'd like to open

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00:14:18,590 --> 00:14:16,770
up to everyone here is there's a lot of

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reactions that I don't realize are

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important but I'm sure everyone here is

350
00:14:24,230 --> 00:14:21,980
more knowledgeable and so if you have

351
00:14:27,680 --> 00:14:24,240
you know the reaction that you're

352
00:14:33,650 --> 00:14:27,690
feeling patriotic about let me know and

353
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I'll convert to your country can't end

354
00:14:38,900 --> 00:14:36,030
without acknowledging the work the

355
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fellow members of the heck lab and thank

356
00:15:00,920 --> 00:14:57,970
awesome questions for Mike hey Great Auk

357
00:15:03,650 --> 00:15:00,930
two things what are the lengths of the

358
00:15:05,390 --> 00:15:03,660
proteins that you were using a hundred

359
00:15:08,840 --> 00:15:05,400
and two amino acids okay so that's not

360
00:15:12,140 --> 00:15:08,850
very long in terms of the proteins that

361
00:15:16,160 --> 00:15:12,150
we have in ourselves today yeah it's

362
00:15:18,670 --> 00:15:16,170
sort of it's sort of the the minimum to

363
00:15:21,050 --> 00:15:18,680
be a protein and not sort of a peptide

364
00:15:24,320 --> 00:15:21,060
there's debate about Erica's insulins of

365
00:15:26,690 --> 00:15:24,330
protein but yeah they're small and the

366
00:15:30,080 --> 00:15:26,700
other thing is I was wondering what is

367
00:15:33,380 --> 00:15:30,090
your hypothesis on why the proteins that

368
00:15:36,170 --> 00:15:33,390
are natural to eco like what they didn't

369
00:15:38,150 --> 00:15:36,180
bind to the metals like I have my theory

370
00:15:41,090 --> 00:15:38,160
but I'm wondering what you think

371
00:15:43,580 --> 00:15:41,100
I'd like to hear your theory but my

372
00:15:50,300 --> 00:15:43,590
hypothesis is sort of the reverse

373
00:15:51,920 --> 00:15:50,310
pressure metal concentrations inside of

374
00:15:54,950 --> 00:15:51,930
the cell are tightly regulated right you

375
00:15:56,660 --> 00:15:54,960
want only the chemistry that you want to

376
00:15:59,720 --> 00:15:56,670
have happen to happen and if there is

377
00:16:04,370 --> 00:15:59,730
some metal binding site that is on the

378
00:16:06,680 --> 00:16:04,380
surface a it might mediate a protein

379
00:16:08,180 --> 00:16:06,690
protein interaction and so two things

380
00:16:11,420 --> 00:16:08,190
with that surface binding I'll stick

381
00:16:14,240 --> 00:16:11,430
together and be it might just mess with

382
00:16:17,920 --> 00:16:14,250
your whole regulation system but we can

383
00:16:25,900 --> 00:16:22,640
hi great oh so we heard that the sizes

384
00:16:30,830 --> 00:16:25,910
are around a hundred and that you get

385
00:16:33,830 --> 00:16:30,840
multiple binding sites per protein so on

386
00:16:38,060 --> 00:16:33,840
one is attainable for you to make them

387
00:16:43,250 --> 00:16:38,070
smaller so that you could get one

388
00:16:46,760 --> 00:16:43,260
binding site per protein it is doable to

389
00:16:48,470 --> 00:16:46,770
make them smaller they become more

390
00:16:50,900 --> 00:16:48,480
disordered the smaller the hydrophobic

391
00:16:54,920 --> 00:16:50,910
chorus and so getting structures on

392
00:16:55,550 --> 00:16:54,930
smaller versions is not something that

393
00:17:06,310 --> 00:16:55,560
we've been

394
00:17:12,220 --> 00:17:09,100
um just to clarify something you started

395
00:17:15,780 --> 00:17:12,230
from talking about how protein design is

396
00:17:19,870 --> 00:17:15,790
at such a point that it can even replace

397
00:17:22,450 --> 00:17:19,880
critical functional proteins and things

398
00:17:24,430 --> 00:17:22,460
like e.coli mmm at the same time about

399
00:17:27,220 --> 00:17:24,440
metalloproteins you know you ended with

400
00:17:29,110 --> 00:17:27,230
but saying that perhaps we can make

401
00:17:31,750 --> 00:17:29,120
something that would have this this

402
00:17:36,310 --> 00:17:31,760
critical function and so is it the case

403
00:17:38,290 --> 00:17:36,320
that it is harder to make Nutella

404
00:17:40,240 --> 00:17:38,300
proteins that would be functional and

405
00:17:41,350 --> 00:17:40,250
the the things that you had in mind at

406
00:17:43,450 --> 00:17:41,360
the beginning just didn't have these

407
00:17:47,220 --> 00:17:43,460
metal cofactors or what's the difference

408
00:17:49,330 --> 00:17:47,230
I think because we haven't specified

409
00:17:51,970 --> 00:17:49,340
from the start that these are designed

410
00:17:54,040 --> 00:17:51,980
to bind a certain metal that in the cell

411
00:17:56,740 --> 00:17:54,050
it's sort of a toss-up if Li well unless

412
00:18:00,820 --> 00:17:56,750
we go that further design step inside

413
00:18:02,920 --> 00:18:00,830
the cell whereas what we did design for

414
00:18:06,850 --> 00:18:02,930
which was just structure was enough to

415
00:18:08,350 --> 00:18:06,860
do certain other functions so improving

416
00:18:11,710 --> 00:18:08,360
the metal binding will make it easier to

417
00:18:15,990 --> 00:18:11,720
then do the next step of metal

418
00:18:21,010 --> 00:18:16,000
dependence in vivo function thank you

419
00:18:22,660 --> 00:18:21,020
great uh any last burning questions all