I WANT TO WELCOME YOU TO THE
WEDNESDAY AFTERNOON LECTURE
SERIES.
I'M A GRADUATE STUDENT AT
GEORGETOWN UNIVERSITY AND I'M
DOING MY RESEARCH HERE AT THE
NATIONAL HUMAN GENOME RESEARCH
INSTITUTE.
AND YEAH, I'M REALLY EXCITED
TODAY TO BE INTRODUCING
DR. DAVID BAKER, WHO'S HERE WITH
US TODAY.
DAVID GOT HIS START WITH A
PH.D. IN BIOCHEMISTRY WORKING
WITH RANDY SHEKMAN AT BERKELEY
AND HE IS NOW PROFESSOR AT THE
UNIVERSITY OF WASHINGTON, HOWARD
HUGHES MEDICAL INVESTIGATOR, AND
INSTITUTE FOR PROTEIN DESIGN.
IN 2021, WELL, MAYBE I SHOULD
JUST SAY LIKE 2021 WAS A HUGE
YEAR FOR THE FIELD AT LARGE BUT
ESPECIALLY FOR THE BAKER GROUP
WITH THE DEVELOPMENT AND RELEASE
OF ROSETAFOLD AND TOGETHER
COMPLETELY TRANSFORMED THE FIELD
WHICH HAPPENS I THINK PRETTY
RARELY, BUT YEAH.
IT WAS PRETTY EXCITING.
IT GIVES ME GOOSE BUMPS.
IT WAS RECOGNIZED, THE TWO WERE
RECOGNIZED FOR THE BREAKTHROUGH
OF THE YEAR IN 2021 BY THE
JOURNAL SCIENCE.
DAVID'S SCIENTIFIC METRICS ARE
COMPLETELY OFF THE CHARTS,
WHETHER YOU LOOK AT
PUBLICATIONS, COMPANIES FOUNDED,
ALL THESE NUMBERS, THEY'RE JUST
HUGE NUMBERS AND I DON'T WANT TO
DOWNPLAY THAT BUT WHAT I WANT TO
EMPHASIZE MAYBE IS THE
DEDICATION HE HAS TO THE
WELL-BEING OF HIS TRAINEES,
WHICH IS A SMALL ARMY.
THE REASON I WANT TO EMPHASIZE
THIS IS THESE ARE THE FOLKS THAT
ARE GOING TO CONTINUE TO
REVOLUTIONIZE THE FIELD FOR
DECADES TO COME IN WAYS WE CAN'T
EVEN IMAGINE.
AND THE LAST THING I WANT TO
TOUCH ON HERE IS, IT'S REALLY A
SPECIAL HONOR FOR ME TO BE
INTRODUCING DAVID TODAY.
HE'S HAD A PRETTY -- EXCUSE ME.
HE'S HAD A SIGNIFICANT IMPACT ON
MY CAREER AT LEAST AS FAR AS
INSPIRING ME TO GET INVOLVED IN
BIOMEDICAL RESEARCH.
WHO DOESN'T WANT TO MAKE
PROTEINS?
IT'S PRETTY ENTICING.
ALSO I WANT TO MENTION TOO,
DAVID IS NOT AFRAID TO THINK
BIG.
HE'S BEEN DEDICATING HIS CAREER
TO A PRETTY DARN DIFFICULT
PROBLEM THAT A LOT OF FOLKS
DIDN'T THINK COULD BE ACHIEVED
IN THIS LIFETIME, AND SO I
REALLY ADMIRE BOTH HIS
PERSISTENCE AND HIS ABILITY TO
APPLY NEW NOVEL METHODS TO TOUGH
PROBLEMS.
SO WITH THAT SAID, THANK YOU,
DAVID, FOR BEING HERE IN PERSON
TODAY.
I'M REALLY EXCITED FOR YOUR TALK
TITLED "THE COMING OF AGE
OF DE NOVO
>>TODAY I WANT TO TELL ABOUT DE
NOVO PROTEIN DESIGN, SO AS YOU
KNOW, PROTEINS AND NATURE CARRY
OUT A REALLY WIDE RANGE OF
IMPORTANT FUNCTIONS AND DE NOVO
DEVINE IS BASICALLY THE PROBLEM
OF MAKING BRAND NEW PROTEINS
WITH NEW FUNCTIONS TO SOLVE
PROBLEMS THAT WEREN'T REALLY
AROUND DURING NATURAL EVOLUTION,
WHEN ALL THE NATURAL PROTEINS
CAME ABOUT.
AND SO FOR MANY YEARS, WE
APPROACH THIS PROBLEM SORT OF
USING A PHYSICALLY-BASED MODEL
WHERE BASED ON THE IDEA THAT
PROTEINS FOLD TO THEIR LOWEST
ENERGY STATES.
SO IF WE WANT TO DESIGN A
PROTEIN FOLD TO A GIVEN
STRUCTURE AND HAVE A GIVEN
FUNCTION, WE SEARCHED FOR AN
AMINO ACID SEQUENCE WHOSE LOWEST
ENERGY STATE WAS THAT STRUCTURE.
NOW MORE RECENTLY, AS YOU'RE ALL
AWARE, DEEP LEARNING HAS BECOME
INCREASINGLY POWERFUL, AND SO
NOW WE CAN USE DEEP LEARNING
METHODS TO DESIGN PROTEINS NOT
USING SORT OF A PHYSICALLY BASED
MODEL BUT USING A DEEP NEURAL
NETWORK.
AND I'LL TALK ABOUT THAT IN THE
SECOND PART OF MY TALK.
I WANT TO SORT OF -- THERE'S
ALSO A THEME OF PANDEMIC
PREPAREDNESS THAT RUNS THROUGH
MY TALK, SO WE'VE BEEN DESIGNING
BOTH VACCINES AND ANTIVIRALS,
AND IN THE FIRST PART OF MY
TALK, I'LL SHOW YOU HOW WE'VE
BEEN ABLE TO MAKE QUITE A NUMBER
OF ANTIVIRALS THAT COULD BE
USEFUL AGAINST FUTURE PANDEMICS,
AND THEN -- AND THAT'S WHAT WE
CALL JUST IN CASE, BECAUSE
THAT'S SORT OF ANTICIPATING
WHERE THE NEXT PROBLEM IS GOING
TO COME FROM, BUT WE ALSO WOULD
LIKE TO HAVE REALLY FAST METHODS
FOR MAKING THERAPEUTICS IN THE
EVENT OF A NEW PANDEMIC
OUTBREAK, AND WHEN I GET TO THE
DEEP LEARNING PART, I'LL SHOW
YOU HOW NOW WE CAN DO EVERYTHING
MUCH, MUCH FASTER THAN WE COULD
BEFORE.
SO IT'S AN EXCITING TIME FOR US.
WE HAVE OUR FIRST
COMPUTATIONALLY DESIGNED
MEDICINE.
THIS IS SOME NUMBER OF YEARS
AGO, WE -- OOPS, LET'S SEE.
WE DESIGNED SELF ASSEMBLING
NANOPARTICLES.
MY COLLEAGUE KNEEL KING, THOSE
NEIL KING PUT THERECEPTOR-BINDIE
CORONAVIRUS ON IT AND IT TURNS
OUT TO ELICIT A REALLY, REALLY
STRONG IMMUNE RESPONSE, QUITE A
BIT STRONGER THAN THE MRNA
VACCINES.
IT'S ALSO VERY, VERY STABLE SO
IT DOESN'T REQUIRE A COLD CHAIN.
SO THIS IS NOW IN USE IN KOREA
AND HOPEFULLY SOON ELSEWHERE.
SO THE FIRST PART OF MY TALK
WILL BE ABOUT DESIGN OF BINDING
PROTEINS.
AND SO WE'VE DEVELOPED GENERAL
METHODS AND THIS IS USING THE
PHYSICALLY-BASED APPROACH TO
DESIGN PROTEINS THAT ARE SHAPED
AND CHEMICALLY CL CL COMPLIMENTY
TARGETS LIKE THE
RECEPTOR-BINDING DOMAIN.
THESE PROTEINS ARE VERY SMALL.
THESE ARE THREE COPIES OF THIS
SMALL PROTEIN SO ONLY 55 AMINO
ACIDS.
IT'S VERY STABLE, IT DOESN'T
MELT.
AND THE STRUCTURES -- SO WE
DESIGN THESE ON THE COMPUTER, WE
DESIGNED A SEQUENCE THAT WAS
JUST FOLDING UP INTO REGULAR
STRUCTURE AND DOCKED TO THE
SPIKE AND IN THIS CRYO-EM
STRUCTURE, YOU CAN SEE THAT THE
DESIGN MODEL IS ACTUALLY VERY
CLOSE TO THE ACTUAL STRUCTURE.
SO WE CAN DESIGN THESE SMALL
PROTEINS VERY ACCURATELY.
NOW, THIS IS A VERY POTENT
PROTEIN, IT PROTECTS MICE AND
HAMSTERS AGAINST THE
CORONAVIRUS.
WHEN ADMINISTERED INTRANASALLY.
TO MAKE IT EVEN MORE POTENT, WE
BASICALLY CONNECTED THREE OF
THESE RECEPTOR LITTLE PROTEINS
THAT BIND TO THE
RECEPTOR-BINDING DOMAIN WITH A
DESIGN TRIMERIC UNIT THAT'S SORT
OF DESIGNED SO THAT IT CAN BIND
ALL THREE AT THE SAME TIME.
HERE'S THE CRYO-EM STRUCTURE,
YOU SEE SORT OF THE TRIPOD-LIKE
STRUCTURE INHIBITOR BOUND TO ALL
TOLETHREE DOMAINS AT THE SAME T.
BECAUSE IT'S BINDING
TRIVALENTLY, IT'S EXTREMELY
TIGHT.
WE CAN'T MEASURE HOW TIGHT IT
IS.
IT NEUTRALIZES ALL OF THE
VARIANTS THAT IT HAS BEEN TESTED
AGAINST WITH VERY HIGH -- AT
VERY HIGH LEVELS, SO AGAIN, IT
CAN GET HARD TO MEASURE
NEUTRALIZATION BELOW 100
PICOMOLAR AND IT ALSO
PROVIDES -- SO BASICALLY THE WAY
IT'S ADMINISTERED IS
INTRANASALLY, YOU CAN GIVE IT UP
TO FOUR DAYS BEFORE EXPOSURE AND
UP TO ONE OR TWO DAYS
AFTERWARDS.
AND IT ALSO BLOCKS TRANSMISSION,
WHICH MAKES SENSE BECAUSE IT'S
BASICALLY JUST BLOCKING THE ACE
2 INTERACTION, BOTH EITHER ON
THE WAY IN OR ON THE WAY OUT.
AND THIS WILL BE STARTING
CLINICAL TRIALS EARLY NEXT YEAR.
SO WE HAVE BEEN EXCITED THEN,
INTERESTED IN SORT OF THIS JUST
IN CASE IDEA, BASICALLY CAN WE
MAKE POTENT INHIBITORS LIKE THIS
FOR OTHER MAJOR CLASSES OF
VIRUSES.
SO -- AND WE'VE BEEN USING THE
SAME BASIC APPROACH WHERE WE
START BY DESIGNING SMALL
PROTEINS, THIS IS THE CASE OF
RSV, WE DESIGNED SMALL PROTEINS
THAT BIND TO THE VIRUS, AND THEN
WE OLIGOMERRIZE THEM, IN THIS
CASE AGAIN, WE HAVE P PICO MOLAR
NEUTRALIZATION OF THESE CHIMERIC
VERSIONS.
THIS IS THE CASE OF MERS, WHERE
AGAIN, WE'RE DOWN WITH
NEUTRALIZATION IC50s IN THE
PICOMOLAR RANGE.
I'M PRETTY CONFIDENT THIS IS A
GENERAL APPROACH FOR MAKING
ANTIVIRALS AND THE NICE THING
ABOUT THESE PROTEINS IS THAT
THEY'RE SO STABLE THAT YOU COULD
COMBINE THEM ALL IN SOME SORT OF
SPRAY THAT INSTEAD OF TAKING
YOUR COVID TEST IN THE MORNING,
YOU WOULD JUST SPRAY A LITTLE
BIT IN YOUR NOSE IF YOU'RE GOING
INTO AN INFECTED AREA.
BUT THERE IS A CAVEAT, WHICH IT
DID TAKE US SOME TIME TO MAKE
THESE.
IT TOOK US -- WE HAD TO SCREEN
QUITE A LARGE NUMBER -- THEY ALL
GET MADE ON COMPUTER BUT THEN WE
HAVE TO TEST A NUMBER.
I'LL SHOW YOU HOW WE CAN MAKE
THESE CLASS OF PROTEINS MUCH
MORE RAPIDLY.
YOU'D REALLY LIKE TO BE ABLE TO
DO THIS IN A NEW VIRUS BREAKS
OUT AND YOU GET THE -- YOU HAVE
THE SEQUENCE AND YOU'D LIKE TO
BE ABLE TO MAKE AN ANTIVIRAL
WITHIN DAYS AND THAT'S OBVIOUSLY
MUCH FASTER THAN YOU CAN DO WITH
ANTIBODIES.
SO WE'VE USED THIS APPROACH TO
MAKE BINDING PROTEINS TO QUITE A
NUMBER OF HUMAN CELL SURFACE
PROTEINS, RECEPTOR SUBUNITS
PRIMARILY.
TYROSINE KINASE SUBRECEPTOR
UNITS, AND WHAT'S EXCITING NOW,
SO WE HAVE ANTAGONISTS FOR ALL
OF THESE, WHICH MAY BE
INTERESTING ON THEIR OWN, BUT
WHAT WE CAN DO IS NOW COMBINE
THEM IN DIFFERENT WAYS TO BRING
RECEPTOR SUBUNITS TOGETHER IN
DIFFERENT ARRANGEMENTS.
AND THAT GIVES A WHOLE HOST OF
NEW AGONIST ACTIVITIES.
SO FOR EXAMPLE, HERE WE HAVE
A -- WE MADE A BINDER AGAINST
THE FGF RECEPTOR, AND WHEN WE
CAN PLACE THAT ON DESIGN
SCAFFOLDS THAT SPACE THESE FGF
BINDING DOMAINS IN DIFFERENT
ARRANGEMENTS AND WE FIND THESE
GIVE DIFFERENT EFFECTS ON CELLS
WHEN THEY'RE ADDED.
SO HERE YOU SEE IN THIS
ARRANGEMENT, WE GET POTENT
CALCIUM SIGNALING BUT IN THIS
ARRANGEMENT, WE DON'T GET ANY --
WE GET MUCH LESS SIGNALING.
SO WE CAN TUNE THE RESPONSE BY
CHANGING THE ARRANGEMENT.
AND THIS IS -- THESE COMPOUNDS
HAVE VERY INTERESTING EFFECTS.
THIS IS -- THAT DIFFER FROM FGF,
SO THIS IS LOOKING AT VASCULAR
DIFFERENTIATION, AND YOU CAN SEE
THE RATIO DURING ENDOTHELIAL
CELL DIFFERENTIATION, THE RATIO
OF ARTERIAL TO VENOUS CELLS THAT
ARE PRODUCED DIFFERS
DRAMATICALLY BETWEEN FGF, WHICH
IS PRIMARILY GOING DOWN THE
VENOUS PATHWAY, WHEREAS THE
SYNTHETIC AGONIST DOWN HERE IS
PRIMARILY GOING DOWN THE
ARTERIAL PATHWAY.
THIS IS, I THINK, REALLY JUST
THE TIP OF THE ICEBERG.
THIS IS AN EXAMPLE HERE WHERE WE
CAN TAKE TWO DIFFERENT BINDERS,
ONE AGAINST THE IL-7 RECEPTOR,
ONE AGAINST THE COMMON GAMMA
CHAIN AND MAKE EXTREMELY POTENT
IL-7 MIMICS.
SO NOW WE'RE EXPLORING DIFFERENT
COMBINATIONS OF THESE LITTLE
DOMAINS, AND SORT OF SEEING WHAT
THE RICHNESS OF INDUCED EFFECTS
CAN BE.
SO I THINK THERE'S A LOT OF
OPPORTUNITY FOR REGENERATIVE
MEDICINE, YOU COULD EVEN IMAGINE
THINGS LIKE TRANS
DIFFERENTIATION, AND OTHER --
AND IN THE CASE OF THE IMMUNE
SYSTEM, BEING ABLE TO -- IT'S
KIND OF AN EXCITING TIME.
SO I TALKED SO FAR ABOUT MAKING
BINDERS AGAINST FOLDED
STRUCTURES, BUT WE CAN ALSO NOW
MAKE BINDERS AGAINST STRUCTURES
WHICH AREN'T FOLDED.
SO IN PARTICULAR, WE CAN MAKE
BINDERS TO PEPTIDES WHICH FORM
AMYLOID.
THIS IS TO THE -- TO A BETA, AND
THE BASIC IDEA IS WE CAN BIND IT
WHEN IT'S IN A BETA STRAND
CONFIRMATION, AND HERE YOU SEE
THE BASIC IDEA, HERE'S THE
DESIGN AND THERE'S THE PEPTIDE
SLOTTED IN.
AND THESE PROTEINS BIND TO THEIR
TARGET PEPTIDES QUITE TIGHTLY,
AND OF COURSE A BETA, IF YOU
JUST LET IT FOLD ON ITS OWN,
WILL ASSEMBLE INTO THESE AMYLOID
FIBRILS.
IF THESE DESIGN PROTEINS ARE
ADDED, THEY COMPLETELY SHUT THIS
PROCESS DOWN AND SO THERE'S A
COLLABORATION WITH THOMAS
NKOWLES LAB, AND HERE A BETA
AGGREGATE VERY RAPIDLY BUT WHEN
THE PROTEINS ARE ADDED, THERE'S
A COMPLETE SUPPRESSION OF
AGGREGATION OVER MULTIPLE DAYS.
AND THIS IS ACTUALLY MUCH
STRONGER THAN HAS BEEN OBSERVED
FOR THE THERAPEUTIC ANTIBODIES.
SO THE ROLE OF THESE
AMYLOID-LIKE STRUCTURES IS STILL
UNCLEAR, BUT WE THINK THIS CLASS
OF PROTEINS WILL PROVIDE A
CLEANER WAY TO PREVENT
AGGREGATION AND SHOULD ALSO
SERVE AS DIAGNOSTICS.
SO ON THIS THEME OF BINDING TO
DISORDERED PARTS OF PROTEINS,
THESE HAVE BEEN HARD TO TARGET
BY CONVENTIONAL METHODS.
PROTEINS BIND TO EXTENDED
PEPTIDES SUCH THAT EVERY OTHER
AMINO ACID OF THE PEPTIDE FITS
INTO A POCKET ON THE PROTEIN.
AND SHE CAN CHANGE THE POCKETS
HERE TO GET SPECIFICITIES FOR
DIFFERENT AMINO ACID SEQUENCES.
AND THESE DESIGN PROTEINS, THE
SEQUENCES THEY BIND TO, ARE, FOR
EXAMPLE, SHOWN HERE.
THIS IS THE SAME POCKET REPEATED
SIX TIMES, SO IT'S THE SEQUENCE
LEUCINE, ARGININE, PROLINE
REPEATED SIX TIMES.
AS YOU CAN SEE HERE FROM THESE
KDs, THEY BIND VERY TIGHTLY,
AND THEY BIND BOTH IN VITRO AND
IN CELLS TO THEIR TARGETS.
AND WHAT'S NEAT IS, NOW SHE CAN
TARGET INTRINSICALLY DISORDERED
REGIONS OF HUMAN PROTEINS, SO
SHE CAN PICK OUT REGIONS WITH --
AND THEN BASICALLY MAKE MODULAR
PROTEINS THAT BIND TO THAT
PEPTIDE SEQUENCE AND SHE CAN USE
THESE PROTEINS, THIS IS ACTUALLY
A COLLABORATIO COLLABORATION WIL
DERIVERY, AND SEE WHAT PROTEINS
COME WITH THEM.
SO THAT'S PRETTY MUCH WHERE WE
ARE WITH USING SORT OF THIS
ENERGY-BASED APPROACH TO
DESIGNING BINDERS, AND AGAIN,
I'LL COME BACK AT THE END OF MY
TALK TO YOU DOING THIS WITH DEEP
LEARNING.
WE'VE ALSO BEEN INTERESTED IN
THE PROBLEM OF DELIVERING
BIOLOGICS, SO HOW DO YOU DELIVER
GUIDE RNAs OR PROTEIN
THERAPEUTICS INTO CELLS?
WE DESIGNED THESE ANTIBODY
NANOCAGES.
THEY'RE MADE OUT OF A DESIGN
PROTEIN LIKE THIS PENT MER IN
GREEN HERE AND THEY BIND TO
ANTIBODIES AS SHOWN HERE, AND
THEY BASICALLY WRAP THE ANTIBODY
INTO A NANOCAGE AND THAT'S SHOWN
HERE.
WE HAVE OTHER ONES THAT WILL
WRAP ANTIBODIES INTO OCTAHEDRAL
CAGES.
THIS IS NICE BECAUSE YOU CAN
TAKE ANY ANTIBODY, ADD THE
CORRESPONDING PROTEIN AND YOU
WILL GET A NICE FOLDED SORT OF
VIRUS-LIKE CAGE FROM IT.
SO FROM THE DELIVERY POINT OF
VIEW, WE CAN BASICALLY PUT ON
THE CAGES AN ANTIBODY WHICH
TARGETS THEM TO A PARTICULAR --
TO PARTICULAR CELLS IN THE BODY.
BUT THEN THE PARTICLES GET TAKEN
UP WITH WHATEVER WE PUT INSIDE
INTO CELLS BUT THEN THEY HAVE TO
GET OUT OF THE ENDO SOME.
WHAT AARON YOUNG HAS DONE IS
FIRST OF ALL TO KEEP THE CARGO
IN, SO IF YOU LOOK AT THIS
ORIGINAL ONE HERE, THERE'S A
HOLE HERE.
AND WHAT ERIN YOUNG DID, A
GRADUATE STUDENT IN THE LAB, SHE
DESIGNED A TRIMER THAT FITS
RIGHT IN THERE.
HERE'S THE CRYO-EM STRUCTURE
NOW, AND YOU CAN SEE DOWN THE
THREE FOLD AND FOUR FOLD AXES OF
THE DESIGN, HERE'S WHERE THE
PLUG SITS, HERE'S WHERE THE
ANTIBODY SITS.
SHE'S DESIGNED THIS TO BE
PH-DEPENDENT SO WHEN IT'S TAKEN
UP INTO THE ENDOSOME THE PLUG
COMES OUT AND WE'RE NOW TRYING
TO OPTIMIZE THE ENTO SOMOL
ESCAPE SO WHAT'S INSIDE THE CAGE
CAN NOW COME OUT BECAUSE IT GETS
OUT OF THE CAGE WHEN THE PLUG
COMES OUT BUT IT'S STILL GOT TO
GET ACROSS THE ENDO SOMOL
MEMBRANE.
SO I'VE TALKED SO FAR ABOUT
SOLUBLE PROTEINS BUT WE CAN ALSO
NOW DESIGN TRANSMEMBRANE
PROTEINS.
SO HERE IS A DESIGNED TRANS
MEMBRANE BETA BARREL.
HERE IS THE CRYSTAL STRUCTURE OF
IT, YOU CAN SEE THEY'RE CLOSE.
SINCE WE'RE DESIGNING THEM, WE
CAN CHANGE THE INTERIOR TO BE
ANYTHING WE WANT, AND THEN GET
SELECTIVITY FOR DIFFERENT TYPES
OF COMPOUNDS.
AS MANY OF YOU KNOW, SINGLE
MOLECULE DNA SEQUENCING CAN BE
DONE WITH NATURALLY OCCURRING
PORES BUT WE CAN REALLY OPTIMIZE
THEM FOR THAT OR FOR PEPTIDE
SEQUENCING OR FOR SOMETHING
ELSE.
AND THESE DESIGN PORES HAVE --
ARE VERY STABLE, THEY HAVE THESE
VERY CLEAR SINGLE CHAJ
CONDUCTANCES AS YOU SEE HERE, SO
WE'RE NOW DESIGNING PROTEINS
WITH DIFFERENT SIZE AND SHAPE
CHANNELS.
SO ONE OF THE THINGS THAT IF YOU
HAVEN'T LOOKED AT A LOT OF
PROTEINS BEFORE, YOU PROBABLY
FIND THESE PICTURES KIND OF HARD
TO LOOK AT.
GENERALLY WHEN THE FIRST PROTEIN
STRUCTURES WERE SOLVED, IT WAS
FELT THEY'RE SO COMPLICATED AND
ASYMMETRIC, AND THAT'S BECAUSE
NATURE REALLY NEVER CARED ABOUT
CREATING PROTEINS THAT WERE EASY
FOR PEOPLE TO LOOK AT.
BUT TIM HUDDY, GRAD STUDENT IN
THE GROUP, IT DIDN'T HAVE TO BE
THAT CASE WAY, SO HE'S DESIGNED
PROTEINS WHICH ARE REALLY MORE
LIKE THE BUILDING BLOCKS YOU
WOULD USE TO, SAY, CONSTRUCT A
HOUSE.
SO HE'S BUILT THESE TOTALLY
STRAIGHT PROTEINS, WHICH ARE
LIKE THESE KIND OF BLOCKS HERE,
AND SINCE THEY'RE JUST MADE OUT
OF REPEATING ELEMENTS, HE CAN
MAKE THEM LONGER, HE CAN PUT IN
RIGHT ANGLE TURNS LIKE THIS, HE
CAN MAKE CURVED VERSIONS LIKE
THIS AND THEN MAKE ONES THAT
INTERACT -- PUT IN INTERFACES
BETWEEN THE BLOCK SO THEY CAN
INTERACT IN DIFFERENT WAYS.
AND FROM THESE, HE CAN BUILD ALL
KINDS OF COOL THINGS.
THIS IS JUST -- UP HERE IS A
CRYSTAL STRUCTURE OF ONE OF THE
BLOCKS.
HERE'S SHOWING WITH DIFFERENT
TYPES OF CORNERS, HE CAN MAKE
TRIANGLES, THIS IS ELECTRON
DENSITY DATA, AND -- OR SQUARES
AND BY PUTTING THE CURVE BLOCKS
TOGETHER, HE CAN MAKE THESE VERY
HOMOGENEOUS POPULATIONS OF
CIRCLES.
NOW BY COMBINING THE CURVED
BLOCKS HERE WITH THESE SORT OF
INTERACTION BLOCKS, HE CAN BUILD
DIFFERENT TYPES OF POLYHEDRA.
THESE ARE EM RECONSTRUCTIONS
REALLY BY JUST PUTTING BLOCKS
TOGETHER, AND YOU CAN SEE THAT
THE ELECTRON DENSITY GOES REALLY
WELL WITH THE DESIGN MODEL.
THE EASIEST ONE TO SEE IS MAYBE
THIS ONE HERE, MADE BY SORT OF
TAKING THIS BLOCK WITH FOUR
SUBUNITS, KIND OF LIKE IN A
CROSS SHAPE, PUTTING IT TOGETHER
WITH THIS RIGHT ANGLE
INTERACTION AND WHAT THAT GIVES
IS THIS CUBE SHOWN HERE.
AND SINCE THESE BLOCKS ARE
PERFECTLY STRAIGHT, HE CAN
SIMPLY CHANGE THE LENGTH OF THIS
BLOCK TO MAKE EITHER A SHORT
CUBE, MEDIUM SIZED CUBE OR A BIG
CUBE, AND ALL THAT'S HAPPENING
IF YOU LOOK AT THESE PICTURES IS
THAT THIS IS GETTING LONGER.
THIS IS, AGAIN, ELECTRON
DENSITY.
SO HE CAN ALSO MAKE UNBOUNDED
STRUCTURES LIKE THESE, WHICH HE
CALLS TRAIN TRACKS, WHICH IS
PUTTING TOGETHER THESE BLOCKS
AND THESE BLOCKS, WHICH -- SO HE
GETS THESE TWO PARALLEL THINGS
WITH THIS CONNECTOR HERE, AND
YOU CAN SEE PICTURES OF THEM
HERE.
AND THEN FINALLY, I SHOWED YOU
THOSE RINGS BEFORE.
HE CAN TAKE AN INNER RING AND AN
OUTER RING AND COMBINE IT WITH
THESE BLOCKS TO MAKE THESE SORTS
OF STRUCTURES HERE.
WHICH WE'RE VERY EXCITED ABOUT
AS LARGER PORES THAT COULD FIT
INTO SILICON WAFERS TO ENABLE
COUPLING WITH WHATEVER'S GOING
THROUGH THE ELECTRONIC READOUT.
SO SO FAR, EVERYTHING I'VE TOLD
YOU ABOUT IS DESIGNING PROTEINS
THAT ARE VERY RIGID, THAT JUST
HAVE ONE STRUCTURE.
AND SO THEY WERE INTERESTED IN
DESIGNING PROTEINS THAT HAVE TWO
DIFFERENT STRUCTURES, BUT VERY
DISCRETE.
SO HERE THIS IS THE SYSTEM, SO
THEY DESIGNED -- THERE'S ONE
STATE HERE AND ANOTHER STATE
WHERE THIS BLUE SORT OF ROTATE
AROUND THE ORANGE.
AND ONLY IN THIS STATE WILL IT
BIND AN EFFECTOR, A PEPTIDE.
SO HERE, THIS IS DEER
SPECTROSCOPY WHICH MONITORING
HOW FAR APART PROBE SPIN LABELS
THAT HAVE BEEN PLACED ON THE
PROTEIN ARE.
SO IN THE PRESENCE OF PEPTIDE,
THESE TWO PROBES IN THE MODEL
COME CLOSER AND INDEED, THIS IS
THE DISTANCE AS READ OUT USING
THIS DEER MESH.
WMEASUREMENT.WHEN THE PEPTIDE IT
THE PROBES ARE FARTHER APART AND
YOU SEE THE DISTANCES MOVE TO
FURTHER APART.
SO WHEN THE PEPTIDE IS ADDED,
YOU SWITCH FROM ONE STATE TO
ANOTHER.
THEY WERE ABLE TO SOLVE THE
CRYSTAL STRUCTURE OF THIS IN
BOTH STATES.
SO HERE'S THE CRYSTAL STRUCTURE
IN THE ABSENCE OF THE PEPTIDE.
IT'S BASICALLY IDENTICAL TO THE
DESIGN MODEL, AND HERE'S THE
CRYSTAL STRUCTURE WITH THE
PEPTIDE, AND YOU CAN SEE IT'S
IDENTICAL TO THE SECOND STATE.
SO AGAIN, WE HAVE TWO VERY
WELL-DEFINED STATES.
SO THEY'VE TAKEN THE SYSTEM AND
BUILT ALLOSTERIC ASSEMBLIES.
HERE'S THE BASIC IDEA.
SO THE SYSTEM IS LIKE THIS
STRAIGHT PROTEIN, WHICH WHEN YOU
ADD THE EFFECTOR, IT BENDS.
THIS IS KIND OF AN IDEALIZED
CASE.
SO IF THESE UNITS HAVE AN
INTERFACE ON THE END THAT ALLOWS
THEM TO INTERACT LIKE THIS, THEN
WHEN THE EFFECTOR IS ADDED, THEN
THAT WILL CHANGE THE OUTCOME
PAUSE NOW THE GEOBECAUSE NOW THF
THESE ARE DIFFERENT, SO HE'S
BEEN ABLE TO MAKE A SERIES OF
DIFFERENT ASSEMBLIES.
SO THIS ONE INVOLVES TWO OF
THESE COMING TOGETHER IN THE
ABSENCE OF THE PEPTIDE, BUT WHEN
THE PEPTIDE BINDS, THE
CONFORMATION CHANGES AND NOW HE
GETS THESE TRIMERS, THESE
TRIANGULAR STRUCTURES.
HERE'S ONE THAT GOES FROM BEING
A CYCLIC TRIMER WITH THREE
COPIES, TO A TETRAMER, WHEN THE
PEPTIDE IS ADDED.
SORRY, I SKIPPED ONE.
WE'RE ABOUT TO GET TO DEEP
LEARNING, AS YOU CAN SEE.
SO IT'S NEAT, SO WE CAN DESIGN
THESE PROTEINS THAT SWITCH
BETWEEN TWO STATES AND WE CAN
USE THEM TO DERIVE CHANGES IN
ASSEMBLY STATE.
HERE'S A PARTICULARLY NICE ONE
THAT GOES FROM BEING A TETRAMER
ON THE RIGHT IN THE ABSENCE OF
THE PEPTIDE, TO A PENTAMER ON
THE LEFT, WHEN THE PEPTIDE IS
ADDED.
OKAY.
SO NOW I'M GOING TO SWITCH GEARS
AND TALK ABOUT DEEP LEARNING,
AND SO WE'VE DEVELOPED THIS
NETWORK CALLED ROSETTAFOLD,
WHICH IS PRETTY GOOD AT
PREDICTING STRUCTURE FROM
SEQUENCE.
AND IT HAS THREE TRACKS.
IT HAS A TRACK SORT OF SEQUENCE
INFORMATION IS GOING THROUGH ON
THIS TRACK, THEN DISTANCE
INFORMATION IS GOING THROUGH ON
THIS TRACK, AND FINALLY
STRUCTURE INFORMATION IS GOING
THROUGH ON THIS TRACK.
SO VARIOUS TYPES OF INFORMATION
COME IN AND THE STRUCTURE COMES
OUT.
SO ONE OF THE THINGS THAT WE'VE
BEEN DOING RECENTLY IS TO EXTEND
TO MORE GENERAL PROBLEMS, SO FOR
EXAMPLE, WE NOW HAVE A VERSION
OF THE NETWORK THAT IS WORK FROM
FRANK DEMAIO THAT WILL TAKE IN
NOT ONLY PROTEIN SEQUENCE BUT
ALSO DNA AND RNA SEQUENCE, SO
MODELS OF PROTEIN RNA OR PROTEIN
DNDNA MODELS AND THESE ARE
EXAMPLES OF SOME OF THE
PREDICTIONED PPREDICTED PROTEINS
THAT IT HAS GENERATED.
I APOLOGIZE FOR HOW COMPLICATED
THIS SLIDE IS.
THIS IS SHOWING YOU WHAT THE
ARCHITECTURE OF ONE OF THESE
NEURAL NETWORKS LOOKS LIKE.
IF YOU HAVEN'T SEEN A LOT OF
THESE BEFORE, THEY'RE KIND OF
COMPLICATED AND DON'T WORRY TOO
MUCH ABOUT THE DETAILS, BUT THIS
DOES THE CLASSIC DESIGN PROGRAM
OF GIVEN THE BACKBONE OF A
PROTEIN, PREDICT WHAT AMINO ACID
SEQUENCES WHICH WILL FOLD TO
THAT BACKBONE.
AND ONE OF THE THINGS WE FOUND,
WE'VE BEEN DEVELOPING THIS
ROSETTA PHYSICALLY-BASED METHOD
FOR MANY YEARS, AND WE FOUND
THAT WHEN WE DEVELOP THIS NEW
METHOD CALLED PROTEIN PMNN AND
WE DID THE CLASSIC TEST OF
TAKING MANY, MANY NATURALLY
OCCURRING PROTEIN STRUCTURES,
THROWING AWAY THE SIDE CHAINS
AND ASKING THE NETWORK TO PRO
DICTIONARY WHAT THE SEQUENCE OF
THE PROTEIN IS THAT THIS
NETWORK DID FAR BETTER, SO THE
IT PREDICTS THE SEQUENCE BASED
ON THE STRUCTURE, AND THEY DID
A REALLY GOOD JOB AT PREDICTING
WHAT THE NATIVE AMINO ACID
SEQUENCES WERE FAR BETTER THAN
ROSETTA.
SO THAT BECAME VERY EVIDENT IN
THE WORK LEADING UP TO THIS
EXPERIMENT HERE.
SO HERE WHAT WE DID IS, IT WAS
OUR FIRST ATTEMPT TO DESIGN
PROTEINS USING ONE OF THESE
NETWORKS.
SO WE STARTED WITH COMPLETELY
RANDOM SEQUENCES AND WE
PREDICTED THE STRUCTURE.
IN THIS CASE, WE USED ALPHA
FOLD, AND A COMPLETELY RANDOM
SEQUENCE WON'T BE PREDICTED TO
HAVE ANY PARTICULAR STRUCTURE AT
ALL.
BUT THEN WE COULD START CHANGING
THE SEQUENCE AND IDENTIFY
CHANGES, BASICALLY DO A MONTE
CARLO SEARCH, IDENTIFY SEQUENCE
CHANGES THAT MADE IT PREDICTED
TO FORM SOME STRUCTURE, AND THEN
WE WOULD GET SEQUENCES STRONGLY
PREDICTED TO FOLD TO A
STRUCTURE.
SO IN THIS EXPERIMENT, WHAT WE
DID WAS TO ASK NOT ONLY THAT THE
SEQUENCE FOLD TO A STRUCTURE BUT
THAT IT WOULD BE TO A STRUCTURE.
NOW WHEN WE DID THIS WE GOT THE
SEQUENCES OUT THAT WERE
PREDICTED BY ALPHAFOLD TO FOLD
TO A VERY NICE OLIGAMERIC
STRUCTURE, BUT WHEN WE MADE THEM
IN THE LAB, AND THE WAY WE'VE
DONE -- EVERYTHING HAS BEEN DONE
THAT I'VE DESCRIBED SO FAR IS WE
GET SYNTHETIC GENES, PUT THEM
INTO BACTERIA, BACTERIA MAKE THE
PROTEIN AND IN THE EXAMPLES I
SHOWED YOU SO FAR, WE GOT THESE
NICE PROTEINS THAT WERE FLU
INHIBITORS OR WHATEVER.
SO IN THIS CASE WE JUST GOT GUNK
AT THE BOTTOM OF THE TUBE.
THEN WE TOOK THE PROTEIN MPMN,
FOUND A NEW SEQUENCE, IN THAT
CASE, THE SUCCESS RATE WAS VERY
HIGH AND WE WERE ABLE TO GET
ULTIMATELY THESE CRYO-EM
STRUCTURES OF THESE VERY LARGE
RINGS THAT AGAIN JUST CAME OUT
FROM THIS RANDOM SEQUENCE
SEARCH.
SO THAT WAS SORT OF OUR FIRST
EXPERIMENTAL EVIDENCE THAT THIS
PROTEIN MPNN COULD REALLY MAKE
PROTEINS THAT FOLD.
AND THESE ARE VERY CLOSE TO THE
DESIGN MODELS.
SO THEN ANOTHER EXAMPLE OF USING
DEEP LEARNING IS ENZYMES.
SO DESIGNING ENZYMES IS HARDER
THAN WHAT I'VE DESCRIBED SO FAR
BECAUSE IT'S NOT JUST BINDING.
YOU HAVE TO SELECTIVELY
STABILIZE THE TRANSITION STATE.
AND THE REACTION THAT ANDY AND
CHRIS CHOSE WAS THIS LUCIFERASE
REACTION, AND THEY MADE PROTEINS
WHICH SELECTIVELY STABILIZE THE
TRANSITION STATE FOR THIS
REACTION.
AND THE WAY THEY DID THIS WAS TO
USE THESE DEEP LEARNING METHODS,
WE CALL IT HALLUCINATION, THE
METHOD I DESCRIBED JUST A MOMENT
AGO.
WE'RE SEARCHING OVER ALL THE
POSSIBLE INPUTS OF THE NETWORK,
IT JUST KIND OF MAKING UP THINGS
FROM SCRATCH.
AND WE SEARCHED FOR SEQUENCES
THAT FOLD UP TO SCAFFOLDS THAT
LOOK LIKE THIS.
AND THEN WE -- IN THIS CASE THIS
WAS SORT OF A HYBRID, WE USED
ROSETTA TO DESIGN SIDE CHAINS
THAT INTERACT WITH THIS SUBTRAIT
FOR THE LUCIFERASE REACTION.
AND WHAT WE FOUND IS WE GOT
QUITE ACTIVE LUCIFERASES OUT OF
THIS, AND THEY WERE CLOSE TO AS
ACTIVE AS NATURALLY OCCURRING
LUCIFERASES FOR THE
ENZYMOLOGISTS WHO MIGHT BE
LISTENING, 10 TO THE SIXTH PER
MOLD PER SECOND, WHICH IS REALLY
PRETTY ACTIVE.
THERE WAS AN INTERESTING
DIFFERENCE FROM -- SORRY.
I KEEP DOING THAT.
FROM NATURALLY OCCURRING
LUCIFERASES, WHICH HAVE VERY
BROAD SUBSTRATE SPECIFICITY.
THIS DESIGN LUCIFERASE IS VERY
HIGHLY SPECIFIC BECAUSE THE
POCKET IS VERY WELL PACKED
AROUND THE SUBSTRATE.
SO ANDY IS NOW TRYING TO MAKE
LUCIFERASES THAT ARE SPECIFIC
FOR OTHER SUBSTRATE.
OKAY.
SO NOW THE REST OF MY TALK, I'M
GOING TO TALK ABOUT REALLY WORK
OF THE LAST THREE MONTH THAT'S
INVOLVED A LOT OF PEOPLE AND IT
VERY EXCITING, SO I HOPE YOU
WILL ENJOY THIS.
SO THIS HALLUCINATION APPROACH
WHERE WE WERE KIND OF JUST
RANDOMLY SEARCHING IN SEQUENCE
SPACE FOR SEQUENCES THAT WILL BE
PREDICTED BY THE NETWORK TO HAVE
THE PROPERTIES THAT WE WANTED
WAS NOT VERY EFFICIENT BECAUSE
EVERY TIME WE CHANGED THE
SEQUENCE, WE HAD TO MAKE A
STRUCTURE PREDICTION AGAIN WITH
ROSETTAFOLD OR ALPHAFOLD.
SO WE COULDN'T REALLY DIRECT IT.
SO WE WANTED REALLY A MORE
DIRECTED WAY OF DOING THIS.
SO THE FIRST WAY WE TRIED TO DO
THIS IS, WE SORT OF TOOK
ROSETTAFOLD AND TREATED IT
LIKE -- WELL, YOU KNOW HOW WHEN
YOU TYPE A FEW WORDS INTO -- ON
A WORD PROCESSOR, IT WILL TRY
AND SOMETIMES COMPLETE YOUR
SENTENCE, SORT OF THAT AUTO
COMPLEEX.
COMPLETION.
WE TRIED TO USE ROSETTAFOLD AS
SORT OF A PROTEIN AUTO
COMPLETER.
WHAT WE DID IS RATHER THAN JUST
TRAINING IT ON SEQUENCE
INFORMATION AND ASKING TO
PREDICT STRUCTURES, SOME OF THE
TIME WE WOULD DELETE SEQUENCE
AND STRUCTURE INFORMATION AND
ASK TO COMPLETE THE MISSING
SEQUENCE AND STRUCTURAL
INFORMATION.
SO THEN WHAT WE COULD DO IS TAKE
JUST PARTS OF PROTEINS, LIKE IN
THIS CASE A DIIRON SITE, WE
COULD JUST TAKE A LITTLE BIT OF
A PROTEIN YOU OUT OF FUNCTION,
LEAVE OUT THE REST OF THE
SEQUENCE AND STRUCTURE AND
ROSETTAFOLD WOULD JUST BUILD IT
IN.
IN THAT WAY, WE WERE ABLE TO GET
PROTEINS THAT IS A CRYSTAL
STRUCTURE ACTUALLY HAD THAT
DESIGN SITE AND BOUND METAL.
WE COULD ALSO -- WE'VE BEEN
OBVIOUSLY INTERESTED IN BETTER
WAYS OF MAKING VACCINES, WE
COULD TAKE EPITOPES FROM A VIRUS
LIKE IN THIS CASE RESPIRATORY
SYNCYTIAL VIRUS, RSVF PROTEIN,
WE COULD TAKE EPITOPES AND ASK
FOR A SINGLE PROTEIN THAT COULD
PRESENT THEM ALL IN THEIR FOLDED
FORM.
SO HERE ARE A COUPLE OF THESE
INPATED DESIGNS MADE USING THIS
APPROACH.
THEY ACTUALLY BIND ALL THREE
DIFFERENT NEUTRALIZING
ANTIBODIES AGAINST THESE THREE
DIFFERENT SITES.
SO WE'RE VERY EXCITED NOW TO SEE
HOW THEY WORK AS IMMUNOGENS.
BUT THERE WAS A PROBLEM, AND
THAT WAS THAT THIS INPAINTING
APPROACH DOES EVERYTHING IN ONE
STEP.
SO WE COULDN'T GET IT, SO IT
ALWAYS -- IT WAS LIKE HAVING A
SENTENCE AUTO COMPLETED THAT
ALWAYS AUTO COMPLETED THE SAME
WAY, AND IN THE SAME WAY THAT A
SENTENCE AUTO COMPLETER WOULD
HAVE, YOU ALWAYS HAD TO GIVE IT
SOME MINIMAL AMOUNT OF
INFORMATION.
YOU GAVE IT TOO SMALL A
FUNCTIONAL MOTIF, IT JUST
COULDN'T FILL IN THE REST.
SO DIFFUSION METHODS HAVE BECOME
VERY POPULAR FOR THINGS LIKE --
FOR BUILDING IMAGES AND THINGS
LIKE DOLLY THAT GO FROM TEXT TO
IMAGES ALL USE DIFFUSION MODELS.
AND THE BASIC WAY THEY WORK IS
YOU START WITH A COMPLETELY
RANDOM NOISE DISTRIBUTION AS
SHOWN ON THE LEFT.
AND THEN YOU SUCCESSIVELY REMOVE
THE NOISE AND YOU DO THAT AT
EACH STEP, YOU REMOVE A SMALL
AMOUNT OF THE NOISE.
AND WHAT WE'RE USING FOR
REMOVING THAT NOISE IS ROSETTA
FOLD.
WE ASK -- WE GIVE ROSETTA FOLD,
WE START WITH COMPLETELY RANDOM
NOISE, WE ASK TO PREDICT WHAT
THE STRUCTURE IS, IT WILL MAKE
SOME GUESS AND THEN WE TAKE A
SMALL STEP IN THAT DIRECTION,
AND THAT'S HOW WE MOVE DOWN TO
LESS AND LESS NOISE.
THEN AS WE GET CLOSE E CLOSER E
REMOVE MORE AND MORE NOISE IT
STARTS TO LOOK MORE AND MORE
LIKE A PROTEIN STRUCTURE.
IN THE LAST FEW MINUTES, I'LL
SHOW YOU EXAMPLES OF HOW WE CAN
APPLY THIS FOR A NUMBER OF
DIFFERENT TYPES OF DESIGN
PROBLEMS.
THE FIRST THING WE CAN DO IS USE
THIS TO MAKE UP BRAND NEW
PROTEIN STRUCTURES.
SO WE START WITH RANDOM NOISE
AND PUT IT THROUGH THIS
DENOISING PROCESS AND EVERY TIME
WE DO THIS, WE GET A DIFFERENT
STRUCTURE OUT.
THESE ARE SOME EXAMPLES OF
ACTUALLY VERY LARGE PROTEINS WE
CAN GET THIS WAY THAT LOOK LIKE
THEY COULD BE PROTEINS, BUT THEY
AREN'T ANY ACTUALLY OCCURRING
PROTEIN.
NOW, WHAT WE CAN ALSO DO IS WE
CAN TAKE THIS RANDOM NOISE AND
THEN WE CAN MAKE COPIES OF IT,
IN THIS CASE THREE COPIES, AND
GO THROUGH THE DENOISING
PROCESS, FORCING THE NOISE TO BE
SYMMETRIC AT EACH STAGE.
SO WHAT HAPPENS THEN IS WE GET
SYMMETRIC OLIGAMERIC STRUCTURES
OUT, AS YOU CAN SEE HERE.
I'LL JUST LET THAT PLAY ONE MORE
TIME.
SO BECAUSE WE'RE STARTING WITH
THE RANDOM NOISE DISTRIBUTION
EVERY TIME WE RUN THIS, WE GET
SOMETHING DIFFERENT OUT.
AND SO HERE ARE SOME OF THE
THINGS THAT COME OUT.
AND IT REALLY QUITE FASCINATING.
SOMETIMES -- THESE ARE JUST
DIFFERENT EXAMPLES AND WHAT YOU
SEE HERE IS EM DATA ON THESE
STRUCTURES.
THIS IS WORK FROM HELEN ISANAC
AND ANDREW -- YOU CAN SEE THEY
CLOSE.
FOR THOSE FAMILIAR WITH NATURAL
PROTEINS KNOW THAT -- WHAT THIS
NETWORK IS DOING IS GENERATING
THINGS THAT LOOK LIKE TIN
BARRELS BUT THEY HAVE 24 STRANDS
AND 24 HELICES -- SO THE NETWORK
HAS BEEN TRAINED ON PROTEIN
STRUCTURES BUT NOW IT CAN GO
CONSIDERABLY BEYOND.
SO WE CAN ALSO ASK TO MAKE, JUST
BY THE WAY IN WHICH WE'RE
SYMMETRI SOO ING THE NOISE, WE
CAN ASK TO MAKE DIE HEE DRAL
STRUCTURES.
SO IT'S GOT LIKE TWO CYCLIC
RINGS OF FOUR SUBUNITS EACH
STACKED ON EACH OTHER.
HERE YOU CAN SEE THE EM
RECONSTRUCTION IS VERY CLOSE TO
THE DESIGN MODEL.
COMING BACK TO THIS VERY FIRST
SLIDE I SHOWED WITH THE COVID
VACCINE, WE CAN JUST GIVE IT A
CERTAIN CHAIN LENGTH.
IN ALL OF THESE CASES WE HAVE TO
SPECIFY THE CHAIN LENGTH AND SAY
MAKE SOMETHING THAT'S
ICOSAHEDRAL.
THIS IS WHAT THE PROTEINS LOOK
LIKE, THESE NICE LITTLE
ICOSAHEDRA, AND THEN THESE ARE
THE NEGATIVE STAIN CLASS
AVERAGES AGAIN MATCH THE -- OR
RECONSTRUCTIONS MATCH THE MODEL.
SO WHAT ABOUT BINDING?
I PROMISED TO GET BACK TO
BINDING.
NOW THIS IS THE VERY LAST FEW
SLIDES.
SO THE FIRST PROBLEM WE CAN DO
IS, THIS IS MDM2, WHICH IS --
WHICH DEGRADES P53, SO IT'S
IMPORTANT TUMOR SUPPRESSOR.
WE CAN TAKE JUST THE P53 PEPTIDE
AND SAY, BUILD A PROTEIN AROUND
IT THAT BINDS MDM2.
AND WHEN WE DO THAT, WE GET OUT
PROTEINS THAT BIND WITH VERY
HIGH AFFINITY TO MDM2.
WE DIDN'T HAVE TO -- THE
PROTEINS BEING MADE IN PLACE IN
THIS CASE, IT'S NOT -- IN THE
CALCULATIONS I TALKED ABOUT IN
THE BEGINNING OF MY TALK, WE
STARTED BY GENERATING A VERY
LARGE SET OF DIFFERENT SHAPED
SCAFFOLDS, THEN WE DOCKED THEM
AGAINST THE TARGET.
HERE WE'RE JUST STARTING WITH
RANDOM NOISE PLACED AGAINST THE
TARGET AND THEN IT SORT OF
ASSEMBLES INTO THIS PROTEIN THAT
INCLUDES THE P53 SEGMENT.
AND NOW FOR THAT PROBLEM I
DESCRIBED AT THE BEGINNING OF
HOW DO YOU CONNECT IF YOU HAVE A
DESIGN PROTEIN BOUND TO A VIRUS,
CAN YOU CONNECT THOSE PROTEINS
IN A MEANINGFUL WAY?
NOW WE CAN DO THIS WITH THIS
VERY QUICK DEEP LEARNING METHOD,
WHERE IT'S ESSENTIALLY BUILDING
A SYMMETRIC TRIMER, BUILDING A
SYMMETRIC SCAFFOLD IN PLACE
THAT'S PERFECT FOR HOLDING THESE
DESIGN BINDERS ON.
SO SOME EXPERIMENTAL
DEMONSTRATION OF THIS SORT OF
SCAFFOLDING, WE CAN TAKE A METAL
BINDING SITE LIKE THIS SQUARE
PLANAR ZINC SITE, AND WE CAN PUT
BASICALLY ADD THE SAME NOISE
DISTRIBUTION TO EACH OF --
BASICALLY AROUND IT, THAT'S SORT
OF SHOWN HERE, AND THEN BY GOING
THROUGH THIS DENOISING
TRAJECTORY, AS YOU CAN SEE HERE,
WE ULTIMATELY END UP WITH POE
PROTEINS THAT ASSEMBLE INTO
SOMETHING WITH -- IT'S A
TETRAMER AND IT HOLDS THESE IN
JUST THE RIGHT ARRANGEMENT.
THESE PROTEINS BIND METAL VERY
TIGHTLY.
THIS IS USING ITC, ISOTHERMAL
TITRATION -- WE CAN'T ACTUALLY
MEASURE HOW TIGHT THAT IS, IT'S
TOO TIGHT, AND THEY HAVE VERY
CLOSE TO THE DESIGN STRUCTURE AS
YOU CAN SEE HERE.
FROM THE EM.
NOW, COMING BACK TO THE BINDER
DESIGN PROBLEM, WHICH I TALKED
ABOUT IN THE BEGINNING, SO HERE
WHAT WE DO IS WE JUST START BY
PICKING A TARGET LIKE, YOU KNOW,
CORONAVIRUS OR WHATEVER, AND A
FEW RESIDUES ON THE TARGET, AND
THEN WE PLACE OUR RANDOM NOISE
CLOUD AROUND THOSE RESIDUES, AND
GO THROUGH THIS DENOISING
PROCESS, AND NOW WE HAVE A
PROTEIN THAT'S RIGHT ABOVE
THE -- THAT'S RIGHT ABOVE THAT
SITE, AND WE HAVE DONE THAT NOW
FOR A NUMBER OF TARGETS.
THIS ACTUALLY TOOK ABOUT A WEEK
IN TOTAL, INCLUDING THE
EXPERIMENTS.
WE WERE TRYING TO SEE HOW FAST
WE COULD DO THIS.
SO THE AFFINITIES ARE NOT GREAT.
THERE'S STILL ROOM FOR
IMPROVEMENT WITH THIS METHOD,
BUT IMPORTANTLY, WE WERE ABLE TO
MAKE BINDERS FROM SCRATCH
WITHOUT TESTING A LARGE NUMBER
OF DESIGNS.
SO BEFORE WE WOULD HAVE TO TEST
TENS OF THOUSANDS OF DESIGNS TO
FIND ONES THAT BOUND, AND HERE
WE'RE JUST TESTING DOZENS AND
GETTING BINDERS IN THE NANOMOLAR
RANGE.
SO IN TERMS OF THE PANDEMIC
RESPONSE, I THINK WE'RE GETTING
CLOSE TO THE POINT THAT IF A NEW
VIRUS DID BREAK OUT AND WE HAD
THE SEQUENCE, WE COULD PREDICT
THE STRUCTURE AND THEN USE THESE
METHODS TO VERY RAPIDLY GENERATE
BINDERS.
THE WORK WE HAVE TO DO IS TO GET
THESE TO BE PICO MO PICOMOLAR IF
NANOMOLAR.
THEN AS MY FINAL SLIDE, THIS
DEMONSTRATION THAT THIS ISN'T
CRAZY, THIS IS TAKING
PARATHYROID HORMONE AND NOW
BASICALLY PUTTING RANDOM
DISTRIBUTION AROUND IT, AND THEN
RUNNING THE DENOISING PROCESS
AND YOU CAN AGAIN SEE A PROTEIN
ASSEMBLING AROUND THE
PARATHYROID HORMONE.
WHEN WE MAKE THESE PROTEINS IN
THE LAB, THEY BIND EXTREMELY
TIGHTLY, SO WE'VE DONE THIS FOR
PARATHYROID HORMONE HERE AND
ANOTHER HELICAL PROTEIN HERE AND
YOU CAN SEE THERE'S ESSENTIALLY
NO DISSOCIATION IN THIS
EXPERIMENT.
SO WE'RE GETTING SUBNANOMOLAR
BINDING STRAIGHT OUT OF THE
COMPUTE E WCOMPUTER, WHICH IS SE
AND OTHERS HAVE NEVER REALLY
SEEN BEFORE FOR ANY KIND OF
BINDING DESIGN THING.
I THINK THIS IS AN EASY CASE
BECAUSE YOU CAN SEE THE PROTEIN
BASICALLY JUST HAS TO CRADLE
THIS LONG HELIX.
BUT I THINK THERE'S HOPE NOW FOR
GENERAL TARGETS AND GETTING
PICOMOLAR BINDERS REALLY QUITE
FAST.
SO THAT'S WHAT I WANTED TO TELL
YOU ABOUT TODAY.
I'VE HAD MANY ABSOLUTELY AMAZING
COLLABORATORS ON THIS.
SO THE ANTIVIRAL DESIGN WORK WAS
DONE WITH MANY, MANY
COLLABORATORS IN MANY DIFFERENT
PLACES FOR ALL THE ANIMAL
EXPERIMENTS.
MIKE DIAMOND'S LAB, WE DID A LOT
OF THE ANIMAL EXPERIMENTS WITH
THE COVID BINDER.
THE AMYLOID PEPTIDE BINDERS WERE
DESIGNED BY DANNY SATO.
THE ANTIBODY CAGES FOR DELIVERY
WERE DESIGNED BY ERIN AND ROBBY
AND ANDREW.
THE PROTEIN NANOPORES, WHAT
WE'RE HOPING FOR THE NEXT
GENERATION PROTEIN SEQUENCING,
FOR EXAMPLE, WERE DESIGNED BY
SAMUEL, SAGARDIP, ANASTASIA AND
CAROLIN.
THE PROTEIN HALLUCINATION
APPROACH I DESCRIENED WAS
DEVELOPED BY ALEXIS, BASILE AND
LUK AS.
ANDY AND CHRIS DESIGNED THE
LUCIFERASE, AND ROSETTAFOLD IS
THE WORK OF MINKYUNG AND FRANK.
JOE AND DAVID REALLY LED THE
EFFORT WITH IMPORTANT
CONTRIBUTIONS FROM EVERYONE
LISTED HERE.
THERE ARE A LOT OF PEOPLE WHO I
NEED TO THANK WHO AREN'T ON THIS
SLIDE, BUT IN PARTICULAR, MY
COLLEAGUES AT THE INSTITUTE FOR
PROTEIN DESIGN WHO REALLY MAKE A
LOT OF THINGS HAPPEN.
SO THANKS FOR YOUR ATTENTION.
I'D BE HAPPY TO TAKE ANY
QUESTIONS.
[APPLAUSE]
>>THANK YOU.
ANYONE IS FREE TO USE THE
MICROPHONES.
WE DO HAVE MANY QUESTIONS
ONLINE, SO LET'S START THERE.
PERHAPS YOU KNOW THIS FELLOW.
OMAR HABIB OVER IN OXFORD.
GREAT TALK, DAVID.
DOES YOUR APPROACH TAKE PTNs
INTO CONTRIBUTION WHEN
PREDICTING STRUCTURE?
>>YEAH, SO WE ARE JUST WORKING
ON THAT NOW.
SO THE ORIGINAL VERSION OF
ROSETTAFOLD, LIKE ALPHAFOLD, DID
NOT INCLUDE POST TRANSLATIONAL
MODIFICATIONS.
BUT WE'RE WORKING ON A VERSION
NOW THAT WILL DO THAT.
>>OKAY.
I'M KNOLL GIST QUESTION.
SINCE THE SYNTHETIC MINI BINDERS
ARE FOREIGN, THE PATIENT'S
IMMUNE SYSTEM CAN DEVELOP
NEUTRALIZING ANTIBODIES AGAINST
THEM.
WOUWOULDN'T THAT LIMIT THEIR LOG
TERM USE?
>>YES AND THAT WAS THE BIG
THING WE WERE WORRIED ABOUT WHEN
WE STARTED PUTTING THESE DESIGN
PROTEINS, WHICH ARE FOREIGN,
INTO ANIMALS.
AND ULTIMATELY IN PEOPLE.
BUT THE RESULTS ARE ACTUALLY
PRETTY CLEAR.
SO THE SMALL PROTEINS DO NOT
ELICIT STRONG IMMUNE RESPONSES,
SO FOR BOTH THE -- BOTH IN THE
CASE OF INFLUENZA AND THE
CORONAVIRUS, I DESCRIBED
THESE -- I TOLD YOU THEY HAD
STRONG -- THEY HAD POTENT
THERAPEUTIC AND PROPHYLACTIC
EFFECT.
YOU COULD ACTUALLY ADMINISTER
THEM OVER A THREE-WEEK PERIOD
EVERY THREE DAYS TO TRY AND
ELICIT IMMUNE RESPONSE.
AND THERE'S NO DECREASE IN
EFFICACY.
SO NEUTRALIZING ANTIBODIES DON'T
SEEM TO GET MADE, AND WHEN WE
LOOK JUST FOR ANTIBODY
RESPONSES, THEY'RE PRETTY WEAK.
AND I THINK THAT'S BECAUSE THE
PROTEINS ARE VERY STABLE,
THEY'RE SMALL, THEY'RE VERY
SOLUBLE, SO THEY'RE PROBABLY NOT
TAKEN UP WELL BY DENDRITIC CELLS
AND THEY'RE VERY STABLE SO
THEY'RE PROBABLY NOT PRESENTED
THAT WELL.
>>OKAY.
FROM ROLAND OWENS, WOULD AN
ARTIFICIAL PROTEIN CAPABLE OF
INDUCING TRANSDIFFERENTIATION
ALSO BE A POTENTIAL TERATOGEN?
>>YEAH, SO I THINK FOR THESE
NOVEL AGONISTS, WHERE WE'RE
TRYING TO GET, SAY, SULFATE
CHANGES, OBVIOUSLY THEY WOULD
HAVE TO BE TARGETED VERY
CAREFULLY.
I SHOULD SAY, WE ALSO HAVE A DE
NOVO DESIGNED CYTOKINE MIMIC
THAT'S IN CLINICAL TRIALS AND
THAT HAS NOT ELICITED ANY
SIGNIFICANT IMMUNE RESPONSE
COMING BACK TO THE PREVIOUS
QUESTION.
>>YOU MENTIONED THAT
ROSETTAFOLD CAN NOW PREDICT
BINDERS TO NUCLEIC ACIDS.
CAN YOU PREDICT WHAT SEQUENCE
PROTEIN MIGHT BE ABLE TO BIND,
AND RELATED QUESTION, CAN YOU DO
SIMILAR THINGS FOR SMALL
MOLECULES TO SAY WHAT ENZYME
MIGHT ACT ON THAT SORT OF THING?
>>YEAH, WELL --
>>I'M SORRY, CAN YOU SUM UP
THAT QUESTION?
I DON'T KNOW IF IT WAS HEARD, IF
THE MIC WAS ON.
>>RIGHT.
SO THE QUESTION IS, I SHOWED YOU
THAT ROSETTAFOLD CAN NOW PREDICT
PROTEIN DNA COMPLEXES AND THE
QUESTION IS CAN IT PREDICT WHAT
DNA SEQUENCE A PROTEIN WILL
BIEND TO AND SIM LAICALLY FOR
SMALL MOLECULES, COULD YOU
PREDICT WHAT SMALL MOLECULE IT
WAS GOING TO BIND TO.
I WOULD SAY AT THIS STAGE, IF
YOU GIVE ROSETTAFOLD A PIECE OF
DNA THAT'S NOT TOO BIG, IT WILL
CORRECTLY BIND TO THE RIGHT
SITE, BUT I THINK THE ABILITY TO
DISCRIMINATE AMONG ALL POSSIBLE
SITES TO PREDICT WHAT THE RIGHT
ONE IS IS NOT SO GOOD, BUT WE'RE
ACTUALLY NOW JUST STARTING TO
EXPLICITLY TRAIN IT BECAUSE
THERE'S A LOT OF EXPERIMENTAL
SPECIFICITY DATA.
THESE DEEP LEARNING MODELS ARE
ONLY AS GOOD AS THE TRAINING
SETS YOU GIVE THEM SO WE THINK
WE CAN IMPROVE THAT.
AND THEN WE AND OTHERS ARE
DEVELOPING ROSETTAFOLD FOR SMALL
MOLECULE BINDING, AND THAT'S
LOOKING PROMISING.
AGAIN, I THINK IT'S EASIER TO
PREDICT THE POSE THAN TO PREDICT
SPECIFICITY BUT I THINK THAT
WILL ALL COME.
>>A FEW MORE QUESTIONS AND
REMINDER WE'RE GOING TO HAVE OUR
RECEPTION, THE FIRST ONE IN MORE
THAN TWO YEARS, JUST OUTSIDE
THIS AUDITORIUM.
OKAY.
QUESTION FROM MATT PEDELTON.
ANY CONSIDERATION FOR ATTACHING
AN FC DOMAIN ON TO THESE VIRAL
INHIBITORS IN ORDER TO INHIBIT
AND ELICIT AN APPROPRIATE
ADAPTIVE IMMUNE EFFECTOR
FUNCTION?
>>SO BRETT CASE AND MIKE
DIAMOND DID THAT, AND WITH THE
CORONAVIRUS BINDERS, AND THEY'RE
VERY -- THEY WORK VERY WELL.
IT'S NOT TOTALLY CLEAR IN THAT
CASE WHAT THE ADVANTAGE OVER
ANTIBODIES ARE.
THE ONE CASE COULD BE THAT IF WE
CAN DESIGN BINDERS TO BIND TO
SITES THAT AREN'T ACCESSIBLE
THAT ANTIBODIES DON'T RECOGNIZE.
BUT YOU CAN PUT FCs ON THEM,
YOU EXTEND THE HALF-LIFE.
DEB FULLER'S LAB HAS DONE THAT
WITH THE FLU AS WELL, SO THAT
CAN CERTAINLY BE DONE.
>>OKAY.
AND MATT FOLLOWS UP.
SO, THIS DENOISING PROCESS IS
NOT STRUCTURALLY CONSTRAINED THE
SAME AS BACKBONE/SIDE CHAIN
PERTURBATION APPROACH, THE WORDS
DEPARTMENT DIDN'T COME THROUGH E
ORIGINAL RO ROSETTA WHERE EACH P
WAS RELATED TO PREVIOUS STEPS BY
SPECIFIC CHANGES IN BACKBONE
ANGLES?
COULD YOU FOLLOW THAT?
>>RIGHT.
SO BASICALLY THE WAY -- SO THIS
PROBLEM OF HOW DO YOU SAMPLE
THROUGH POSSIBLE BACKBONES TO
MAKE UP NEW STRUCTURES IS ONE
THAT WE WORKED ON FOR A LONG
TIME IN THE CONTEXT OF THE
ROSETTA PHYSICAL MODEL.
IN THAT CASE, YOU KEEP THE CHAIN
CONNECTED AND ALL THE BOND LINKS
AND BOND ANGLES ARE IDEAL, AND
YOU'RE JUST SAMPLING THE
BACKBONE TORSION ANGLES.
THAT'S HOW YOU SEARCH AROUND IN
SPACE.
IN THIS DIFFUSION APPROACH, IT'S
NOT LIKE THAT.
THE RESIDUES ARE ALL SEPARATE.
BASICALLY EACH RESIDUE HAS A
POSITION AND AN ORIENTATION, AND
WHAT YOU'RE DIFFUSING OVER, WHAT
YOU'RE SAMPLING ARE THOSE
POSITIONS AND ORIENTATIONS.
AND SO IT'S REALLY QUITE
DIFFERENT IN THAT WAY.
WE'VE TRIED TO PUT SOME OF THE
PHYSICS THAT'S IN ROSETTA INTO
ROSETTAFOLD BUT IT REALLY HASN'T
HELPED THAT MUCH AND I THINK
THAT'S SORT OF A TESTAMENT TO
HOW RICH THE PROTEIN STRUCTURE
DATABASE IS.
THERE'S SO MUCH INFORMATION IN
THERE, THE NETWORK KIND OF
LEARNS ALL THE RULES ABOUT
CONNECTIVITY WITHOUT HAVING TO
BE PROVIDED WITH ADDITIONAL
INFORMATION.
>>OKAY.
HAVE YOU CONSIDERED EXPANDING
BEYOND THE CHEMISTRY OF THE
CANONICAL AMINO ACIDS?
>>YEAH, SO THAT'S AN IMPORTANT
DIFFERENCE BETWEEN THE
PHYSICALLY BASED APPROACH AND
THE DEEP LEARNING APPROACH.
WITH THE PHYSICALLY BASED
APPROACH, WE'VE DONE THAT, AND
SO WE CAN -- BUT THERE THE
PROBLEM IS MAKING THE PROTEINS,
BECAUSE IF YOU'RE PUTTING IN A
LOT OF UNNATURAL CHEMISTRIES, IT
GETS HARDER AND HARDER TO MAKE
THEM IN CELLS.
SO WE'VE REALLY FOCUSED ON SMALL
SICYCLIC PEPTIDES, WE'VE DESIGND
THEM SO THEY CAN GET ACROSS
MEMBRANES VERY READILY AND THEY
HAVE RIGID STRUCTURES SO NOW
WE'RE DEVELOPING METHODS TO MAKE
THEM BIND TIGHTLY TO -- OF
INTEREST BUT WE HAVE TO MAKE
THOSE CHEMICALLY.
>>FOR YOUR RF DIFFUSION BINDING
FIBERS THAT YOU'RE ONLY GETTING
NANOMOLAR AND YOU NEED
PICOMOLAR, WOULD YOU DO -- CAN
YOU DO THAT IN SILICO OR WITH
A.I. NOWADAYS OR IS THAT
SOMETHING IN THE PIPELINE?
>>RIGHT, YOU'RE ABSOLUTELY
RIGHT.
SO THE WAY THAT WE HAD BEEN
DOING THAT WITH THE ROSETTA
METHOD, LIKE THE CORONAVIRUS
BINDERS AND THE OTHER VIRUS
BINDERS I DESCRIBED AT THE TOP,
WE WOULD GET THE ORIGINAL HITS
FROM ROSETTA BUT THEN WE WOULD
BASICALLY BHAIK A LIBRARY WHERE
WE MUTATED EVERY RESIDUE TO
EVERY OTHER ONE AT A TIME AND
IDENTIFY SUBSTITUTIONS THAT
INCREASED AFFINITY AND WE WOULD
DEFINE THEM TO GET TO PICOMOLAR
LEVEL.
THAT'S SLOW, SO YOU'RE
ABSOLUTELY RIGHT, SO NOW OUR
CHALLENGES IS, AND I THINK IT'S
EMINENTLY SOLVABLE, IS DEVELOPED
SORT OF INSILICO WAYS OF DOING
THAT EXACTLY.
>>THERE'S A FEW MORE IF YOU'RE
STILL GOING STRONG?
>>SURE.
>>THE CME CODE -- OH, GOOD.
44435.
I WAS GOING TO RELY ON MY OWN
MEMORY, BUT IT GOT FLASHED UP
THERE.
OKAY.
WILLIAM ANDERSON ASKS, THANK YOU
FOR SHARING YOUR WORK.
THIS IS FASCINATING.
IN REGARDS TO THE DE NOVO
PROTEINS FOR
TREATMENT/THERAPIES, IS THERE --
IS THE GREATER CHALLENGE
DESIGNING EFFECTIVE PROTEINS OR
DELIVERING THOSE PROTEINS IN A
CORRECT WAY TO THE CORRECT
LOCATION?
>>THOSE ARE BOTH VERY IMPORTANT
THINGS.
I WOULD SAY THE EVEN MORE
IMPORTANT THING IS SORT OF THE
BIOLOGY OR MEDICAL CONCEPT.
WE CAN NOW DESIGN PROTEINS THAT
BIND ALMOST ANYTHING.
THE QUESTION IS, HOW DO YOU CURE
DISEASE WITH THAT?
AND THE VIRUSES ARE PARTICULARLY
A CLEAR CASE, YOU JUST BLOCK THE
VIRUS.
AND THEN MAKING THESE NEW TYPES
OF AGONISTS, NEW TYPES OF
CYTOKINE OR GROWTH FACTOR
MIMICS.
SO I THINK YOU HAVE TO HAVE THE
BIOLOGICAL CONCEPT FOR HOW
YOU'RE GOING TO CURE DISEASE.
ANOTHER THING WE'VE BEEN LOOKING
AT IS MAKING PROTEINS THAT WILL
TARGET SPECIFIC PROTEINS FOR
DEGRADATION.
BUT BEYOND THAT, YOUR POINT IS
ABSOLUTELY RIGHT THAT NOT ONLY
DO YOU NEED BINDING BUT YOU NEED
THAT BINDING ACTIVITY AT THE
RIGHT PLACE AT THE RIGHT TIME.
SO WE'RE ALSO WORKING ON SORT OF
PROTEIN LOGIC SYSTEMS SO THAT
YOU ONLY HAVE THAT ACTIVITY IN
THE RIGHT PLACE.
>>OKAY.
AND RELATED, RISHI ASKS, CAN YOU
COMMENT ON THE IMMUNOGENICITY
AND CLEARANCE OF THESE DESIGN
PROTEINS IN VIVO?
>>SO AS I MENTIONED EARLIER,
THE IMMUNOGENICITY OF THE DESIGN
PROTEINS IS LOWER THAN YOU WOULD
EXPECT.
SO THERE HAVE BEEN CLINICAL
TRIALS NOW OF OUR DESIGNED
CYTOKINE MIMIC, AND IT ELICITED
VERY LITTLE ANTIBODIES, AND ALL
THE ANTIVIRAL PROTEINS HAVE BEEN
IN MANY ANIMAL SYSTEMS AND
THERE'S NO SIGN OF ANY
NEUTRALIZING ANTIBODY RESPONSE.
CLEARANCE, THOUGH, CLEARANCE WE
UNDERSTAND VERY WELL SO THESE
ARE ALL SMALL PROTEIN SO THEY
GET FILTERED TOUT IN OUT IN TH.
IF YOU DON'T DO ANYTHING ELSE.
AND THAT'S JUST A FUNCTION OF
SIZE.
SO FOR SOME APPLICATIONS, THAT'S
GOOD.
SO IF YOU WANT TO -- IF YOU'RE
DOING RADIONUCLIDE THERAPY, SO
YOU'RE ATTACHING A VERY POTENT
RADIO EMITTER TO THESE PROTEINS
AND TARGETING THEM TO A TUMOR,
FOR EXAMPLE, HAVING THINGS
WASHED OUT QUICKLY IS GOOD.
IF YOU WANT LONGER HALF LIVES,
THEN YOU HAVE TO EXTEND -- YOU
HAVE TO DO THINGS TO EXTEND THE
HALF-LIFE.
>>OKAY.
>>IF YOU PUT THEM IN -- THAT'S
THE NICE THING ABOUT THESE
RESPIRATORY ANTIVIRALS, YOU PUT
THEM INTO THE RESPIRATORY SYSTEM
AND THEY JUST STAY THERE.
SO LIKE I SAID, YOU CAN GET
PROTECTION FOUR DAYS IN ADVANCE.
>>OKAY.
LOOKS LIKE OUR NEXT PERSON IS
WATCHING THE WEDNESDAY EVENING
LECTURE SERIES FROM GERMANY.
I WAS VERY EXCITED TO SEE THE
PRE-PRINT FOR ROSETTAFOLD NA.
HOW FAR AWAY ARE YOU FROM MAKING
A SOFTWARE FOR THE DESIGN OF
NUCLEIC ACID STRUCTURES?
EXAMPLE, RATIONALLY DESIGNED
APTOMERS AND RIBOSOMES OR
DNAZYMES?
>>GIVEN A PARTICULAR DOUBLE
STRANDED DNA SEQUENCE, MAKE A
BINDER TO IT.
WE'RE ACTUALLY MAKING A LOT OF
PROGRESS ON THAT PROBLEM.
SO THAT'S THE EASIEST PROBLEM
BECAUSE THERE, YOU KNOW WHAT
THE -- YOU HAVE A PRETTY GOOD
IDEA THAT'S B FORM DNA.
I THINK THE OTHER PROBLEMS ARE A
LITTLE BIT HARDER BECAUSE THE
STRUCTURE IS LESS REGULAR, BUT I
THINK THOSE ARE ALL CURRENT
PROBLEMS WHICH COULD BE
APPROACHED NOW.
AND I SHOULD SAY WE'RE ALWAYS
INTERESTED IN COLLABORATION, SO
IF PEOPLE HAVE THINGS WHERE
THEY'D LIKE BINDERS, LET ME
KNOW.
>>OKAY.
MAYBE ONE MORE AND IT'S A
SELF-DESCRIBED NAIVE QUESTION
BUT IT'S INTERESTING TO ME.
A NAIVE QUESTION, BUT CAN THESE
STRATEGIES BE USED TO PRODUCE
ARTIFICIAL BLOOD OR SPECIFIC
BLOOD TYPES?
>>WELL, I THINK WHAT THEY CAN
BE -- THEY CAN BE USED FOR --
ONE OF THE USES OF THESE
ARTIFICIAL GROWTH FACTORS, IT'S
NOT QUITE IN ANSWER TO THAT
QUESTION, BUT YOU KNOW, SERUM IS
VERY -- IF YOU GROW CELLS FOR
ARTIFICIAL MEAT OR GROWING UP
T-CELLS FOR ADOPTIVE CELL
THERAPIES, YOU NEED TO ADD
GROWTH FACTORS AND CYTOKINES.
SO ONE OF THE THINGS WE CAN DO
IS SUBSTITUTE THOSE WHICH CAN BE
HARD TO MAKE AND EXPENSIVE AND
NOT VERY STABLE FOR MUCH CHEAPER
SYNTHETIC VERSIONS.
AS FAR AS BLOOD GOES, YOU KNOW,
YOU'D HAVE TO LOOK AT WHAT THE
ACTIVITIES -- WHAT ARE THE, I
GUESS HEMOGLOBIN, YOU WOULD NEED
SOMETHING THAT WOULD BE AN
OXYGEN CARRIER.
I THINK ONE COULD GO THROUGH IT.
I'M NOT ENOUGH OF AN EXPERT TO
KNOW WHAT THE KEY COMPONENTS ARE
THAT ONE WOULD NEED, BUT I THINK
IT'S A VERY INTERESTING
QUESTION.
>>OKAY.
WELL, IF THERE'S NOTHING ELSE
FROM HERE, I WANTED TO REMIND
PEOPLE THERE'S A RECEPTION JUST
OUTSIDE HERE.
NOT THE ONE ON FAS TEARS, THAT'S
A HOLIDAY PARTY AND YOU WOULD BE
CRASHING THAT, BUT JUST OUTSIDE
THESE DOORS.
SO THANK YOU FOR THIS GREAT
TALK.
>>OKAY.
THANK YOU.
[APPLAUSE]