>>WELCOME, EVERYONE, TO THE
NIGMS JUDITH H. GREENBERG EARLY
CAREER INVESTIGATOR LECTURE.
I'M JON LORSCH.
AS WE BEGIN TODAY'S WEBINAR,
PLEASE NOTE THE SESSION IS BEING
RECORDED.
WE LAUNCHED THE JUDITH H.
GREENBERG EARLY CAREER
INVESTIGATOR LECTURE IN 2016 IN
ORDER TO SHOWCASE NIGMS FUNDED
EARLY CAREER INVESTIGATORS AND
THE VALUABLE CONTRIBUTIONS
THEY'RE MAKING TO PUBLIC HEALTH.
THE LECTURE SERIES IS NAMED FOR
JUDITH GREENBERG, FORMER DEPUTY
DIRECTOR OF NIGMS AND TWO-TIME
ACTING DIRECTOR OF THE INSTITUTE
WHO HAD DEEP COMMITMENT TO
SUPPORTING EARLY CAREER
INVESTIGATORS.
WE GEAR THESE LECTURES TO
STUDENTS TO GIVE THEM THE
OPPORTUNITY TO HEAR AND MEET
YOUNG SCIENTISTS DOING
REMARKABLE THINGS IN BIOMEDICAL
RESEARCH.
OUR HOPE IS THAT YOU'LL BE
INSPIRED TO FOLLOW CAREERS IN
THIS AREA.
THE LECTURE WILL BEGIN WITH A
SPEAKER, DR. CAESAR DE LA
FUENTE, GIVING A 30-MINUTE TALK
ON HIS RESEARCH AND CAREER PATH
FOLLOWED BY A 30-MINUTE QUESTION
AND ANSWER SESSION.
I ENCOURAGE ALL OF YOU JOINING
US ON THE ZOOM TODAY TO ASK
QUESTIONS ON DR. DE LA FUENTE'S
RESEARCH, CAREER AND SCIENCE BY
SUBMITTING THEM TO ME, JON
LORSCH, THROUGH THE ZOOM CHAT
BOX FUNCTION.
SO TODAY'S SPEAKER.
AS I MENTIONED, OUR SPEAKER
TODAY IS CAESAR DE LA FUENTE.
A PRESIDENTIAL ASSISTANT
PROFESSOR IN THE DEPARTMENTS OF
BIOENGINEERING, PSYCHIATRY AT
THE UNIVERSITY OF PENNSYLVANIA.
HIS RESEARCH GROUP BELIEVES THAT
INNOVATIONS IN ARTIFICIAL
INTELLIGENCE MAY HELP TO
REPLENISH OUR ARSENAL OF
EFFECTIVE DRUGS SUCH AS THOSE
USED TO TREAT ANTIBIOTIC
RESISTANT BACTERIAL INFECTIONS.
SPECIFICALLY, CESAR PIONEERED
THE DEVELOPMENT OF THE FIRST
ANTIBIOTIC DESIGNED BY A
COMPUTER WITH EFFICACY IN
ANIMALS, DESIGNED ALGORITHMS FOR
ANTIBIOTIC DISCOVERY,
REPROGRAMMED VENOMS TO
ANTIMICROBIALS, CREATED NOVEL
RESISTANCE-PROOF ANTIMICROBIAL
TEARS AND DIAGNOSTICS FOR
COVID-19 AND OTHER INFECTIONS.
HE HAS RECEIVED WIDE RECOGNITION
FOR HIS PIONEERING RESEARCH THAT
SPANS OVER 100 PUBLICATIONS.
NIGMS IS VERY PROUD TO HAVE
FUNDED CESAR'S PROGRAM, THE MIRA
PROGRAM, SINCE 2020.
CESAR, WELCOME AND THANK YOU FOR
JOINING US TODAY.
>>JON, THANK YOU VERY MUCH FOR
THE VERY, VERY KIND
INTRODUCTION.
I'D LIKE TO START OFF BY
EXPRESSING THAT IT'S REALLY A
PRIVILEGE TO GIVE THIS LECTURE
IN HONOR OF DR. JUDITH
GREENBERG, WHO ACTUALLY HAPPENS
TO BE HERE IN THE AUDIENCE, SO
THANK YOU, DR. GREENBERG, FOR
TAKING THE TIME.
I REALLY APPRECIATE IT.
AND I'M DEFINITELY INSPIRED BY
YOUR COMMITMENT THROUGHOUT YOUR
CAREER TO ADVANCING WOMEN IN
BIOMEDICAL SCIENCE.
SO THANK YOU FOR THAT.
AND IT'S ALSO HUMBLING TO FOLLOW
IN THE FOOTSTEPS OF LUMINARIES
IN THEIR RESPECTIVE FIELDS THAT
HAVE DELIVERED THIS LECTURE
PREVIOUSLY, SO I REALLY FEEL
VERY GRATEFUL FOR THIS
OPPORTUNITY.
I THOUGHT I'D TELL YOU
TODAY IS A LITTLE BIT ABOUT OUR
EFFORTS AROUND HOW TO USE
ARTIFICIAL INTELLIGENCE FOR
ANTIBIOTIC DISCOVERY.
BUT FIRST BEFORE I BEGIN WITH
ALL THE SCIENCE, I WOULD LIKE TO
TELL YOU A LITTLE BIT ABOUT MY
JOURNEY IN SCIENCE.
SO LET'S START FROM THE VERY
BEGINNING.
THAT'S ME AS A KID, READING.
I'VE ALWAYS BEEN VERY CURIOUS
ABOUT LEARNING NEW THINGS AND I
THINK THAT HAS ALWAYS BEEN A
GREAT SORT OF FIRE IN ME TO
LEARN.
IN SCIENCE, THERE IS NO LACK OF
LEARNING AND YOU'RE ALWAYS --
EVEN WHEN EXPERIMENTS DON'T
WORK, THAT ALWAYS SERVES AS A
LEARNING PROCESS, SO SCIENCE
REALLY -- FOR ME, IT MEANS -- IT
ENTAILS THE PERFECT FRAMEWORK
FOR LEARNING AND BEING CREATIVE
AND SO ON.
SINCE I WAS A VERY LITTLE KID,
I'VE ALWAYS BEEN FASCINATED WITH
ENGINEERING AND BIOLOGY.
I REMEMBER TRYING TO BUILD
FLYING MACHINES SUCH AS THE ONE
ON THE RIGHT, FOR EXAMPLE, AND I
WOULD CO CONVINCE MYSELF AND MY
SIBLINGS TO GET ON THESE FLYING
MACHINES AND ONE DAY WE SET IT
ON FIRE AND NOTHING HAPPENED.
BUT ANYWAYS, ALWAYS VERY CURIOUS
ABOUT EXPERIMENTING BOTH WITH
PHYSICAL THINGS AND WITH NATURAL
THINGS.
OTHER THINGS THAT I REMEMBER AS
A CHILD, BEING FASCINATED -- I
GREW UP BY THE OCEAN, SO BEING
FASCINATED BY MARINE ORGANISMS
OR JUST PONDERING ABOUT THE
ABILITY, THE INCREDIBLE ABILITY
OF GE C GECKOS TO REGENERATE THR
TAIL.
SUCH THINGS WE TAKE FOR GRANTED
AS WE BECOME ADULTS BUT THEY'RE
REALLY QUITE REMARKABLE, AND
THAT JUST SPEAKS TO HOW BIOLOGY
AND SCIENCE, YOU KNOW, IT SEEMS
LIKE SCIENCE FICTION SOMETIMES,
AND I THINK THAT MAKES IT
INCREDIBLY FUN FOR ME.
SO I DID MY UNDERGRAD IN
BIOTECHNOLOGY, SO THAT'S WHERE I
LEARNED HOW TO THINK ABOUT
SCIENCE A LITTLE BIT AND THAT
WAS A VERY FORMATIVE -- VERY
FORMATIVE YEARS FOR MYSELF.
FROM THEN, I TOOK A LEAP AND I
WENT TO UNIVERSITY IN VANCOUVER,
CANADA, BEAUTIFUL CITY, AND IN A
BEAUTIFUL CAMPUS, AND I HAD THE
OPPORTUNITY THERE TO WORK WITH
BOB HANCOCK, ONE OF THE PIONEERS
IN ANTIBIOTIC DESIGN AND
BACTERIAL PATHOGENESIS.
AND I HAVE TO SAY A QUICK STORY
HERE, I APPLIED TO CANADA
BECAUSE IT WAS ONE OF THE FEW
PLACES -- I WAS LUCKY TO GO FOR
AN INTERVIEW, AND I THINK THE
LESSON HERE IS TO HAVE A PLAN B
OR C OR D AND I THINK THAT THE
NEWER GENERATION IS MUCH MORE
PREPARED.
I DON'T KNOW WHAT I WOULD HAVE
DONE IN MY ENTIRE JOURNEY,
REALLY.
SO I'M ALSO GOING TO MOVE -- I
HAD TO LEARN ENGLISH, AND, YOU
KNOW, LEARN A NEW CULTURE AND
ALWAYS MOVING FROM ONE PLACE TO
ANOTHER AND START FACING SOME OF
THESE ADVERSITIES, I THINK HAS
HELPED ME A LOT IN SCIENCE.
NOT ONLY HOPEFULLY TO BECOME A
BETTER SCIENTIST BUT ALSO TO
BECOME PERHAPS A BETTER PERSON
AND TO ALWAYS TRY TO GROW.
THEN AFTER I COMPLETED MY PH.D.,
I WENT TO MIT WHERE I DID MY
POSTDOC, AND THERE I DID MORE --
I WORKED MORE IN SYNTHETIC
BIOLOGY AND COMPUTATIONAL
BIOLOGY APPROACHES TO ANTIBIOTIC
DISCOVERY AND TO MICROBIOLOGY,
AND MY TIME THERE OPENED A LOT
OF HORIZONS AS TO HOW TO THINK
FROM AN INTERDISCIPLINARY
PERSPECTIVE ABOUT PROBLEMS AND
HOW WE CAN SOLVE THEM BY
BRINGING TOGETHER PEOPLE THAT
COME FROM COMPLETELY DIFFERENT
DISCIPLINES ALL TOGETHER.
AND THEN FROM THERE, I WAS VERY
FORTUNATE TO BE RECRUITED TO ONE
OF THE OLDEST UNIVERSITIES IN
THE COUNTRY, UNIVERSITY OF
PENNSYLVANIA, THAT WAS FOUNDED
BY BENJAMIN FRANKLIN IN 1740, SO
IT'S INCREDIBLY OLD, AND IT'S A
BEAUTIFUL CAMPUS, IF YOU'VE
NEVER BEEN, I RECOMMEND IT.
VERY COMPACT CAMPUS WHERE YOU
HAVE THE ENGINEERING SCHOOL, THE
SCHOOL OF MEDICINE, THE SCHOOL
OF ARTS AND SCIENCES ALL
TOGETHER, SO WE CAN REACH WITHIN
WALKING DISTANCE OF INCREDIBLE
COLLEAGUES WHERE YOU CAN SPARC
CONVERSATIONS AND INITIATE
COLLABORATIONS AND EXECUTE NEW
IDEAS.
SO THAT'S ABOUT ME BUT I'D BE
HAPPY TO DISCUSS ANY OF THIS
JOURNEY THROUGHOUT THE Q & A
SESSION AT THE END.
SO A LITTLE ABOUT THE SCIENCE,
WE'RE INTERESTED IN USING
ARTIFICIAL INTELLIGENCE FOR
ANTIBIOTIC DISCOVERY.
DEVELOPING DRUGS IS A VERY
DIFFICULT PROCESS.
SO AS WE CAN SEE HERE ON THE
LEFT, TO DEVELOP A DRUG FROM THE
MOMENT THAT YOU DISCOVER IT IN
THE LABORATORY, THE FIRST TIME,
IN THAT EUREKA MOMENT, TO THE
MOMENT WHERE THAT DRUG ACTUALLY
HAS AN IMPACT ON PATIENTS IS AN
INCREDIBLY LONG AND WINDY ROAD,
IT CAN TAKE ON AVERAGE 10 YEARS.
SO IT'S A LONG TIME.
IT'S ALSO A HIGHLY COSTLY
ENDEAVOR.
TO DEVELOP A DRUG THERE'S A
PREDICTION IT COSTS ABOUT
$2.6 BILLION TO DEVELOP A DRUG.
THAT'S ACTUALLY MORE THAN THE
BUDGET THAT NASA HAS TO TAKE A
ROCKET ALL THE WAY TO THE MOON.
SO TO DEVELOP A DRUG IS EVEN
MORE COMPLICATED ENDEAVOR IN
SOME WAYS THAN TAKING A ROCKET
ALL THE WAY TO THE MOON, WHICH
IS INCREDIBLE TO THINK ABOUT.
WE ARE OPTIMISTIC, AND WE THINK
THAT THERE ARE A COUPLE OF
TRENDS THAT HAVE BEEN GOING ON
FOR SEVERAL DECADES THAT CAN
HELP ADDRESS SOME OF THESE GAPS
THAT WE SEE.
SO THE FIRST ONE IS THE
EVER-INCREASING COMPUTE POWER.
THIS IS FOLLOWING A MOORE'S LAW
THAT TELLS US THE NUMBER OF
TRANSISTORS WE CAN FIT IN A CHIP
DOUBLES EVERY TWO YEARS OR SO,
MEANING WE HAVE MORE AND MORE
CAPACITY TO PROCESS INFORMATION
ON COMPUTERS.
AND THE SECOND TREND IS OUR
ABILITY TO GENERATE DATA.
WE CAN REALLY GENERATE VAST
AMOUNTS OF DATA NOWADAYS, AND
THAT'S THANKS TO ADVANCES IN
AUTOMATION AND IN
HIGH-THROUGHPUT SCREENING.
SO WE THINK THESE TWO TRENDS CAN
HELP US REDUCE THE TIME THAT IT
TAKES TO DEVELOP ANTIBIOTICS AND
ALSO HELP US REDUCE THE COSTS
ASSOCIATE WITH THIS PROCESS.
WHY DO WE FOCUS ON ANTIBIOTICS?
ANTIBIOTIC RESISTANCE IS A HUGE
GLOBAL HEALTH PROBLEM.
IT'S CURRENTLY PREDICTED TO LEAD
TO THE DEATH OF 10 MILLION
PEOPLE PER YEAR BY 2050,
SURPASSING EVERY OTHER MAJOR
CAUSE OF DEATH IN OUR SOCIETY.
THOSE 10 MILLION DEATHS PER
YEAR, IF YOU RUN A CRUDE
CALCULATION, THEY CORRESPOND TO
ABOUT ONE DEATH EVERY
THREE SECONDS.
SO THIS IS THE FUTURE THAT WE'RE
HEADING TOWARDS UNLESS WE COME
UP WITH SOLUTIONS, WE COME UP
WITH NOVEL TYPES OF ANTIBIOTICS
THAT WE CAN USE TO TREAT THESE
CURRENTLY UNTREATABLE
INFECTIONS.
I ALWAYS LIKE TO HIGHLIGHT THAT
ANTIBIOTICS ARE NOT ONLY USEFUL
WHEN WE HAVE AN INFECTION AND WE
TAKE THEM AND WE GET CURED, BUT
THEY'RE ACTUALLY ESSENTIAL FOR
MODERN MEDICINE AS WE KNOW IT.
SO INTERVENTIONS LIKE CHILDBIRTH
OR CHEMOTHERAPY TREATMENTS FOR
CANCER PATIENTS OR ORGAN
TRANSPLANTATIONS OR SURGERIES
WOULD NOT BE FEASIBLE WITHOUT
EFFECTIVE ANTIBIOTICS.
SO IT'S REALLY IMPORTANT TO
EMPHASIZE THIS.
NATURE, I'M SHOWING A PHOTO OF
THE GLOBE HERE BECAUSE NATURE
HAS BEEN A GREAT INSPIRATION OF
LIKE A SOURCE OF ANTIBIOTICS.
FROM THE VERY FIRST ONE, WHICH
WAS PENICILLIN, WHICH WAS
DISCOVERED BY ALEXANDER FLEMING
IN 1928, NATURE HAS REALLY GIVEN
US EVERY MAJOR CLASS OF
ANTIBIOTICS THAT WE HAVE
NOWADAYS IN HOSPITALS.
AND SO AGAIN, IT HAS BEEN A
GREAT SOURCE OF INSPIRATION AND
HAS GIVEN US DIFFERENT
CHEMISTRIES THAT WE'VE USED FOR
MANY DECADES TO SAVE LIVES, BUT
THE PROBLEM THAT WE'RE FACING
TODAY IS THAT FOR DECADES, THE
SCIENTIFIC COMMUNITY, WE'VE BEEN
UNABLE TO FIND TRULY NOVEL
CLASSES OF ANTIBIOTICS IN
NATURE.
SO IN MY LAB, THE WAY WE THINK
ABOUT THIS, INSTEAD OF RELYING
ON NATURE TO GIVE US ALL THESE
LIFE-SAVING MOLECULES, WHY DON'T
WE TRY TO TRANSLATE THE CHEMICAL
COMPLEXITY OF MOLECULES INTO THE
BINARY CODE OF ONES AND ZEROS SO
THAT MACHINES CAN TAKE CARE OF
THE DISCOVERY PROCESS.
AND IN PARTICULAR, WE FOCUS
MOSTLY ON SMALL PROTEINS CALLED
PEPTIDES IN MY LAB.
OF COURSE HOW DO WE ENGINEER
PEPTIDES OR PROTEINS?
WE FOLLOW VERY BASIC -- THE
STRUCTURE OF OUR PROTEIN
DETERMINES ITS FUNCTION.
SO IF WE CAN CONTROL THE
SEQUENCE OF AMINO ACIDS WHICH
ARE THE BUILDING BLOCKS OF
PEPTIDES AND PROTEINS, WE'LL BE
ABLE TO CONTROL THEIR FUNCTION
AND THEN CAN YOU TUNE IT.
THIS IS A LITTLE LIKE PLAYING
LEGO.
WE'RE TRYING TO ARRANGE
DIFFERENT AMINO ACIDS, BUILDING
BLOCKS IN DIFFERENT POSITIONS,
TO BUILD SYNTHETIC MOLECULES
THAT ARE CAPABLE OF DOING WHAT
WE WANT THEM TO DO.
SO THIS IS A TRADITIONAL WAY,
AND MORE RECENTLY, WE'VE
INCORPORATED ASPECTS OF
COMPUTATIONAL BIOLOGY AND A.I.
TO HELP US ACCELERATE ANTIBIOTIC
DISCOVERY.
COMPUTERS, THEY CAN HELP US DO A
NUMBER OF THINGS, AND I'M GOING
TO TELL YOU ABOUT THREE OF THEM.
THAT ARE, I THINK, QUITE
SIGNIFICANT.
THEY CAN HELP US EXPLORE
SEQUENCE BASE.
AGAIN, SEQUENCE BASE AS I'LL
MENTION IS INCREDIBLY VAST FOR
ANY MOLECULES BUT INCLUDING ALSO
ANY PROTEINS OR ANY PEPTIDES.
IT'S ALMOST INFINITE.
SO COMPUTERS CAN HELP US EXPLORE
THIS.
THE SECOND THING IT CAN HELP US
DO, MACHINES IS HELP US GENERATE
NEW MOLECULES.
HERE WE ENTER A LITTLE BIT INTO
THE REALM OF CREATIVITY THAT WE
TYPICALLY ASSOCIATE WITH THE
HUMAN BRAIN, BUT I'LL PROVIDE
SOME EARLY EVIDENCE THAT
COMPUTERS ALSO -- AND WE CAN
TRAIN COMPUTERS TO START TO GET
A LITTLE BIT CREATIVE IN TERMS
OF THEIR ABILITY TO CREATE NOVEL
MOLECULES.
LESS BUT NOT LEAST, MACHINES CAN
HELP US MINE BIOLOGY TO TRY TO
FIND NOVEL DRUGS IN OUR CASE,
NOVEL ANTIMICROBIALS.
SO I'LL TALK ABOUT EACH OF THESE
THREE POINTS.
SO SEQUENCE SPACE.
HOW DO WE THINK ABOUT SEQUENCE
SPACE?
SO ILLUSTRATE THIS CONCEPT A
LITTLE BIT BETTER, I'M GOING TO
SHOW YOU THE SPACE OF CONCEPTS
THAT PERHAPS THE AUDIENCE IS
MORE FAMILIAR WITH.
FOR EXAMPLE, THE NUMBER OF
PEOPLE ON EARTH IS 10 TO THE 10.
THE NUMBER OF BACTERIA IN HUMAN
IS 10 TO THE 13.
WE'RE SURROUNDED BY MICROBES,
THE MAJORITY OF WHICH DO GOOD
THINGS FOR US.
IF WE KEEP GOING HIGHER, HIGHER
ORDER OF MAGNITUDE, THE NUMBER
OF STARS IN THE UNIVERSE, 10 TO
THE 31.
AND ALMOST UNIMAGINABLE NUMBER.
VERY DIFFICULT TO GRASP.
AND NOW I WOULD ASK YOU TO
CONSIDER A VERY SMALL PROTEIN, A
PEPTIDE COMPOSED OF ONLY 25
AMINO ACIDS, IN A LINEAR CHAIN,
ONE UP TO THE OTHER, SORT OF
LIKE THE COLOR OF PEARLS.
I'LL JUST TELL YOU THAT THE
COMMUNE TORE YAL SEQUENCE BASE
OF THAT VERY SMALL MOLECULE,
AGAIN, VERY SMALL, MUCH SMALLER
THAN ANY PROTEIN IN OUR BODIES
WHICH ARE COMPRISED OF HUNDREDS
OF AMINO ACIDS, IT HAS A
SEQUENCE BASE THAT IS SUPERIOR
TO THE NUMBER OF STARS IN THE
UNIVERSE.
SO THIS IS INCREDIBLE TO THINK
ABOUT, WE NEED COMPUTERS TO
START EXPLORING THIS.
TO MAKE THINGS MORE COMPLEX AND
THEREFORE OF COURSE MORE
INTERESTING FROM A SCIENTIFIC
PERSPECTIVE, BIOLOGICAL
EVOLUTION THROUGHOUT BILLIONS OF
YEARS HAS ONLY SAMPLED A TINY
FRACTION OF THE ENTIRE SPACE OF
POSSIBILITIES OF ALL POTENTIAL
MOLECULES, PEPTIDES, PROTEINS.
SO EVERYTHING THAT HAS BEEN
EXPLORED, WE CAN FIT IT INTO
THIS LITTLE PINK OVAL HERE.
AND ALL THESE WHITE AREAS REMAIN
UNEXPLORED.
COMPUTERS CAN HELP US EXPAND THE
SEQUENCE SPACE FOR THE WHITE
SPACES OR AREAS THAT WE THINK
MAY HARBOR MOLECULE SEQUENCES
THAT CAN HELP US SOLVE
PRESENT-DAY PROBLEMS, FOR
EXAMPLE, ANTIBIOTIC RESISTANCE.
SO OKAY, CLEARLY I HOPE I
CONVINCED YOU THAT WE NEED
COMPUTERS TO BE ABLE TO TACKLE
SOME OF THESE PROBLEMS AND THE
NEXT QUESTION WE ASKED OURSELVES
WAS HOW CAN WE TRAIN A COMPUTER
TO CREATE DIVERSITY AT THE
MOLECULAR LEVEL, TO INNOVATE AT
THE MOLECULAR LEVEL?
AFTER THINKING ABOUT THIS WITH
OUR COLLABORATORS, WE DECIDED
THE BEST WAY TO DO THIS WAS TO
TRAIN A COMPUTER TO MIMIC THE
GREATEST ENGINE WE HAVE FOR
INNOVATION AND THAT'S, OF
COURSE, NOTHING ELSE THAN
EVOLUTION ITSELF.
SO WE DECIDED TO TEACH THE
COMPUTER HOW TO EXECUTE DARWIN,
ON A MACHINE IT BECOMES A THEORY
OF ARTIFICIAL SELECTION INSTEAD
OF HAVING TO WAIT MILLIONS OF
YEARS FOR A MOLECULE TO EVOLVE,
WE CAN DO IT ON A COMPUTER IN A
TIME SCALE OF DAYS TO WEEKS.
THIS IS HOW WE DID IT.
WE STARTED WITH AN INITIAL
POPULATION OF SMALL PEPTIDES
FROM NATURE, AND THEN LIKE I
SAID, WE TRAINED THE COMPUTER TO
EVOLVE THEM THROUGH MUTATION,
SELECTION, RECOMBINATION, WHICH
ARE THE ESSENTIAL PROCESSES OF
EVOLUTION, AND ITERATIVELY IN A
WAY THE COMPUTER IS ABLE TO
EVOLVE THE MOLECULES IN REALTIME
INSILICO.
SO THIS IS WHAT IT LOOKS LIKE,
UPON INCREASING NUMBER OF
ITERATIONS, COMPUTER EVOLVES THE
MOLECULES TOWARD VALUES THAT
CORRELATE WITH PREDICTED
ANTIBIOTIC ACTIVITY.
SO AT LEAST IN PRINCIPLE, THE
COMPUTER IS MAKING THE MOLECULES
BECOME BETTER AT -- BACTERIA.
IN THE PROCESS OF DOING THIS,
WHICH IS REALLY INTERESTING, THE
COMPUTER IS ALSO CAPABLE OF OF
EXPLORING PREVIOUSLY UNEXPLORED
SPACES, REMEMBER A FEW SLIDES
AGO, THE WHITE SPACES, WHITE
AREAS NOT EXPLORED THROUGH
EVOLUTIONARY PROCESS, AND YOU
CAN START TO INTERROGATE THOSE
REGIONS YIELDING MOLECULES
DIFFERENT FROM WHAT WE SEE IN
BIOLOGY.
SO WITH AMINO ACID RATIOS AND
COMPOSITIONS THAT ARE DIFFERENT,
ATYPICAL, NOT WHAT WE TYPICALLY
SEE THAT HAS BEEN GENERATED
THROUGHOUT THE EVOLUTIONARY
PROCESS.
OKAY.
SO WHAT I'M SHOWING YOU HERE IS
A BUNCH OF DIFFERENT PEPTIDE
MOLECULES.
YOU CAN SEE THOSE BEAUTIFUL
STRUCTURES, BUT EVERYTHING I'M
SHOWING YOU HERE JUST GENERATED
BY THE MACHINE WITH VERY MINIMAL
HUMAN INTERVENTION UP TO THIS
POINT.
AND HERE, WE REACH A ROADBLOCK
VERY COMMON TO ANY PROJECTS
INVOLVING COMPUTERS AND THAT IS
THAT EVERYTHING THAT WE SEE HERE
IS JUST BASED ON COMPUTATIONAL
ASSUMPTIONS.
IN OTHER WORDS, THE COMPUTER
THINKS THAT THESE MOLECULES WILL
BE GREAT ANTIBIOTICS, THEY WILL
BE GREAT AT KILLING BACTERIA.
BUT WE DON'T KNOW THAT FOR SURE,
RIGHT?
THIS IS VERY MUCH AN EMERGING
FIELD SO WE CAN'T REALLY TRUST
THE COMPUTER'S ASSUMPTION.
WE NEED TO VALIDATE ABSOLUTELY
EVERYTHING.
SO WE CHEMICALLY SYNTHESIZE
THESE MOLECULES AND WE TESTED
THEM AGAINST REAL BACTERIA FROM
THE HOSPITALS AND FROM THE
LABORATORY, TO SEE IF THEY WERE
ACTUALLY ABLE TO KILL THEM.
SO THROUGH A NUMBER OF SCREENING
EFFORTS WE WERE ABLE TO ISOLATE
A LEAD PEPTIDE THAT WAS ACTUALLY
VERY POTENT AT KILLING
PATHOGENS.
THEN WE WENT ON TO EE LEWIS DADE
THE THREE-DIMENSIONAL STRUCTURE
OF THE PEPTIDE WHICH WE SEE, WE
CALL THIS GUAVANIN 2.
AND OF COURSE WE'RE NOT SEAD
WSATISFIED WITH THIS, WE WANTED
TO SEE IF THIS COMPUTER-MADE
MOLECULE WAS CAPABLE OF REDUCING
INFECTIONS IN A REALISTIC
CLINICAL MOUSE MODEL.
SO WE DEVELOPED THIS SKIN
INFECTION MODEL AND WE INFECTED
THE MICE AND THEN WE TREATED
THEM.
WE TREATED THEM WITH TWO OF THE
PEPTIDES THE COMPUTER STARTED
THE PROCESS WITH, THESE TWO
HERE, AND THIS IS THE
MACHINE-MADE MODEL, GUA VANIN
2 THAT LED TO BETTER RESOLUTION
OF THE INFECTION.
THIS IS A CONTROL GROUP OF MICE
SO YOU GET A SENSE OF HOW MANY
BACTERIA IN THOSE CORE MICE LEFT
UNTREATED, YOU CAN SEE THE
GUAVANIN 2 MOLECULE IS ACTUALLY
CAPABLE OF REDUCING THE
INFECTION MUCH MORE EFFECTIVELY
THAN EVERY OTHER GROUP.
SO JUST TO WRAP UP THIS PART, WE
CAN USE COMPUTERS TO ASSIGN
NOVEL ANTIBIOTICS NOT ONLY
PRETTY TO LOOK AT ON THE SCREEN
BUT THEY ACTUALLY KILL, YOU
KNOW, PATHOGENIC BACTERIA THAT
ARE CLINICALLY RELEVANT IN VITRO
AND ALSO IN A MOUSE MODEL.
SO THIS IS ANOTHER PROJECT WHERE
WE IDENTIFIED A MOTIF,
ESSENTIALLY FIVE AMINO ACIDS
COLORED IN RED THAT WERE
ASSOCIATED WITH TWO FUNCTIONS.
DIRECT ANTIMICROBIAL ACTIVITY
AND IMMUNOMODULATORY.
THE HYPOTHESIS THAT WE REACHED
IS, WHY DON'T WE TAKE THIS MOTIF
THAT WE PREDICT HAS THESE TWO
DIFFERENT FUNCTIONS AND WE'LL
TRY TO STITCH IT INTO ANOTHER
MOLECULE TO SEE IF IT CAN
INVOLVE THAT OTHER MOLECULE WITH
TWO FUNCTIONS OF INTEREST.
SO IT'S A LITTLE LIKE PLAYING
LEGO.
SO WE STARTED WITH THIS
TEMPLATE.
ACTUALLY A TOXIC PEPTIDE, AND WE
BASICALLY ENGINEERED IN THE
PEPTIDE MOTIF TO ITS END
TERMINUS TO CREATE THIS MOLECULE
WE CALLED MAST-MO.
ESSENTIALLY WE'RE ABLE TO SHOW
THROUGH A NUMBER OF ASSAYS IN
VIVO AND IN VITRO THAT THAT
SYNTHETIC MOLECULE WAS CAPABLE
OF DISPLAYING THE DUAL ACTIVITY.
SO NOT ONLY THE CONVENTIONAL
PARADIGM FOR TREATING INFECTIONS
WHERE WITH ANTIBIOTICS YOU
TARGET THE PATHOGEN AND YOU
CLEAR IT AND THAT RESOLVES THE
INFORECAINFECTION BUT ALSO IN AW
PARADIGM, WHERE WHAT YOU'RE
DOING IS RESOLVING THE INFECTION
BY BOOSTING THE HOST'S OWN
IMMUNE SYSTEM, IN THIS CASE, THE
MOUSE IMMUNE SYSTEM, TO THEN --
YOU BOOST IT IN SUCH A WAY THAT
THE MOUSE'S IMMUNE SYSTEM IS
ABLE TO CLEAR THE INFECTION.
SO WE'RE ABLE TO DEMONSTRATE
THIS CONCEPT BOTH IN VITRO IN A
SKIN INFECTION MODEL AND ALSO IN
A SEPSIS INFECTION MODEL, WHICH
IS A HUGE PROBLEM, SEPSIS IS A
HUGE PROBLEM IN THE WORLD AS I
MENTIONED, HERE ARE SOME OF THE
NUMBERS, AND HERE TREATMENT WITH
THE SYNTHETIC PEPTIDES WERE
CAPABLE OF COMPARING PROTECTION
TO MICE AGAINST OTHERWISE LETHAL
INFECTIONS OF LABORATORY STRAINS
LIKE E. COLI AND STAPH AUREUS
BUT ALSO HIGHLY DRUG RESISTANT
STRAINS PROBLEMATIC IN THE
HOSPITAL, SUCH AS E. COLI KPC
POSITIVE AND STAPH AUREUS
MRSA -- HIGHLY RESISTANT TO
NUMBER OF ANTIBIOTICS.
SO THAT WAS ALSO ENCOURAGING AND
WE'RE NOW PURSUING FURTHER
DEVELOPMENT OF THIS APPROACH FOR
TREATING INFECTIONS THROUGH DUAL
MECHANISMS.
OKAY, SO I TOLD YOU HOW WE CAN
USE COMPUTATIONAL TOOLS COUPLED
WITH EXPERIMENTS TO DESIGN NOVEL
APPROACHES FOR ANTIBIOTIC
TREATMENT.
OR ANTIBIOTIC DESIGN.
NOW I'D LIKE TO TELL YOU HOW WE
USE -- BIOLOGICAL INFORMATION,
THINGS THAT HAVEN'T REALLY BEEN
EXPLORED BEFORE VERY WELL, AND
TO TACKLE THIS, WE TAKE
INSPIRATION FROM IMAGE AND
SPEECH RECOGNITION ALGORITHMS
BUT INSTEAD OF RECOGNIZING
FACIAL EXPRESSIONS OR SOUNDS, WE
WANT TO RECOGNIZE MOLECULAR
PATTERNS ASSOCIATED WITH
POTENTIAL ANTIBIOTICS.
AND WE WORK WITH PEPTIDES, WE
LOOK AT AMINO ACID PATTERNS, AND
THE ANALOGY THAT I LIKE TO USE
HERE IS THAT THESE ALGORITHMS
OPERATE A LITTLE LIKE THE SEARCH
FUNCTION IN MICROSOFT WORD,
WHERE LET'S SAY YOU HAVE A HUGE
WORD DOCUMENT LIKE -- WITH
HUNDREDS OF PAGES AND YOU WANT
TO FIND THE WORD -- ONE
PARTICULAR WORD, LET'S SAY
NIGMS.
YOU GO INTO THE SEARCH FUNCTION,
YOU TYPE NIGMS, AND THE
ALGORITHM IDENTIFIES OR FINDS
THE NIGMS IN WHATEVER INSTANCE
IT APPEARS IN THE DOCUMENT.
VERY EFFECTIVELY.
SOL ALGORITHMS OPERATE IN A
SIMILAR FASHION WHERE WE KNOW
WHAT WE'RE LOOKING FOR, THE
PATTERNS, INSTEAD OF USING
LETTERS, WE USE AMINO ACID CODE
AND INSTEAD OF SEARCHING OR
BROWSING -- WE BROWSE THROUGH
GENOMES AND PROTEINS AND WE CAN
IDENTIFY POTENTIAL NEW
ANTIMICROBIALS.
SO TO ILLUSTRATE THIS CONCEPT IN
A MORE VISUAL WAY, WE CAN HAVE
ESSENTIALLY A PROTEIN IN THREE
DIMENSIONS, WE CAN THEN DISPLAY
IT IN TWO DIMENSIONS HERE, AND
THE ALGORITHM ESSENTIALLY WILL
RUN THROUGH THE CODE AND WILL
IDENTIFY REGIONS WITHIN THE
AMINO ACID CODE THAT ARE
PROSPECTIVE ANTIBIOTICS.
THAT'S GIVEN IN THE DIFFERENT
COLORS HERE, THE DIFFERENT
COLORS THAT WE SEE.
AGAIN, ONLY A PREDICTION.
BUT REALLY A VERY POWERFUL ONE
BECAUSE IT ALLOWS YOU TO
IDENTIFY WI WITHIN ENTIRE PROTES
FRAGMENTS, FOR EXAMPLE THIS
FRAGMENT IN YELLOW THAT
REPRESENT A POTENTIAL
ANTIBIOTIC.
WE CAN CONSTRUCT THAT ANTIBIOTIC
AND PLAY AROUND WITH IT IN
LABORATORY TO SEE IF IT CAN
BECOME A POTENTIAL THERAPEUTIC.
SO WE'VE BASICALLY UTILIZED
THESE METHODS TO EXPLORE THE
WHOLE -- TO PERFORM THE FIRST
PROTEIN-WIDE EXPLORATION OF THE
HUMAN BODY AS A SOURCE OF
POTENTIAL ANTIBIOTICS.
WE FOUND THOUSANDS OF WHAT WE
CALL ENCRYPTED PEPTIDE
ANTIBIOTICS AND WHAT I THINK IS
REALLY QUITE FASCINATING IS THAT
WE DIDN'T ONLY FIND THEM IN THE
INNATE IMMUNE SYSTEM, WHICH IS
WHERE YOU WOULD IMAGINE YOU
WOULD FIND ANTIMICROBIAL
MOLECULES.
THE IMMUNE SYSTEM HELPS OF FIGHT
OFF INVADING ORGANISMS,
PATHOGENS.
BUT WE WE ALSO FIND THEM ALL
THROUGHOUT THE BODY.
THIS TAKES US TO OUR CURRENT
HYPOTHESIS, WHERE WE THINK THAT
PERHAPS IMMUNOLOGICAL RESPONSE
IS NOT ONLY RESPONSIBILITY OF
THE INNATE IMMUNE SYSTEM, BUT WE
HAVE ALL THESE OTHER BODY
SYSTEMS WORKING IN COLLABORATION
TO PROVIDE -- TO FIGHT OFF
INVADING ORGANISMS EITHER
DIRECTLY OR INDIRECTLY.
SO AGAIN, WE FOUND THOUSANDS OF
THESE MOLECULES, AND THEN WE
SYNTHESIZE 56 OF THEM USING
CHEMICAL METHODS, USING CHEMICAL
SYNTHESIS.
WE TRY TO LEARN AS MUCH AS
POSSIBLE FROM THIS EM.
SO I'M GOING TO TELL YOU SOME OF
THE LEARNINGS THAT WE TOOK FROM
SOME OF THOSE EXPERIMENTS.
FOR EXAMPLE, IF YOU TAKEN
CRYPTED PEPTIDES THAT ARE
ENCODED IN THE SAME
BIOGEOGRAPHICAL AREA OF YOUR --
THE SAME AREA OF THE BODY, LET'S
SAY THE BLOODSTREAM, AND YOU
COMBINE IT IN COCKTAILS, YOU'RE
ACTUALLY ABLE TO POTENTIATE THE
ACTIVITY TO TARGET PATHOGENS,
WHICH IS REALLY INTERESTING.
AND WE DEMONSTRATED THIS IN
SYNERGISTIC INTERACTION ASSAYS,
AND THIS TAKES US TO A VIEW OF
SOME OF THESE INCRYPTED
MOLECULES WHERE YOU CAN ENVISION
HAVING HUNDREDS OF THEM ALL
THROUGHOUT YOUR BODY WORKING IN
CONJUNCTION TO PROVIDE AN
IMMUNOLOGICAL RESPONSE.
ANOTHER THING THAT WE WANTED TO
SEE IS WHETHER OUR TARGET
DEVELOPED RESISTANCE TO THESE
INCRYPTED PEPTIDES AND TARGET
BACTERIA, SO WE RAN RESISTANCE
DEVELOPMENT ASSAYS IN THIS CASE
AGAINST GRAM-NEGATIVE HIGHLY
DRUG-RESISTANT BACTERIA.
AND WE EXPOSED THE BACTERIA OVER
TIME, OVER 30 DAYS,
CONSISTENTLY, WITH POLYMYXIN B
AND THREE ENCRYPTED -- WE FIND
IN OUR OWN BODIES.
THE BACTERIAL PATHOGEN
EVENTUALLY DEVELOPED RESISTANCE
TO POLYMYXIN B BUT NOT THE
ENCRYPTED PEPTIDES.
SO IT SEEMS THE BACTERIA DID NOT
READILY DEVELOP PERSISTENCE TO
THESE MOLECULES.
SO THAT'S ALSO ENCOURAGING.
THEN, OF COURSE, WE WENT ON TO
SEE WHAT WAS THE INFECTIVE
PROPERTY.
WE WENT TO ALL THIS INTERESTING
TRANSLATING TO SEE IF WE COULD
TURN SOME OF THESE MOLECULES
INTO ANTIBIOTICS THAT EVENTUALLY
THEY HELP PEOPLE.
AND WE WERE ABLE TO SEE THAT
TREATMENT, MONOTHERAPY TREATMENT
WITH ONE PEPTIDE OR ANOTHER
PEPTIDE REDUCED THE INFECTION.
IN THIS CASE, PSEUDOMONAS
INFECTION IN MICE, THIS IS THE
UNTREATED CONTROL GROUP, BUT
THEN THAT EFFECT WAS EMPHASIZED
AND COMBINED IN COCKTAIL, SO WE
WERE ABLE TO RECAPITULATE THE
DATA WE GOT IN VITRO, ALSO IN
VIVO.
SO THIS IS COMBINING THESE TWO
MOLECULES HERE, WE CAN SEE AN
ENHANCED EFFECT AT REDUCING THE
INFECTION.
AND THE SAME BASICALLY FOR A.
BAUMANNIL.
BUT THEN WE WENT ON AND WE
TESTED THE INFECTIVE EFFICACY IN
A PRE-CLINICAL INFECTION MODEL,
TYPICALLY THE ONE THAT THE FDA
WANTS TO SEE FOR -- ANTIBIOTICS.
ONCE AGAIN WE SEE THE TREATED
CONTROL GROUP, WE HAVE AROUND 10
TO THE 9 BACTERIA IN THOSE MICE,
AND THEN WE HAVE MONOTHERAPY
WITH ONE PEPTIDE, MONOTHERAPY
WITH ANOTHER PEPTIDE AND THEN
COMBINATION THERAPY.
SO WE SEE THE POWER, THE FORCE
OF COMBINING COCKTAILS AND HOW
THAT ACTUALLY ENHANCES THE
ANTIMICROBIAL ACTIVITY IN VIVO
VERY SUBSTANTIALLY.
THIS IS JUST TO SHOW THAT SOME
OF THESE ENCRYPTED PEPTIDES
ACTUALLY PRODUCE -- IN THE BODY.
WE DID IT THROUGH A LITERATURE
SEARCH AND WE HAVE FOUR EXAMPLES
HERE, THE CUB DOMAIN 3,
FIBROBLAST GROWTH FACTOR 5,
NATRIURET IC PEPTIDE AND VON
WILLEBRAND FACTOR, FROM
DIFFERENT LEVELS, LOW TO MEDIUM
TO HIGH, IN DIFFERENT AREAS OF
THE BODY.
THIS IS SUPPORT THAT THESE
MOLECULES ARE PRODUCED NATURALLY
IN OUR BODIES, FRAGMENTS OF
LARGER PROTEINS, AND THEY ARE
CONFERRING IMMUNOLOGICAL EFFECT.
SO I'M GOING TO SHIFT GEARS A
LITTLE BIT.
SO ONE -- THIS IS A FUN
COLLABORATION WITH A COLLEAGUE
OF MINE IN BARCELONA, AND THE
CONCEPT HERE IS THAT ANTIBIOTICS
ARE VERY PASSIVE MOLECULES.
WE INTRODUCE THEM INTO, FOR
EXAMPLE, THE BLOODSTREAM AND
THEY GO ALONG FOR THE RIDE
THROUGH THE BLOODSTREAM.
THEY DON'T HAVE ANY SORT OF
DIRECTION.
THEY DON'T KNOW WHERE THE TARGET
SITE IS, SO WE DECIDED TO SEE IF
WE COULD CONFER ANTIBIOTICS WITH
SOME SORT OF DIRECTIONALITY.
AND WHAT WE DID IS WE USED
NANOMACHINES AND WE LOADED THEM
WITH ANTIBIOTIC AND THE
NANOMACHINES CAN SELF-PROPEL
THROUGH A DISTANCE, AND THE
ANALOGY HERE WOULD BE LIKE IF
YOU HAD A TRACK, RIGHT?
YOU'D LOAD IT WITH A BUNCH OF
ANTIBIOTIC AND THEN THE TRACK
CAN MOVE AND IT CAN TRANSPORT
ANTIBIOTIC.
AND THEN AS IT TRANSPORTS
ANTIBIOTIC, THE ANTIBIOTIC IS
CAPABLE OF RESOLVING THE
INFECTION, BUT IN A DIRECTIVE
WAY, IN AN AUTONOMOUS WAY.
SO WHAT YOU'LL SEE HERE IN THE
DIFFERENT QUADRANTS, YOU'LL SEE
MOVEMENT OF NANOMACHINES THAT
ARE BEING SELF-PROPELLED AND
TRANSPORTING ANTIBIOTIC AND
CLEARING AN INFECTION, AND IN
THIS QUADRANT HERE, YOU WON'T
SEE MATCH BECAUSE THE SUBSTRATE
THAT DRIVES THE INTERACTION IS
NOT PRESENT SO ALL YOU'LL SEE IS
A BIT OF -- MOTION IN THIS
QUADRANT.
SO HERE'S A LITTLE VIDEO.
YOU CAN SEE IN MOST CASES YOU
HAVE DIRECTIONALITY AND THE NANO
MACHINES ARE MOVING ANTIBIOTIC,
I THINK THIS OPENS AVENUES FOR
THINKING ABOUT IN DESIGNING
ANTIBIOTICS THAT CAN REACH THE
TARGET SITE IN AN AUTONOMOUS
FASHION AND THAT WOULD AVOID A
LOT OF THE SIDE EFFECTS WE HAVE
WITH ANTIBIOTICS THAT GO ALL
THROUGHOUT OUR BODIES.
THEN JUST TO WRAP UP A LITTLE
BIT, SO WE'RE REALLY FASCINATED
BY COMPUTERS AND THEIR ABILITY
TO DESIGN AND DISCOVER NOVEL
DRUGS, FOCUSED ON ANTIBIOTICS,
AND NOW WHAT WE WOULD LIKE TO DO
IS WE'D LIKE TO STREAMLINE THIS
SO THAT ROBOTS CAN MAKE THE
MOLECULES THE COMPUTER TELLS
THEM TO MAKE.
AND THEN STRE STREAMLINE THIS
FURTHER, CONNECT THEM TO A
SCREENING FACILITY THAT ALLOWS
US TO SCREEN THOSE MOLECULES FOR
ANTIBIOTIC ACTIVITY OR OTHER
TYPES OF ACTIVITY THAT YOU MIGHT
BE INTERESTED IN, WHETHER CANCER
OR OTHERS.
WE'RE BUILDING MACHINE LEARNING
MODELS TO TIE BACK THE
FUNCTIONAL INFORMATION FROM
THOSE BIOASSAYS BACK TO THE
COMPUTER SO WE CAN HAVE A
SELF-LEARNING PLATFORM THAT
ALLOWS US TO DISCOVER A NOVEL --
NOVEL ANTIBIOTICS REALLY IN A
VERY RAPID MANNER.
THIS IS JUST TO ILLUSTRATE HOW
YOUNG THIS FIELD IS.
AT THE INTERSECTION OF A.I. AND
ANTIBIOTIC DISCOVERY.
THIS IS A RETROSPECTIVE STUDY WE
RUN USING THE QUERIES --
CONTINUING THE QUERIES OF
ANTIBIOTICS AND A.I., CANCER
THERAPEUTICS A.I. AND DRUGS A.I.
YOU CAN SEE WE HAD PRACTICALLY
NO PUBLICATIONS UNTIL 2018 SO
REALLY WE WERE IN THE MIDST OF A
YOUNG AND EMERGING FIELD.
I ALWAYS LIKE TO --
INTERDISCIPLINARY RESEARCH
COMING FROM BIOLOGY,
MICROBIOLOGY, PHYSICS, COMPUTER
SCIENCE, DOESN'T REALLY MATTER,
CHEMISTRY, THAT IT WOULD BE
WONDERFUL TO HAVE YOU JOIN THESE
EFFORTS.
AGAIN -- AFFECTS EVERY CORNER OF
THE WORLD AND THEY'RE REALLY
MUCH NEEDED SO REALLY TRIED TO
USE YOUR TALENT AND YOUR
INGENUITY AND YOUR PASSION TO
TRY TO MOVE THIS FIELD FORWARD
WOULD BE REALLY FANTASTIC.
SO IF ANYBODY WANTS TO GET IN
TOUCH, PLEASE DO SO.
I'D LIKE TO ALSO MENTION THEY
ASKED ME TO ADD THIS TO MY TALKS
AND THIS IS A BOOK THAT WE
RECENTLY PUBLISHED ON MACHINE
LEARNING FOR DRUG DISCOVERY.
I THINK IT SERVES AS A GOOD
PRIMER FOR BEGINNERS TO TRY TO
GET INTO THIS FIELD SO IF
ANYBODY IS INTERESTED, I'M ABLE
TO GET A FREE COPY.
I'D LIKE TO END HERE.
I'D LIKE TO THANK THE LAB
MEMBERS.
IT'S REALLY A PRIVILEGE TO WORK
WITH THEM.
HIGHLY INTERDISCIPLINARY PEOPLE,
VERY SMART.
A LOT OF THE THINGS I'VE SHOWN
TODAY WAS DONE BY PEOPLE IN MY
LAB AND MY COLLABORATORS, SO
REALLY OUTSTANDING.
OUR FUNDERS FOR ALLOWING US TO
DO THE WORK WE DO AND PRIMARILY
I'D LIKE TO THANK NIGMS FOR
THEIR SUPPORT, REALLY
INSTRUMENTAL AT A CRUCIAL POINT
IN MY CAREER, REALLY MADE ME
BELIEVE THAT I COULD ATTRACT
FUNDS TO FUND SOME OF OUR IDEAS.
SO THAT WAS A REALLY KEY MOMENT.
HERE JUST LINKS TO OUR WEBSITE,
EMAIL ADDRESS AND TWITTER HANDLE
IN CASE ANYBODY WANTS TO GET IN
TOUCH THROUGH ANY OF THOSE
MEANS, I'M ALWAYS HAPPY TO CHAT
ABOUT SCIENCE OR CAREER ADVICE
OR ANYTHING LIKE THAT SO ONCE
AGAIN IT'S REALLY AN HONOR TO
HAVE DELIVER THIS LECTURE AND
THANK YOU FOR YOUR ATTENTION AND
I'D LOVE TO TAKE ANY QUESTIONS
AND HAVE A DISCUSSION WITH THE
AUDIENCE.
THANK YOU.
>>THANK YOU SO MUCH, CESAR.
THAT WAS REALLY TERRIFIC.
WE HAVE SOME QUESTIONS.
WE'LL START WITH A VERY GENERAL
ONE.
HOW DID YOU GET THE IDEA TO WORK
ON THIS PARTICULAR VERY
FASCINATING AREA OF RESEARCH?
>>THAT'S A GREAT QUESTION.
I THINK MY PH.D., I SAW IT BEING
VERY MUCH TRIAL AND ERROR AND I
THOUGHT THEY HAD TO BE A BETTER
SORT OF ENGINEERING SOLUTION TO
IT THEN WITH THE ADVANCES IN
COMPUTE POWER AND COMPUTER
GENERATION I THOUGHT
COMPUTATIONAL TOOLS WOULD BE THE
BEST TOOL FOR THE PROBLEM.
I'M INCREDIBLY PASSIONATE AND
THAT'S WHAT TOOK ME TO SORT OF
MORE TRIAL AND ERROR RESEARCH AT
THE TIME WHERE I HAD TO ENGINEER
A PEPTIDE AND TRY IT OUT AND SO
ON, NOW WE CAN DO IT ON THE
COMPUTER IN A MUCH FASTER SCALE
AND IT REALLY ACCELERATES
EVERYTHING, MANY, MANY
[INAUDIBLE]
>>GREAT.
LET'S SEE, WE GOT A NUMBER OF
MORE QUESTIONS HERE.
DO YOU HAVE ANY ADVICE FOR
UNDERGRADUATES WHO ARE
INTERESTED IN DOING RESEARCH,
AND CERTAINLY YOU SUGGESTED
PEOPLE COULD COME WORK WITH YOU,
BUT IN GENERAL, WHAT KIND OF
ADVICE WOULD YOU HAVE FOR
UNDERGRADUATES WHO WANT TO GET
INTO THIS KIND OF AREA?
>>I WOULD SAY FOR UNDERGRADS,
YOU'RE AT A STAGE IN YOUR LIFE,
YOUR CAREER, THAT I THINK IT'S
VERY IMPORTANT TO EXPLORE
DIFFERENT OPTIONS, TO EXPLORE
REALLY WHAT CALLS YOU, WHAT YOU
REALLY LIKE DOING DAY IN AND DAY
OUT, WHAT YOU'RE TRULY
PASSIONATE ABOUT.
IF YOU'RE INTERESTED IN
RESEARCH, EXPLORE DIFFERENT
RESEARCH AREAS, GO TO DIFFERENT
LABS FOR THE SUMMER AND THEN TRY
TO FIND SOMETHING -- YOU MIGHT
BE LUCKY, YOU GO TO THE FIRST
ONE AND YOU LOVE IT AND THAT'S
WHAT YOU DO THE REST OF YOUR
LIFE.
BUT THAT MIGHT NOT BE THE CASE
AND THAT'S OKAY TOO.
YOU MIGHT NEED TO EXPLORE
DIFFERENT THINGS.
COMPUTATIONAL RESEARCH IS VERY
DIFFERENT FROM EXPERIMENTAL
RESEARCH.
SOME PEOPLE LIKE ONE AND NOT THE
OTHER, SOME PEOPLE LIKE BOTH, SO
YOU HAVE TO FIND YOUR OWN
CALLING WITHIN RESEARCH.
RESEARCH IS INCREDIBLY WIDE
ENDEAVOR SO THERE'S A PLACE FOR
EVERYBODY AND I THINK YOU JUST
HAVE TO EXPLORE IN ORDER TO FIND
YOUR PLACE IN IT.
>>GREAT.
WE'VE GOT A NUMBER OF QUESTIONS
SORT OF RELATED TO EACH OTHER
BUT THE FIRST IS WHAT DO YOU
KNOW ABOUT THE MECHANISM OF
ACTION OF THESE PEPTIDES?
ARE THEY ALL THE SAME, ARE THEY
DOING DIFFERENT THINGS?
DO YOU HAVE SPECIFIC EVIDENCE?
>>FANTASTIC QUESTION.
SO A LOT OF THEM, THEY TARGET
THE MEMBRANE.
SO ONE THING I DIDN'T GET INTO
IN THE TALK BUT THE EVOLUTIONARY
ALGORITHM WE USE IS DRIVEN BY A
FITNESS FUNCTION THAT
ESSENTIALLY SELECTS FOR MINIMAL
PEPTIDE STRUCTURES THAT WILL
TATARGET BACTERIAL MEMBRANE.
WE USE DESCRIPTORS THAT YOU NEED
TO HAVE SOME POSITIVE CHARGES IN
THE PEPTIDE AND HYDROPHOBIC
AMINO ACIDS.
THE POSITIVE CHARGES HAD, THEY
ALLOW THE PEPTIDE TO
ELECTROSTATICALLY CHARGE WITH
BACTERIAL MEMBRANE, THAT ALLOWS
TO APPROACH THE MEMBRANE AND IT
ALLOWS TO TRANS LOCATE INTO THE
MEMBRANE CREATING A PORE THAT
TYPICALLY LEADS TO CELL DEATH.
SO THOSE ARE SOME OF THE
CONCEPTS THAT WE'RE USING TO
BUILD MOLECULES THAT WILL TARGET
AND KILL BACTERIA.
>>THAT SEGUES TO THE SECOND
QUESTION WE'VE GOTTEN A NUMBER
OF EXAMPLES OF, GIVEN MANY OF
THEM SEEM TO WORK BY MAKING A
WHOLE IN THE MEMBRANE
ESSENTIALLY, WHAT ABOUT
TOXICITY?
SOME OF THESE EXPERIMENTS WERE
IN MICE, SOME WERE IN CULTURE, I
GUESS, BUT WHAT DO YOU KNOW
ABOUT TOXICITY OF THE VARIOUS
COMPOUNDS AND CAN YOU USE A.I.
TO PREDICT IT I GUESS AS A
FOLLOW-UP TO THAT
>>FASCINATING QUESTION.
TOXICITY, OF COURSE BEFORE WE
TRANSITION ANY OF THE MOLECULES
TO MICE, WE ALWAYS TEST FOR
TOXICITY AGAINST MAMMALIAN CELL
LINES OR MOUSE CELL LINES.
ANYTHING TO MICE IS NON-TOXIC
BUT SOMETIMES WE COME ACROSS POP
TIDES THAT ARE TOXIC SO WE RULE
THEM OUT.
WE HAVE MACHINE LEARNING MODELS
THAT ALLOW US TO PREDICT
TOXICITY AS WELL AND THE HOPE IN
THE FUTURE IS THAT AS PART OF
OUR ALGORITHM, WE ARE NOT ONLY
ABLE TO OPTIMIZE TO MAKE THE
MOLECULES BETTER AT THAT BUT
HOPEFULLY WE CAN ALSO OPTIMIZE
TO COUP TER SELECT FOR
CYTOTOXICITY AND THINGS LIKE
THAT THAT WE DON'T WANT.
THE DREAM THAT WE HAVE IS TO
CREATE ANTIBIOTICS SO TO HAVE
SOMETHING THAT IS ACTIVE,
NON-TOXIC, SUFFICIENTLY STABLE,
THAT HAS GOOD PHARMACOKINETIC,
PHARMACODYNAMIC PROPERTIES AND
SO ON.
SO THE OVERARCHING GOAL IS TO
BUILD A MOLECULE OR SUBSET OF
MOLECULES THAT INCORPORATE ALL
THOSE ATTRIBUTES.
>>GREAT.
THAT SEGUES INTO A QUESTION BY
IAN MORGAN, HOW DO YOU TRAIN THE
MODELS TO DETERMINE WHICH
COMPOUNDS WOULD MAKE THE BEST
ANTIBIOTICS?
I THINK YOU KIND OF SHOWED A FEW
PARAMETERS, BUT HOW DID YOU
DETERMINE WHICH ONES TO USE AND
HOW TO USE THOSE TO TRAIN THE
MODELS?
>>GREAT QUESTION.
FOR THE GENETIC ALGORITHM, WE
USED ISOMERS -- LOOKING AT
HYDROPHOBIC AMINO ACIDS AND
BASED ON -- SCALE, SO AGAIN THIS
IS JUST TO BUILD A MINIMAL
STRUCTURE -- THE BACTERIAL
MEMBRANE, BUT SINCE THEN, WE'VE
CONTINUED THAT WORK AND NOW
WE'RE USING MANY DIMENSIONS OF
PHYSIOCHEMICAL DESCRIPTORS AT A
TIME, SO UP TO 500 OR MORE.
BASICALLY AS MUCH INFORMATION AS
POSSIBLE TO DESCRIBE AN AMINO
ACID WE CAN SEQUENCE.
>>GREAT.
IN DEVELOPING YOUR MODELS, YOUR
A.I. MODELS FOR ANTIBIOTICS,
HAVE YOU OR THE MODELS FOUND
NOVEL TRENDS IN PROTEIN DESIGN
THAT YOU HAVEN'T SEEN BEFORE OR
WERE SURPRISED TO FIND OUT WAS
MORE IMPORTANT THAN YOU
PREVIOUSLY THOUGHT?
>>INTERESTING QUESTION.
I THINK A LOT OF THE PEPTIDES
THEY TEND TO BE HELICAL WHEN IN
CONTACT WITH THE MEMBRANE BUT WE
HAVE FOUND SOME BENEFITS IN
THERE AND DIFFERENT SECONDARY
STRUG STRUCTURES.
NOT EXACTLY RELATED TO THIS BUT
ONE THING I FOUND QUITE
INTERESTING IN EVOLUTIONARY
ALGORITHM, IF WE ACTUALLY LET
THE ALGORITHM GO FOR TOO LONG
AND REACH A PLATEAU IN TERMS OF
ITS OPTIMAL SOLUTION, IT STARTED
BASICALLY SPITTING OUT SEQUENCES
THAT WERE VERY SIMILAR TO WHAT
NATURE HAS CREATED.
SO DIVERSITY AT THE MICRO LEVEL
WE HAVE TO STOP THE ALGORITHM
BEFORE IT REACHES A PLATEAU AND
THAT'S WHERE IT STARTED
GENERATING DIVERSITY.
I STILL DON'T KNOW WHAT TO MAKE
OF THOSE RESULTS BUT I THINK
IT'S AN INTERESTING -- IT'S
INTERESTING CONCEPTUALLY TO
THINK ABOUT THAT.
>>INTERESTING.
I'M SURE YOU'RE AWARE OF ALPHA
FOLD, THIS IF YOU APPROACH TO
PREDICTING PROTEIN STRUCTURE
USING ARTIFICIAL INTELLIGENCE
MACHINE LEARNING.
HAS THAT APPROACH INFLUENCED
YOUR WORK AT ALL?
>>YEAH, I THINK IT'S A
FANTASTIC TOOL FOR PROTEIN
STRUCTURE PREDICTION.
FOR PEPTIDES IT DOESN'T WORK
THAT WELL.
WE'VE TRIED A COUPLE TIMES BUT
IT'S JUST BECAUSE THE ALGORITHMS
WERE NOT TRAINED ON PEPTIDE
DATA, SO THEY CAN REALLY -- THEY
CAN'T REALLY ELUCIDATE THE
STRUCTURE OF SOMETHING THAT
THEY'VE NEVER SEEN.
YOU KNOW, A.I. IS NOT QUITE YET
AT THAT LEVEL, SO WE'VE TRIED TO
USE IT FOR A COUPLE THINGS, WE
HAVEN'T -- NOT VERY
SUCCESSFULLY, BUT I DO THINK
IT'S AN INCREDIBLE TOOL FOR
SCIENTISTS ALL AROUND THE WORLD
WORKING ON PROTEINS AND I'M
PRETTY SURE SOON IT WILL BE
APPLIED TO SMALLER PEPTIDES AS
WELL AND IT'S
Q.US A MATTER OF TIME.
>>A NUMBER OF PEOPLE WANT TO
KNOW WHAT THE NEXT STEPS ARE FOR
THIS REALLY PROMISING PEPTIDES
YOU HAVE IN TERMS OF REALLY
GETTING THEM INTO THE CLINICS.
>>THAT'S EVENTUALLY THE DREAM
THAT WE HAVE.
FOR A LOT OF THEM, WE PROBABLY
NEED TO DO SYSTEMATIC TOXICITY
STUDIES JUST TO MAKE SURE AND
SYSTEMATIC AND PROBABLY PKPV,
PHARMACOKINETIC
PHARMACODYNAMICS, HOW THE
PEPTIDES ARE DISTRIBUTED
THROUGHOUT THE BODY.
WE HAVEN'T DONE THOSE STUDIES IN
DETAIL.
SO THOSE ARE PROBABLY NEXT
STEPS.
THEN AFTER THAT, OF COURSE, YOU
HAVE IND ENABLING STUDIES, NEW
DRUG STUDIES, AND THEN AFTER
THAT IF THAT'S SUCCESSFUL, YOU
GO TO PHASE 1 CLINICAL TRIALS,
PHASE 2, PHASE 3, WHERE
ESSENTIALLY WHAT YOU LOOK FOR IS
SAFETY AND EFFICACY IN HUMANS.
BUT WE'RE STILL NOT THERE.
>>IS THERE ENOUGH DATA ON PK-PD
AND PEPTIDES FOR YOU TO USE AN
ARTIFICIAL INTELLIGENCE APPROACH
TO PREDICT WHICH ONES WOULD HAVE
THE BEST PROPERTIES?
>>UNFORTUNATELY NOT.
THAT'S SOMETHING I WOULD LOVE TO
COMBINE, COMPILE, PUT TOGETHER A
DATASET THAT HAS SUFFICIENT PKPB
DATA, BUT THERE ARE VERY FEW
STUDIES, USUALLY USING
RADIOACTIVE APPROACHES AND NOT A
LOT OF PEOPLE DO IT AND THERE'S
A SCARCITY OF DATA.
SO THAT'S THE ROADBLOCK THAT
WE'RE REACHING IN A LOT OF THESE
THINGS, IS THAT YOU ACTUALLY
NEED TO HAVE QUITE A LOT OF
DATA.
WE GENERATE A LOT IN HOUSE AND
SO WE FOCUS ON A COUPLE THINGS
AND THEN WE GENERATE THE
DATASETS WE THEN USE TO DEVELOP
OUR MODELS.
BUT RELYING ON PUBLIC DATABASES
SOMETIMES NOT -- THEY'RE NOT THE
MOST RELIABLE BECAUSE PEOPLE DO
THINGS DIFFERENTLY USING
DIFFERENT MEDIA, DIFFERENT
METHODS AND SO -- BUT WITH PKPB,
WE HAVE THAT HUGE ROADBLOCK
WHERE WE DON'T HAVE ENOUGH DATA.
IT'S ACTUALLY VERY EXPENSIVE AND
VERY DIFFICULT TO GENERATE
PKP -- DATA.
>>THERE'S A QUESTION ABOUT GRAM
POSITIVE DATA.
YOU SAW MOSTLY GRAM-NEGATIVE.
DO YOU HAVE DATA FOR GRAM
POSITIVE AS WELL?
>>WE HAVE DATA FOR GRAM
POSITIVE.
ANOTHER BIG PROJECT WE HAVE IN
THE LAB IS THAT WE'RE TRYING TO
ALSO DESIGN MOLECULES THAT
SPECIFICALLY TARGET
GRAM-NEGATIVE AND NOT GRAM
POSITIVE AND VICE VERSA AND EVEN
TO MORE GRANULAR LEVEL LIKE
SPECIFICITY LEVEL, GINO
SPECIFICITY AND SO ON.
SO THIS IS A BIG PROJECT WE'RE
CURRENTLY CARRYING OUT.
>>THAT BRINGS UP ANOTHER SET OF
QUESTIONS ABOUT WHAT HAPPENS TO
THE GUT MICROBIOME OR WHAT WOULD
YOU THINK WOULD HAPPEN WHEN YOU
USE THESE PEPTIDES?
>>THAT'S A GREAT QUESTION.
OF COURSE YOU DON'T WANT TO KILL
THE GUT MICROBIOME, RIGHT?
WHEN YOU'RE TAKING AN
ANTIBIOTIC.
THAT'S ONE OF THE UNINTENDED
CONSEQUENCES THAT A LOT OF
CONVENTIONAL ANTIBIOTICS DO.
THEY JUST BLAST EVERYTHING LIKE
A BOMB ESSENTIALLY.
SO WE ARE WORKING ON ANTIBIOTICS
THAT ONLY TARGET PATHOGENS AND
NOT GOOD BACTERIA.
YEAH, THAT'S SOMETHING THAT --
IT'S PART OF OUR EFFORTS IN
TRYING TO ACHIEVE WHAT WE CALL
TARGETTABILITY, WHICH IS ONLY
WHAT YOU WANT AND NOTHING ELSE.
THAT CAN MEAN YOU WANT TO TARGET
TWO BACTERIA AT A TIME BUT NOT
THESE OTHER THREE OR YOU WANT TO
TARGET FIVE AND NOT THIS OTHER
ONE.
SO IT'S A VERY COMPLICATED
PROBLEM BECAUSE OF COURSE YOU
KNOW, WE DON'T KNOW ENOUGH ABOUT
EACH BACTERIAL STRAIN IN TERMS
OF PHYSIOLOGY, MEMBRANE
COMPOSITION AND SO ON, SO IT'S A
VERY HARD PROBLEM, BUT WE'RE
TRYING OUR BEST.
>>QUESTION ABOUT WHETHER OR NOT
YOUR APPROACH IS AMABLE TO OTHEO
OTHER DISEASES BESIDES
ANTIBIOTIC RESISTANT INFECTIONS.
>>THAT'S AN INTERESTING NOTION.
I THINK OBVIOUSLY OUR FOCUS IS
ON ANTIBIOTICS, BUT WE NOW HAVE
A COLLABORATION TO DEVELOP
ANTICANCER PEPTIDES, SO WE
HAVEN'T VALIDATED THEM
EXPERIMENTALLY YET SO I CAN'T
REALLY SAY, BUT I DO THINK AT
LEAST CONCEPTUALLY, SOME OF THE
PIPELINES AND SOME OF THE
APPROACHES WE'RE COMING UP WITH,
I THINK THEY COULD POTENTIALLY
BE EXTRAPOLATED TO OTHER
INDICATIONS, NOT ONLY BACTERIAL
INFECTIONS BUT ALSO MAYBE VIRAL
INFECTIONS, MAYBE CANCER.
WE'VE HAD PROJECTS IN THE PAST
IN MALARIA AS WELL, SO I DO
THINK SOME OF THESE THINGS MIGHT
BE USEFUL IN OTHER AREAS.
>>INTERESTING.
ANOTHER CAREER QUESTION.
SO THEY'RE A POSTDOC WITH A
BACKGROUND IN COMPUTER SCIENCE
AND MACHINE LEARNING, WANT TO
KNOW WHAT YOU THINK THE NEXT
STEPS WOULD BE TO PURSUE THE
SORT OF RESEARCH YOU'RE DOING
WITH SOMEONE WHO HAS A
BACKGROUND IN THOSE AREAS.
>>SO IF THIS IS TO THE POSTDOC,
DEFINITELY REACH OUT TO LABS,
TALK TO PEOPLE, AND YOU WANT TO
MAKE SURE YOU ALIGN WITH THE
LABS THEY ARE GOING TO.
IF IT'S TO GO FOR THE NEXT STEP,
FOR A FACULTY JOB OR A CAREER IN
INDUSTRY, TRY TO FIND A GOOD
PLACE.
WHERE THE MISSION ALIGNS VERY
MUCH WITH YOUR INTERESTS.
IF YOU WANT TO DO COMPUTATIONAL
WORK IN THE CONTEXT OF DRUG
DISCOVERY, TRY TO GO TO A PLACE
WHERE YOU'RE GOING TO BE HAPPY,
WHERE YOU'RE GOING TO BE
ALIGNING WELL AND DOING YOUR
WORK EVERY DAY OR MOST DAYS.
>>GREAT.
A LOT OF THE PEPTIDES THAT YOU
FOUND THE QUESTION UNDERSTOOD
CORRECTLY SEEMED TO BE THE ONES
IN THE GENOME ALREADY, SEEMED TO
BE IN THE CONTEXT OFTEN OF A
LARGER PROTEIN.
SO THE QUESTION IS, DO YOU THINK
THOSE PEPTIDES HAVE THE
ANTIBIOTIC OR ANTIBACTERIAL
FUNCTION IN THE CONTEXT OF THE
LARGER PROTEIN OR IS IT JUST A
COINCIDENCE THAT THEY HAPPEN TO
HAVE THAT FUNCTION?
>>YEAH, THIS IS A VERY GOOD
QUESTION.
SO ONE OF THE THINGS THAT I
DON'T THINK I GOT INTO IT BUT SO
A LOT OF THE ENCRYPTED PEPTIDES
ARE ACTUALLY PREDICTED TO BE
CLEAVED UP BY PROTEASES.
>>INTERESTING.
>>SO YOU HAVE YOUR ENTIRE
PROTEIN, THEN A PROTEASE COMES
ALONG PRESENT IN A PARTICULAR
ENVIRONMENT IN THE BODY.
IT CLEAVES OFF THE FRAGMENT AND
THAT FRAGMENT IS WHAT HAS THE
ANTIMICROBIAL ACTIVITY.
SO THIS OBVIOUSLY SPARKS A LOT
OF POTENTIAL THOUGHTS.
ONE OF THEM IS THE NOTION OF
GENOMIC -- PRESERVING ENERGY AT
THE GENOMIC LEVEL WHERE ONE GENE
ENCODES FOR ONE PROTEIN BUT THAT
ONE PROTEIN HAS MULTIPLE
FUNCTIONS.
THAT CAN BE ACTIVATED BY AREAS
CLEAVED BY PROTEASES,
ESSENTIALLY -- PARTICULAR
FRAGMENTS FROM A PROTEIN.
SO PRESERVING ENERGY AT THE
GENOMIC LEVEL.
THINKING ABOUT PROTEINS NOT ONLY
HAVING ONE FUNCTION BUT HAVING
MULTIPLE FUNCTIONS THAT CAN BE
ACTIVATED.
>>WOULD THAT BE USEFUL FOR YOUR
IDEA OF TARGETING THEM TO
SPECIFIC AREAS SO IF THE MAIN
PROTEIN CARRIED THEM TO THE
RIGHT AREA AND THEN IT GOT
CLEAVED OFF, IS THAT A STRATEGY?
>>THAT MIGHT BE ONE OF THE
THINGS THAT WE MIGHT EXPLORE.
YOU CAN ALSO -- THE WAY I THINK
ABOUT THIS, YOU HAVE A PROTEIN,
DOING DAILY ROUTINE, RIGHT?
A PROTEIN, FOR EXAMPLE, INVOLVED
IN THE NERVOUS SYSTEM.
UNDER PARTICULAR CONDITIONS,
MAYBE AN INFECTION, I DON'T
KNOW, THIS IS JUST SPECULATION,
BUT MAYBE THE PERSON GETS
INFECTED, A MICROENVIRONMENT
WHERE PROTEASES GET RELEASED,
CLEAVING OFF -- MAYBE DOZENS OF
THEM IN A PARTICULAR AREA, AND
THEN YOU HAVE ALL THESE
COCKTAILS OF ENCRYPTED PEPTIDE
ANTIBIOTICS THAT ARE WORKING --
THAT'S ME DREAMING A LITTLE BIT,
IMAGINING THE SITUATION, BUT
THAT COULD BE, YOU KNOW,
POTENTIALLY WHAT MIGHT BE
HAPPENING.
>>VERY COOL.
RELATED TO THE TARGETING IDEA,
AND I HOPE YOU KNOW WHAT THIS
QUESTION MEANS, WOULD FERRAL
FLUIDS BE AN OPTION TO DIRECT
ANTIBIOTICS TO A TARGET SITE?
>>I'M NOT SURE ACTUALLY.
I HAVEN'T REALLY EXPLORED THAT.
THAT CONCEPT.
FLEURN.
.WOULD LOVE TO LEARN MORE.
>>ON THAT NOTE, LEILA WOULD
LIKE TO KNOW HOW TO GET IN TOUCH
WITH YOU, IF SHE'S INTERESTED IN
WORKING IN YOUR LAB IN THE
FUTURE.
SO I ASSUME VIA EMAIL?
IS THAT THE BEST WAY?
>>YEAH, I WOULD SAY EMAIL OR
TWITTER OR -- EMAIL OR TWITTER.
SO LET ME -- I CAN PUT IN THE
CHAT.
>>PUT IT IN THE CHAT, THAT
WOULD BE GREAT.
>>THAT'S MY EMAIL.
THEN TWITTER HANDLE IS ?SH SO
ANY OF THOSE TWO WORK VERY WELL.
>>ALL RIGHT.
WELL, I THINK THOSE ARE A LOT OF
QUESTIONS, FANTASTIC QUESTIONS,
BUT I THINK THAT'S WHAT WE HAD.
SO CESAR, THANK YOU SO MUCH,
THIS WAS A TERRIFIC LECTURE,
TERRIFIC WORK, AND REALLY
PLEASED TO SEE IT PROGRESSING SO
WELL.
>>JOHN, THANK YOU SO MUCH,
AGAIN, FOR THE OPPORTUNITY, AND
THIS HAS BEEN REALLY SUPER FUN
AND A PRIVILEGE.
>>THANK YOU SO MUCH.
THANKS, EVERYONE, FOR JOINING.
>>THANKS, EVERYBODY.
TAKE CARE.
>>BYE-BYE.