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>>WELCOME, EVERYONE, TO THE

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NIGMS JUDITH H. GREENBERG EARLY

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CAREER INVESTIGATOR LECTURE.

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I'M JON LORSCH.

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AS WE BEGIN TODAY'S WEBINAR,

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PLEASE NOTE THE SESSION IS BEING

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RECORDED.

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WE LAUNCHED THE JUDITH H.

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GREENBERG EARLY CAREER

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INVESTIGATOR LECTURE IN 2016 IN

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ORDER TO SHOWCASE NIGMS FUNDED

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EARLY CAREER INVESTIGATORS AND

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THE VALUABLE CONTRIBUTIONS

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THEY'RE MAKING TO PUBLIC HEALTH.

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THE LECTURE SERIES IS NAMED FOR

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JUDITH GREENBERG, FORMER DEPUTY

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DIRECTOR OF NIGMS AND TWO-TIME

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ACTING DIRECTOR OF THE INSTITUTE

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WHO HAD DEEP COMMITMENT TO

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SUPPORTING EARLY CAREER

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INVESTIGATORS.

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WE GEAR THESE LECTURES TO

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STUDENTS TO GIVE THEM THE

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OPPORTUNITY TO HEAR AND MEET

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YOUNG SCIENTISTS DOING

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REMARKABLE THINGS IN BIOMEDICAL

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RESEARCH.

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OUR HOPE IS THAT YOU'LL BE

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INSPIRED TO FOLLOW CAREERS IN

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THIS AREA.

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THE LECTURE WILL BEGIN WITH A

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SPEAKER, DR. CAESAR DE LA

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FUENTE, GIVING A 30-MINUTE TALK

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ON HIS RESEARCH AND CAREER PATH

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FOLLOWED BY A 30-MINUTE QUESTION

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AND ANSWER SESSION.

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I ENCOURAGE ALL OF YOU JOINING

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US ON THE ZOOM TODAY TO ASK

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QUESTIONS ON DR. DE LA FUENTE'S

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RESEARCH, CAREER AND SCIENCE BY

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SUBMITTING THEM TO ME, JON

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LORSCH, THROUGH THE ZOOM CHAT

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BOX FUNCTION.

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SO TODAY'S SPEAKER.

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AS I MENTIONED, OUR SPEAKER

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TODAY IS CAESAR DE LA FUENTE.

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A PRESIDENTIAL ASSISTANT

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PROFESSOR IN THE DEPARTMENTS OF

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BIOENGINEERING, PSYCHIATRY AT

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THE UNIVERSITY OF PENNSYLVANIA.

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HIS RESEARCH GROUP BELIEVES THAT

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INNOVATIONS IN ARTIFICIAL

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INTELLIGENCE MAY HELP TO

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REPLENISH OUR ARSENAL OF

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EFFECTIVE DRUGS SUCH AS THOSE

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USED TO TREAT ANTIBIOTIC

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RESISTANT BACTERIAL INFECTIONS.

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SPECIFICALLY, CESAR PIONEERED

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THE DEVELOPMENT OF THE FIRST

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ANTIBIOTIC DESIGNED BY A

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COMPUTER WITH EFFICACY IN

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ANIMALS, DESIGNED ALGORITHMS FOR

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ANTIBIOTIC DISCOVERY,

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REPROGRAMMED VENOMS TO

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ANTIMICROBIALS, CREATED NOVEL

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RESISTANCE-PROOF ANTIMICROBIAL

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TEARS AND DIAGNOSTICS FOR

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COVID-19 AND OTHER INFECTIONS.

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HE HAS RECEIVED WIDE RECOGNITION

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FOR HIS PIONEERING RESEARCH THAT

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SPANS OVER 100 PUBLICATIONS.

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NIGMS IS VERY PROUD TO HAVE

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FUNDED CESAR'S PROGRAM, THE MIRA

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PROGRAM, SINCE 2020.

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CESAR, WELCOME AND THANK YOU FOR

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JOINING US TODAY.

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>>JON, THANK YOU VERY
MUCH FOR

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THE VERY, VERY KIND

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INTRODUCTION.

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I'D LIKE TO START OFF BY

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EXPRESSING THAT IT'S REALLY A

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PRIVILEGE TO GIVE THIS LECTURE

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IN HONOR OF DR. JUDITH

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GREENBERG, WHO ACTUALLY HAPPENS

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TO BE HERE IN THE AUDIENCE, SO

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THANK YOU, DR. GREENBERG, FOR

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TAKING THE TIME.

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I REALLY APPRECIATE IT.

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AND I'M DEFINITELY INSPIRED BY

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YOUR COMMITMENT THROUGHOUT YOUR

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CAREER TO ADVANCING WOMEN IN

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BIOMEDICAL SCIENCE.

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SO THANK YOU FOR THAT.

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AND IT'S ALSO HUMBLING TO FOLLOW

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IN THE FOOTSTEPS OF LUMINARIES

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IN THEIR RESPECTIVE FIELDS THAT

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HAVE DELIVERED THIS LECTURE

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PREVIOUSLY, SO I REALLY FEEL

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VERY GRATEFUL FOR THIS

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OPPORTUNITY.

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I THOUGHT I'D TELL YOU

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TODAY IS A LITTLE BIT ABOUT OUR

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EFFORTS AROUND HOW TO USE

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ARTIFICIAL INTELLIGENCE FOR

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ANTIBIOTIC DISCOVERY.

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BUT FIRST BEFORE I BEGIN WITH

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ALL THE SCIENCE, I WOULD LIKE TO

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TELL YOU A LITTLE BIT ABOUT MY

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JOURNEY IN SCIENCE.

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SO LET'S START FROM THE VERY

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BEGINNING.

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THAT'S ME AS A KID, READING.

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I'VE ALWAYS BEEN VERY CURIOUS

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ABOUT LEARNING NEW THINGS AND I

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THINK THAT HAS ALWAYS BEEN A

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GREAT SORT OF FIRE IN ME TO

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LEARN.

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IN SCIENCE, THERE IS NO LACK OF

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LEARNING AND YOU'RE ALWAYS --

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EVEN WHEN EXPERIMENTS DON'T

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WORK, THAT ALWAYS SERVES AS A

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LEARNING PROCESS, SO SCIENCE

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REALLY -- FOR ME, IT MEANS -- IT

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ENTAILS THE PERFECT FRAMEWORK

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FOR LEARNING AND BEING CREATIVE

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AND SO ON.

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SINCE I WAS A VERY LITTLE KID,

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I'VE ALWAYS BEEN FASCINATED WITH

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ENGINEERING AND BIOLOGY.

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I REMEMBER TRYING TO BUILD

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FLYING MACHINES SUCH AS THE ONE

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ON THE RIGHT, FOR EXAMPLE, AND I

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WOULD CO CONVINCE MYSELF AND MY

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SIBLINGS TO GET ON THESE FLYING

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MACHINES AND ONE DAY WE SET IT

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ON FIRE AND NOTHING HAPPENED.

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BUT ANYWAYS, ALWAYS VERY CURIOUS

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ABOUT EXPERIMENTING BOTH WITH

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PHYSICAL THINGS AND WITH NATURAL

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THINGS.

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OTHER THINGS THAT I REMEMBER AS

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A CHILD, BEING FASCINATED -- I

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GREW UP BY THE OCEAN, SO BEING

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FASCINATED BY MARINE ORGANISMS

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OR JUST PONDERING ABOUT THE

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ABILITY, THE INCREDIBLE ABILITY

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OF GE C GECKOS TO REGENERATE THR

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TAIL.

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SUCH THINGS WE TAKE FOR GRANTED

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AS WE BECOME ADULTS BUT THEY'RE

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REALLY QUITE REMARKABLE, AND

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THAT JUST SPEAKS TO HOW BIOLOGY

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AND SCIENCE, YOU KNOW, IT SEEMS

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LIKE SCIENCE FICTION SOMETIMES,

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AND I THINK THAT MAKES IT

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INCREDIBLY FUN FOR ME.

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SO I DID MY UNDERGRAD IN

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BIOTECHNOLOGY, SO THAT'S WHERE I

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LEARNED HOW TO THINK ABOUT

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SCIENCE A LITTLE BIT AND THAT

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WAS A VERY FORMATIVE -- VERY

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FORMATIVE YEARS FOR MYSELF.

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FROM THEN, I TOOK A LEAP AND I

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WENT TO UNIVERSITY IN VANCOUVER,

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CANADA, BEAUTIFUL CITY, AND IN A

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BEAUTIFUL CAMPUS, AND I HAD THE

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OPPORTUNITY THERE TO WORK WITH

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BOB HANCOCK, ONE OF THE PIONEERS

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IN ANTIBIOTIC DESIGN AND

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BACTERIAL PATHOGENESIS.

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AND I HAVE TO SAY A QUICK STORY

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HERE, I APPLIED TO CANADA

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BECAUSE IT WAS ONE OF THE FEW

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PLACES -- I WAS LUCKY TO GO FOR

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AN INTERVIEW, AND I THINK THE

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LESSON HERE IS TO HAVE A PLAN B

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OR C OR D AND I THINK THAT THE

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NEWER GENERATION IS MUCH MORE

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PREPARED.

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I DON'T KNOW WHAT I WOULD HAVE

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DONE IN MY ENTIRE JOURNEY,

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REALLY.

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SO I'M ALSO GOING TO MOVE -- I

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HAD TO LEARN ENGLISH, AND, YOU

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KNOW, LEARN A NEW CULTURE AND

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ALWAYS MOVING FROM ONE PLACE TO

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ANOTHER AND START FACING SOME OF

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THESE ADVERSITIES, I THINK HAS

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HELPED ME A LOT IN SCIENCE.

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NOT ONLY HOPEFULLY TO BECOME A

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BETTER SCIENTIST BUT ALSO TO

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BECOME PERHAPS A BETTER PERSON

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AND TO ALWAYS TRY TO GROW.

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THEN AFTER I COMPLETED MY PH.D.,

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I WENT TO MIT WHERE I DID MY

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POSTDOC, AND THERE I DID MORE --

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I WORKED MORE IN SYNTHETIC

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BIOLOGY AND COMPUTATIONAL

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BIOLOGY APPROACHES TO ANTIBIOTIC

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DISCOVERY AND TO MICROBIOLOGY,

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AND MY TIME THERE OPENED A LOT

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OF HORIZONS AS TO HOW TO THINK

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FROM AN INTERDISCIPLINARY

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PERSPECTIVE ABOUT PROBLEMS AND

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HOW WE CAN SOLVE THEM BY

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BRINGING TOGETHER PEOPLE THAT

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COME FROM COMPLETELY DIFFERENT

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DISCIPLINES ALL TOGETHER.

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AND THEN FROM THERE, I WAS VERY

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FORTUNATE TO BE RECRUITED TO ONE

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OF THE OLDEST UNIVERSITIES IN

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THE COUNTRY, UNIVERSITY OF

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PENNSYLVANIA, THAT WAS FOUNDED

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BY BENJAMIN FRANKLIN IN 1740, SO

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IT'S INCREDIBLY OLD, AND IT'S A

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BEAUTIFUL CAMPUS, IF YOU'VE

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NEVER BEEN, I RECOMMEND IT.

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VERY COMPACT CAMPUS WHERE YOU

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HAVE THE ENGINEERING SCHOOL, THE

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SCHOOL OF MEDICINE, THE SCHOOL

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OF ARTS AND SCIENCES ALL

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TOGETHER, SO WE CAN REACH WITHIN

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WALKING DISTANCE OF INCREDIBLE

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COLLEAGUES WHERE YOU CAN SPARC

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CONVERSATIONS AND INITIATE

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COLLABORATIONS AND EXECUTE NEW

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IDEAS.

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SO THAT'S ABOUT ME BUT I'D BE

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HAPPY TO DISCUSS ANY OF THIS

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JOURNEY THROUGHOUT THE Q & A

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SESSION AT THE END.

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SO A LITTLE ABOUT THE SCIENCE,

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WE'RE INTERESTED IN USING

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ARTIFICIAL INTELLIGENCE FOR

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ANTIBIOTIC DISCOVERY.

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DEVELOPING DRUGS IS A VERY

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DIFFICULT PROCESS.

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SO AS WE CAN SEE HERE ON THE

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LEFT, TO DEVELOP A DRUG FROM THE

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MOMENT THAT YOU DISCOVER IT IN

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THE LABORATORY, THE FIRST TIME,

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IN THAT EUREKA MOMENT, TO THE

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MOMENT WHERE THAT DRUG ACTUALLY

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HAS AN IMPACT ON PATIENTS IS AN

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INCREDIBLY LONG AND WINDY ROAD,

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IT CAN TAKE ON AVERAGE 10 YEARS.

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SO IT'S A LONG TIME.

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IT'S ALSO A HIGHLY COSTLY

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ENDEAVOR.

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TO DEVELOP A DRUG THERE'S A

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PREDICTION IT COSTS ABOUT

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$2.6 BILLION TO DEVELOP A DRUG.

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THAT'S ACTUALLY MORE THAN THE

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BUDGET THAT NASA HAS TO TAKE A

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ROCKET ALL THE WAY TO THE MOON.

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SO TO DEVELOP A DRUG IS EVEN

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MORE COMPLICATED ENDEAVOR IN

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SOME WAYS THAN TAKING A ROCKET

259
00:09:45,880 --> 00:09:46,880
ALL THE WAY TO THE MOON, WHICH

260
00:09:46,880 --> 00:09:49,040
IS INCREDIBLE TO THINK ABOUT.

261
00:09:49,040 --> 00:09:50,800
WE ARE OPTIMISTIC, AND WE THINK

262
00:09:50,800 --> 00:09:54,080
THAT THERE ARE A COUPLE OF

263
00:09:54,080 --> 00:09:56,280
TRENDS THAT HAVE BEEN GOING ON

264
00:09:56,280 --> 00:09:57,800
FOR SEVERAL DECADES THAT CAN

265
00:09:57,800 --> 00:09:58,800
HELP ADDRESS SOME OF THESE GAPS

266
00:09:58,800 --> 00:09:59,560
THAT WE SEE.

267
00:09:59,560 --> 00:10:02,280
SO THE FIRST ONE IS THE

268
00:10:02,280 --> 00:10:04,680
EVER-INCREASING COMPUTE POWER.

269
00:10:04,680 --> 00:10:09,200
THIS IS FOLLOWING A MOORE'S LAW

270
00:10:09,200 --> 00:10:10,720
THAT TELLS US THE NUMBER OF

271
00:10:10,720 --> 00:10:12,120
TRANSISTORS WE CAN FIT IN A CHIP

272
00:10:12,120 --> 00:10:13,320
DOUBLES EVERY TWO YEARS OR SO,

273
00:10:13,320 --> 00:10:16,080
MEANING WE HAVE MORE AND MORE

274
00:10:16,080 --> 00:10:17,920
CAPACITY TO PROCESS INFORMATION

275
00:10:17,920 --> 00:10:19,480
ON COMPUTERS.

276
00:10:19,480 --> 00:10:21,520
AND THE SECOND TREND IS OUR

277
00:10:21,520 --> 00:10:22,960
ABILITY TO GENERATE DATA.

278
00:10:22,960 --> 00:10:24,720
WE CAN REALLY GENERATE VAST

279
00:10:24,720 --> 00:10:27,760
AMOUNTS OF DATA NOWADAYS, AND

280
00:10:27,760 --> 00:10:29,640
THAT'S THANKS TO ADVANCES IN

281
00:10:29,640 --> 00:10:31,160
AUTOMATION AND IN

282
00:10:31,160 --> 00:10:32,160
HIGH-THROUGHPUT SCREENING.

283
00:10:32,160 --> 00:10:34,280
SO WE THINK THESE TWO TRENDS CAN

284
00:10:34,280 --> 00:10:36,760
HELP US REDUCE THE TIME THAT IT

285
00:10:36,760 --> 00:10:39,240
TAKES TO DEVELOP ANTIBIOTICS AND

286
00:10:39,240 --> 00:10:42,640
ALSO HELP US REDUCE THE COSTS

287
00:10:42,640 --> 00:10:46,880
ASSOCIATE WITH THIS PROCESS.

288
00:10:46,880 --> 00:10:49,960
WHY DO WE FOCUS ON ANTIBIOTICS?

289
00:10:49,960 --> 00:10:51,320
ANTIBIOTIC RESISTANCE IS A HUGE

290
00:10:51,320 --> 00:10:53,040
GLOBAL HEALTH PROBLEM.

291
00:10:53,040 --> 00:10:55,200
IT'S CURRENTLY PREDICTED TO LEAD

292
00:10:55,200 --> 00:10:57,480
TO THE DEATH OF 10 MILLION

293
00:10:57,480 --> 00:11:01,680
PEOPLE PER YEAR BY 2050,

294
00:11:01,680 --> 00:11:02,760
SURPASSING EVERY OTHER MAJOR

295
00:11:02,760 --> 00:11:06,400
CAUSE OF DEATH IN OUR SOCIETY.

296
00:11:06,400 --> 00:11:08,200
THOSE 10 MILLION DEATHS PER

297
00:11:08,200 --> 00:11:10,240
YEAR, IF YOU RUN A CRUDE

298
00:11:10,240 --> 00:11:11,640
CALCULATION, THEY CORRESPOND TO

299
00:11:11,640 --> 00:11:12,480
ABOUT ONE DEATH EVERY

300
00:11:12,480 --> 00:11:13,160
THREE SECONDS.

301
00:11:13,160 --> 00:11:15,000
SO THIS IS THE FUTURE THAT WE'RE

302
00:11:15,000 --> 00:11:16,320
HEADING TOWARDS UNLESS WE COME

303
00:11:16,320 --> 00:11:19,080
UP WITH SOLUTIONS, WE COME UP

304
00:11:19,080 --> 00:11:20,120
WITH NOVEL TYPES OF ANTIBIOTICS

305
00:11:20,120 --> 00:11:23,000
THAT WE CAN USE TO TREAT THESE

306
00:11:23,000 --> 00:11:23,880
CURRENTLY UNTREATABLE

307
00:11:23,880 --> 00:11:24,480
INFECTIONS.

308
00:11:24,480 --> 00:11:26,280
I ALWAYS LIKE TO HIGHLIGHT THAT

309
00:11:26,280 --> 00:11:27,520
ANTIBIOTICS ARE NOT ONLY USEFUL

310
00:11:27,520 --> 00:11:28,920
WHEN WE HAVE AN INFECTION AND WE

311
00:11:28,920 --> 00:11:30,960
TAKE THEM AND WE GET CURED, BUT

312
00:11:30,960 --> 00:11:32,640
THEY'RE ACTUALLY ESSENTIAL FOR

313
00:11:32,640 --> 00:11:33,840
MODERN MEDICINE AS WE KNOW IT.

314
00:11:33,840 --> 00:11:36,440
SO INTERVENTIONS LIKE CHILDBIRTH

315
00:11:36,440 --> 00:11:38,200
OR CHEMOTHERAPY TREATMENTS FOR

316
00:11:38,200 --> 00:11:42,040
CANCER PATIENTS OR ORGAN

317
00:11:42,040 --> 00:11:42,840
TRANSPLANTATIONS OR SURGERIES

318
00:11:42,840 --> 00:11:44,880
WOULD NOT BE FEASIBLE WITHOUT

319
00:11:44,880 --> 00:11:45,640
EFFECTIVE ANTIBIOTICS.

320
00:11:45,640 --> 00:11:47,640
SO IT'S REALLY IMPORTANT TO

321
00:11:47,640 --> 00:11:51,080
EMPHASIZE THIS.

322
00:11:51,080 --> 00:11:52,280
NATURE, I'M SHOWING A PHOTO OF

323
00:11:52,280 --> 00:11:54,720
THE GLOBE HERE BECAUSE NATURE

324
00:11:54,720 --> 00:11:57,520
HAS BEEN A GREAT INSPIRATION OF

325
00:11:57,520 --> 00:11:59,120
LIKE A SOURCE OF ANTIBIOTICS.

326
00:11:59,120 --> 00:12:00,960
FROM THE VERY FIRST ONE, WHICH

327
00:12:00,960 --> 00:12:02,800
WAS PENICILLIN, WHICH WAS

328
00:12:02,800 --> 00:12:04,760
DISCOVERED BY ALEXANDER FLEMING

329
00:12:04,760 --> 00:12:06,880
IN 1928, NATURE HAS REALLY GIVEN

330
00:12:06,880 --> 00:12:08,600
US EVERY MAJOR CLASS OF

331
00:12:08,600 --> 00:12:09,480
ANTIBIOTICS THAT WE HAVE

332
00:12:09,480 --> 00:12:11,360
NOWADAYS IN HOSPITALS.

333
00:12:11,360 --> 00:12:14,080
AND SO AGAIN, IT HAS BEEN A

334
00:12:14,080 --> 00:12:15,400
GREAT SOURCE OF INSPIRATION AND

335
00:12:15,400 --> 00:12:17,440
HAS GIVEN US DIFFERENT

336
00:12:17,440 --> 00:12:19,320
CHEMISTRIES THAT WE'VE USED FOR

337
00:12:19,320 --> 00:12:20,840
MANY DECADES TO SAVE LIVES, BUT

338
00:12:20,840 --> 00:12:22,640
THE PROBLEM THAT WE'RE FACING

339
00:12:22,640 --> 00:12:24,800
TODAY IS THAT FOR DECADES, THE

340
00:12:24,800 --> 00:12:27,440
SCIENTIFIC COMMUNITY, WE'VE BEEN

341
00:12:27,440 --> 00:12:29,600
UNABLE TO FIND TRULY NOVEL

342
00:12:29,600 --> 00:12:30,360
CLASSES OF ANTIBIOTICS IN

343
00:12:30,360 --> 00:12:32,920
NATURE.

344
00:12:32,920 --> 00:12:35,080
SO IN MY LAB, THE WAY WE THINK

345
00:12:35,080 --> 00:12:36,520
ABOUT THIS, INSTEAD OF RELYING

346
00:12:36,520 --> 00:12:38,800
ON NATURE TO GIVE US ALL THESE

347
00:12:38,800 --> 00:12:40,120
LIFE-SAVING MOLECULES, WHY DON'T

348
00:12:40,120 --> 00:12:43,960
WE TRY TO TRANSLATE THE CHEMICAL

349
00:12:43,960 --> 00:12:47,360
COMPLEXITY OF MOLECULES INTO THE

350
00:12:47,360 --> 00:12:48,640
BINARY CODE OF ONES AND ZEROS SO

351
00:12:48,640 --> 00:12:50,200
THAT MACHINES CAN TAKE CARE OF

352
00:12:50,200 --> 00:12:52,360
THE DISCOVERY PROCESS.

353
00:12:52,360 --> 00:12:53,800
AND IN PARTICULAR, WE FOCUS

354
00:12:53,800 --> 00:12:55,440
MOSTLY ON SMALL PROTEINS CALLED

355
00:12:55,440 --> 00:13:00,360
PEPTIDES IN MY LAB.

356
00:13:00,360 --> 00:13:02,080
OF COURSE HOW DO WE ENGINEER

357
00:13:02,080 --> 00:13:04,520
PEPTIDES OR PROTEINS?

358
00:13:04,520 --> 00:13:07,560
WE FOLLOW VERY BASIC -- THE

359
00:13:07,560 --> 00:13:08,960
STRUCTURE OF OUR PROTEIN

360
00:13:08,960 --> 00:13:09,720
DETERMINES ITS FUNCTION.

361
00:13:09,720 --> 00:13:11,160
SO IF WE CAN CONTROL THE

362
00:13:11,160 --> 00:13:12,480
SEQUENCE OF AMINO ACIDS WHICH

363
00:13:12,480 --> 00:13:15,440
ARE THE BUILDING BLOCKS OF

364
00:13:15,440 --> 00:13:18,080
PEPTIDES AND PROTEINS, WE'LL BE

365
00:13:18,080 --> 00:13:19,040
ABLE TO CONTROL THEIR FUNCTION

366
00:13:19,040 --> 00:13:19,920
AND THEN CAN YOU TUNE IT.

367
00:13:19,920 --> 00:13:21,680
THIS IS A LITTLE LIKE PLAYING

368
00:13:21,680 --> 00:13:22,000
LEGO.

369
00:13:22,000 --> 00:13:24,640
WE'RE TRYING TO ARRANGE

370
00:13:24,640 --> 00:13:27,160
DIFFERENT AMINO ACIDS, BUILDING

371
00:13:27,160 --> 00:13:28,240
BLOCKS IN DIFFERENT POSITIONS,

372
00:13:28,240 --> 00:13:29,480
TO BUILD SYNTHETIC MOLECULES

373
00:13:29,480 --> 00:13:30,680
THAT ARE CAPABLE OF DOING WHAT

374
00:13:30,680 --> 00:13:32,080
WE WANT THEM TO DO.

375
00:13:32,080 --> 00:13:34,360
SO THIS IS A TRADITIONAL WAY,

376
00:13:34,360 --> 00:13:36,760
AND MORE RECENTLY, WE'VE

377
00:13:36,760 --> 00:13:38,200
INCORPORATED ASPECTS OF

378
00:13:38,200 --> 00:13:39,640
COMPUTATIONAL BIOLOGY AND A.I.

379
00:13:39,640 --> 00:13:42,120
TO HELP US ACCELERATE ANTIBIOTIC

380
00:13:42,120 --> 00:13:44,440
DISCOVERY.

381
00:13:44,440 --> 00:13:45,760
COMPUTERS, THEY CAN HELP US DO A

382
00:13:45,760 --> 00:13:47,280
NUMBER OF THINGS, AND I'M GOING

383
00:13:47,280 --> 00:13:50,200
TO TELL YOU ABOUT THREE OF THEM.

384
00:13:50,200 --> 00:13:53,200
THAT ARE, I THINK, QUITE

385
00:13:53,200 --> 00:13:53,720
SIGNIFICANT.

386
00:13:53,720 --> 00:13:56,240
THEY CAN HELP US EXPLORE

387
00:13:56,240 --> 00:13:57,240
SEQUENCE BASE.

388
00:13:57,240 --> 00:13:58,560
AGAIN, SEQUENCE BASE AS I'LL

389
00:13:58,560 --> 00:13:59,840
MENTION IS INCREDIBLY VAST FOR

390
00:13:59,840 --> 00:14:01,400
ANY MOLECULES BUT INCLUDING ALSO

391
00:14:01,400 --> 00:14:04,440
ANY PROTEINS OR ANY PEPTIDES.

392
00:14:04,440 --> 00:14:06,080
IT'S ALMOST INFINITE.

393
00:14:06,080 --> 00:14:10,320
SO COMPUTERS CAN HELP US EXPLORE

394
00:14:10,320 --> 00:14:11,160
THIS.

395
00:14:11,160 --> 00:14:12,440
THE SECOND THING IT CAN HELP US

396
00:14:12,440 --> 00:14:14,240
DO, MACHINES IS HELP US GENERATE

397
00:14:14,240 --> 00:14:14,920
NEW MOLECULES.

398
00:14:14,920 --> 00:14:16,280
HERE WE ENTER A LITTLE BIT INTO

399
00:14:16,280 --> 00:14:18,120
THE REALM OF CREATIVITY THAT WE

400
00:14:18,120 --> 00:14:20,760
TYPICALLY ASSOCIATE WITH THE

401
00:14:20,760 --> 00:14:22,160
HUMAN BRAIN, BUT I'LL PROVIDE

402
00:14:22,160 --> 00:14:26,000
SOME EARLY EVIDENCE THAT

403
00:14:26,000 --> 00:14:27,520
COMPUTERS ALSO -- AND WE CAN

404
00:14:27,520 --> 00:14:28,600
TRAIN COMPUTERS TO START TO GET

405
00:14:28,600 --> 00:14:30,000
A LITTLE BIT CREATIVE IN TERMS

406
00:14:30,000 --> 00:14:34,640
OF THEIR ABILITY TO CREATE NOVEL

407
00:14:34,640 --> 00:14:35,160
MOLECULES.

408
00:14:35,160 --> 00:14:37,160
LESS BUT NOT LEAST, MACHINES CAN

409
00:14:37,160 --> 00:14:39,440
HELP US MINE BIOLOGY TO TRY TO

410
00:14:39,440 --> 00:14:42,280
FIND NOVEL DRUGS IN OUR CASE,

411
00:14:42,280 --> 00:14:42,720
NOVEL ANTIMICROBIALS.

412
00:14:42,720 --> 00:14:44,240
SO I'LL TALK ABOUT EACH OF THESE

413
00:14:44,240 --> 00:14:49,720
THREE POINTS.

414
00:14:49,720 --> 00:14:50,360
SO SEQUENCE SPACE.

415
00:14:50,360 --> 00:14:52,360
HOW DO WE THINK ABOUT SEQUENCE

416
00:14:52,360 --> 00:14:53,000
SPACE?

417
00:14:53,000 --> 00:14:53,960
SO ILLUSTRATE THIS CONCEPT A

418
00:14:53,960 --> 00:14:55,080
LITTLE BIT BETTER, I'M GOING TO

419
00:14:55,080 --> 00:14:59,520
SHOW YOU THE SPACE OF CONCEPTS

420
00:14:59,520 --> 00:15:01,000
THAT PERHAPS THE AUDIENCE IS

421
00:15:01,000 --> 00:15:02,040
MORE FAMILIAR WITH.

422
00:15:02,040 --> 00:15:03,280
FOR EXAMPLE, THE NUMBER OF

423
00:15:03,280 --> 00:15:05,000
PEOPLE ON EARTH IS 10 TO THE 10.

424
00:15:05,000 --> 00:15:06,680
THE NUMBER OF BACTERIA IN HUMAN

425
00:15:06,680 --> 00:15:07,640
IS 10 TO THE 13.

426
00:15:07,640 --> 00:15:09,080
WE'RE SURROUNDED BY MICROBES,

427
00:15:09,080 --> 00:15:10,480
THE MAJORITY OF WHICH DO GOOD

428
00:15:10,480 --> 00:15:11,840
THINGS FOR US.

429
00:15:11,840 --> 00:15:14,440
IF WE KEEP GOING HIGHER, HIGHER

430
00:15:14,440 --> 00:15:15,880
ORDER OF MAGNITUDE, THE NUMBER

431
00:15:15,880 --> 00:15:17,520
OF STARS IN THE UNIVERSE, 10 TO

432
00:15:17,520 --> 00:15:18,120
THE 31.

433
00:15:18,120 --> 00:15:20,480
AND ALMOST UNIMAGINABLE NUMBER.

434
00:15:20,480 --> 00:15:24,160
VERY DIFFICULT TO GRASP.

435
00:15:24,160 --> 00:15:25,720
AND NOW I WOULD ASK YOU TO

436
00:15:25,720 --> 00:15:28,960
CONSIDER A VERY SMALL PROTEIN, A

437
00:15:28,960 --> 00:15:30,920
PEPTIDE COMPOSED OF ONLY 25

438
00:15:30,920 --> 00:15:34,360
AMINO ACIDS, IN A LINEAR CHAIN,

439
00:15:34,360 --> 00:15:37,320
ONE UP TO THE OTHER, SORT OF

440
00:15:37,320 --> 00:15:38,720
LIKE THE COLOR OF PEARLS.

441
00:15:38,720 --> 00:15:40,000
I'LL JUST TELL YOU THAT THE

442
00:15:40,000 --> 00:15:41,040
COMMUNE TORE YAL SEQUENCE BASE

443
00:15:41,040 --> 00:15:43,440
OF THAT VERY SMALL MOLECULE,

444
00:15:43,440 --> 00:15:45,520
AGAIN, VERY SMALL, MUCH SMALLER

445
00:15:45,520 --> 00:15:48,120
THAN ANY PROTEIN IN OUR BODIES

446
00:15:48,120 --> 00:15:49,800
WHICH ARE COMPRISED OF HUNDREDS

447
00:15:49,800 --> 00:15:51,360
OF AMINO ACIDS, IT HAS A

448
00:15:51,360 --> 00:15:52,240
SEQUENCE BASE THAT IS SUPERIOR

449
00:15:52,240 --> 00:15:54,600
TO THE NUMBER OF STARS IN THE

450
00:15:54,600 --> 00:15:55,240
UNIVERSE.

451
00:15:55,240 --> 00:16:04,480
SO THIS IS INCREDIBLE TO THINK

452
00:16:04,480 --> 00:16:05,520
ABOUT, WE NEED COMPUTERS TO

453
00:16:05,520 --> 00:16:06,480
START EXPLORING THIS.

454
00:16:06,480 --> 00:16:08,240
TO MAKE THINGS MORE COMPLEX AND

455
00:16:08,240 --> 00:16:09,120
THEREFORE OF COURSE MORE

456
00:16:09,120 --> 00:16:12,960
INTERESTING FROM A SCIENTIFIC

457
00:16:12,960 --> 00:16:13,880
PERSPECTIVE, BIOLOGICAL

458
00:16:13,880 --> 00:16:15,240
EVOLUTION THROUGHOUT BILLIONS OF

459
00:16:15,240 --> 00:16:17,080
YEARS HAS ONLY SAMPLED A TINY

460
00:16:17,080 --> 00:16:18,520
FRACTION OF THE ENTIRE SPACE OF

461
00:16:18,520 --> 00:16:20,280
POSSIBILITIES OF ALL POTENTIAL

462
00:16:20,280 --> 00:16:22,360
MOLECULES, PEPTIDES, PROTEINS.

463
00:16:22,360 --> 00:16:23,760
SO EVERYTHING THAT HAS BEEN

464
00:16:23,760 --> 00:16:25,400
EXPLORED, WE CAN FIT IT INTO

465
00:16:25,400 --> 00:16:27,600
THIS LITTLE PINK OVAL HERE.

466
00:16:27,600 --> 00:16:35,160
AND ALL THESE WHITE AREAS REMAIN

467
00:16:35,160 --> 00:16:35,560
UNEXPLORED.

468
00:16:35,560 --> 00:16:39,760
COMPUTERS CAN HELP US EXPAND THE

469
00:16:39,760 --> 00:16:41,400
SEQUENCE SPACE FOR THE WHITE

470
00:16:41,400 --> 00:16:42,920
SPACES OR AREAS THAT WE THINK

471
00:16:42,920 --> 00:16:44,040
MAY HARBOR MOLECULE SEQUENCES

472
00:16:44,040 --> 00:16:46,320
THAT CAN HELP US SOLVE

473
00:16:46,320 --> 00:16:48,360
PRESENT-DAY PROBLEMS, FOR

474
00:16:48,360 --> 00:16:50,120
EXAMPLE, ANTIBIOTIC RESISTANCE.

475
00:16:50,120 --> 00:16:52,000
SO OKAY, CLEARLY I HOPE I

476
00:16:52,000 --> 00:16:53,080
CONVINCED YOU THAT WE NEED

477
00:16:53,080 --> 00:16:54,280
COMPUTERS TO BE ABLE TO TACKLE

478
00:16:54,280 --> 00:16:59,200
SOME OF THESE PROBLEMS AND THE

479
00:16:59,200 --> 00:17:00,520
NEXT QUESTION WE ASKED OURSELVES

480
00:17:00,520 --> 00:17:02,080
WAS HOW CAN WE TRAIN A COMPUTER

481
00:17:02,080 --> 00:17:03,960
TO CREATE DIVERSITY AT THE

482
00:17:03,960 --> 00:17:06,360
MOLECULAR LEVEL, TO INNOVATE AT

483
00:17:06,360 --> 00:17:07,400
THE MOLECULAR LEVEL?

484
00:17:07,400 --> 00:17:09,080
AFTER THINKING ABOUT THIS WITH

485
00:17:09,080 --> 00:17:11,680
OUR COLLABORATORS, WE DECIDED

486
00:17:11,680 --> 00:17:14,080
THE BEST WAY TO DO THIS WAS TO

487
00:17:14,080 --> 00:17:17,440
TRAIN A COMPUTER TO MIMIC THE

488
00:17:17,440 --> 00:17:22,200
GREATEST ENGINE WE HAVE FOR

489
00:17:22,200 --> 00:17:23,160
INNOVATION AND THAT'S, OF

490
00:17:23,160 --> 00:17:24,000
COURSE, NOTHING ELSE THAN

491
00:17:24,000 --> 00:17:28,600
EVOLUTION ITSELF.

492
00:17:28,600 --> 00:17:30,920
SO WE DECIDED TO TEACH THE

493
00:17:30,920 --> 00:17:37,840
COMPUTER HOW TO EXECUTE DARWIN,

494
00:17:37,840 --> 00:17:39,920
ON A MACHINE IT BECOMES A THEORY

495
00:17:39,920 --> 00:17:41,560
OF ARTIFICIAL SELECTION INSTEAD

496
00:17:41,560 --> 00:17:43,840
OF HAVING TO WAIT MILLIONS OF

497
00:17:43,840 --> 00:17:45,480
YEARS FOR A MOLECULE TO EVOLVE,

498
00:17:45,480 --> 00:17:47,800
WE CAN DO IT ON A COMPUTER IN A

499
00:17:47,800 --> 00:17:50,440
TIME SCALE OF DAYS TO WEEKS.

500
00:17:50,440 --> 00:17:51,720
THIS IS HOW WE DID IT.

501
00:17:51,720 --> 00:17:53,040
WE STARTED WITH AN INITIAL

502
00:17:53,040 --> 00:17:53,960
POPULATION OF SMALL PEPTIDES

503
00:17:53,960 --> 00:17:57,280
FROM NATURE, AND THEN LIKE I

504
00:17:57,280 --> 00:17:58,600
SAID, WE TRAINED THE COMPUTER TO

505
00:17:58,600 --> 00:18:01,000
EVOLVE THEM THROUGH MUTATION,

506
00:18:01,000 --> 00:18:04,320
SELECTION, RECOMBINATION, WHICH

507
00:18:04,320 --> 00:18:09,200
ARE THE ESSENTIAL PROCESSES OF

508
00:18:09,200 --> 00:18:14,760
EVOLUTION, AND ITERATIVELY IN A

509
00:18:14,760 --> 00:18:15,960
WAY THE COMPUTER IS ABLE TO

510
00:18:15,960 --> 00:18:18,280
EVOLVE THE MOLECULES IN REALTIME

511
00:18:18,280 --> 00:18:19,400
INSILICO.

512
00:18:19,400 --> 00:18:21,600
SO THIS IS WHAT IT LOOKS LIKE,

513
00:18:21,600 --> 00:18:23,320
UPON INCREASING NUMBER OF

514
00:18:23,320 --> 00:18:26,280
ITERATIONS, COMPUTER EVOLVES THE

515
00:18:26,280 --> 00:18:27,680
MOLECULES TOWARD VALUES THAT

516
00:18:27,680 --> 00:18:28,640
CORRELATE WITH PREDICTED

517
00:18:28,640 --> 00:18:29,640
ANTIBIOTIC ACTIVITY.

518
00:18:29,640 --> 00:18:31,320
SO AT LEAST IN PRINCIPLE, THE

519
00:18:31,320 --> 00:18:32,720
COMPUTER IS MAKING THE MOLECULES

520
00:18:32,720 --> 00:18:36,440
BECOME BETTER AT -- BACTERIA.

521
00:18:36,440 --> 00:18:38,200
IN THE PROCESS OF DOING THIS,

522
00:18:38,200 --> 00:18:39,400
WHICH IS REALLY INTERESTING, THE

523
00:18:39,400 --> 00:18:44,400
COMPUTER IS ALSO CAPABLE OF OF

524
00:18:44,400 --> 00:18:46,280
EXPLORING PREVIOUSLY UNEXPLORED

525
00:18:46,280 --> 00:18:48,040
SPACES, REMEMBER A FEW SLIDES

526
00:18:48,040 --> 00:18:50,080
AGO, THE WHITE SPACES, WHITE

527
00:18:50,080 --> 00:18:51,560
AREAS NOT EXPLORED THROUGH

528
00:18:51,560 --> 00:18:52,360
EVOLUTIONARY PROCESS, AND YOU

529
00:18:52,360 --> 00:18:58,240
CAN START TO INTERROGATE THOSE

530
00:18:58,240 --> 00:18:59,760
REGIONS YIELDING MOLECULES

531
00:18:59,760 --> 00:19:00,960
DIFFERENT FROM WHAT WE SEE IN

532
00:19:00,960 --> 00:19:01,280
BIOLOGY.

533
00:19:01,280 --> 00:19:03,840
SO WITH AMINO ACID RATIOS AND

534
00:19:03,840 --> 00:19:05,320
COMPOSITIONS THAT ARE DIFFERENT,

535
00:19:05,320 --> 00:19:06,880
ATYPICAL, NOT WHAT WE TYPICALLY

536
00:19:06,880 --> 00:19:08,480
SEE THAT HAS BEEN GENERATED

537
00:19:08,480 --> 00:19:10,920
THROUGHOUT THE EVOLUTIONARY

538
00:19:10,920 --> 00:19:11,120
PROCESS.

539
00:19:11,120 --> 00:19:11,440
OKAY.

540
00:19:11,440 --> 00:19:13,120
SO WHAT I'M SHOWING YOU HERE IS

541
00:19:13,120 --> 00:19:14,920
A BUNCH OF DIFFERENT PEPTIDE

542
00:19:14,920 --> 00:19:15,320
MOLECULES.

543
00:19:15,320 --> 00:19:18,000
YOU CAN SEE THOSE BEAUTIFUL

544
00:19:18,000 --> 00:19:18,880
STRUCTURES, BUT EVERYTHING I'M

545
00:19:18,880 --> 00:19:20,560
SHOWING YOU HERE JUST GENERATED

546
00:19:20,560 --> 00:19:23,040
BY THE MACHINE WITH VERY MINIMAL

547
00:19:23,040 --> 00:19:26,000
HUMAN INTERVENTION UP TO THIS

548
00:19:26,000 --> 00:19:26,200
POINT.

549
00:19:26,200 --> 00:19:29,040
AND HERE, WE REACH A ROADBLOCK

550
00:19:29,040 --> 00:19:30,600
VERY COMMON TO ANY PROJECTS

551
00:19:30,600 --> 00:19:31,680
INVOLVING COMPUTERS AND THAT IS

552
00:19:31,680 --> 00:19:32,880
THAT EVERYTHING THAT WE SEE HERE

553
00:19:32,880 --> 00:19:35,960
IS JUST BASED ON COMPUTATIONAL

554
00:19:35,960 --> 00:19:36,240
ASSUMPTIONS.

555
00:19:36,240 --> 00:19:37,480
IN OTHER WORDS, THE COMPUTER

556
00:19:37,480 --> 00:19:39,240
THINKS THAT THESE MOLECULES WILL

557
00:19:39,240 --> 00:19:41,080
BE GREAT ANTIBIOTICS, THEY WILL

558
00:19:41,080 --> 00:19:42,280
BE GREAT AT KILLING BACTERIA.

559
00:19:42,280 --> 00:19:46,320
BUT WE DON'T KNOW THAT FOR SURE,

560
00:19:46,320 --> 00:19:46,680
RIGHT?

561
00:19:46,680 --> 00:19:48,320
THIS IS VERY MUCH AN EMERGING

562
00:19:48,320 --> 00:19:49,720
FIELD SO WE CAN'T REALLY TRUST

563
00:19:49,720 --> 00:19:52,360
THE COMPUTER'S ASSUMPTION.

564
00:19:52,360 --> 00:19:54,840
WE NEED TO VALIDATE ABSOLUTELY

565
00:19:54,840 --> 00:19:55,320
EVERYTHING.

566
00:19:55,320 --> 00:19:58,280
SO WE CHEMICALLY SYNTHESIZE

567
00:19:58,280 --> 00:19:59,680
THESE MOLECULES AND WE TESTED

568
00:19:59,680 --> 00:20:01,000
THEM AGAINST REAL BACTERIA FROM

569
00:20:01,000 --> 00:20:02,760
THE HOSPITALS AND FROM THE

570
00:20:02,760 --> 00:20:04,280
LABORATORY, TO SEE IF THEY WERE

571
00:20:04,280 --> 00:20:08,840
ACTUALLY ABLE TO KILL THEM.

572
00:20:08,840 --> 00:20:10,240
SO THROUGH A NUMBER OF SCREENING

573
00:20:10,240 --> 00:20:12,040
EFFORTS WE WERE ABLE TO ISOLATE

574
00:20:12,040 --> 00:20:13,560
A LEAD PEPTIDE THAT WAS ACTUALLY

575
00:20:13,560 --> 00:20:15,120
VERY POTENT AT KILLING

576
00:20:15,120 --> 00:20:20,360
PATHOGENS.

577
00:20:20,360 --> 00:20:22,760
THEN WE WENT ON TO EE LEWIS DADE

578
00:20:22,760 --> 00:20:24,120
THE THREE-DIMENSIONAL STRUCTURE

579
00:20:24,120 --> 00:20:29,120
OF THE PEPTIDE WHICH WE SEE, WE

580
00:20:29,120 --> 00:20:32,040
CALL THIS GUAVANIN 2.

581
00:20:32,040 --> 00:20:34,480
AND OF COURSE WE'RE NOT SEAD

582
00:20:34,480 --> 00:20:37,000
WSATISFIED WITH THIS, WE WANTED

583
00:20:37,000 --> 00:20:39,160
TO SEE IF THIS COMPUTER-MADE

584
00:20:39,160 --> 00:20:41,240
MOLECULE WAS CAPABLE OF REDUCING

585
00:20:41,240 --> 00:20:43,240
INFECTIONS IN A REALISTIC

586
00:20:43,240 --> 00:20:44,080
CLINICAL MOUSE MODEL.

587
00:20:44,080 --> 00:20:46,080
SO WE DEVELOPED THIS SKIN

588
00:20:46,080 --> 00:20:47,280
INFECTION MODEL AND WE INFECTED

589
00:20:47,280 --> 00:20:48,760
THE MICE AND THEN WE TREATED

590
00:20:48,760 --> 00:20:49,040
THEM.

591
00:20:49,040 --> 00:20:53,720
WE TREATED THEM WITH TWO OF THE

592
00:20:53,720 --> 00:20:55,920
PEPTIDES THE COMPUTER STARTED

593
00:20:55,920 --> 00:20:57,040
THE PROCESS WITH, THESE TWO

594
00:20:57,040 --> 00:20:59,320
HERE, AND THIS IS THE

595
00:20:59,320 --> 00:21:03,360
MACHINE-MADE MODEL, GUA VANIN

596
00:21:03,360 --> 00:21:06,160
2 THAT LED TO BETTER RESOLUTION

597
00:21:06,160 --> 00:21:07,200
OF THE INFECTION.

598
00:21:07,200 --> 00:21:08,040
THIS IS A CONTROL GROUP OF MICE

599
00:21:08,040 --> 00:21:10,360
SO YOU GET A SENSE OF HOW MANY

600
00:21:10,360 --> 00:21:12,320
BACTERIA IN THOSE CORE MICE LEFT

601
00:21:12,320 --> 00:21:15,600
UNTREATED, YOU CAN SEE THE

602
00:21:15,600 --> 00:21:16,680
GUAVANIN 2 MOLECULE IS ACTUALLY

603
00:21:16,680 --> 00:21:17,800
CAPABLE OF REDUCING THE

604
00:21:17,800 --> 00:21:20,400
INFECTION MUCH MORE EFFECTIVELY

605
00:21:20,400 --> 00:21:24,360
THAN EVERY OTHER GROUP.

606
00:21:24,360 --> 00:21:25,680
SO JUST TO WRAP UP THIS PART, WE

607
00:21:25,680 --> 00:21:28,960
CAN USE COMPUTERS TO ASSIGN

608
00:21:28,960 --> 00:21:30,280
NOVEL ANTIBIOTICS NOT ONLY

609
00:21:30,280 --> 00:21:31,360
PRETTY TO LOOK AT ON THE SCREEN

610
00:21:31,360 --> 00:21:33,120
BUT THEY ACTUALLY KILL, YOU

611
00:21:33,120 --> 00:21:35,360
KNOW, PATHOGENIC BACTERIA THAT

612
00:21:35,360 --> 00:21:37,040
ARE CLINICALLY RELEVANT IN VITRO

613
00:21:37,040 --> 00:21:42,800
AND ALSO IN A MOUSE MODEL.

614
00:21:42,800 --> 00:21:44,640
SO THIS IS ANOTHER PROJECT WHERE

615
00:21:44,640 --> 00:21:48,840
WE IDENTIFIED A MOTIF,

616
00:21:48,840 --> 00:21:49,880
ESSENTIALLY FIVE AMINO ACIDS

617
00:21:49,880 --> 00:21:51,000
COLORED IN RED THAT WERE

618
00:21:51,000 --> 00:21:53,960
ASSOCIATED WITH TWO FUNCTIONS.

619
00:21:53,960 --> 00:21:57,560
DIRECT ANTIMICROBIAL ACTIVITY

620
00:21:57,560 --> 00:21:58,600
AND IMMUNOMODULATORY.

621
00:21:58,600 --> 00:22:00,720
THE HYPOTHESIS THAT WE REACHED

622
00:22:00,720 --> 00:22:03,120
IS, WHY DON'T WE TAKE THIS MOTIF

623
00:22:03,120 --> 00:22:05,440
THAT WE PREDICT HAS THESE TWO

624
00:22:05,440 --> 00:22:06,400
DIFFERENT FUNCTIONS AND WE'LL

625
00:22:06,400 --> 00:22:07,840
TRY TO STITCH IT INTO ANOTHER

626
00:22:07,840 --> 00:22:11,120
MOLECULE TO SEE IF IT CAN

627
00:22:11,120 --> 00:22:12,400
INVOLVE THAT OTHER MOLECULE WITH

628
00:22:12,400 --> 00:22:13,320
TWO FUNCTIONS OF INTEREST.

629
00:22:13,320 --> 00:22:14,840
SO IT'S A LITTLE LIKE PLAYING

630
00:22:14,840 --> 00:22:15,200
LEGO.

631
00:22:15,200 --> 00:22:19,000
SO WE STARTED WITH THIS

632
00:22:19,000 --> 00:22:19,640
TEMPLATE.

633
00:22:19,640 --> 00:22:22,960
ACTUALLY A TOXIC PEPTIDE, AND WE

634
00:22:22,960 --> 00:22:24,160
BASICALLY ENGINEERED IN THE

635
00:22:24,160 --> 00:22:27,280
PEPTIDE MOTIF TO ITS END

636
00:22:27,280 --> 00:22:29,600
TERMINUS TO CREATE THIS MOLECULE

637
00:22:29,600 --> 00:22:32,040
WE CALLED MAST-MO.

638
00:22:32,040 --> 00:22:32,920
ESSENTIALLY WE'RE ABLE TO SHOW

639
00:22:32,920 --> 00:22:34,800
THROUGH A NUMBER OF ASSAYS IN

640
00:22:34,800 --> 00:22:39,560
VIVO AND IN VITRO THAT THAT

641
00:22:39,560 --> 00:22:40,760
SYNTHETIC MOLECULE WAS CAPABLE

642
00:22:40,760 --> 00:22:43,720
OF DISPLAYING THE DUAL ACTIVITY.

643
00:22:43,720 --> 00:22:47,440
SO NOT ONLY THE CONVENTIONAL

644
00:22:47,440 --> 00:22:51,000
PARADIGM FOR TREATING INFECTIONS

645
00:22:51,000 --> 00:22:52,400
WHERE WITH ANTIBIOTICS YOU

646
00:22:52,400 --> 00:22:53,440
TARGET THE PATHOGEN AND YOU

647
00:22:53,440 --> 00:22:55,520
CLEAR IT AND THAT RESOLVES THE

648
00:22:55,520 --> 00:22:59,480
INFORECAINFECTION BUT ALSO IN AW

649
00:22:59,480 --> 00:23:00,320
PARADIGM, WHERE WHAT YOU'RE

650
00:23:00,320 --> 00:23:02,480
DOING IS RESOLVING THE INFECTION

651
00:23:02,480 --> 00:23:04,080
BY BOOSTING THE HOST'S OWN

652
00:23:04,080 --> 00:23:05,600
IMMUNE SYSTEM, IN THIS CASE, THE

653
00:23:05,600 --> 00:23:07,640
MOUSE IMMUNE SYSTEM, TO THEN --

654
00:23:07,640 --> 00:23:09,520
YOU BOOST IT IN SUCH A WAY THAT

655
00:23:09,520 --> 00:23:12,280
THE MOUSE'S IMMUNE SYSTEM IS

656
00:23:12,280 --> 00:23:13,480
ABLE TO CLEAR THE INFECTION.

657
00:23:13,480 --> 00:23:15,560
SO WE'RE ABLE TO DEMONSTRATE

658
00:23:15,560 --> 00:23:17,600
THIS CONCEPT BOTH IN VITRO IN A

659
00:23:17,600 --> 00:23:19,160
SKIN INFECTION MODEL AND ALSO IN

660
00:23:19,160 --> 00:23:20,560
A SEPSIS INFECTION MODEL, WHICH

661
00:23:20,560 --> 00:23:22,320
IS A HUGE PROBLEM, SEPSIS IS A

662
00:23:22,320 --> 00:23:23,640
HUGE PROBLEM IN THE WORLD AS I

663
00:23:23,640 --> 00:23:25,640
MENTIONED, HERE ARE SOME OF THE

664
00:23:25,640 --> 00:23:27,680
NUMBERS, AND HERE TREATMENT WITH

665
00:23:27,680 --> 00:23:29,640
THE SYNTHETIC PEPTIDES WERE

666
00:23:29,640 --> 00:23:32,080
CAPABLE OF COMPARING PROTECTION

667
00:23:32,080 --> 00:23:35,120
TO MICE AGAINST OTHERWISE LETHAL

668
00:23:35,120 --> 00:23:37,640
INFECTIONS OF LABORATORY STRAINS

669
00:23:37,640 --> 00:23:39,360
LIKE E. COLI AND STAPH AUREUS

670
00:23:39,360 --> 00:23:41,120
BUT ALSO HIGHLY DRUG RESISTANT

671
00:23:41,120 --> 00:23:42,240
STRAINS PROBLEMATIC IN THE

672
00:23:42,240 --> 00:23:46,720
HOSPITAL, SUCH AS E. COLI KPC

673
00:23:46,720 --> 00:23:49,440
POSITIVE AND STAPH AUREUS

674
00:23:49,440 --> 00:23:52,000
MRSA -- HIGHLY RESISTANT TO

675
00:23:52,000 --> 00:23:53,400
NUMBER OF ANTIBIOTICS.

676
00:23:53,400 --> 00:23:55,000
SO THAT WAS ALSO ENCOURAGING AND

677
00:23:55,000 --> 00:23:57,880
WE'RE NOW PURSUING FURTHER

678
00:23:57,880 --> 00:24:01,440
DEVELOPMENT OF THIS APPROACH FOR

679
00:24:01,440 --> 00:24:02,720
TREATING INFECTIONS THROUGH DUAL

680
00:24:02,720 --> 00:24:03,680
MECHANISMS.

681
00:24:03,680 --> 00:24:05,360
OKAY, SO I TOLD YOU HOW WE CAN

682
00:24:05,360 --> 00:24:07,560
USE COMPUTATIONAL TOOLS COUPLED

683
00:24:07,560 --> 00:24:11,240
WITH EXPERIMENTS TO DESIGN NOVEL

684
00:24:11,240 --> 00:24:12,120
APPROACHES FOR ANTIBIOTIC

685
00:24:12,120 --> 00:24:15,080
TREATMENT.

686
00:24:15,080 --> 00:24:16,320
OR ANTIBIOTIC DESIGN.

687
00:24:16,320 --> 00:24:22,160
NOW I'D LIKE TO TELL YOU HOW WE

688
00:24:22,160 --> 00:24:23,520
USE -- BIOLOGICAL INFORMATION,

689
00:24:23,520 --> 00:24:25,040
THINGS THAT HAVEN'T REALLY BEEN

690
00:24:25,040 --> 00:24:26,320
EXPLORED BEFORE VERY WELL, AND

691
00:24:26,320 --> 00:24:28,080
TO TACKLE THIS, WE TAKE

692
00:24:28,080 --> 00:24:29,280
INSPIRATION FROM IMAGE AND

693
00:24:29,280 --> 00:24:30,720
SPEECH RECOGNITION ALGORITHMS

694
00:24:30,720 --> 00:24:33,640
BUT INSTEAD OF RECOGNIZING

695
00:24:33,640 --> 00:24:34,760
FACIAL EXPRESSIONS OR SOUNDS, WE

696
00:24:34,760 --> 00:24:38,280
WANT TO RECOGNIZE MOLECULAR

697
00:24:38,280 --> 00:24:39,560
PATTERNS ASSOCIATED WITH

698
00:24:39,560 --> 00:24:41,080
POTENTIAL ANTIBIOTICS.

699
00:24:41,080 --> 00:24:43,840
AND WE WORK WITH PEPTIDES, WE

700
00:24:43,840 --> 00:24:46,520
LOOK AT AMINO ACID PATTERNS, AND

701
00:24:46,520 --> 00:24:47,920
THE ANALOGY THAT I LIKE TO USE

702
00:24:47,920 --> 00:24:51,280
HERE IS THAT THESE ALGORITHMS

703
00:24:51,280 --> 00:24:52,920
OPERATE A LITTLE LIKE THE SEARCH

704
00:24:52,920 --> 00:24:54,320
FUNCTION IN MICROSOFT WORD,

705
00:24:54,320 --> 00:24:55,960
WHERE LET'S SAY YOU HAVE A HUGE

706
00:24:55,960 --> 00:25:00,440
WORD DOCUMENT LIKE -- WITH

707
00:25:00,440 --> 00:25:01,360
HUNDREDS OF PAGES AND YOU WANT

708
00:25:01,360 --> 00:25:03,200
TO FIND THE WORD -- ONE

709
00:25:03,200 --> 00:25:05,280
PARTICULAR WORD, LET'S SAY

710
00:25:05,280 --> 00:25:07,680
NIGMS.

711
00:25:07,680 --> 00:25:09,120
YOU GO INTO THE SEARCH FUNCTION,

712
00:25:09,120 --> 00:25:13,600
YOU TYPE NIGMS, AND THE

713
00:25:13,600 --> 00:25:17,200
ALGORITHM IDENTIFIES OR FINDS

714
00:25:17,200 --> 00:25:19,400
THE NIGMS IN WHATEVER INSTANCE

715
00:25:19,400 --> 00:25:22,800
IT APPEARS IN THE DOCUMENT.

716
00:25:22,800 --> 00:25:23,880
VERY EFFECTIVELY.

717
00:25:23,880 --> 00:25:25,640
SOL ALGORITHMS OPERATE IN A

718
00:25:25,640 --> 00:25:26,960
SIMILAR FASHION WHERE WE KNOW

719
00:25:26,960 --> 00:25:28,680
WHAT WE'RE LOOKING FOR, THE

720
00:25:28,680 --> 00:25:32,520
PATTERNS, INSTEAD OF USING

721
00:25:32,520 --> 00:25:34,920
LETTERS, WE USE AMINO ACID CODE

722
00:25:34,920 --> 00:25:37,560
AND INSTEAD OF SEARCHING OR

723
00:25:37,560 --> 00:25:38,640
BROWSING -- WE BROWSE THROUGH

724
00:25:38,640 --> 00:25:40,480
GENOMES AND PROTEINS AND WE CAN

725
00:25:40,480 --> 00:25:43,800
IDENTIFY POTENTIAL NEW

726
00:25:43,800 --> 00:25:44,320
ANTIMICROBIALS.

727
00:25:44,320 --> 00:25:46,280
SO TO ILLUSTRATE THIS CONCEPT IN

728
00:25:46,280 --> 00:25:52,400
A MORE VISUAL WAY, WE CAN HAVE

729
00:25:52,400 --> 00:25:53,720
ESSENTIALLY A PROTEIN IN THREE

730
00:25:53,720 --> 00:25:55,160
DIMENSIONS, WE CAN THEN DISPLAY

731
00:25:55,160 --> 00:25:58,960
IT IN TWO DIMENSIONS HERE, AND

732
00:25:58,960 --> 00:26:00,320
THE ALGORITHM ESSENTIALLY WILL

733
00:26:00,320 --> 00:26:04,880
RUN THROUGH THE CODE AND WILL

734
00:26:04,880 --> 00:26:07,200
IDENTIFY REGIONS WITHIN THE

735
00:26:07,200 --> 00:26:11,040
AMINO ACID CODE THAT ARE

736
00:26:11,040 --> 00:26:11,880
PROSPECTIVE ANTIBIOTICS.

737
00:26:11,880 --> 00:26:13,080
THAT'S GIVEN IN THE DIFFERENT

738
00:26:13,080 --> 00:26:14,040
COLORS HERE, THE DIFFERENT

739
00:26:14,040 --> 00:26:14,840
COLORS THAT WE SEE.

740
00:26:14,840 --> 00:26:16,480
AGAIN, ONLY A PREDICTION.

741
00:26:16,480 --> 00:26:18,240
BUT REALLY A VERY POWERFUL ONE

742
00:26:18,240 --> 00:26:19,240
BECAUSE IT ALLOWS YOU TO

743
00:26:19,240 --> 00:26:23,920
IDENTIFY WI WITHIN ENTIRE PROTES

744
00:26:23,920 --> 00:26:25,920
FRAGMENTS, FOR EXAMPLE THIS

745
00:26:25,920 --> 00:26:27,880
FRAGMENT IN YELLOW THAT

746
00:26:27,880 --> 00:26:29,240
REPRESENT A POTENTIAL

747
00:26:29,240 --> 00:26:29,720
ANTIBIOTIC.

748
00:26:29,720 --> 00:26:31,240
WE CAN CONSTRUCT THAT ANTIBIOTIC

749
00:26:31,240 --> 00:26:32,880
AND PLAY AROUND WITH IT IN

750
00:26:32,880 --> 00:26:34,120
LABORATORY TO SEE IF IT CAN

751
00:26:34,120 --> 00:26:35,840
BECOME A POTENTIAL THERAPEUTIC.

752
00:26:35,840 --> 00:26:39,600
SO WE'VE BASICALLY UTILIZED

753
00:26:39,600 --> 00:26:41,760
THESE METHODS TO EXPLORE THE

754
00:26:41,760 --> 00:26:44,640
WHOLE -- TO PERFORM THE FIRST

755
00:26:44,640 --> 00:26:47,040
PROTEIN-WIDE EXPLORATION OF THE

756
00:26:47,040 --> 00:26:48,760
HUMAN BODY AS A SOURCE OF

757
00:26:48,760 --> 00:26:49,840
POTENTIAL ANTIBIOTICS.

758
00:26:49,840 --> 00:26:51,600
WE FOUND THOUSANDS OF WHAT WE

759
00:26:51,600 --> 00:26:53,360
CALL ENCRYPTED PEPTIDE

760
00:26:53,360 --> 00:26:54,520
ANTIBIOTICS AND WHAT I THINK IS

761
00:26:54,520 --> 00:26:55,760
REALLY QUITE FASCINATING IS THAT

762
00:26:55,760 --> 00:26:58,920
WE DIDN'T ONLY FIND THEM IN THE

763
00:26:58,920 --> 00:27:00,560
INNATE IMMUNE SYSTEM, WHICH IS

764
00:27:00,560 --> 00:27:01,760
WHERE YOU WOULD IMAGINE YOU

765
00:27:01,760 --> 00:27:06,560
WOULD FIND ANTIMICROBIAL

766
00:27:06,560 --> 00:27:07,560
MOLECULES.

767
00:27:07,560 --> 00:27:09,560
THE IMMUNE SYSTEM HELPS OF FIGHT

768
00:27:09,560 --> 00:27:11,960
OFF INVADING ORGANISMS,

769
00:27:11,960 --> 00:27:13,160
PATHOGENS.

770
00:27:13,160 --> 00:27:17,040
BUT WE WE ALSO FIND THEM ALL

771
00:27:17,040 --> 00:27:17,960
THROUGHOUT THE BODY.

772
00:27:17,960 --> 00:27:19,240
THIS TAKES US TO OUR CURRENT

773
00:27:19,240 --> 00:27:22,120
HYPOTHESIS, WHERE WE THINK THAT

774
00:27:22,120 --> 00:27:22,760
PERHAPS IMMUNOLOGICAL RESPONSE

775
00:27:22,760 --> 00:27:24,200
IS NOT ONLY RESPONSIBILITY OF

776
00:27:24,200 --> 00:27:26,680
THE INNATE IMMUNE SYSTEM, BUT WE

777
00:27:26,680 --> 00:27:28,320
HAVE ALL THESE OTHER BODY

778
00:27:28,320 --> 00:27:33,920
SYSTEMS WORKING IN COLLABORATION

779
00:27:33,920 --> 00:27:35,680
TO PROVIDE -- TO FIGHT OFF

780
00:27:35,680 --> 00:27:38,080
INVADING ORGANISMS EITHER

781
00:27:38,080 --> 00:27:38,960
DIRECTLY OR INDIRECTLY.

782
00:27:38,960 --> 00:27:40,600
SO AGAIN, WE FOUND THOUSANDS OF

783
00:27:40,600 --> 00:27:45,400
THESE MOLECULES, AND THEN WE

784
00:27:45,400 --> 00:27:46,520
SYNTHESIZE 56 OF THEM USING

785
00:27:46,520 --> 00:27:52,200
CHEMICAL METHODS, USING CHEMICAL

786
00:27:52,200 --> 00:27:52,840
SYNTHESIS.

787
00:27:52,840 --> 00:27:53,840
WE TRY TO LEARN AS MUCH AS

788
00:27:53,840 --> 00:27:55,920
POSSIBLE FROM THIS EM.

789
00:27:55,920 --> 00:27:57,200
SO I'M GOING TO TELL YOU SOME OF

790
00:27:57,200 --> 00:27:58,640
THE LEARNINGS THAT WE TOOK FROM

791
00:27:58,640 --> 00:28:00,640
SOME OF THOSE EXPERIMENTS.

792
00:28:00,640 --> 00:28:05,000
FOR EXAMPLE, IF YOU TAKEN

793
00:28:05,000 --> 00:28:06,640
CRYPTED PEPTIDES THAT ARE

794
00:28:06,640 --> 00:28:08,360
ENCODED IN THE SAME

795
00:28:08,360 --> 00:28:11,440
BIOGEOGRAPHICAL AREA OF YOUR --

796
00:28:11,440 --> 00:28:12,760
THE SAME AREA OF THE BODY, LET'S

797
00:28:12,760 --> 00:28:14,280
SAY THE BLOODSTREAM, AND YOU

798
00:28:14,280 --> 00:28:17,360
COMBINE IT IN COCKTAILS, YOU'RE

799
00:28:17,360 --> 00:28:19,400
ACTUALLY ABLE TO POTENTIATE THE

800
00:28:19,400 --> 00:28:20,400
ACTIVITY TO TARGET PATHOGENS,

801
00:28:20,400 --> 00:28:23,040
WHICH IS REALLY INTERESTING.

802
00:28:23,040 --> 00:28:25,240
AND WE DEMONSTRATED THIS IN

803
00:28:25,240 --> 00:28:26,880
SYNERGISTIC INTERACTION ASSAYS,

804
00:28:26,880 --> 00:28:29,920
AND THIS TAKES US TO A VIEW OF

805
00:28:29,920 --> 00:28:32,280
SOME OF THESE INCRYPTED

806
00:28:32,280 --> 00:28:33,680
MOLECULES WHERE YOU CAN ENVISION

807
00:28:33,680 --> 00:28:34,960
HAVING HUNDREDS OF THEM ALL

808
00:28:34,960 --> 00:28:36,400
THROUGHOUT YOUR BODY WORKING IN

809
00:28:36,400 --> 00:28:38,560
CONJUNCTION TO PROVIDE AN

810
00:28:38,560 --> 00:28:39,240
IMMUNOLOGICAL RESPONSE.

811
00:28:39,240 --> 00:28:41,200
ANOTHER THING THAT WE WANTED TO

812
00:28:41,200 --> 00:28:42,960
SEE IS WHETHER OUR TARGET

813
00:28:42,960 --> 00:28:46,360
DEVELOPED RESISTANCE TO THESE

814
00:28:46,360 --> 00:28:48,640
INCRYPTED PEPTIDES AND TARGET

815
00:28:48,640 --> 00:28:50,960
BACTERIA, SO WE RAN RESISTANCE

816
00:28:50,960 --> 00:28:55,400
DEVELOPMENT ASSAYS IN THIS CASE

817
00:28:55,400 --> 00:28:57,480
AGAINST GRAM-NEGATIVE HIGHLY

818
00:28:57,480 --> 00:28:58,160
DRUG-RESISTANT BACTERIA.

819
00:28:58,160 --> 00:29:01,240
AND WE EXPOSED THE BACTERIA OVER

820
00:29:01,240 --> 00:29:07,320
TIME, OVER 30 DAYS,

821
00:29:07,320 --> 00:29:12,800
CONSISTENTLY, WITH POLYMYXIN B

822
00:29:12,800 --> 00:29:14,680
AND THREE ENCRYPTED -- WE FIND

823
00:29:14,680 --> 00:29:17,560
IN OUR OWN BODIES.

824
00:29:17,560 --> 00:29:19,160
THE BACTERIAL PATHOGEN

825
00:29:19,160 --> 00:29:21,000
EVENTUALLY DEVELOPED RESISTANCE

826
00:29:21,000 --> 00:29:24,600
TO POLYMYXIN B BUT NOT THE

827
00:29:24,600 --> 00:29:25,280
ENCRYPTED PEPTIDES.

828
00:29:25,280 --> 00:29:28,440
SO IT SEEMS THE BACTERIA DID NOT

829
00:29:28,440 --> 00:29:30,200
READILY DEVELOP PERSISTENCE TO

830
00:29:30,200 --> 00:29:30,840
THESE MOLECULES.

831
00:29:30,840 --> 00:29:33,400
SO THAT'S ALSO ENCOURAGING.

832
00:29:33,400 --> 00:29:34,800
THEN, OF COURSE, WE WENT ON TO

833
00:29:34,800 --> 00:29:37,520
SEE WHAT WAS THE INFECTIVE

834
00:29:37,520 --> 00:29:38,680
PROPERTY.

835
00:29:38,680 --> 00:29:41,880
WE WENT TO ALL THIS INTERESTING

836
00:29:41,880 --> 00:29:43,000
TRANSLATING TO SEE IF WE COULD

837
00:29:43,000 --> 00:29:45,440
TURN SOME OF THESE MOLECULES

838
00:29:45,440 --> 00:29:46,840
INTO ANTIBIOTICS THAT EVENTUALLY

839
00:29:46,840 --> 00:29:47,520
THEY HELP PEOPLE.

840
00:29:47,520 --> 00:29:53,160
AND WE WERE ABLE TO SEE THAT

841
00:29:53,160 --> 00:29:54,160
TREATMENT, MONOTHERAPY TREATMENT

842
00:29:54,160 --> 00:29:56,440
WITH ONE PEPTIDE OR ANOTHER

843
00:29:56,440 --> 00:29:57,560
PEPTIDE REDUCED THE INFECTION.

844
00:29:57,560 --> 00:30:00,080
IN THIS CASE, PSEUDOMONAS

845
00:30:00,080 --> 00:30:01,600
INFECTION IN MICE, THIS IS THE

846
00:30:01,600 --> 00:30:02,680
UNTREATED CONTROL GROUP, BUT

847
00:30:02,680 --> 00:30:06,040
THEN THAT EFFECT WAS EMPHASIZED

848
00:30:06,040 --> 00:30:08,040
AND COMBINED IN COCKTAIL, SO WE

849
00:30:08,040 --> 00:30:10,120
WERE ABLE TO RECAPITULATE THE

850
00:30:10,120 --> 00:30:12,240
DATA WE GOT IN VITRO, ALSO IN

851
00:30:12,240 --> 00:30:12,880
VIVO.

852
00:30:12,880 --> 00:30:14,920
SO THIS IS COMBINING THESE TWO

853
00:30:14,920 --> 00:30:16,600
MOLECULES HERE, WE CAN SEE AN

854
00:30:16,600 --> 00:30:18,000
ENHANCED EFFECT AT REDUCING THE

855
00:30:18,000 --> 00:30:18,720
INFECTION.

856
00:30:18,720 --> 00:30:28,600
AND THE SAME BASICALLY FOR A.

857
00:30:28,600 --> 00:30:29,120
BAUMANNIL.

858
00:30:29,120 --> 00:30:32,120
BUT THEN WE WENT ON AND WE

859
00:30:32,120 --> 00:30:34,000
TESTED THE INFECTIVE EFFICACY IN

860
00:30:34,000 --> 00:30:36,040
A PRE-CLINICAL INFECTION MODEL,

861
00:30:36,040 --> 00:30:37,160
TYPICALLY THE ONE THAT THE FDA

862
00:30:37,160 --> 00:30:41,960
WANTS TO SEE FOR -- ANTIBIOTICS.

863
00:30:41,960 --> 00:30:43,280
ONCE AGAIN WE SEE THE TREATED

864
00:30:43,280 --> 00:30:44,680
CONTROL GROUP, WE HAVE AROUND 10

865
00:30:44,680 --> 00:30:47,080
TO THE 9 BACTERIA IN THOSE MICE,

866
00:30:47,080 --> 00:30:49,080
AND THEN WE HAVE MONOTHERAPY

867
00:30:49,080 --> 00:30:51,000
WITH ONE PEPTIDE, MONOTHERAPY

868
00:30:51,000 --> 00:30:52,680
WITH ANOTHER PEPTIDE AND THEN

869
00:30:52,680 --> 00:30:53,240
COMBINATION THERAPY.

870
00:30:53,240 --> 00:30:55,080
SO WE SEE THE POWER, THE FORCE

871
00:30:55,080 --> 00:30:56,600
OF COMBINING COCKTAILS AND HOW

872
00:30:56,600 --> 00:31:00,440
THAT ACTUALLY ENHANCES THE

873
00:31:00,440 --> 00:31:02,320
ANTIMICROBIAL ACTIVITY IN VIVO

874
00:31:02,320 --> 00:31:03,520
VERY SUBSTANTIALLY.

875
00:31:03,520 --> 00:31:05,600
THIS IS JUST TO SHOW THAT SOME

876
00:31:05,600 --> 00:31:09,000
OF THESE ENCRYPTED PEPTIDES

877
00:31:09,000 --> 00:31:10,400
ACTUALLY PRODUCE -- IN THE BODY.

878
00:31:10,400 --> 00:31:11,720
WE DID IT THROUGH A LITERATURE

879
00:31:11,720 --> 00:31:13,240
SEARCH AND WE HAVE FOUR EXAMPLES

880
00:31:13,240 --> 00:31:15,640
HERE, THE CUB DOMAIN 3,

881
00:31:15,640 --> 00:31:20,800
FIBROBLAST GROWTH FACTOR 5,

882
00:31:20,800 --> 00:31:22,880
NATRIURET IC PEPTIDE AND VON

883
00:31:22,880 --> 00:31:24,400
WILLEBRAND FACTOR, FROM

884
00:31:24,400 --> 00:31:26,520
DIFFERENT LEVELS, LOW TO MEDIUM

885
00:31:26,520 --> 00:31:27,880
TO HIGH, IN DIFFERENT AREAS OF

886
00:31:27,880 --> 00:31:29,880
THE BODY.

887
00:31:29,880 --> 00:31:31,960
THIS IS SUPPORT THAT THESE

888
00:31:31,960 --> 00:31:33,080
MOLECULES ARE PRODUCED NATURALLY

889
00:31:33,080 --> 00:31:35,760
IN OUR BODIES, FRAGMENTS OF

890
00:31:35,760 --> 00:31:38,720
LARGER PROTEINS, AND THEY ARE

891
00:31:38,720 --> 00:31:39,800
CONFERRING IMMUNOLOGICAL EFFECT.

892
00:31:39,800 --> 00:31:41,240
SO I'M GOING TO SHIFT GEARS A

893
00:31:41,240 --> 00:31:43,200
LITTLE BIT.

894
00:31:43,200 --> 00:31:44,960
SO ONE -- THIS IS A FUN

895
00:31:44,960 --> 00:31:46,600
COLLABORATION WITH A COLLEAGUE

896
00:31:46,600 --> 00:31:50,440
OF MINE IN BARCELONA, AND THE

897
00:31:50,440 --> 00:31:52,280
CONCEPT HERE IS THAT ANTIBIOTICS

898
00:31:52,280 --> 00:31:55,560
ARE VERY PASSIVE MOLECULES.

899
00:31:55,560 --> 00:31:57,080
WE INTRODUCE THEM INTO, FOR

900
00:31:57,080 --> 00:31:58,080
EXAMPLE, THE BLOODSTREAM AND

901
00:31:58,080 --> 00:31:59,160
THEY GO ALONG FOR THE RIDE

902
00:31:59,160 --> 00:31:59,920
THROUGH THE BLOODSTREAM.

903
00:31:59,920 --> 00:32:03,240
THEY DON'T HAVE ANY SORT OF

904
00:32:03,240 --> 00:32:03,520
DIRECTION.

905
00:32:03,520 --> 00:32:05,080
THEY DON'T KNOW WHERE THE TARGET

906
00:32:05,080 --> 00:32:10,000
SITE IS, SO WE DECIDED TO SEE IF

907
00:32:10,000 --> 00:32:12,040
WE COULD CONFER ANTIBIOTICS WITH

908
00:32:12,040 --> 00:32:13,280
SOME SORT OF DIRECTIONALITY.

909
00:32:13,280 --> 00:32:16,200
AND WHAT WE DID IS WE USED

910
00:32:16,200 --> 00:32:17,680
NANOMACHINES AND WE LOADED THEM

911
00:32:17,680 --> 00:32:19,000
WITH ANTIBIOTIC AND THE

912
00:32:19,000 --> 00:32:20,280
NANOMACHINES CAN SELF-PROPEL

913
00:32:20,280 --> 00:32:23,120
THROUGH A DISTANCE, AND THE

914
00:32:23,120 --> 00:32:24,040
ANALOGY HERE WOULD BE LIKE IF

915
00:32:24,040 --> 00:32:25,520
YOU HAD A TRACK, RIGHT?

916
00:32:25,520 --> 00:32:28,680
YOU'D LOAD IT WITH A BUNCH OF

917
00:32:28,680 --> 00:32:29,480
ANTIBIOTIC AND THEN THE TRACK

918
00:32:29,480 --> 00:32:31,240
CAN MOVE AND IT CAN TRANSPORT

919
00:32:31,240 --> 00:32:32,360
ANTIBIOTIC.

920
00:32:32,360 --> 00:32:34,320
AND THEN AS IT TRANSPORTS

921
00:32:34,320 --> 00:32:35,160
ANTIBIOTIC, THE ANTIBIOTIC IS

922
00:32:35,160 --> 00:32:37,560
CAPABLE OF RESOLVING THE

923
00:32:37,560 --> 00:32:39,760
INFECTION, BUT IN A DIRECTIVE

924
00:32:39,760 --> 00:32:41,320
WAY, IN AN AUTONOMOUS WAY.

925
00:32:41,320 --> 00:32:42,840
SO WHAT YOU'LL SEE HERE IN THE

926
00:32:42,840 --> 00:32:45,000
DIFFERENT QUADRANTS, YOU'LL SEE

927
00:32:45,000 --> 00:32:48,080
MOVEMENT OF NANOMACHINES THAT

928
00:32:48,080 --> 00:32:50,480
ARE BEING SELF-PROPELLED AND

929
00:32:50,480 --> 00:32:51,040
TRANSPORTING ANTIBIOTIC AND

930
00:32:51,040 --> 00:32:52,560
CLEARING AN INFECTION, AND IN

931
00:32:52,560 --> 00:32:54,280
THIS QUADRANT HERE, YOU WON'T

932
00:32:54,280 --> 00:32:55,640
SEE MATCH BECAUSE THE SUBSTRATE

933
00:32:55,640 --> 00:32:58,240
THAT DRIVES THE INTERACTION IS

934
00:32:58,240 --> 00:33:00,400
NOT PRESENT SO ALL YOU'LL SEE IS

935
00:33:00,400 --> 00:33:03,280
A BIT OF -- MOTION IN THIS

936
00:33:03,280 --> 00:33:03,600
QUADRANT.

937
00:33:03,600 --> 00:33:10,040
SO HERE'S A LITTLE VIDEO.

938
00:33:10,040 --> 00:33:11,360
YOU CAN SEE IN MOST CASES YOU

939
00:33:11,360 --> 00:33:13,440
HAVE DIRECTIONALITY AND THE NANO

940
00:33:13,440 --> 00:33:17,800
MACHINES ARE MOVING ANTIBIOTIC,

941
00:33:17,800 --> 00:33:20,120
I THINK THIS OPENS AVENUES FOR

942
00:33:20,120 --> 00:33:21,800
THINKING ABOUT IN DESIGNING

943
00:33:21,800 --> 00:33:24,600
ANTIBIOTICS THAT CAN REACH THE

944
00:33:24,600 --> 00:33:26,440
TARGET SITE IN AN AUTONOMOUS

945
00:33:26,440 --> 00:33:30,520
FASHION AND THAT WOULD AVOID A

946
00:33:30,520 --> 00:33:33,040
LOT OF THE SIDE EFFECTS WE HAVE

947
00:33:33,040 --> 00:33:34,240
WITH ANTIBIOTICS THAT GO ALL

948
00:33:34,240 --> 00:33:40,120
THROUGHOUT OUR BODIES.

949
00:33:40,120 --> 00:33:44,080
THEN JUST TO WRAP UP A LITTLE

950
00:33:44,080 --> 00:33:46,480
BIT, SO WE'RE REALLY FASCINATED

951
00:33:46,480 --> 00:33:47,920
BY COMPUTERS AND THEIR ABILITY

952
00:33:47,920 --> 00:33:51,080
TO DESIGN AND DISCOVER NOVEL

953
00:33:51,080 --> 00:33:52,600
DRUGS, FOCUSED ON ANTIBIOTICS,

954
00:33:52,600 --> 00:33:54,120
AND NOW WHAT WE WOULD LIKE TO DO

955
00:33:54,120 --> 00:33:55,680
IS WE'D LIKE TO STREAMLINE THIS

956
00:33:55,680 --> 00:33:57,640
SO THAT ROBOTS CAN MAKE THE

957
00:33:57,640 --> 00:33:58,720
MOLECULES THE COMPUTER TELLS

958
00:33:58,720 --> 00:34:00,600
THEM TO MAKE.

959
00:34:00,600 --> 00:34:03,040
AND THEN STRE STREAMLINE THIS

960
00:34:03,040 --> 00:34:04,520
FURTHER, CONNECT THEM TO A

961
00:34:04,520 --> 00:34:07,400
SCREENING FACILITY THAT ALLOWS

962
00:34:07,400 --> 00:34:09,560
US TO SCREEN THOSE MOLECULES FOR

963
00:34:09,560 --> 00:34:11,120
ANTIBIOTIC ACTIVITY OR OTHER

964
00:34:11,120 --> 00:34:12,320
TYPES OF ACTIVITY THAT YOU MIGHT

965
00:34:12,320 --> 00:34:14,080
BE INTERESTED IN, WHETHER CANCER

966
00:34:14,080 --> 00:34:15,040
OR OTHERS.

967
00:34:15,040 --> 00:34:16,040
WE'RE BUILDING MACHINE LEARNING

968
00:34:16,040 --> 00:34:17,120
MODELS TO TIE BACK THE

969
00:34:17,120 --> 00:34:18,320
FUNCTIONAL INFORMATION FROM

970
00:34:18,320 --> 00:34:19,400
THOSE BIOASSAYS BACK TO THE

971
00:34:19,400 --> 00:34:21,720
COMPUTER SO WE CAN HAVE A

972
00:34:21,720 --> 00:34:22,560
SELF-LEARNING PLATFORM THAT

973
00:34:22,560 --> 00:34:27,600
ALLOWS US TO DISCOVER A NOVEL --

974
00:34:27,600 --> 00:34:28,840
NOVEL ANTIBIOTICS REALLY IN A

975
00:34:28,840 --> 00:34:31,640
VERY RAPID MANNER.

976
00:34:31,640 --> 00:34:33,440
THIS IS JUST TO ILLUSTRATE HOW

977
00:34:33,440 --> 00:34:34,640
YOUNG THIS FIELD IS.

978
00:34:34,640 --> 00:34:39,000
AT THE INTERSECTION OF A.I. AND

979
00:34:39,000 --> 00:34:39,560
ANTIBIOTIC DISCOVERY.

980
00:34:39,560 --> 00:34:42,720
THIS IS A RETROSPECTIVE STUDY WE

981
00:34:42,720 --> 00:34:44,480
RUN USING THE QUERIES --

982
00:34:44,480 --> 00:34:46,200
CONTINUING THE QUERIES OF

983
00:34:46,200 --> 00:34:51,040
ANTIBIOTICS AND A.I., CANCER

984
00:34:51,040 --> 00:34:53,000
THERAPEUTICS A.I. AND DRUGS A.I.

985
00:34:53,000 --> 00:34:54,760
YOU CAN SEE WE HAD PRACTICALLY

986
00:34:54,760 --> 00:34:58,600
NO PUBLICATIONS UNTIL 2018 SO

987
00:34:58,600 --> 00:35:01,000
REALLY WE WERE IN THE MIDST OF A

988
00:35:01,000 --> 00:35:02,200
YOUNG AND EMERGING FIELD.

989
00:35:02,200 --> 00:35:07,000
I ALWAYS LIKE TO --

990
00:35:07,000 --> 00:35:07,560
INTERDISCIPLINARY RESEARCH

991
00:35:07,560 --> 00:35:08,960
COMING FROM BIOLOGY,

992
00:35:08,960 --> 00:35:10,640
MICROBIOLOGY, PHYSICS, COMPUTER

993
00:35:10,640 --> 00:35:14,440
SCIENCE, DOESN'T REALLY MATTER,

994
00:35:14,440 --> 00:35:17,600
CHEMISTRY, THAT IT WOULD BE

995
00:35:17,600 --> 00:35:18,920
WONDERFUL TO HAVE YOU JOIN THESE

996
00:35:18,920 --> 00:35:22,720
EFFORTS.

997
00:35:22,720 --> 00:35:23,920
AGAIN -- AFFECTS EVERY CORNER OF

998
00:35:23,920 --> 00:35:27,160
THE WORLD AND THEY'RE REALLY

999
00:35:27,160 --> 00:35:30,960
MUCH NEEDED SO REALLY TRIED TO

1000
00:35:30,960 --> 00:35:32,800
USE YOUR TALENT AND YOUR

1001
00:35:32,800 --> 00:35:35,640
INGENUITY AND YOUR PASSION TO

1002
00:35:35,640 --> 00:35:36,640
TRY TO MOVE THIS FIELD FORWARD

1003
00:35:36,640 --> 00:35:37,840
WOULD BE REALLY FANTASTIC.

1004
00:35:37,840 --> 00:35:41,640
SO IF ANYBODY WANTS TO GET IN

1005
00:35:41,640 --> 00:35:42,640
TOUCH, PLEASE DO SO.

1006
00:35:42,640 --> 00:35:46,360
I'D LIKE TO ALSO MENTION THEY

1007
00:35:46,360 --> 00:35:47,960
ASKED ME TO ADD THIS TO MY TALKS

1008
00:35:47,960 --> 00:35:49,320
AND THIS IS A BOOK THAT WE

1009
00:35:49,320 --> 00:35:50,760
RECENTLY PUBLISHED ON MACHINE

1010
00:35:50,760 --> 00:35:52,720
LEARNING FOR DRUG DISCOVERY.

1011
00:35:52,720 --> 00:35:54,120
I THINK IT SERVES AS A GOOD

1012
00:35:54,120 --> 00:35:56,120
PRIMER FOR BEGINNERS TO TRY TO

1013
00:35:56,120 --> 00:35:57,200
GET INTO THIS FIELD SO IF

1014
00:35:57,200 --> 00:36:05,120
ANYBODY IS INTERESTED, I'M ABLE

1015
00:36:05,120 --> 00:36:06,480
TO GET A FREE COPY.

1016
00:36:06,480 --> 00:36:07,480
I'D LIKE TO END HERE.

1017
00:36:07,480 --> 00:36:12,400
I'D LIKE TO THANK THE LAB

1018
00:36:12,400 --> 00:36:12,640
MEMBERS.

1019
00:36:12,640 --> 00:36:13,960
IT'S REALLY A PRIVILEGE TO WORK

1020
00:36:13,960 --> 00:36:17,040
WITH THEM.

1021
00:36:17,040 --> 00:36:18,560
HIGHLY INTERDISCIPLINARY PEOPLE,

1022
00:36:18,560 --> 00:36:20,200
VERY SMART.

1023
00:36:20,200 --> 00:36:21,480
A LOT OF THE THINGS I'VE SHOWN

1024
00:36:21,480 --> 00:36:23,160
TODAY WAS DONE BY PEOPLE IN MY

1025
00:36:23,160 --> 00:36:26,560
LAB AND MY COLLABORATORS, SO

1026
00:36:26,560 --> 00:36:27,000
REALLY OUTSTANDING.

1027
00:36:27,000 --> 00:36:28,400
OUR FUNDERS FOR ALLOWING US TO

1028
00:36:28,400 --> 00:36:30,200
DO THE WORK WE DO AND PRIMARILY

1029
00:36:30,200 --> 00:36:33,880
I'D LIKE TO THANK NIGMS FOR

1030
00:36:33,880 --> 00:36:38,040
THEIR SUPPORT, REALLY

1031
00:36:38,040 --> 00:36:39,440
INSTRUMENTAL AT A CRUCIAL POINT

1032
00:36:39,440 --> 00:36:41,000
IN MY CAREER, REALLY MADE ME

1033
00:36:41,000 --> 00:36:43,280
BELIEVE THAT I COULD ATTRACT

1034
00:36:43,280 --> 00:36:44,920
FUNDS TO FUND SOME OF OUR IDEAS.

1035
00:36:44,920 --> 00:36:47,560
SO THAT WAS A REALLY KEY MOMENT.

1036
00:36:47,560 --> 00:36:49,840
HERE JUST LINKS TO OUR WEBSITE,

1037
00:36:49,840 --> 00:36:51,280
EMAIL ADDRESS AND TWITTER HANDLE

1038
00:36:51,280 --> 00:36:52,800
IN CASE ANYBODY WANTS TO GET IN

1039
00:36:52,800 --> 00:36:54,040
TOUCH THROUGH ANY OF THOSE

1040
00:36:54,040 --> 00:36:56,240
MEANS, I'M ALWAYS HAPPY TO CHAT

1041
00:36:56,240 --> 00:36:58,840
ABOUT SCIENCE OR CAREER ADVICE

1042
00:36:58,840 --> 00:37:01,680
OR ANYTHING LIKE THAT SO ONCE

1043
00:37:01,680 --> 00:37:04,400
AGAIN IT'S REALLY AN HONOR TO

1044
00:37:04,400 --> 00:37:05,480
HAVE DELIVER THIS LECTURE AND

1045
00:37:05,480 --> 00:37:06,520
THANK YOU FOR YOUR ATTENTION AND

1046
00:37:06,520 --> 00:37:07,720
I'D LOVE TO TAKE ANY QUESTIONS

1047
00:37:07,720 --> 00:37:09,360
AND HAVE A DISCUSSION WITH THE

1048
00:37:09,360 --> 00:37:09,600
AUDIENCE.

1049
00:37:09,600 --> 00:37:14,280
THANK YOU.

1050
00:37:14,280 --> 00:37:18,160
>>THANK YOU SO MUCH,
CESAR.

1051
00:37:18,160 --> 00:37:20,680
THAT WAS REALLY TERRIFIC.

1052
00:37:20,680 --> 00:37:21,760
WE HAVE SOME QUESTIONS.

1053
00:37:21,760 --> 00:37:22,760
WE'LL START WITH A VERY GENERAL

1054
00:37:22,760 --> 00:37:22,920
ONE.

1055
00:37:22,920 --> 00:37:24,720
HOW DID YOU GET THE IDEA TO WORK

1056
00:37:24,720 --> 00:37:27,120
ON THIS PARTICULAR VERY

1057
00:37:27,120 --> 00:37:29,880
FASCINATING AREA OF RESEARCH?

1058
00:37:29,880 --> 00:37:33,560
>>THAT'S A GREAT
QUESTION.

1059
00:37:33,560 --> 00:37:41,640
I THINK MY PH.D., I SAW IT BEING

1060
00:37:41,640 --> 00:37:43,320
VERY MUCH TRIAL AND ERROR AND I

1061
00:37:43,320 --> 00:37:44,960
THOUGHT THEY HAD TO BE A BETTER

1062
00:37:44,960 --> 00:37:53,040
SORT OF ENGINEERING SOLUTION TO

1063
00:37:53,040 --> 00:37:58,640
IT THEN WITH THE ADVANCES IN

1064
00:37:58,640 --> 00:37:59,840
COMPUTE POWER AND COMPUTER

1065
00:37:59,840 --> 00:38:03,120
GENERATION I THOUGHT

1066
00:38:03,120 --> 00:38:04,480
COMPUTATIONAL TOOLS WOULD BE THE

1067
00:38:04,480 --> 00:38:07,160
BEST TOOL FOR THE PROBLEM.

1068
00:38:07,160 --> 00:38:08,520
I'M INCREDIBLY PASSIONATE AND

1069
00:38:08,520 --> 00:38:10,360
THAT'S WHAT TOOK ME TO SORT OF

1070
00:38:10,360 --> 00:38:12,240
MORE TRIAL AND ERROR RESEARCH AT

1071
00:38:12,240 --> 00:38:13,760
THE TIME WHERE I HAD TO ENGINEER

1072
00:38:13,760 --> 00:38:15,760
A PEPTIDE AND TRY IT OUT AND SO

1073
00:38:15,760 --> 00:38:18,040
ON, NOW WE CAN DO IT ON THE

1074
00:38:18,040 --> 00:38:21,560
COMPUTER IN A MUCH FASTER SCALE

1075
00:38:21,560 --> 00:38:23,320
AND IT REALLY ACCELERATES

1076
00:38:23,320 --> 00:38:28,240
EVERYTHING, MANY, MANY

1077
00:38:28,240 --> 00:38:28,520
[INAUDIBLE]

1078
00:38:28,520 --> 00:38:29,000
>>GREAT.

1079
00:38:29,000 --> 00:38:30,760
LET'S SEE, WE GOT A NUMBER OF

1080
00:38:30,760 --> 00:38:33,720
MORE QUESTIONS HERE.

1081
00:38:33,720 --> 00:38:35,360
DO YOU HAVE ANY ADVICE FOR

1082
00:38:35,360 --> 00:38:36,000
UNDERGRADUATES WHO ARE

1083
00:38:36,000 --> 00:38:37,320
INTERESTED IN DOING RESEARCH,

1084
00:38:37,320 --> 00:38:39,000
AND CERTAINLY YOU SUGGESTED

1085
00:38:39,000 --> 00:38:41,160
PEOPLE COULD COME WORK WITH YOU,

1086
00:38:41,160 --> 00:38:42,720
BUT IN GENERAL, WHAT KIND OF

1087
00:38:42,720 --> 00:38:44,480
ADVICE WOULD YOU HAVE FOR

1088
00:38:44,480 --> 00:38:45,240
UNDERGRADUATES WHO WANT TO GET

1089
00:38:45,240 --> 00:38:47,880
INTO THIS KIND OF AREA?

1090
00:38:47,880 --> 00:38:49,840
>>I WOULD SAY FOR
UNDERGRADS,

1091
00:38:49,840 --> 00:38:53,000
YOU'RE AT A STAGE IN YOUR LIFE,

1092
00:38:53,000 --> 00:38:54,000
YOUR CAREER, THAT I THINK IT'S

1093
00:38:54,000 --> 00:38:55,000
VERY IMPORTANT TO EXPLORE

1094
00:38:55,000 --> 00:39:00,560
DIFFERENT OPTIONS, TO EXPLORE

1095
00:39:00,560 --> 00:39:02,080
REALLY WHAT CALLS YOU, WHAT YOU

1096
00:39:02,080 --> 00:39:04,840
REALLY LIKE DOING DAY IN AND DAY

1097
00:39:04,840 --> 00:39:06,480
OUT, WHAT YOU'RE TRULY

1098
00:39:06,480 --> 00:39:06,920
PASSIONATE ABOUT.

1099
00:39:06,920 --> 00:39:08,240
IF YOU'RE INTERESTED IN

1100
00:39:08,240 --> 00:39:09,960
RESEARCH, EXPLORE DIFFERENT

1101
00:39:09,960 --> 00:39:10,600
RESEARCH AREAS, GO TO DIFFERENT

1102
00:39:10,600 --> 00:39:11,880
LABS FOR THE SUMMER AND THEN TRY

1103
00:39:11,880 --> 00:39:16,000
TO FIND SOMETHING -- YOU MIGHT

1104
00:39:16,000 --> 00:39:17,320
BE LUCKY, YOU GO TO THE FIRST

1105
00:39:17,320 --> 00:39:18,120
ONE AND YOU LOVE IT AND THAT'S

1106
00:39:18,120 --> 00:39:19,120
WHAT YOU DO THE REST OF YOUR

1107
00:39:19,120 --> 00:39:19,960
LIFE.

1108
00:39:19,960 --> 00:39:21,040
BUT THAT MIGHT NOT BE THE CASE

1109
00:39:21,040 --> 00:39:22,360
AND THAT'S OKAY TOO.

1110
00:39:22,360 --> 00:39:23,480
YOU MIGHT NEED TO EXPLORE

1111
00:39:23,480 --> 00:39:24,240
DIFFERENT THINGS.

1112
00:39:24,240 --> 00:39:25,440
COMPUTATIONAL RESEARCH IS VERY

1113
00:39:25,440 --> 00:39:26,600
DIFFERENT FROM EXPERIMENTAL

1114
00:39:26,600 --> 00:39:26,880
RESEARCH.

1115
00:39:26,880 --> 00:39:28,400
SOME PEOPLE LIKE ONE AND NOT THE

1116
00:39:28,400 --> 00:39:29,760
OTHER, SOME PEOPLE LIKE BOTH, SO

1117
00:39:29,760 --> 00:39:33,800
YOU HAVE TO FIND YOUR OWN

1118
00:39:33,800 --> 00:39:34,800
CALLING WITHIN RESEARCH.

1119
00:39:34,800 --> 00:39:42,320
RESEARCH IS INCREDIBLY WIDE

1120
00:39:42,320 --> 00:39:44,640
ENDEAVOR SO THERE'S A PLACE FOR

1121
00:39:44,640 --> 00:39:45,520
EVERYBODY AND I THINK YOU JUST

1122
00:39:45,520 --> 00:39:46,720
HAVE TO EXPLORE IN ORDER TO FIND

1123
00:39:46,720 --> 00:39:50,520
YOUR PLACE IN IT.

1124
00:39:50,520 --> 00:39:50,960
>>GREAT.

1125
00:39:50,960 --> 00:39:52,200
WE'VE GOT A NUMBER OF QUESTIONS

1126
00:39:52,200 --> 00:39:53,160
SORT OF RELATED TO EACH OTHER

1127
00:39:53,160 --> 00:39:58,440
BUT THE FIRST IS WHAT DO YOU

1128
00:39:58,440 --> 00:39:59,640
KNOW ABOUT THE MECHANISM OF

1129
00:39:59,640 --> 00:40:00,640
ACTION OF THESE PEPTIDES?

1130
00:40:00,640 --> 00:40:02,200
ARE THEY ALL THE SAME, ARE THEY

1131
00:40:02,200 --> 00:40:02,720
DOING DIFFERENT THINGS?

1132
00:40:02,720 --> 00:40:04,400
DO YOU HAVE SPECIFIC EVIDENCE?

1133
00:40:04,400 --> 00:40:05,600
>>FANTASTIC QUESTION.

1134
00:40:05,600 --> 00:40:07,040
SO A LOT OF THEM, THEY TARGET

1135
00:40:07,040 --> 00:40:11,280
THE MEMBRANE.

1136
00:40:11,280 --> 00:40:12,600
SO ONE THING I DIDN'T GET INTO

1137
00:40:12,600 --> 00:40:14,240
IN THE TALK BUT THE EVOLUTIONARY

1138
00:40:14,240 --> 00:40:16,200
ALGORITHM WE USE IS DRIVEN BY A

1139
00:40:16,200 --> 00:40:18,080
FITNESS FUNCTION THAT

1140
00:40:18,080 --> 00:40:19,400
ESSENTIALLY SELECTS FOR MINIMAL

1141
00:40:19,400 --> 00:40:24,120
PEPTIDE STRUCTURES THAT WILL

1142
00:40:24,120 --> 00:40:27,720
TATARGET BACTERIAL MEMBRANE.

1143
00:40:27,720 --> 00:40:29,240
WE USE DESCRIPTORS THAT YOU NEED

1144
00:40:29,240 --> 00:40:30,520
TO HAVE SOME POSITIVE CHARGES IN

1145
00:40:30,520 --> 00:40:32,760
THE PEPTIDE AND HYDROPHOBIC

1146
00:40:32,760 --> 00:40:34,640
AMINO ACIDS.

1147
00:40:34,640 --> 00:40:40,080
THE POSITIVE CHARGES HAD, THEY

1148
00:40:40,080 --> 00:40:43,840
ALLOW THE PEPTIDE TO

1149
00:40:43,840 --> 00:40:45,120
ELECTROSTATICALLY CHARGE WITH

1150
00:40:45,120 --> 00:40:46,680
BACTERIAL MEMBRANE, THAT ALLOWS

1151
00:40:46,680 --> 00:40:48,760
TO APPROACH THE MEMBRANE AND IT

1152
00:40:48,760 --> 00:40:50,280
ALLOWS TO TRANS LOCATE INTO THE

1153
00:40:50,280 --> 00:40:51,920
MEMBRANE CREATING A PORE THAT

1154
00:40:51,920 --> 00:40:53,280
TYPICALLY LEADS TO CELL DEATH.

1155
00:40:53,280 --> 00:40:57,880
SO THOSE ARE SOME OF THE

1156
00:40:57,880 --> 00:40:58,880
CONCEPTS THAT WE'RE USING TO

1157
00:40:58,880 --> 00:41:05,000
BUILD MOLECULES THAT WILL TARGET

1158
00:41:05,000 --> 00:41:06,040
AND KILL BACTERIA.

1159
00:41:06,040 --> 00:41:07,520
>>THAT SEGUES TO THE
SECOND

1160
00:41:07,520 --> 00:41:08,480
QUESTION WE'VE GOTTEN A NUMBER

1161
00:41:08,480 --> 00:41:10,320
OF EXAMPLES OF, GIVEN MANY OF

1162
00:41:10,320 --> 00:41:11,600
THEM SEEM TO WORK BY MAKING A

1163
00:41:11,600 --> 00:41:12,560
WHOLE IN THE MEMBRANE

1164
00:41:12,560 --> 00:41:14,760
ESSENTIALLY, WHAT ABOUT

1165
00:41:14,760 --> 00:41:15,400
TOXICITY?

1166
00:41:15,400 --> 00:41:17,840
SOME OF THESE EXPERIMENTS WERE

1167
00:41:17,840 --> 00:41:19,800
IN MICE, SOME WERE IN CULTURE, I

1168
00:41:19,800 --> 00:41:20,920
GUESS, BUT WHAT DO YOU KNOW

1169
00:41:20,920 --> 00:41:22,680
ABOUT TOXICITY OF THE VARIOUS

1170
00:41:22,680 --> 00:41:24,840
COMPOUNDS AND CAN YOU USE A.I.

1171
00:41:24,840 --> 00:41:26,280
TO PREDICT IT I GUESS AS A

1172
00:41:26,280 --> 00:41:27,120
FOLLOW-UP TO THAT

1173
00:41:27,120 --> 00:41:28,640
>>FASCINATING QUESTION.

1174
00:41:28,640 --> 00:41:29,760
TOXICITY, OF COURSE BEFORE WE

1175
00:41:29,760 --> 00:41:30,800
TRANSITION ANY OF THE MOLECULES

1176
00:41:30,800 --> 00:41:38,600
TO MICE, WE ALWAYS TEST FOR

1177
00:41:38,600 --> 00:41:39,880
TOXICITY AGAINST MAMMALIAN CELL

1178
00:41:39,880 --> 00:41:47,560
LINES OR MOUSE CELL LINES.

1179
00:41:47,560 --> 00:41:49,840
ANYTHING TO MICE IS NON-TOXIC

1180
00:41:49,840 --> 00:41:53,120
BUT SOMETIMES WE COME ACROSS POP

1181
00:41:53,120 --> 00:41:55,080
TIDES THAT ARE TOXIC SO WE RULE

1182
00:41:55,080 --> 00:41:55,760
THEM OUT.

1183
00:41:55,760 --> 00:41:57,080
WE HAVE MACHINE LEARNING MODELS

1184
00:41:57,080 --> 00:41:58,520
THAT ALLOW US TO PREDICT

1185
00:41:58,520 --> 00:42:00,320
TOXICITY AS WELL AND THE HOPE IN

1186
00:42:00,320 --> 00:42:03,320
THE FUTURE IS THAT AS PART OF

1187
00:42:03,320 --> 00:42:05,560
OUR ALGORITHM, WE ARE NOT ONLY

1188
00:42:05,560 --> 00:42:07,520
ABLE TO OPTIMIZE TO MAKE THE

1189
00:42:07,520 --> 00:42:10,920
MOLECULES BETTER AT THAT BUT

1190
00:42:10,920 --> 00:42:12,560
HOPEFULLY WE CAN ALSO OPTIMIZE

1191
00:42:12,560 --> 00:42:15,080
TO COUP TER SELECT FOR

1192
00:42:15,080 --> 00:42:15,760
CYTOTOXICITY AND THINGS LIKE

1193
00:42:15,760 --> 00:42:16,960
THAT THAT WE DON'T WANT.

1194
00:42:16,960 --> 00:42:19,160
THE DREAM THAT WE HAVE IS TO

1195
00:42:19,160 --> 00:42:20,240
CREATE ANTIBIOTICS SO TO HAVE

1196
00:42:20,240 --> 00:42:24,240
SOMETHING THAT IS ACTIVE,

1197
00:42:24,240 --> 00:42:25,280
NON-TOXIC, SUFFICIENTLY STABLE,

1198
00:42:25,280 --> 00:42:28,360
THAT HAS GOOD PHARMACOKINETIC,

1199
00:42:28,360 --> 00:42:28,960
PHARMACODYNAMIC PROPERTIES AND

1200
00:42:28,960 --> 00:42:30,760
SO ON.

1201
00:42:30,760 --> 00:42:32,320
SO THE OVERARCHING GOAL IS TO

1202
00:42:32,320 --> 00:42:34,520
BUILD A MOLECULE OR SUBSET OF

1203
00:42:34,520 --> 00:42:35,800
MOLECULES THAT INCORPORATE ALL

1204
00:42:35,800 --> 00:42:42,800
THOSE ATTRIBUTES.

1205
00:42:42,800 --> 00:42:43,080
>>GREAT.

1206
00:42:43,080 --> 00:42:45,880
THAT SEGUES INTO A QUESTION BY

1207
00:42:45,880 --> 00:42:47,960
IAN MORGAN, HOW DO YOU TRAIN THE

1208
00:42:47,960 --> 00:42:48,880
MODELS TO DETERMINE WHICH

1209
00:42:48,880 --> 00:42:49,880
COMPOUNDS WOULD MAKE THE BEST

1210
00:42:49,880 --> 00:42:50,200
ANTIBIOTICS?

1211
00:42:50,200 --> 00:42:52,400
I THINK YOU KIND OF SHOWED A FEW

1212
00:42:52,400 --> 00:42:53,400
PARAMETERS, BUT HOW DID YOU

1213
00:42:53,400 --> 00:42:54,720
DETERMINE WHICH ONES TO USE AND

1214
00:42:54,720 --> 00:43:01,400
HOW TO USE THOSE TO TRAIN THE

1215
00:43:01,400 --> 00:43:01,640
MODELS?

1216
00:43:01,640 --> 00:43:02,040
>>GREAT QUESTION.

1217
00:43:02,040 --> 00:43:05,320
FOR THE GENETIC ALGORITHM, WE

1218
00:43:05,320 --> 00:43:09,720
USED ISOMERS -- LOOKING AT

1219
00:43:09,720 --> 00:43:12,120
HYDROPHOBIC AMINO ACIDS AND

1220
00:43:12,120 --> 00:43:14,200
BASED ON -- SCALE, SO AGAIN THIS

1221
00:43:14,200 --> 00:43:18,040
IS JUST TO BUILD A MINIMAL

1222
00:43:18,040 --> 00:43:19,240
STRUCTURE -- THE BACTERIAL

1223
00:43:19,240 --> 00:43:22,400
MEMBRANE, BUT SINCE THEN, WE'VE

1224
00:43:22,400 --> 00:43:26,560
CONTINUED THAT WORK AND NOW

1225
00:43:26,560 --> 00:43:30,240
WE'RE USING MANY DIMENSIONS OF

1226
00:43:30,240 --> 00:43:31,280
PHYSIOCHEMICAL DESCRIPTORS AT A

1227
00:43:31,280 --> 00:43:40,640
TIME, SO UP TO 500 OR MORE.

1228
00:43:40,640 --> 00:43:41,920
BASICALLY AS MUCH INFORMATION AS

1229
00:43:41,920 --> 00:43:45,360
POSSIBLE TO DESCRIBE AN AMINO

1230
00:43:45,360 --> 00:43:46,240
ACID WE CAN SEQUENCE.

1231
00:43:46,240 --> 00:43:46,640
>>GREAT.

1232
00:43:46,640 --> 00:43:48,640
IN DEVELOPING YOUR MODELS, YOUR

1233
00:43:48,640 --> 00:43:50,400
A.I. MODELS FOR ANTIBIOTICS,

1234
00:43:50,400 --> 00:43:52,440
HAVE YOU OR THE MODELS FOUND

1235
00:43:52,440 --> 00:43:55,160
NOVEL TRENDS IN PROTEIN DESIGN

1236
00:43:55,160 --> 00:43:57,000
THAT YOU HAVEN'T SEEN BEFORE OR

1237
00:43:57,000 --> 00:43:58,320
WERE SURPRISED TO FIND OUT WAS

1238
00:43:58,320 --> 00:43:59,960
MORE IMPORTANT THAN YOU

1239
00:43:59,960 --> 00:44:00,880
PREVIOUSLY THOUGHT?

1240
00:44:00,880 --> 00:44:06,440
>>INTERESTING QUESTION.

1241
00:44:06,440 --> 00:44:08,200
I THINK A LOT OF THE PEPTIDES

1242
00:44:08,200 --> 00:44:10,560
THEY TEND TO BE HELICAL WHEN IN

1243
00:44:10,560 --> 00:44:11,920
CONTACT WITH THE MEMBRANE BUT WE

1244
00:44:11,920 --> 00:44:13,240
HAVE FOUND SOME BENEFITS IN

1245
00:44:13,240 --> 00:44:14,880
THERE AND DIFFERENT SECONDARY

1246
00:44:14,880 --> 00:44:17,840
STRUG STRUCTURES.

1247
00:44:17,840 --> 00:44:20,360
NOT EXACTLY RELATED TO THIS BUT

1248
00:44:20,360 --> 00:44:21,240
ONE THING I FOUND QUITE

1249
00:44:21,240 --> 00:44:22,800
INTERESTING IN EVOLUTIONARY

1250
00:44:22,800 --> 00:44:23,880
ALGORITHM, IF WE ACTUALLY LET

1251
00:44:23,880 --> 00:44:26,320
THE ALGORITHM GO FOR TOO LONG

1252
00:44:26,320 --> 00:44:28,520
AND REACH A PLATEAU IN TERMS OF

1253
00:44:28,520 --> 00:44:31,240
ITS OPTIMAL SOLUTION, IT STARTED

1254
00:44:31,240 --> 00:44:32,680
BASICALLY SPITTING OUT SEQUENCES

1255
00:44:32,680 --> 00:44:35,080
THAT WERE VERY SIMILAR TO WHAT

1256
00:44:35,080 --> 00:44:38,640
NATURE HAS CREATED.

1257
00:44:38,640 --> 00:44:40,480
SO DIVERSITY AT THE MICRO LEVEL

1258
00:44:40,480 --> 00:44:41,800
WE HAVE TO STOP THE ALGORITHM

1259
00:44:41,800 --> 00:44:43,440
BEFORE IT REACHES A PLATEAU AND

1260
00:44:43,440 --> 00:44:47,280
THAT'S WHERE IT STARTED

1261
00:44:47,280 --> 00:44:49,320
GENERATING DIVERSITY.

1262
00:44:49,320 --> 00:44:50,440
I STILL DON'T KNOW WHAT TO MAKE

1263
00:44:50,440 --> 00:44:51,760
OF THOSE RESULTS BUT I THINK

1264
00:44:51,760 --> 00:44:54,640
IT'S AN INTERESTING -- IT'S

1265
00:44:54,640 --> 00:44:55,720
INTERESTING CONCEPTUALLY TO

1266
00:44:55,720 --> 00:44:56,240
THINK ABOUT THAT.

1267
00:44:56,240 --> 00:44:58,240
>>INTERESTING.

1268
00:44:58,240 --> 00:45:00,160
I'M SURE YOU'RE AWARE OF ALPHA

1269
00:45:00,160 --> 00:45:01,680
FOLD, THIS IF YOU APPROACH TO

1270
00:45:01,680 --> 00:45:04,280
PREDICTING PROTEIN STRUCTURE

1271
00:45:04,280 --> 00:45:05,640
USING ARTIFICIAL INTELLIGENCE

1272
00:45:05,640 --> 00:45:06,120
MACHINE LEARNING.

1273
00:45:06,120 --> 00:45:09,920
HAS THAT APPROACH INFLUENCED

1274
00:45:09,920 --> 00:45:10,560
YOUR WORK AT ALL?

1275
00:45:10,560 --> 00:45:12,320
>>YEAH, I THINK IT'S A

1276
00:45:12,320 --> 00:45:15,440
FANTASTIC TOOL FOR PROTEIN

1277
00:45:15,440 --> 00:45:16,200
STRUCTURE PREDICTION.

1278
00:45:16,200 --> 00:45:17,280
FOR PEPTIDES IT DOESN'T WORK

1279
00:45:17,280 --> 00:45:17,920
THAT WELL.

1280
00:45:17,920 --> 00:45:19,360
WE'VE TRIED A COUPLE TIMES BUT

1281
00:45:19,360 --> 00:45:20,920
IT'S JUST BECAUSE THE ALGORITHMS

1282
00:45:20,920 --> 00:45:23,720
WERE NOT TRAINED ON PEPTIDE

1283
00:45:23,720 --> 00:45:26,800
DATA, SO THEY CAN REALLY -- THEY

1284
00:45:26,800 --> 00:45:28,800
CAN'T REALLY ELUCIDATE THE

1285
00:45:28,800 --> 00:45:29,680
STRUCTURE OF SOMETHING THAT

1286
00:45:29,680 --> 00:45:30,960
THEY'VE NEVER SEEN.

1287
00:45:30,960 --> 00:45:33,280
YOU KNOW, A.I. IS NOT QUITE YET

1288
00:45:33,280 --> 00:45:35,840
AT THAT LEVEL, SO WE'VE TRIED TO

1289
00:45:35,840 --> 00:45:38,680
USE IT FOR A COUPLE THINGS, WE

1290
00:45:38,680 --> 00:45:39,760
HAVEN'T -- NOT VERY

1291
00:45:39,760 --> 00:45:41,760
SUCCESSFULLY, BUT I DO THINK

1292
00:45:41,760 --> 00:45:43,480
IT'S AN INCREDIBLE TOOL FOR

1293
00:45:43,480 --> 00:45:44,720
SCIENTISTS ALL AROUND THE WORLD

1294
00:45:44,720 --> 00:45:46,240
WORKING ON PROTEINS AND I'M

1295
00:45:46,240 --> 00:45:47,360
PRETTY SURE SOON IT WILL BE

1296
00:45:47,360 --> 00:45:49,320
APPLIED TO SMALLER PEPTIDES AS

1297
00:45:49,320 --> 00:45:49,760
WELL AND IT'S

1298
00:45:49,760 --> 00:45:54,120
Q.US A MATTER OF TIME.

1299
00:45:54,120 --> 00:45:57,440
>>A NUMBER OF PEOPLE WANT
TO

1300
00:45:57,440 --> 00:45:58,680
KNOW WHAT THE NEXT STEPS ARE FOR

1301
00:45:58,680 --> 00:46:00,320
THIS REALLY PROMISING PEPTIDES

1302
00:46:00,320 --> 00:46:01,440
YOU HAVE IN TERMS OF REALLY

1303
00:46:01,440 --> 00:46:03,120
GETTING THEM INTO THE CLINICS.

1304
00:46:03,120 --> 00:46:05,600
>>THAT'S EVENTUALLY THE
DREAM

1305
00:46:05,600 --> 00:46:09,560
THAT WE HAVE.

1306
00:46:09,560 --> 00:46:11,000
FOR A LOT OF THEM, WE PROBABLY

1307
00:46:11,000 --> 00:46:12,960
NEED TO DO SYSTEMATIC TOXICITY

1308
00:46:12,960 --> 00:46:14,840
STUDIES JUST TO MAKE SURE AND

1309
00:46:14,840 --> 00:46:19,440
SYSTEMATIC AND PROBABLY PKPV,

1310
00:46:19,440 --> 00:46:20,000
PHARMACOKINETIC

1311
00:46:20,000 --> 00:46:20,680
PHARMACODYNAMICS, HOW THE

1312
00:46:20,680 --> 00:46:21,320
PEPTIDES ARE DISTRIBUTED

1313
00:46:21,320 --> 00:46:22,160
THROUGHOUT THE BODY.

1314
00:46:22,160 --> 00:46:26,000
WE HAVEN'T DONE THOSE STUDIES IN

1315
00:46:26,000 --> 00:46:26,960
DETAIL.

1316
00:46:26,960 --> 00:46:28,000
SO THOSE ARE PROBABLY NEXT

1317
00:46:28,000 --> 00:46:28,320
STEPS.

1318
00:46:28,320 --> 00:46:29,520
THEN AFTER THAT, OF COURSE, YOU

1319
00:46:29,520 --> 00:46:33,600
HAVE IND ENABLING STUDIES, NEW

1320
00:46:33,600 --> 00:46:35,760
DRUG STUDIES, AND THEN AFTER

1321
00:46:35,760 --> 00:46:38,600
THAT IF THAT'S SUCCESSFUL, YOU

1322
00:46:38,600 --> 00:46:40,280
GO TO PHASE 1 CLINICAL TRIALS,

1323
00:46:40,280 --> 00:46:42,160
PHASE 2, PHASE 3, WHERE

1324
00:46:42,160 --> 00:46:43,680
ESSENTIALLY WHAT YOU LOOK FOR IS

1325
00:46:43,680 --> 00:46:45,360
SAFETY AND EFFICACY IN HUMANS.

1326
00:46:45,360 --> 00:46:47,040
BUT WE'RE STILL NOT THERE.

1327
00:46:47,040 --> 00:46:51,240
>>IS THERE ENOUGH DATA ON
PK-PD

1328
00:46:51,240 --> 00:46:53,840
AND PEPTIDES FOR YOU TO USE AN

1329
00:46:53,840 --> 00:46:54,880
ARTIFICIAL INTELLIGENCE APPROACH

1330
00:46:54,880 --> 00:46:56,080
TO PREDICT WHICH ONES WOULD HAVE

1331
00:46:56,080 --> 00:46:56,640
THE BEST PROPERTIES?

1332
00:46:56,640 --> 00:46:59,960
>>UNFORTUNATELY NOT.

1333
00:46:59,960 --> 00:47:02,480
THAT'S SOMETHING I WOULD LOVE TO

1334
00:47:02,480 --> 00:47:04,120
COMBINE, COMPILE, PUT TOGETHER A

1335
00:47:04,120 --> 00:47:07,320
DATASET THAT HAS SUFFICIENT PKPB

1336
00:47:07,320 --> 00:47:11,560
DATA, BUT THERE ARE VERY FEW

1337
00:47:11,560 --> 00:47:14,120
STUDIES, USUALLY USING

1338
00:47:14,120 --> 00:47:15,720
RADIOACTIVE APPROACHES AND NOT A

1339
00:47:15,720 --> 00:47:17,840
LOT OF PEOPLE DO IT AND THERE'S

1340
00:47:17,840 --> 00:47:18,840
A SCARCITY OF DATA.

1341
00:47:18,840 --> 00:47:20,080
SO THAT'S THE ROADBLOCK THAT

1342
00:47:20,080 --> 00:47:21,280
WE'RE REACHING IN A LOT OF THESE

1343
00:47:21,280 --> 00:47:22,160
THINGS, IS THAT YOU ACTUALLY

1344
00:47:22,160 --> 00:47:24,040
NEED TO HAVE QUITE A LOT OF

1345
00:47:24,040 --> 00:47:24,480
DATA.

1346
00:47:24,480 --> 00:47:26,040
WE GENERATE A LOT IN HOUSE AND

1347
00:47:26,040 --> 00:47:27,680
SO WE FOCUS ON A COUPLE THINGS

1348
00:47:27,680 --> 00:47:33,040
AND THEN WE GENERATE THE

1349
00:47:33,040 --> 00:47:34,240
DATASETS WE THEN USE TO DEVELOP

1350
00:47:34,240 --> 00:47:37,400
OUR MODELS.

1351
00:47:37,400 --> 00:47:42,480
BUT RELYING ON PUBLIC DATABASES

1352
00:47:42,480 --> 00:47:43,760
SOMETIMES NOT -- THEY'RE NOT THE

1353
00:47:43,760 --> 00:47:45,440
MOST RELIABLE BECAUSE PEOPLE DO

1354
00:47:45,440 --> 00:47:46,320
THINGS DIFFERENTLY USING

1355
00:47:46,320 --> 00:47:47,440
DIFFERENT MEDIA, DIFFERENT

1356
00:47:47,440 --> 00:47:52,160
METHODS AND SO -- BUT WITH PKPB,

1357
00:47:52,160 --> 00:47:53,520
WE HAVE THAT HUGE ROADBLOCK

1358
00:47:53,520 --> 00:47:55,240
WHERE WE DON'T HAVE ENOUGH DATA.

1359
00:47:55,240 --> 00:47:57,640
IT'S ACTUALLY VERY EXPENSIVE AND

1360
00:47:57,640 --> 00:48:03,000
VERY DIFFICULT TO GENERATE

1361
00:48:03,000 --> 00:48:03,840
PKP -- DATA.

1362
00:48:03,840 --> 00:48:05,880
>>THERE'S A QUESTION
ABOUT GRAM

1363
00:48:05,880 --> 00:48:07,080
POSITIVE DATA.

1364
00:48:07,080 --> 00:48:09,360
YOU SAW MOSTLY GRAM-NEGATIVE.

1365
00:48:09,360 --> 00:48:11,480
DO YOU HAVE DATA FOR GRAM

1366
00:48:11,480 --> 00:48:12,120
POSITIVE AS WELL?

1367
00:48:12,120 --> 00:48:13,440
>>WE HAVE DATA FOR GRAM

1368
00:48:13,440 --> 00:48:14,360
POSITIVE.

1369
00:48:14,360 --> 00:48:15,080
ANOTHER BIG PROJECT WE HAVE IN

1370
00:48:15,080 --> 00:48:17,640
THE LAB IS THAT WE'RE TRYING TO

1371
00:48:17,640 --> 00:48:20,920
ALSO DESIGN MOLECULES THAT

1372
00:48:20,920 --> 00:48:23,000
SPECIFICALLY TARGET

1373
00:48:23,000 --> 00:48:24,000
GRAM-NEGATIVE AND NOT GRAM

1374
00:48:24,000 --> 00:48:25,800
POSITIVE AND VICE VERSA AND EVEN

1375
00:48:25,800 --> 00:48:30,040
TO MORE GRANULAR LEVEL LIKE

1376
00:48:30,040 --> 00:48:31,360
SPECIFICITY LEVEL, GINO

1377
00:48:31,360 --> 00:48:32,080
SPECIFICITY AND SO ON.

1378
00:48:32,080 --> 00:48:34,560
SO THIS IS A BIG PROJECT WE'RE

1379
00:48:34,560 --> 00:48:35,720
CURRENTLY CARRYING OUT.

1380
00:48:35,720 --> 00:48:38,720
>>THAT BRINGS UP ANOTHER
SET OF

1381
00:48:38,720 --> 00:48:41,040
QUESTIONS ABOUT WHAT HAPPENS TO

1382
00:48:41,040 --> 00:48:42,920
THE GUT MICROBIOME OR WHAT WOULD

1383
00:48:42,920 --> 00:48:44,000
YOU THINK WOULD HAPPEN WHEN YOU

1384
00:48:44,000 --> 00:48:44,800
USE THESE PEPTIDES?

1385
00:48:44,800 --> 00:48:45,720
>>THAT'S A GREAT
QUESTION.

1386
00:48:45,720 --> 00:48:48,080
OF COURSE YOU DON'T WANT TO KILL

1387
00:48:48,080 --> 00:48:49,960
THE GUT MICROBIOME, RIGHT?

1388
00:48:49,960 --> 00:48:51,280
WHEN YOU'RE TAKING AN

1389
00:48:51,280 --> 00:48:52,240
ANTIBIOTIC.

1390
00:48:52,240 --> 00:48:53,240
THAT'S ONE OF THE UNINTENDED

1391
00:48:53,240 --> 00:48:54,880
CONSEQUENCES THAT A LOT OF

1392
00:48:54,880 --> 00:48:55,800
CONVENTIONAL ANTIBIOTICS DO.

1393
00:48:55,800 --> 00:48:57,560
THEY JUST BLAST EVERYTHING LIKE

1394
00:48:57,560 --> 00:48:59,640
A BOMB ESSENTIALLY.

1395
00:48:59,640 --> 00:49:02,800
SO WE ARE WORKING ON ANTIBIOTICS

1396
00:49:02,800 --> 00:49:04,240
THAT ONLY TARGET PATHOGENS AND

1397
00:49:04,240 --> 00:49:07,880
NOT GOOD BACTERIA.

1398
00:49:07,880 --> 00:49:08,920
YEAH, THAT'S SOMETHING THAT --

1399
00:49:08,920 --> 00:49:11,240
IT'S PART OF OUR EFFORTS IN

1400
00:49:11,240 --> 00:49:14,000
TRYING TO ACHIEVE WHAT WE CALL

1401
00:49:14,000 --> 00:49:14,680
TARGETTABILITY, WHICH IS ONLY

1402
00:49:14,680 --> 00:49:17,280
WHAT YOU WANT AND NOTHING ELSE.

1403
00:49:17,280 --> 00:49:18,880
THAT CAN MEAN YOU WANT TO TARGET

1404
00:49:18,880 --> 00:49:20,360
TWO BACTERIA AT A TIME BUT NOT

1405
00:49:20,360 --> 00:49:23,640
THESE OTHER THREE OR YOU WANT TO

1406
00:49:23,640 --> 00:49:24,400
TARGET FIVE AND NOT THIS OTHER

1407
00:49:24,400 --> 00:49:24,760
ONE.

1408
00:49:24,760 --> 00:49:26,200
SO IT'S A VERY COMPLICATED

1409
00:49:26,200 --> 00:49:28,840
PROBLEM BECAUSE OF COURSE YOU

1410
00:49:28,840 --> 00:49:32,040
KNOW, WE DON'T KNOW ENOUGH ABOUT

1411
00:49:32,040 --> 00:49:34,800
EACH BACTERIAL STRAIN IN TERMS

1412
00:49:34,800 --> 00:49:36,240
OF PHYSIOLOGY, MEMBRANE

1413
00:49:36,240 --> 00:49:37,560
COMPOSITION AND SO ON, SO IT'S A

1414
00:49:37,560 --> 00:49:38,880
VERY HARD PROBLEM, BUT WE'RE

1415
00:49:38,880 --> 00:49:42,080
TRYING OUR BEST.

1416
00:49:42,080 --> 00:49:45,960
>>QUESTION ABOUT WHETHER
OR NOT

1417
00:49:45,960 --> 00:49:50,000
YOUR APPROACH IS AMABLE TO OTHEO

1418
00:49:50,000 --> 00:49:51,600
OTHER DISEASES BESIDES

1419
00:49:51,600 --> 00:49:52,640
ANTIBIOTIC RESISTANT INFECTIONS.

1420
00:49:52,640 --> 00:49:53,920
>>THAT'S AN INTERESTING
NOTION.

1421
00:49:53,920 --> 00:49:55,760
I THINK OBVIOUSLY OUR FOCUS IS

1422
00:49:55,760 --> 00:49:57,760
ON ANTIBIOTICS, BUT WE NOW HAVE

1423
00:49:57,760 --> 00:50:01,920
A COLLABORATION TO DEVELOP

1424
00:50:01,920 --> 00:50:05,120
ANTICANCER PEPTIDES, SO WE

1425
00:50:05,120 --> 00:50:06,560
HAVEN'T VALIDATED THEM

1426
00:50:06,560 --> 00:50:07,320
EXPERIMENTALLY YET SO I CAN'T

1427
00:50:07,320 --> 00:50:09,440
REALLY SAY, BUT I DO THINK AT

1428
00:50:09,440 --> 00:50:12,920
LEAST CONCEPTUALLY, SOME OF THE

1429
00:50:12,920 --> 00:50:14,280
PIPELINES AND SOME OF THE

1430
00:50:14,280 --> 00:50:15,120
APPROACHES WE'RE COMING UP WITH,

1431
00:50:15,120 --> 00:50:17,120
I THINK THEY COULD POTENTIALLY

1432
00:50:17,120 --> 00:50:18,680
BE EXTRAPOLATED TO OTHER

1433
00:50:18,680 --> 00:50:20,440
INDICATIONS, NOT ONLY BACTERIAL

1434
00:50:20,440 --> 00:50:23,640
INFECTIONS BUT ALSO MAYBE VIRAL

1435
00:50:23,640 --> 00:50:24,440
INFECTIONS, MAYBE CANCER.

1436
00:50:24,440 --> 00:50:25,960
WE'VE HAD PROJECTS IN THE PAST

1437
00:50:25,960 --> 00:50:29,680
IN MALARIA AS WELL, SO I DO

1438
00:50:29,680 --> 00:50:31,000
THINK SOME OF THESE THINGS MIGHT

1439
00:50:31,000 --> 00:50:32,800
BE USEFUL IN OTHER AREAS.

1440
00:50:32,800 --> 00:50:34,760
>>INTERESTING.

1441
00:50:34,760 --> 00:50:41,320
ANOTHER CAREER QUESTION.

1442
00:50:41,320 --> 00:50:42,640
SO THEY'RE A POSTDOC WITH A

1443
00:50:42,640 --> 00:50:44,000
BACKGROUND IN COMPUTER SCIENCE

1444
00:50:44,000 --> 00:50:44,960
AND MACHINE LEARNING, WANT TO

1445
00:50:44,960 --> 00:50:46,080
KNOW WHAT YOU THINK THE NEXT

1446
00:50:46,080 --> 00:50:47,840
STEPS WOULD BE TO PURSUE THE

1447
00:50:47,840 --> 00:50:49,600
SORT OF RESEARCH YOU'RE DOING

1448
00:50:49,600 --> 00:50:51,000
WITH SOMEONE WHO HAS A

1449
00:50:51,000 --> 00:50:56,200
BACKGROUND IN THOSE AREAS.

1450
00:50:56,200 --> 00:50:59,360
>>SO IF THIS IS TO THE
POSTDOC,

1451
00:50:59,360 --> 00:51:00,920
DEFINITELY REACH OUT TO LABS,

1452
00:51:00,920 --> 00:51:01,960
TALK TO PEOPLE, AND YOU WANT TO

1453
00:51:01,960 --> 00:51:05,760
MAKE SURE YOU ALIGN WITH THE

1454
00:51:05,760 --> 00:51:06,840
LABS THEY ARE GOING TO.

1455
00:51:06,840 --> 00:51:08,520
IF IT'S TO GO FOR THE NEXT STEP,

1456
00:51:08,520 --> 00:51:10,400
FOR A FACULTY JOB OR A CAREER IN

1457
00:51:10,400 --> 00:51:14,360
INDUSTRY, TRY TO FIND A GOOD

1458
00:51:14,360 --> 00:51:14,560
PLACE.

1459
00:51:14,560 --> 00:51:15,800
WHERE THE MISSION ALIGNS VERY

1460
00:51:15,800 --> 00:51:16,680
MUCH WITH YOUR INTERESTS.

1461
00:51:16,680 --> 00:51:20,640
IF YOU WANT TO DO COMPUTATIONAL

1462
00:51:20,640 --> 00:51:22,280
WORK IN THE CONTEXT OF DRUG

1463
00:51:22,280 --> 00:51:23,960
DISCOVERY, TRY TO GO TO A PLACE

1464
00:51:23,960 --> 00:51:25,080
WHERE YOU'RE GOING TO BE HAPPY,

1465
00:51:25,080 --> 00:51:28,440
WHERE YOU'RE GOING TO BE

1466
00:51:28,440 --> 00:51:29,480
ALIGNING WELL AND DOING YOUR

1467
00:51:29,480 --> 00:51:33,160
WORK EVERY DAY OR MOST DAYS.

1468
00:51:33,160 --> 00:51:33,760
>>GREAT.

1469
00:51:33,760 --> 00:51:38,800
A LOT OF THE PEPTIDES THAT YOU

1470
00:51:38,800 --> 00:51:40,000
FOUND THE QUESTION UNDERSTOOD

1471
00:51:40,000 --> 00:51:41,560
CORRECTLY SEEMED TO BE THE ONES

1472
00:51:41,560 --> 00:51:42,640
IN THE GENOME ALREADY, SEEMED TO

1473
00:51:42,640 --> 00:51:44,400
BE IN THE CONTEXT OFTEN OF A

1474
00:51:44,400 --> 00:51:47,480
LARGER PROTEIN.

1475
00:51:47,480 --> 00:51:49,040
SO THE QUESTION IS, DO YOU THINK

1476
00:51:49,040 --> 00:51:52,760
THOSE PEPTIDES HAVE THE

1477
00:51:52,760 --> 00:51:54,160
ANTIBIOTIC OR ANTIBACTERIAL

1478
00:51:54,160 --> 00:51:55,520
FUNCTION IN THE CONTEXT OF THE

1479
00:51:55,520 --> 00:51:56,960
LARGER PROTEIN OR IS IT JUST A

1480
00:51:56,960 --> 00:51:57,960
COINCIDENCE THAT THEY HAPPEN TO

1481
00:51:57,960 --> 00:51:59,160
HAVE THAT FUNCTION?

1482
00:51:59,160 --> 00:52:00,880
>>YEAH, THIS IS A VERY
GOOD

1483
00:52:00,880 --> 00:52:01,600
QUESTION.

1484
00:52:01,600 --> 00:52:02,480
SO ONE OF THE THINGS THAT I

1485
00:52:02,480 --> 00:52:04,960
DON'T THINK I GOT INTO IT BUT SO

1486
00:52:04,960 --> 00:52:06,600
A LOT OF THE ENCRYPTED PEPTIDES

1487
00:52:06,600 --> 00:52:08,200
ARE ACTUALLY PREDICTED TO BE

1488
00:52:08,200 --> 00:52:10,160
CLEAVED UP BY PROTEASES.

1489
00:52:10,160 --> 00:52:11,160
>>INTERESTING.

1490
00:52:11,160 --> 00:52:13,080
>>SO YOU HAVE YOUR ENTIRE

1491
00:52:13,080 --> 00:52:15,920
PROTEIN, THEN A PROTEASE COMES

1492
00:52:15,920 --> 00:52:16,960
ALONG PRESENT IN A PARTICULAR

1493
00:52:16,960 --> 00:52:19,240
ENVIRONMENT IN THE BODY.

1494
00:52:19,240 --> 00:52:21,320
IT CLEAVES OFF THE FRAGMENT AND

1495
00:52:21,320 --> 00:52:24,320
THAT FRAGMENT IS WHAT HAS THE

1496
00:52:24,320 --> 00:52:25,080
ANTIMICROBIAL ACTIVITY.

1497
00:52:25,080 --> 00:52:29,000
SO THIS OBVIOUSLY SPARKS A LOT

1498
00:52:29,000 --> 00:52:29,880
OF POTENTIAL THOUGHTS.

1499
00:52:29,880 --> 00:52:33,040
ONE OF THEM IS THE NOTION OF

1500
00:52:33,040 --> 00:52:35,120
GENOMIC -- PRESERVING ENERGY AT

1501
00:52:35,120 --> 00:52:36,920
THE GENOMIC LEVEL WHERE ONE GENE

1502
00:52:36,920 --> 00:52:38,960
ENCODES FOR ONE PROTEIN BUT THAT

1503
00:52:38,960 --> 00:52:40,160
ONE PROTEIN HAS MULTIPLE

1504
00:52:40,160 --> 00:52:40,400
FUNCTIONS.

1505
00:52:40,400 --> 00:52:48,880
THAT CAN BE ACTIVATED BY AREAS

1506
00:52:48,880 --> 00:52:53,040
CLEAVED BY PROTEASES,

1507
00:52:53,040 --> 00:52:53,600
ESSENTIALLY -- PARTICULAR

1508
00:52:53,600 --> 00:53:00,600
FRAGMENTS FROM A PROTEIN.

1509
00:53:00,600 --> 00:53:01,880
SO PRESERVING ENERGY AT THE

1510
00:53:01,880 --> 00:53:02,480
GENOMIC LEVEL.

1511
00:53:02,480 --> 00:53:04,120
THINKING ABOUT PROTEINS NOT ONLY

1512
00:53:04,120 --> 00:53:05,280
HAVING ONE FUNCTION BUT HAVING

1513
00:53:05,280 --> 00:53:06,400
MULTIPLE FUNCTIONS THAT CAN BE

1514
00:53:06,400 --> 00:53:06,960
ACTIVATED.

1515
00:53:06,960 --> 00:53:09,240
>>WOULD THAT BE USEFUL
FOR YOUR

1516
00:53:09,240 --> 00:53:10,640
IDEA OF TARGETING THEM TO

1517
00:53:10,640 --> 00:53:13,440
SPECIFIC AREAS SO IF THE MAIN

1518
00:53:13,440 --> 00:53:14,560
PROTEIN CARRIED THEM TO THE

1519
00:53:14,560 --> 00:53:16,160
RIGHT AREA AND THEN IT GOT

1520
00:53:16,160 --> 00:53:18,560
CLEAVED OFF, IS THAT A STRATEGY?

1521
00:53:18,560 --> 00:53:20,960
>>THAT MIGHT BE ONE OF
THE

1522
00:53:20,960 --> 00:53:23,120
THINGS THAT WE MIGHT EXPLORE.

1523
00:53:23,120 --> 00:53:26,040
YOU CAN ALSO -- THE WAY I THINK

1524
00:53:26,040 --> 00:53:30,520
ABOUT THIS, YOU HAVE A PROTEIN,

1525
00:53:30,520 --> 00:53:31,960
DOING DAILY ROUTINE, RIGHT?

1526
00:53:31,960 --> 00:53:34,160
A PROTEIN, FOR EXAMPLE, INVOLVED

1527
00:53:34,160 --> 00:53:38,680
IN THE NERVOUS SYSTEM.

1528
00:53:38,680 --> 00:53:40,640
UNDER PARTICULAR CONDITIONS,

1529
00:53:40,640 --> 00:53:41,880
MAYBE AN INFECTION, I DON'T

1530
00:53:41,880 --> 00:53:43,320
KNOW, THIS IS JUST SPECULATION,

1531
00:53:43,320 --> 00:53:48,320
BUT MAYBE THE PERSON GETS

1532
00:53:48,320 --> 00:53:51,640
INFECTED, A MICROENVIRONMENT

1533
00:53:51,640 --> 00:53:53,560
WHERE PROTEASES GET RELEASED,

1534
00:53:53,560 --> 00:53:56,040
CLEAVING OFF -- MAYBE DOZENS OF

1535
00:53:56,040 --> 00:53:57,360
THEM IN A PARTICULAR AREA, AND

1536
00:53:57,360 --> 00:53:58,840
THEN YOU HAVE ALL THESE

1537
00:53:58,840 --> 00:54:01,360
COCKTAILS OF ENCRYPTED PEPTIDE

1538
00:54:01,360 --> 00:54:05,400
ANTIBIOTICS THAT ARE WORKING --

1539
00:54:05,400 --> 00:54:07,080
THAT'S ME DREAMING A LITTLE BIT,

1540
00:54:07,080 --> 00:54:07,880
IMAGINING THE SITUATION, BUT

1541
00:54:07,880 --> 00:54:09,280
THAT COULD BE, YOU KNOW,

1542
00:54:09,280 --> 00:54:09,920
POTENTIALLY WHAT MIGHT BE

1543
00:54:09,920 --> 00:54:11,320
HAPPENING.

1544
00:54:11,320 --> 00:54:12,720
>>VERY COOL.

1545
00:54:12,720 --> 00:54:14,640
RELATED TO THE TARGETING IDEA,

1546
00:54:14,640 --> 00:54:16,520
AND I HOPE YOU KNOW WHAT THIS

1547
00:54:16,520 --> 00:54:22,960
QUESTION MEANS, WOULD FERRAL

1548
00:54:22,960 --> 00:54:26,680
FLUIDS BE AN OPTION TO DIRECT

1549
00:54:26,680 --> 00:54:27,760
ANTIBIOTICS TO A TARGET SITE?

1550
00:54:27,760 --> 00:54:29,480
>>I'M NOT SURE ACTUALLY.

1551
00:54:29,480 --> 00:54:31,440
I HAVEN'T REALLY EXPLORED THAT.

1552
00:54:31,440 --> 00:54:32,320
THAT CONCEPT.

1553
00:54:32,320 --> 00:54:32,760
FLEURN.

1554
00:54:32,760 --> 00:54:38,400
.WOULD LOVE TO LEARN MORE.

1555
00:54:38,400 --> 00:54:41,200
>>ON THAT NOTE, LEILA
WOULD

1556
00:54:41,200 --> 00:54:42,480
LIKE TO KNOW HOW TO GET IN TOUCH

1557
00:54:42,480 --> 00:54:44,560
WITH YOU, IF SHE'S INTERESTED IN

1558
00:54:44,560 --> 00:54:45,640
WORKING IN YOUR LAB IN THE

1559
00:54:45,640 --> 00:54:46,720
FUTURE.

1560
00:54:46,720 --> 00:54:49,000
SO I ASSUME VIA EMAIL?

1561
00:54:49,000 --> 00:54:49,960
IS THAT THE BEST WAY?

1562
00:54:49,960 --> 00:54:52,360
>>YEAH, I WOULD SAY EMAIL
OR

1563
00:54:52,360 --> 00:54:54,120
TWITTER OR -- EMAIL OR TWITTER.

1564
00:54:54,120 --> 00:54:56,000
SO LET ME -- I CAN PUT IN THE

1565
00:54:56,000 --> 00:54:56,200
CHAT.

1566
00:54:56,200 --> 00:54:57,280
>>PUT IT IN THE CHAT,
THAT

1567
00:54:57,280 --> 00:55:07,560
WOULD BE GREAT.

1568
00:55:12,600 --> 00:55:15,880
>>THAT'S MY EMAIL.

1569
00:55:15,880 --> 00:55:20,920
THEN TWITTER HANDLE IS ?SH SO

1570
00:55:20,920 --> 00:55:24,040
ANY OF THOSE TWO WORK VERY WELL.

1571
00:55:24,040 --> 00:55:24,600
>>ALL RIGHT.

1572
00:55:24,600 --> 00:55:26,840
WELL, I THINK THOSE ARE A LOT OF

1573
00:55:26,840 --> 00:55:27,800
QUESTIONS, FANTASTIC QUESTIONS,

1574
00:55:27,800 --> 00:55:31,200
BUT I THINK THAT'S WHAT WE HAD.

1575
00:55:31,200 --> 00:55:33,000
SO CESAR, THANK YOU SO MUCH,

1576
00:55:33,000 --> 00:55:34,280
THIS WAS A TERRIFIC LECTURE,

1577
00:55:34,280 --> 00:55:35,800
TERRIFIC WORK, AND REALLY

1578
00:55:35,800 --> 00:55:38,640
PLEASED TO SEE IT PROGRESSING SO

1579
00:55:38,640 --> 00:55:38,960
WELL.

1580
00:55:38,960 --> 00:55:40,000
>>JOHN, THANK YOU SO
MUCH,

1581
00:55:40,000 --> 00:55:41,520
AGAIN, FOR THE OPPORTUNITY, AND

1582
00:55:41,520 --> 00:55:43,680
THIS HAS BEEN REALLY SUPER FUN

1583
00:55:43,680 --> 00:55:44,720
AND A PRIVILEGE.

1584
00:55:44,720 --> 00:55:45,720
>>THANK YOU SO MUCH.

1585
00:55:45,720 --> 00:55:47,480
THANKS, EVERYONE, FOR JOINING.

1586
00:55:47,480 --> 00:55:48,480
>>THANKS, EVERYBODY.

1587
00:55:48,480 --> 00:55:49,480
TAKE CARE.

1588
00:55:49,480 --> 00:00:00,000
>>BYE-BYE.