AIRR-C Seminar Series, February 22nd, 2024 - Brian Hie, Stanford University, US
3.4 هزار بار بازدید -
5 ماه پیش
-
Learning to read and write
Learning to read and write antibody evolution
Established Speaker: Brian Hie, Stanford University
Talk abstract
Evolution is the powerful force driving both the real-time emergence of pathogen resistance to drugs and immunity, as well as the diversity of natural forms and functions that have emerged over longer timescales. Modern evolutionary models, especially those that leverage advances in machine learning, can improve our ability to design new proteins in the laboratory. This talk will cover how models of protein sequences and structures can learn evolutionary rules that help guide the artificial evolution of human antibodies. First, we will cover how algorithms known as protein language models can guide the affinity maturation of antibodies against diverse viral antigens using sequence information alone and without requiring any task-specific data. Next, we will cover how multimodal language models, which also take into account information about protein structure, can further improve the ability for unsupervised models to guide antibody evolution, which we use to improve the neutralization potency of clinical antibodies against viral escape variants.
Speaker bio
Brian Hie is an Assistant Professor of Chemical Engineering and Data Science at Stanford University and an Innovation Investigator at Arc Institute, where he conducts research at the intersection of biology and machine learning. He was previously a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI. He completed his Ph.D. at MIT CSAIL and was an undergraduate at Stanford University.
AIRR-C Seminar Series website: https://www.antibodysociety.org/the-a...
Established Speaker: Brian Hie, Stanford University
Talk abstract
Evolution is the powerful force driving both the real-time emergence of pathogen resistance to drugs and immunity, as well as the diversity of natural forms and functions that have emerged over longer timescales. Modern evolutionary models, especially those that leverage advances in machine learning, can improve our ability to design new proteins in the laboratory. This talk will cover how models of protein sequences and structures can learn evolutionary rules that help guide the artificial evolution of human antibodies. First, we will cover how algorithms known as protein language models can guide the affinity maturation of antibodies against diverse viral antigens using sequence information alone and without requiring any task-specific data. Next, we will cover how multimodal language models, which also take into account information about protein structure, can further improve the ability for unsupervised models to guide antibody evolution, which we use to improve the neutralization potency of clinical antibodies against viral escape variants.
Speaker bio
Brian Hie is an Assistant Professor of Chemical Engineering and Data Science at Stanford University and an Innovation Investigator at Arc Institute, where he conducts research at the intersection of biology and machine learning. He was previously a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI. He completed his Ph.D. at MIT CSAIL and was an undergraduate at Stanford University.
AIRR-C Seminar Series website: https://www.antibodysociety.org/the-a...
5 ماه پیش
در تاریخ 1402/12/07 منتشر شده
است.
3,456
بـار بازدید شده