Re-Imagining Biology With Computational Protein Design
Event details
| Date | 27.06.2025 |
| Hour | 10:00 › 11:00 |
| Speaker | Prof. Bruno Correia, Institute of Bioengineering, School of Engineering, EPFL, Lausanne (CH) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
SPECIAL BIOENGINEERING SEMINAR
Abstract:
Proteins are essential molecular machines that drive nearly every biological process in living organisms. Yet despite their centrality, the rational design of functional proteins remains a formidable challenge due to the astronomical size of sequence space and the complex, dynamic relationship between sequence, structure, and function. Recent advances in machine and structural biology have redefined what is possible, enabling us to move beyond nature’s repertoire and engineer proteins with novel structures and programmable functions.
In our laboratory, we develop computational frameworks that leverage geometric deep learning, protein structure prediction, and generative modeling to design functional proteins. I will discuss our work in the context of the basic unit of life – the cell. Specifically, I will discuss how we have been repurposing structure prediction networks to design novel protein folds – enlarging the available protein building blocks for the design of new biological functions. In the next level of complexity, cells rely in protein interaction networks for propagating signals across its different compartments. I will describe our work on learned protein representations for the design of de novo protein-protein interactions – a long-standing challenge in the field of protein design. I will bring several of these aspects together and present our efforts to encode state-switching capabilities in designer proteins and how we have assembled minimal signaling networks. Finally, I will present some synthetic biology applications and how we are using these components in cellular systems to control the output of these complex biological devices. Altogether, computational protein design is poised to have a transformative impact both on biotechnology as well as to enhance our understanding of the complexity of natural biological systems.
Bio:
Bruno Correia was trained in world-renowned laboratories and institutions in the United States of America (University of Washington and The Scripps Research Institute). Very early in his scientific career he found his fascination about protein structure and function. His PhD studies evolved in the direction of immunogen design and vaccine engineering which sparked his interest in the many needs and opportunities in vaccinology and translational research. During his postdoctoral studies he joined a chemical biology laboratory at the Scripps Research Institute where he developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In 2015, he joined the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland as a tenure track assistant professor and in 2021 was promoted to associate professor.
The focus of Bruno Correia' sresearch group has been to develop computational tools for protein design with particular emphasis on applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). Correia has made a number of contributions to this field, exploring novel molecular representations and machine learning strategies for computational protein design. His laboratory has been awarded three prestigious research grants from the European Research Council (and SNSF replacement scheme). Bruno has won several awards for scientific excellence such as the Latsis University Award and The Protein Society Young Investigator Award. He is an ELLIS member and a Radcliffe Fellow at Harvard University. Lastly, he has been awarded the prize for best teacher of Life sciences in 2019.
Zoom link for attending remotely: https://epfl.zoom.us/j/61826493220
Instructions for 1st-year Ph.D. students planning to attend this talk, who are under EDBB’s mandatory seminar attendance rule:
IN CASE you cannot attend in-person in the room, please make sure to
Abstract:
Proteins are essential molecular machines that drive nearly every biological process in living organisms. Yet despite their centrality, the rational design of functional proteins remains a formidable challenge due to the astronomical size of sequence space and the complex, dynamic relationship between sequence, structure, and function. Recent advances in machine and structural biology have redefined what is possible, enabling us to move beyond nature’s repertoire and engineer proteins with novel structures and programmable functions.
In our laboratory, we develop computational frameworks that leverage geometric deep learning, protein structure prediction, and generative modeling to design functional proteins. I will discuss our work in the context of the basic unit of life – the cell. Specifically, I will discuss how we have been repurposing structure prediction networks to design novel protein folds – enlarging the available protein building blocks for the design of new biological functions. In the next level of complexity, cells rely in protein interaction networks for propagating signals across its different compartments. I will describe our work on learned protein representations for the design of de novo protein-protein interactions – a long-standing challenge in the field of protein design. I will bring several of these aspects together and present our efforts to encode state-switching capabilities in designer proteins and how we have assembled minimal signaling networks. Finally, I will present some synthetic biology applications and how we are using these components in cellular systems to control the output of these complex biological devices. Altogether, computational protein design is poised to have a transformative impact both on biotechnology as well as to enhance our understanding of the complexity of natural biological systems.
Bio:
Bruno Correia was trained in world-renowned laboratories and institutions in the United States of America (University of Washington and The Scripps Research Institute). Very early in his scientific career he found his fascination about protein structure and function. His PhD studies evolved in the direction of immunogen design and vaccine engineering which sparked his interest in the many needs and opportunities in vaccinology and translational research. During his postdoctoral studies he joined a chemical biology laboratory at the Scripps Research Institute where he developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In 2015, he joined the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland as a tenure track assistant professor and in 2021 was promoted to associate professor.
The focus of Bruno Correia' sresearch group has been to develop computational tools for protein design with particular emphasis on applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). Correia has made a number of contributions to this field, exploring novel molecular representations and machine learning strategies for computational protein design. His laboratory has been awarded three prestigious research grants from the European Research Council (and SNSF replacement scheme). Bruno has won several awards for scientific excellence such as the Latsis University Award and The Protein Society Young Investigator Award. He is an ELLIS member and a Radcliffe Fellow at Harvard University. Lastly, he has been awarded the prize for best teacher of Life sciences in 2019.
Zoom link for attending remotely: https://epfl.zoom.us/j/61826493220
Instructions for 1st-year Ph.D. students planning to attend this talk, who are under EDBB’s mandatory seminar attendance rule:
IN CASE you cannot attend in-person in the room, please make sure to
- send D. Reinhard a note well ahead of time (ideally before seminar day), informing that you plan to attend the talk online, and, during seminar:
- be signed in on Zoom with a recognizable user name (not any alias making it difficult or impossible to identify you).
Practical information
- Informed public
- Free
Organizer
Contact
- Institute of Bioengineering (IBI), Dietrich REINHARD