CIS - "Get to know your neighbors" Seminar series - Prof. Bruno Correia

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Event details

Date and time 01.02.2021 15:1516:15  
Place and room
Online https://epfl.zoom.us/j/88145568014
Speaker Prof. Bruno Correia
Category Conferences - Seminars
Title: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning 
Abstract: Predicting interactions between proteins and other biomolecules solely based on structure remains a central challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein’s modes of interactions with other biomolecules.
The underlying hypothesizes of our work is that proteins interacting with similar molecules may share common fingerprints, independent of their sequence and overall structural fold. These structural fingerprints are difficult to grasp by visual analysis but may be learned from large-scale datasets.
I will discuss MaSIF (Molecular Surface Interaction Fingerprinting), a conceptual framework based on a geometric deep learning method, to capture structural fingerprints that are important for specific biomolecular interactions. I will also present current developments of the framework to make it end-to-end differentiable. Finally, I will describe our approach based on MaSIF to design de novo protein-protein interactions, which may have important applications in the development of new protein-based drugs.   
We anticipate that this conceptual framework will lead to improvements in our understanding of protein function and design.
Bio: Throughout my PhD and postdoctoral studies, I 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 my scientific career I found out my fascination about protein structure and function. My PhD studies evolved in the direction of immunogen design and vaccine engineering, which sparked my interest in the many needs and opportunities in vaccinology and translational research. My efforts resulted in an enlightening piece of work where for the first time, computationally designed immunogens elicited potent neutralizing antibodies. During my postdoctoral studies, I joined a chemical biology laboratory at the Scripps Research Institute. In this stage, I developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In March 2015, I joined the École Polytechnique Fédérale de Lausanne (EPFL) – Switzerland as a tenure track assistant professor. The focus of my research group is to develop computational tools for protein design with particular emphasis in applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). The activities in my laboratory focus on computational design methods development and experimental characterization of the designed proteins. Our laboratory has been awarded with 2 prestigious research grants from the European Research Council. Lastly, I have been awarded the prize for best teacher of Life sciences in 2019. 

The Center for Intelligent Systems at EPFL (CIS) is a collaboration among IC, SB, STI and ENAC that brings together researchers working on different aspects of Intelligent Systems.
 
In order to promote exchanges among researchers and encourage the creation of new, collaborative projects, CIS is organizing a "Get to know your neighbors" series. Each seminar will consist of one short overview presentation geared to the general public at EPFL.
 
The CIS seminar will take place live on Zoom:https://epfl.zoom.us/j/88145568014


 
Monday, 1st February 2021 from 3:15 to 4:15 pm


NB: Video recordings of the seminars will be made available on our website and published on our social media pages

Practical information

  • General public
  • Free

Organizer

  • CIS

Contact

Tags

CISSBSTIICENACApprentissage automatique Intelligence artificielle Robotique Vision par ordinateur Artificial intelligence AI Robotics Computer vision

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