QBI presents a seminar with Patrick Barth, Associate Professor at the Swiss Federal Institute of Technology (EPFL) in Lausanne.

Professor Barth leads the Protein and Cell Engineering Laboratory at the Swiss Federal Institute of Technology (EPFL) in Lausanne. He studied Physics, Chemistry, and Biology at the University of Paris and École Normale Supérieure and carried out his PhD at the  Commissariat à l'Énergie Atomique in Saclay, France. He later pursued postdoctoral research at UC Berkeley and the University of Washington, working with Tom Alber and David Baker to develop cutting-edge computational methods for protein structure prediction and design. After launching his independent career at Baylor College of Medicine, Professor Barth continues his work at EPFL, integrating computational and experimental approaches to advance protein design, explore the molecular mechanisms of signal transduction, and engineer proteins with novel functions for synthetic biology and therapeutic applications.

Proteins are the molecular machines that power life. Although recent advances have greatly improved our ability to determine and predict protein structures, proteins are far from static. Many function as dynamic molecular switches that transition between distinct 
conformational states in response to external cues such as ligand binding, light, or mechanical forces. These transitions underlie fundamental biological processes, from cellular signaling to gene regulation, through a phenomenon known as allostery. Understanding how protein motions give rise to function remains a major challenge.

In this seminar, Dr. Barth will present his efforts to address this problem by combining molecular simulations, novel deep learning, and protein design methods to model and engineer protein dynamics. He will discuss how these approaches provide new insights into allosteric regulation and enable the design of programmable biosensors, signaling proteins, and therapeutic ligands. Together, these advances are opening new opportunities to understand and engineer protein function for applications in synthetic biology and medicine.