Postdoctoral Position in Deep Learning for Structural Biology and Protein Design
The successful candidate will collaborate with both laboratories to develop and train deep learning models for modelling protein allostery and designing protein switches. Proteins are not static molecules. They often behave as switches, alternating between states that carry out distinct functions. Hence, switching is a powerful mechanism of biological regulation and typically occurs upon structural changes triggered by a wide variety of external stimuli (e.g. from photon absorption to the binding of another protein) through a process referred as allostery. Understanding how this switching behavior occurs at the molecular level remains challenging. The advent of deep learning offers new opportunities to explore and predict protein motions but these approaches have mostly been applied to static representations of protein structures. Here, the candidate will tackle the modelling of switching dynamics using specially designed geometric deep learning techniques applied to molecular representations. We expect that this approach will lead to a new understanding of the mechanisms underpinning these switches and, eventually, to generative approaches allowing to engineer novel molecular switches.
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