Topic: conformational ensembles

Develop deep-learning methods for generative protein design at the interface of machine learning, molecular simulation, structural biology and experimental protein engineering. Projects can investigate conformational ensembles, integrate protein language models with physics, or design proteins for challenging membrane and sugar interfaces. The researcher can develop an independent direction while collaborating with computational and experimental colleagues. Applicants submit a CV, research-interest statement and three referee contacts to the laboratory contact listed on its recruitment page.

Evidence regarding protein structure and function manifest the imperative role that dynamics play in proteins, underlining reconsideration of the unanimated sequence-to-structure-to-function paradigm. Structural dynamics portray a heterogeneous energy landscape described by conformational ensembles where each structural representation can be responsible for unique functions or enable macromolecular assemblies. Using the human p27/Cdk2/Cyclin A ternary complex as an example, we highlight the vital role of intra- and intermolecular dynamics for target recognition, binding, and inhibition as a cr