Topic: Experimental validation

David Baker presents recent advances in designing proteins for functions that natural evolution has not been required to solve. The approach begins with a desired structure or activity and uses deep learning to propose amino acid sequences expected to realise it. The resulting designs are translated into synthetic genes, produced as proteins and tested experimentally. The lecture connects computational design with laboratory characterisation, explaining how the combination can create new molecular functions relevant to medicine, technology and sustainability. Its focus is the construction and

Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensori-motor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning t