The Coming of Age of De Novo Protein Design

Proteins mediate the critical processes of life and beautifully solve the challenges faced during evolution. Our goal is to design a new generation of proteins that address current-day problems not faced during evolution. In contrast to traditional protein engineering efforts, which have focused on modifying naturally occurring proteins, we design new proteins from scratch to optimally solve the problem at hand. We now use two approaches. First, guided by Anfinsen’s principle that proteins fold to their global free energy minimum, we use the physically based Rosetta method to compute sequences for which the desired target structure has the lowest energy. Second, we use deep learning methods to design sequences predicted to fold to the desired structures. In both cases, we produce synthetic genes encoding these novel protein sequences and characterize them experimentally. In this talk, I will describe recent achievements in de novo protein design, including the development of novel self-assembling protein nanomaterials. I will also highlight RFdiffusion, a powerful new guided diffusion model that generates proteins in a manner similar to the popular image-generating tool DALL-E.