Exploring “dark-matter” protein folds using deep learning @CellSystemsCP • Introducing Genesis VAE, a convolutional variational autoencoder that transforms low-resolution protein fold sketches into designable, stable 3D models. • Genesis VAE enables rapid exploration of “dark-matter” protein folds, creating novel protein architectures that have never been observed in nature. • Coupled with trRosetta, Genesis can design protein sequences for both known and novel folds, validated through experimental protease resistance assays. • One major innovation is Genesis’ ability to learn structural patterns from low-resolution sketches, refining them into native-like protein backbones. • The framework successfully designed five native folds and three novel “dark-matter” folds, demonstrating its capacity to generalize beyond natural protein spaces. • High-throughput experimental validation showed several of the designed proteins were stable, with folding behavior on par with natural proteins. • Genesis opens the door for the de novo design of proteins with entirely new functions, breaking through current limitations in protein design. @zanderharteveld @mmbronstein @befcorreia @JoshSouthern13 @CasperGoverde @m_deff @loukasa_tweet @trekkinglemon 💻Code: github.com/zanderharteveld/g… 📜Paper: cell.com/cell-systems/fullte…