EnzyShell: Dynamic Shell-Guided Enzyme Backbone Generation

Enzyme design requires generating protein backbones that scaffold catalytic motifs and accommodate substrates. However, directly generating full backbones from these functional constraints involves two distinct requirements: inferring a suitable local protein environment around the catalytic motif and substrate, and generating the full backbone to support this environment. To address this challenge, we propose EnzyShell, a dynamic shell-guided framework for enzyme backbone generation. We explicitly model this local environment as a functional shell comprising the motif and residues surrounding either the motif or the substrate. We then train two complementary flow models for full-backbone generation, conditioned on the substrate together with either the motif or the functional shell. During sampling, we dynamically construct the functional shell from backbone predictions of the motif-conditioned model, provide it to the shell-conditioned model, and combine their predicted vector fields to guide enzyme backbone generation. Evaluated on the EnzyBind benchmark spanning six EC classes, EnzyShell improves backbone designability, EC match rate, and predicted $k_{\mathrm{cat}}$ over the evaluated baselines, while maintaining comparable catalytic-motif geometry.