ANCHORZYME: Anchor-Conditioned Dual-Memory Enzyme Generation for Unseen Substrates

Designing enzymes for substrates with no known natural catalyst is a central AI for Science problem because it could expand biocatalysis to new reactions in sustainable synthesis, drug and materials manufacturing, and environmental remediation; we study strict substrate-only zero-shot enzyme generation, where the input is only a small query molecule and the output is a candidate catalytic protein sequence. The main obstacle is an activity–structure trade-off: in this setting, edits that increase predicted enzyme–substrate compatibility often reduce foldability or structural confidence. We show that this trade-off is amplified when retrieved enzymes are used as a single undifferentiated prompt, because global scaffold similarity, which supports foldability, is mixed with local pocket and active-site signals, which support substrate recognition and catalysis. To separate these roles, we propose AnchorZyme, a retrieval-augmented, anchor-conditioned editing framework that selects a scaffold anchor, retrieves donor enzymes, aligns them to define site, shell, and distal edit regions, trains a teacher-guided generator to make better-than-anchor edits, and combines a catalytic-gain discriminator with a frozen sequence-plausibility discriminator for sampling and reranking. On RealUnknown-389, a curated benchmark of 389 currently enzyme-unknown substrates, AnchorZyme achieves the best UniKP ${\log }_{10}(kcat)$ and normalized pLDDT, reaching 0.384 and 0.770, and improves over SENZ from 0.310 to 0.384 in catalytic score while raising structural confidence from 0.499 to 0.770, establishing scaffold-preserving local editing as a practical route toward discovery-oriented zero-shot enzyme design for substrates beyond currently known biocatalysis.Our code is available at https://anonymous.4open.science/r/code-EEE1.