Computational enzyme design by catalytic motif scaffolding.

ABSTRACT: Enzymes find broad use as biocatalysts in industry and medicine owing to their exquisite selectivity, efficiency and mild reaction conditions. Custom-designed enzymes can produce tailor-made biocatalysts with potential applications that extend beyond natural reactions. However, current design methods require testing a large number of designs and mostly produce de novo enzymes with low catalytic activities^1-3. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable^4,5. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by design

SUBMITTER: Braun M 

PROVIDER: S-EPMC12727513 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature