Computational design of metallohydrolases.

ABSTRACT: De novo enzyme design seeks to build proteins containing ideal active sites with catalytic residues surrounding and stabilizing the transition state(s) of the target chemical reaction^1-7. The generative artificial intelligence method RFdiffusion^8,9 solves this problem, but requires specifying both the sequence position and backbone coordinates for each catalytic residue, limiting sampling. Here we introduce RFdiffusion2, which eliminates these requirements, and use it to design zinc metallohydrolases starting from quantum chemistry-derived active site geometries. From an initial set of 96 designs tested experimentally, the most active has a catalytic efficiency (k_cat/K_M) of 16,000 M^-1 s^-1, orders of magnitude higher than pre

SUBMITTER: Kim D 

PROVIDER: S-EPMC12727532 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature