# Inverse folding

The functional diversity and mutational potential of beta-lactamases in environmental microbial communities were only partly understood. Combining protein language models with ensemble learning identified more than 46,000 putative beta-lactamases across all four Ambler classes from environmental metagenomes; two class C candidates sharing about 30% sequence identity with known enzymes conferred resistance to multiple beta-lactam antibiotics when expressed in E. coli, and mutations found by combining an inverse-folding model with protein language models altered resistance profiles, including increased carbapenem resistance.

The functional diversity and mutational potential of beta-lactamases in environmental microbial communities were only partly understood. Combining protein language models with ensemble learning identified more than 46,000 putative beta-lactamases across all four Ambler classes from environmental metagenomes; two class C candidates sharing about 30% sequence identity with known enzymes conferred resistance to multiple beta-lactam antibiotics when expressed in E. coli, and mutations found by combining an inverse-folding model with protein language models altered resistance profiles, including increased carbapenem resistance.

The inverse-folding models used to design peptides emit a probability over exactly twenty natural residues at each position, and adding an unnatural one means supplying its structural data and retraining the whole model. AtomWeaver instead grows side chains as atoms in three dimensions under a fixed backbone and a target protein, then matches each finished atom cloud against a reference library of residue templates to read off identity. On PeptideArena, 353 backbones against twelve targets, it stayed competitive with twenty-residue methods and held the same performance regime with a 300-residue vocabulary.

The inverse-folding models used to design peptides emit a probability over exactly twenty natural residues at each position, and adding an unnatural one means supplying its structural data and retraining the whole model. AtomWeaver instead grows side chains as atoms in three dimensions under a fixed backbone and a target protein, then matches each finished atom cloud against a reference library of residue templates to read off identity. On PeptideArena, 353 backbones against twelve targets, it stayed competitive with twenty-residue methods and held the same performance regime with a 300-residue vocabulary.