Abstract
ABSTRACT
The standard numbering scheme for class B β-lactamases (metallo-β-lactamases, MBLs), the BBL scheme, was proposed by Galleni et al. in 2001 and updated by Garau et al. in 2004 from the small number of MBL structures then solved. Hundreds of additional class B sequences have since been catalogued, but most have never been structurally solved, so whether BBL correspondences hold across this larger diversity has not been directly tested. Following the precedent set for the sibling class D enzymes, we used ColabFold (AlphaFold2/MMseqs2) to model 87 class B families across subclasses B1, B2, and B3 lacking an experimental structure, after validating the pipeline against an eight-enzyme benchmark spanning all three subclasses (RMSD to experimental structures 0.35–0.46 Å). Applying BBL zinc-ligand numbering to the 87 models via paired sequence- and structure-based residue-transfer methods, the canonical zinc-coordination motif transferred cleanly in the large majority of families, and 11 of the 16 sequence-level exceptions were accounted for by a literature-supported biological cause (a subclass-appropriate substitution or a previously proposed, unconfirmed fourth subgroup), while two motif-retaining families showed predicted local structural divergence at the active-site loop, flagged by structural superposition, that could not be resolved to exact zinc-ligand numbering and warrants direct experimental follow-up. These results show that the BBL scheme, formulated before metagenomic sequencing or deep-learning structure prediction existed, generalizes robustly into the structural space these technologies have since opened, and identify a small set of variants whose active sites merit experimental attention.
IMPORTANCE
Metallo-β-lactamases inactivate carbapenems, antibiotics of last resort for multidrug-resistant Gram-negative infections, and new MBL variants are discovered faster than they can be structurally characterized. The BBL numbering scheme underlies how researchers compare active-site residues across this diverse family, but it was built from only a handful of solved structures and has not been re-examined against the much larger sequence diversity now known. We show that validated AlphaFold2 prediction can test whether a numbering scheme built on a small structural sample still holds for unsolved enzymes, and that most of class B is well described by the existing scheme. This approach also flags enzymes whose active site appears diverged from the canonical arrangement, prioritizing them for structural work and giving surveillance and inhibitor-design efforts a validated numbering reference covering far more class B diversity than the eight enzymes on which the scheme was built.