bioRxivMicrobiologyPreprint
Structure-Informed Stress-Testing of the Standard Numbering Scheme for Class B β-Lactamases Using AlphaFold2
Abstract
The standard numbering scheme for class B beta-lactamases (metallo-beta-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 A). 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.
- assay: protein structure
- research focus: protein structure prediction
- research focus: benchmarking
- research focus: variant effect prediction
- approach: deep learning
- approach: transformer / foundation model
- Citations
- 0
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