Rapid classification of β-lactamase activity through AI-based structure prediction and electric field analysis
- 1University of Bristol
Abstract
Antibiotic resistance driven by {beta}-lactamases has become a major threat to public health. Here, we propose a computational protocol for rapid estimation of {beta}-lactamase activity towards carbapenem antibiotics from sequence alone. Starting from AI-predicted acylenzyme complexes, only approximate transition state ensembles are sampled to compute catalytic electric fields, which strongly correlate with experimental activation barriers (R2 = 0.8). Compared with full quantum mechanics/molecular mechanics (QM/MM) reaction simulations, the protocol substantially reduces the amount of configurational sampling and thus computational cost. Our protocol enables efficient identification of carbapenem breakdown efficiency, further highlighting the important role of electric fields in enzyme catalysis.
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BibTeX
@misc{wang2026rapid,
title = {{Rapid classification of β-lactamase activity through AI-based structure prediction and electric field analysis}},
author = {D. Wang and A. H. Lima and M. W. van der Kamp},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.23.753769},
url = {https://www.biorxiv.org/content/10.64898/2026.09.23.753769v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Rapid classification of β-lactamase activity through AI-based structure prediction and electric field analysis
AU - D. Wang
AU - A. H. Lima
AU - M. W. van der Kamp
PY - 2026
DA - 2026/09/23
PB - bioRxiv
DO - 10.64898/2026.09.23.753769
UR - https://www.biorxiv.org/content/10.64898/2026.09.23.753769v1
AB - Antibiotic resistance driven by {beta}-lactamases has become a major threat to public health. Here, we propose a computational protocol for rapid estimation of {beta}-lactamase activity towards carbapenem antibiotics from sequence alone. Starting from AI-predicted acylenzyme complexes, only approximate transition state ensembles are sampled to compute catalytic electric fields, which strongly correlate with experimental activation barriers (R2 = 0.8). Compared with full quantum mechanics/molecular mechanics (QM/MM) reaction simulations, the protocol substantially reduces the amount of configurational sampling and thus computational cost. Our protocol enables efficient identification of carbapenem breakdown efficiency, further highlighting the important role of electric fields in enzyme catalysis.
ER -