Structural and Kinetic Basis for the Rational Design of Next-Generation β-Lactamase Inhibitors

The global spread of β-lactamase-mediated resistance poses a threat to β-lactam antibiotics. Boron-based β-lactamase inhibitors (BLIs) represent a promising class of reversible covalent inhibitors, yet the molecular basis of their recognition and dissociation remains poorly understood. Using <i>Pseudomonas</i>-derived cephalosporinase-3 (PDC-3) as a model, we employed enhanced sampling strategies with machine learning and steady-state kinetic assays to investigate the binding and unbinding dynamics of LP06, a boronate BLI. We identify three binding pathways, governed by hydrophobic recognition motifs and a conserved arginine anchor that together steer the ligand toward the precovalent state. Sequence alignment of nearly 7000 class C β-lactamases supports the conservation of these determinants, and structural analyses suggest that R349 may act as a shared anchoring point across serine β-lactamases. Additionally, hydrogen-bonding interactions were found to delay productive binding by stabilizing nonproductive conformations. Our findings provide fundamental insights into β-lactamase inhibition and establish design principles for next-generation β-lactamase inhibitors.

Authors

Institutions

Publication Details

Journal
Journal of Medicinal Chemistry
Published
2026-03-16
DOI
https://doi.org/10.1021/acs.jmedchem.5c03315
Primary Topic
Antibiotic Resistance in Bacteria
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Structural and Kinetic Basis for the Rational Design of Next-Generation β-Lactamase Inhibitors

Sergio Decherchi, Min Xu, Shozeb Haider, Christopher R. Bethel et al.
Journal of Medicinal Chemistry
Antibiotic Resistance in Bacteria
article

Structural and Kinetic Basis for the Rational Design of Next-Generation β-Lactamase Inhibitors

Sergio Decherchi, Min Xu, Shozeb Haider, Christopher R. Bethel, Andrea M. Hujer, Shuang Chen, Magdalena A. Taracila, Muchen Yu, Robert A. Bonomo
article en

Abstract

The global spread of β-lactamase-mediated resistance poses a threat to β-lactam antibiotics. Boron-based β-lactamase inhibitors (BLIs) represent a promising class of reversible covalent inhibitors, yet the molecular basis of their recognition and dissociation remains poorly understood. Using <i>Pseudomonas</i>-derived cephalosporinase-3 (PDC-3) as a model, we employed enhanced sampling strategies with machine learning and steady-state kinetic assays to investigate the binding and unbinding dynamics of LP06, a boronate BLI. We identify three binding pathways, governed by hydrophobic recognition motifs and a conserved arginine anchor that together steer the ligand toward the precovalent state. Sequence alignment of nearly 7000 class C β-lactamases supports the conservation of these determinants, and structural analyses suggest that R349 may act as a shared anchoring point across serine β-lactamases. Additionally, hydrogen-bonding interactions were found to delay productive binding by stabilizing nonproductive conformations. Our findings provide fundamental insights into β-lactamase inhibition and establish design principles for next-generation β-lactamase inhibitors.

Journal of Medicinal ChemistryVol. 69(6)
Hokkaido Pharmaceutical University (JP), Italian Institute of Technology (IT), Fahd bin Sultan University (SA), Louis Stokes Cleveland VA Medical Center (US), Migrant Clinicians Network (US), University College London (GB), Case Western Reserve University (US), University of Tabuk (SA)
Openalex Percentile: Top 19%
Antibiotic Resistance in Bacteria
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.