Why Computational Predictions Fall Short in Subclass B1 Metallo-β-Lactamase Inhibitor Discovery: Progress, Pitfalls, and Translational Opportunities

Abstract The global dissemination of subclass B1 metallo-β-lactamases (MBLs), especially New Delhi metallo-β-lactamase-1 (NDM-1), Verona integron-encoded metallo-β-lactamase-2 (VIM-2), and imipenemase-1 (IMP-1), threatens the clinical utility of carbapenems and other β-lactam antibiotics. This challenge is compounded by the absence of any commercially available MBL inhibitors for clinical use, underscoring the urgent need for novel inhibitor development. Computational approaches now guide much of MBL inhibitor discovery, yet many compounds predicted to bind B1 MBLs, or even shown to inhibit purified enzymes, fail to become biologically useful β-lactam rescue agents. This Perspective argues that this prediction-to-translation gap arises less from the absence of computational tools than from their fragmented use across metal coordination chemistry, enzyme dynamics, bacterial permeability, efflux, zinc availability, and β-lactam potentiation. Drawing on recent advances from 2018 to 2026 and selected foundational studies, we examine why predictions fail and how the field can improve translation. Major failure points include inaccurate treatment of the dizinc active site, uncertain protonation states, poor modeling of catalytic and structural water molecules, insufficient sampling of L3 and L10 loop dynamics, incomplete treatment of conformational, solvation, and other entropic contributions, overinterpretation of docking scores, and extrapolation from NDM-1-biased datasets to broader NDM, VIM, and IMP inhibition. We propose that standardized multiscale workflows integrating metal-aware docking, validated zinc models, replicate molecular dynamics simulations, mechanistic quantum mechanics/molecular mechanics refinement, permeability-aware prioritization, and multi-enzyme validation can reduce avoidable prediction failures and improve the likelihood that computational hits progress toward biologically relevant MBL inhibition and β-lactam potentiation.

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Publication Details

Journal
ACS Infectious Diseases
Published
2026-10-09
DOI
https://doi.org/10.1021/acsinfecdis.6c00608
Primary Topic
Antibiotic Resistance in Bacteria
Type
article
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article

Why Computational Predictions Fall Short in Subclass B1 Metallo-β-Lactamase Inhibitor Discovery: Progress, Pitfalls, and Translational Opportunities

Tricia Naicker, Alessandra Moraes Balieiro, Hendrik Gerhardus Kruger, Letisha Girdhari et al.
ACS Infectious Diseases
Antibiotic Resistance in Bacteria
article

Why Computational Predictions Fall Short in Subclass B1 Metallo-β-Lactamase Inhibitor Discovery: Progress, Pitfalls, and Translational Opportunities

Tricia Naicker, Alessandra Moraes Balieiro, Hendrik Gerhardus Kruger, Letisha Girdhari, Thavendran Govender, José Rogério A. Silva, Gilson Mateus Bittencourt Fernandes
article en

Abstract

Abstract The global dissemination of subclass B1 metallo-β-lactamases (MBLs), especially New Delhi metallo-β-lactamase-1 (NDM-1), Verona integron-encoded metallo-β-lactamase-2 (VIM-2), and imipenemase-1 (IMP-1), threatens the clinical utility of carbapenems and other β-lactam antibiotics. This challenge is compounded by the absence of any commercially available MBL inhibitors for clinical use, underscoring the urgent need for novel inhibitor development. Computational approaches now guide much of MBL inhibitor discovery, yet many compounds predicted to bind B1 MBLs, or even shown to inhibit purified enzymes, fail to become biologically useful β-lactam rescue agents. This Perspective argues that this prediction-to-translation gap arises less from the absence of computational tools than from their fragmented use across metal coordination chemistry, enzyme dynamics, bacterial permeability, efflux, zinc availability, and β-lactam potentiation. Drawing on recent advances from 2018 to 2026 and selected foundational studies, we examine why predictions fail and how the field can improve translation. Major failure points include inaccurate treatment of the dizinc active site, uncertain protonation states, poor modeling of catalytic and structural water molecules, insufficient sampling of L3 and L10 loop dynamics, incomplete treatment of conformational, solvation, and other entropic contributions, overinterpretation of docking scores, and extrapolation from NDM-1-biased datasets to broader NDM, VIM, and IMP inhibition. We propose that standardized multiscale workflows integrating metal-aware docking, validated zinc models, replicate molecular dynamics simulations, mechanistic quantum mechanics/molecular mechanics refinement, permeability-aware prioritization, and multi-enzyme validation can reduce avoidable prediction failures and improve the likelihood that computational hits progress toward biologically relevant MBL inhibition and β-lactam potentiation.

ACS Infectious Diseases
Ansys (United States) (US), Universidade Federal do Pará (BR), University of KwaZulu-Natal (ZA)
Openalex Percentile: Top 22%
Antibiotic Resistance in Bacteria
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