Abstract
Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.
Original language | English |
---|---|
Pages (from-to) | 372-384 |
Number of pages | 13 |
Journal | Proteins: Structure, Function, and Bioinformatics |
Volume | 90 |
Issue number | 2 |
Early online date | 29 Aug 2021 |
DOIs | |
Publication status | Published - Feb 2022 |
Bibliographical note
Funding Information:This study was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol— http://www.bris.ac.uk/acrc/ . This study was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) to James Spencer and Graciela Mahler under Award Number R01AI100560. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Franco Vairoletti is grateful for a fellowship from Agencia Nacional de Investigación e Innovación Uruguay (ANII): ANII (POS_NAC_2018_1_151488). This study was supported by Engineering and Physical Sciences Research Council (grant number EP/M022609/1, Adrian J. Mulholland) and MRC to (grant number MR/T016035/1, Adrian J. Mulholland and James Spencer).
Funding Information:
This study was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol—http://www.bris.ac.uk/acrc/. This study was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) to James Spencer and Graciela Mahler under Award Number R01AI100560. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Franco Vairoletti is grateful for a fellowship from Agencia Nacional de Investigación e Innovación Uruguay (ANII): ANII (POS_NAC_2018_1_151488). This study was supported by Engineering and Physical Sciences Research Council (grant number EP/M022609/1, Adrian J. Mulholland) and MRC to (grant number MR/T016035/1, Adrian J. Mulholland and James Spencer).
Publisher Copyright:
© 2021 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.
Keywords
- antibiotic resistance
- IMP-1
- MBL inhibitor
- metallo β-lactamases
- metalloenzymes
- thiazolidine
- zinc enzymes
Fingerprint
Dive into the research topics of 'A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors'. Together they form a unique fingerprint.Equipment
-
HPC (High Performance Computing) and HTC (High Throughput Computing) Facilities
Alam, S. R. (Manager), Williams, D. A. G. (Manager), Eccleston, P. E. (Manager) & Greene, D. (Manager)
Facility/equipment: Facility