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</script>Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase
Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase
The beta-lactamase enzyme provides effective resistance to beta-lactam antibiotics due to substrate recognition controlled by point mutations. Recently, extended-spectrum and inhibitor-resistant mutants have become a global health problem. Here, the functional dynamics that control substrate recognition in TEM beta-lactamase are investigated using all-atom molecular dynamics simulations. Comparisons are made between wild-type TEM-1 and TEM-2 and the extended-spectrum mutants TEM-10 and TEM-52, both in apo form and in complex with four different antibiotics (ampicillin, amoxicillin, cefotaxime and ceftazidime). Dynamic allostery is predicted based on a quasi-harmonic normal mode analysis using a perturbation scan. An allosteric mechanism known to inhibit enzymatic function in TEM beta-lactamase is identified, along with other allosteric binding targets. Mechanisms for substrate recognition are elucidated using multivariate comparative analysis of molecular dynamics trajectories to identify changes in dynamics resulting from point mutations and ligand binding, and the conserved dynamics, which are functionally important, are extracted as well. The results suggest that the H10-H11 loop (residues 214-221) is a secondary anchor for larger extended spectrum ligands, while the H9-H10 loop (residues 194-202) is distal from the active site and stabilizes the protein against structural changes. These secondary non-catalytically-active loops offer attractive targets for novel noncompetitive inhibitors of TEM beta-lactamase.
- University of North Carolina at Chapel Hill United States
- University of North Carolina at Charlotte United States
beta-lactam antibiotics, Science, Physics, QC1-999, dynamic allostery, Q, beta-lactamase; beta-lactam antibiotics; molecular dynamics; dynamic allostery; functional dynamics; machine learning; SPLOC, Astrophysics, molecular dynamics, Article, QB460-466, machine learning, beta-lactamase, functional dynamics
beta-lactam antibiotics, Science, Physics, QC1-999, dynamic allostery, Q, beta-lactamase; beta-lactam antibiotics; molecular dynamics; dynamic allostery; functional dynamics; machine learning; SPLOC, Astrophysics, molecular dynamics, Article, QB460-466, machine learning, beta-lactamase, functional dynamics
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