Zhao, Zhuoran; (2023) Structural Dynamics of L1 and L2 β-lactamase. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Stenotrophomonas maltophilia is a Gram-negative bacterium, found in several different environments, such as soil, water and hospital. It can cause multiple infections but also has strong resistance to many antibiotics such as cephalosporins, carbapenems, and aminoglycosides. S. maltophilia confers antibiotic resistance through expression of two different β-lactamases: L1-metallo-β-lactamase (L1 MBL) and L2 β-lactamase. L1 MBL is a class B3 β-lactamase and is the only known tetrameric β-lactamase known to humans. L2 is a class A β-lactamase which has been recently identified. In L1 MBL, there are, two loops (α3-β7 and β12-α5) known as the gating loops, that enclose the active site. The “open” and “close” conformations of these two loops were observed in the molecular dynamic simulation. These two conformations allow the gate loops have the ability of controlling the volume of the zinc binding pocket. The pocket size affects the substrate binding and further influence the catalytic activity of the whole protein. Therefore, gating loops are thought to have an important role in substrate binding and catalysis. In this thesis, the dynamics of the gating loops is explored through Markov state models. The “open” and “closed” confirmations are defined and three key interactions (salt bridge between R236 and D150c, the π–π stacking between H151 and Y227 and the orientation of P225) were identified that play an important role in controlling the conformation of the gating loops. Furthermore, as a tetramer, the correlation between the four subunits was also explored through CVAE-based deep learning and network analysis. The results revealed a ‘dimer of dimer’ dynamics in L1 MBL. The second part of the thesis focuses on exploring the dynamics of L2 β-lactamase family consisting of L2a, L2b, L2c and L2d enzymes. Homology modelling, MDLovofit, Markov state models, dynamic cross-correlation analysis and CVAE-based deep learning were employed for identifying potential key interactions and dynamic correlations between each subtype. Two dynamic combinations regions were revealed (α1 helix/N-terminal, β9-α15 loop, β7-β8 loop, hinge region, and C terminal, β1-β2 loop, β8-β9 loop) which exist in all four L2 β-lactamases. Stabilising these two combinations could possibly help inhibit the function of L2 β-lactamases. Besides, several potential key residues which result in high dynamic regions were also identified. Since very few research targeted on L2 β-lactamases, this work could be a starting point for the following-up work. The improved understanding of the dynamics of L1 and L2 β-lactamases will help in the design of their inhibitors and discovery of novel resistance breakers.

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