The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases

Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning

L2 β-lactamases, serine-based class A β-lactamases expressed by Stenotrophomonas maltophilia, play a pivotal role in antimicrobial resistance (AMR). However, limited studies have been conducted on these important enzymes. To understand the coevolutionary dynamics of L2 β-lactamase, innovative computational methodologies, including adaptive sampling molecular dynamics simulations, and deep learning methods (convolutional variational autoencoders and BindSiteS-CNN) explored conformational changes and correlations within the L2 β-lactamase family together with other representative class A enzymes including SME-1 and KPC-2. This work also investigated the potential role of hydrophobic nodes and binding site residues in facilitating the functional mechanisms. The convergence of analytical approaches utilized in this effort yielded comprehensive insights into the dynamic behavior of the β-lactamases, specifically from an evolutionary standpoint. In addition, this analysis presents a promising approach for understanding how the class A β-lactamases evolve in response to environmental pressure and establishes a theoretical foundation for forthcoming endeavors in drug development aimed at combating AMR.

Multilevel superposition for deciphering the conformational variability of protein ensembles

The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase

β-Lactam antibiotics are the most important and widely used antibacterial agents across the world. However, the widespread dissemination of β-lactamases among pathogenic bacteria limits the efficacy of β-lactam antibiotics. This has created a major public health crisis. The use of β-lactamase inhibitors has proven useful in restoring the activity of β-lactam antibiotics, yet, effective clinically approved inhibitors against class B metallo-β-lactamases are not available. L1, a class B3 enzyme expressed by Stenotrophomonas maltophilia, is a significant contributor to the β-lactam resistance displayed by this opportunistic pathogen. Structurally, L1 is a tetramer with two elongated loops, α3-β7 and β12-α5, present around the active site of each monomer. Residues in these two loops influence substrate/inhibitor binding. To study how the conformational changes of the elongated loops affect the active site in each monomer, enhanced sampling molecular dynamics simulations were performed, Markov State Models were built, and convolutional variational autoencoder-based deep learning was applied. The key identified residues (D150a, H151, P225, Y227, and R236) were mutated and the activity of the generated L1 variants was evaluated in cell-based experiments. The results demonstrate that there are extremely significant gating interactions between α3-β7 and β12-α5 loops. Taken together, the gating interactions with the conformational changes of the key residues play an important role in the structural remodeling of the active site. These observations offer insights into the potential for novel drug development exploiting these gating interactions.

Allosteric cooperation in ß-lactam binding to a non-classical transpeptidase

L,D-transpeptidase function predominates in atypical 3®3 transpeptide networking of peptidoglycan (PG) layer in Mycobacterium tuberculosis. Prior studies of L,D-transpeptidases have identified only the catalytic site that binds to peptide moiety of the PG substrate or ß-lactam antibiotics. This insight was leveraged to develop mechanism of its activity and inhibition by ß-lactams. Here we report identification of an allosteric site at a distance of 21 Å from the catalytic site that binds the sugar moiety of PG substrates (hereafter referred to as the S-pocket). This site also binds a second ß-lactam molecule and influences binding at the catalytic site. We provide evidence that two ß-lactam molecules bind co-operatively to this enzyme, one non-covalently at the S-pocket and one covalently at the catalytic site. This dual ß-lactam binding phenomenon is previously unknown and is an observation that may offer novel approaches for the structure-based design of new drugs against M. tuberculosis./em>.