Biology subjects

Mojica, M. F.

Publications and source records attributed to Mojica, M. F..

3 recordsLinked to original sources

bioRxiv2024

Deciphering the co-evolutionary dynamics of L2 β-lactamases via Deep learning

L2 {beta}-lactamases, a serine-based class A {beta}-lactamases expressed by Stenotrophomonas maltophilia plays a pivotal role in antimicrobial resistance. However, limited studies have been conducted on these important enzymes. To understand the co-evolutionary dynamics of L2 {beta}-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 {beta}-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 behaviour of the {beta}-lactamases, specifically from an evolutionary standpoint. In addition, this analysis presents a promising approach for understanding how the class A {beta}-lactamases evolve in response to environmental pressure and establishes a theoretical foundation for forthcoming endeavours in drug development aimed at combating antimicrobial resistance. SynopsisDeep learning is used to reveal the dynamic co-evolutionary patterns of L2 {beta}-lactamases. O_LIAnalysis of hydrophobic nodes and binding site residues provides a detailed understanding of both local and global dynamic evolution, which explain the functional divergences. C_LIO_LIThe employment of two distinct deep learning models, the Convolutional Variational Autoencoder (CVAE) and BindSiteS-CNN, facilitates the investigation of conformational shifts, thereby depicting the dynamic evolution of L2 {beta}-lactamases. C_LIO_LIThe effectiveness of CVAE and BindSiteS-CNN in dynamic classification is corroborated with selected features. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/575584v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1db15forg.highwire.dtl.DTLVardef@168043eorg.highwire.dtl.DTLVardef@1eebb96org.highwire.dtl.DTLVardef@5d8019_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

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

{beta}-lactam antibiotics are the most important and widely used antibacterial agents across the world. However, the widespread dissemination of {beta}-lactamases among pathogenic bacteria limits the efficacy of {beta}-lactam antibiotics. This has created a major public health crisis. The use of {beta}-lactamase inhibitors has proven useful in restoring the activity of {beta}-lactam antibiotics, yet, effective clinically approved inhibitors against class B metallo-{beta}-lactamases (MBLs) are not available. L1, a class B3 enzyme expressed by Stenotrophomonas maltophilia, is a significant contributor to the {beta}-lactam resistance displayed by this opportunistic pathogen. Structurally, L1 is a tetramer with two elongated loops, 3-{beta}7 and {beta}12-5, 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 (MD) simulations were performed, Markov State Models (MSM) were built, and convolutional variational autoencoder (CVAE)-based deep learning was applied. The key identified residues (D150a, H151, P225, Y227, 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 3-{beta}7 and {beta}12-5 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.

biophysics↗

bioRxiv2021

Allosteric communication in Class A β-lactamases occurs via Cooperative Coupling of Loop Dynamics

Allosteric effects control protein (e.g. enzyme) activity in ways that are not fully understood. Better understanding of allosteric effects, and tools to identify them, would offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways from two distant ligand binding sites to important active site structural elements that control enzymatic activity in two prototypical class A {beta}-lactamases, TEM-1 and KPC-2. Both of these enzymes are important determinants of antibiotic resistance in widespread bacterial pathogens. The simulations show that the allosteric sites are connected to the active site in both enzymes, (e.g. affecting the conformation of the {Omega}-loop) highlighting how allosteric inhibitors may exert their effects. Nonequilibrium simulations reveal pathways of communication operating over distances of 30 [A] or more. In these identified signaling pathways, the propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more clinically relevant amino acid substitutions in each enzyme map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences.

biophysics↗