Dynamical aspects of TEM-1 beta-lactamase probed by molecular dynamics

Theoretical Study on the Mechanism of the Acylate Reaction of β-Lactamase

Using density functional theory and a cluster approach, we study the reaction potential surface and compute Gibbs free energies for the acylate reaction of β-lactamase with penicillin G, where the solvent effect is important and taken into consideration. Two reaction paths are investigated: one is a multi-step process with a rate-limit energy barrier of 19.1 kcal/mol, which is relatively small, and the reaction can easily occur; the other is a one-step process with a barrier of 45.0 kcal/mol, which is large and thus makes the reaction hard to occur. The reason why the two paths have different barriers is explained.

Can We Exploit β-Lactamases Intrinsic Dynamics for Designing More Effective Inhibitors?

β-lactamases (BLs) represent the most frequent cause of antimicrobial resistance in Gram-negative bacteria. Despite the continuous efforts in the development of BL inhibitors (BLIs), new BLs able to hydrolyze the last developed antibiotics rapidly emerge. Moreover, the insurgence rate of effective mutations is far higher than the release of BLIs able to counteract them. This results in a shortage of antibiotics that is menacing the effective treating of infectious diseases. The situation is made even worse by the co-expression in bacteria of BLs with different mechanisms and hydrolysis spectra, and by the lack of inhibitors able to hit them all. Differently from other targets, BL flexibility has not been deeply exploited for drug design, possibly because of the small protein size, for their apparent rigidity and their high fold conservation. In this mini-review, we discuss the evidence for BL binding site dynamics being crucial for catalytic efficiency, mutation effect, and for the design of new inhibitors. Then, we report on identified allosteric sites in BLs and on possible allosteric inhibitors, as a strategy to overcome the frequent occurrence of mutations in BLs and the difficulty of competing efficaciously with substrates. Nevertheless, allosteric inhibitors could work synergistically with traditional inhibitors, increasing the chances of restoring bacterial susceptibility towards available antibiotics.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

BACKGROUND

The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

OBJECTIVE

This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

METHODS

31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

RESULTS

Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

CONCLUSION

Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

β-Lactamase of Mycobacterium tuberculosis Shows Dynamics in the Active Site That Increase upon Inhibitor Binding

The Mycobacterium tuberculosis β-lactamase BlaC is a broad-spectrum β-lactamase that can convert a range of β-lactam antibiotics. Enzymes with low specificity are expected to exhibit active-site flexibility. To probe the motions in BlaC, we studied the dynamic behavior in solution using nuclear magnetic resonance (NMR) spectroscopy. ¹⁵N relaxation experiments show that BlaC is mostly rigid on the pico- to nanosecond timescale. Saturation transfer experiments indicate that also on the high-millisecond timescale BlaC is not dynamic. Using relaxation dispersion experiments, clear evidence was obtained for dynamics in the low-millisecond range, with an exchange rate of ca. 860 s^-1 The dynamic amide groups are localized in the active site. Upon formation of an adduct with the inhibitor avibactam, extensive line broadening occurs, indicating an increase in magnitude of the active-site dynamics. Furthermore, the rate of the motions increases significantly. Upon reaction with the inhibitor clavulanic acid, similar line broadening is accompanied by duplication of NMR signals, indicative of at least one additional, slower exchange process (exchange rate, k _ex, of <100 s^-1), while for this inhibitor also loss of pico- to nanosecond timescale rigidity is observed for some amides in the α domain. Possible sources of the observed dynamics, such as motions in the omega loop and rearrangements of active-site residues, are discussed. The increase in dynamics upon ligand binding argues against a model of inhibitor binding through conformational selection. Rather, the induced dynamics may serve to maximize the likelihood of sampling the optimal conformation for hydrolysis of the bound ligand.

The Role of the Ω-Loop in Regulation of the Catalytic Activity of TEM-Type β-Lactamases

Bacterial resistance to β-lactams, the most commonly used class of antibiotics, poses a global challenge. This resistance is caused by the production of bacterial enzymes that are termed β-lactamases (βLs). The evolution of serine-class A β-lactamases from penicillin-binding proteins (PBPs) is related to the formation of the Ω-loop at the entrance to the enzyme’s active site. In this loop, the Glu166 residue plays a key role in the two-step catalytic cycle of hydrolysis. This residue in TEM–type β-lactamases, together with Asn170, is involved in the formation of a hydrogen bonding network with a water molecule, leading to the deacylation of the acyl–enzyme complex and the hydrolysis of the β-lactam ring of the antibiotic. The activity exhibited by the Ω-loop is attributed to the positioning of its N-terminal residues near the catalytically important residues of the active site. The structure of the Ω-loop of TEM-type β-lactamases is characterized by low mutability, a stable topology, and structural flexibility. All of the revealed features of the Ω-loop, as well as the mechanisms related to its involvement in catalysis, make it a potential target for novel allosteric inhibitors of β-lactamases.

Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 β-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for β-lactamases functional and evolutional studies in general.

Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods

Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ₁₋₄₀) and its “Arctic” variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.

[Investigation the role of mutations M182T and Q39K in structure of beta-lactamase TEM-72 by molecular dynamics method]

Synthesis of b-lactamases is one of the common mechanisms of bacterial resistance to b-lactam antibiotics including penicillins and cephalosporins. The widespread use of antibiotics results in appearance of numerous extended-spectrum b-lactamase variants or resistance to inhibitors. Mutations of 92 residues of TEM type were found. Several mutations are the key mutations that determine the extension of spectrum of substrates. However, roles of the most associated mutations, located far from active site, remain unknown. We have investigated the role of associated mutations in structure of b-lactamase TEM-72, which contain two key mutation (G238S, E240K) and two associated mutations (Q39K, M182T) by means of simulation of molecular dynamics. The key mutation lead to destabilization of the protein globule, characterized by increased mobility of amino acid residues at high temperature of modelling. Mutation M182T lead to stabilization protein, whereas mutation Q39K is destabilizing mutation. It seems that the last mutation serves for optimization of conformational mobility of b-lactamase and may influence on enzyme activity.

Chimeric β-lactamases: global conservation of parental function and fast time-scale dynamics with increased slow motions

Enzyme engineering has been facilitated by recombination of close homologues, followed by functional screening. In one such effort, chimeras of two class-A β-lactamases – TEM-1 and PSE-4 – were created according to structure-guided protein recombination and selected for their capacity to promote bacterial proliferation in the presence of ampicillin (Voigt et al., Nat. Struct. Biol. 2002 9:553). To provide a more detailed assessment of the effects of protein recombination on the structure and function of the resulting chimeric enzymes, we characterized a series of functional TEM-1/PSE-4 chimeras possessing between 17 and 92 substitutions relative to TEM-1 β-lactamase. Circular dichroism and thermal scanning fluorimetry revealed that the chimeras were generally well folded. Despite harbouring important sequence variation relative to either of the two ‘parental’ β-lactamases, the chimeric β-lactamases displayed substrate recognition spectra and reactivity similar to their most closely-related parent. To gain further insight into the changes induced by chimerization, the chimera with 17 substitutions was investigated by NMR spin relaxation. While high order was conserved on the ps-ns timescale, a hallmark of class A β-lactamases, evidence of additional slow motions on the µs-ms timescale was extracted from model-free calculations. This is consistent with the greater number of resonances that could not be assigned in this chimera relative to the parental β-lactamases, and is consistent with this well-folded and functional chimeric β-lactamase displaying increased slow time-scale motions.

Molecular dynamics of class A β-lactamases-effects of substrate binding

The effects of substrate binding on class A β-lactamase dynamics were studied using molecular dynamics simulations of two model enzymes; 40 100-ns trajectories of the free and substrate-bound forms of TEM-1 (with benzylpenicillin) and PSE-4 (with carbenicillin) were recorded (totaling 4.0 μs). Substrates were parameterized with the CHARMM General Force Field. In both enzymes, the Ω loop exhibits a marked flexibility increase upon substrate binding, supporting the hypothesis of substrate gating. However, specific interactions that are formed or broken in the Ω loop upon binding differ between the two enzymes: dynamics are conserved, but not specific interactions. Substrate binding also has a global structuring effect on TEM-1, but not on PSE-4. Changes in TEM-1's normal modes show long-range effects of substrate binding on enzyme dynamics. Hydrogen bonds observed in the active site are mostly preserved upon substrate binding, and new, transient interactions are also formed. Agreement between NMR relaxation parameters and our theoretical results highlights the dynamic duality of class A β-lactamases: enzymes that are highly structured on the ps-ns timescale, with important flexibility on the μs-ms timescale in regions such as the Ω loop.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

TEM-1 backbone dynamics-insights from combined molecular dynamics and nuclear magnetic resonance

Dynamic properties of class A beta-lactamase TEM-1 are investigated from molecular dynamics (MD) simulations. Comparison of MD-derived order parameters with those obtained from model-free analysis of nuclear magnetic resonance (NMR) relaxation data shows high agreement for N-H moieties within alpha- and beta-secondary structures, but significant deviation for those in loops. This was expected, because motions slower than the protein global tumbling often take place in loop regions. As previously shown using NMR, TEM-1 is a highly ordered protein. Motions are observed within the Omega loop that could, upon substrate binding, stabilize E166 in a catalytically efficient position as the cavity between the protein core and the Omega loop is partially filled. The rigidity of active site residues is consistent with the enzyme high turnover number. MD data are also shown to be useful during the model selection step of model-free analysis: local N-H motions observed over the course of the trajectories help assess whether a peptide plan undergoes low or high amplitude motions on one or more timescales. This joint use of MD and NMR provides a better description of protein dynamics than would be possible using either technique alone.

Multiple molecular dynamics simulations of TEM beta-lactamase: dynamics and water binding of the omega-loop

The Omega-loop of TEM beta-lactamase is involved in substrate recognition and catalysis. Its dynamical properties and interaction with water molecules were investigated by performing multiple molecular dynamics simulations of up to 50 ns. Protein flexibility was assessed by calculating the root mean-square fluctuations and the generalized order parameter, S(2). The residues in secondary structure elements are highly ordered, whereas loop regions are more flexible, which is in agreement with previous experimental observations. Interestingly, the Omega-loop (residues 161-179) is rigid with order parameters similar to secondary structure elements, with the exception of the tip of the loop (residues 173-177) that has a considerably higher flexibility and performs an opening and closing motion on the 50-ns timescale. The rigidity of the main part of the Omega-loop is mediated by stabilizing and highly conserved water bridges inside a cavity lined by the Omega-loop and residues 65-69 of the protein core. In contrast, the flexible tip of the Omega-loop lacks these interactions. Hydration of the cavity and exchange of the water molecules with the bulk solvent occurs via two pathways: the flexible tip that serves as a door to the cavity, and a temporary water channel involving the side chain of Arg(164).

NMR dynamics of PSE-4 beta-lactamase: an interplay of ps-ns order and mus-ms motions in the active site

The backbone dynamics for the 29.5 kDa class A beta-lactamase PSE-4 is presented. This solution NMR study was performed using multiple field (15)N spin relaxation and amide exchange data in the EX2 regime. Analysis was carried out with the relax program and includes the Lipari-Szabo model-free approach. Showing similarity to the homologous enzyme TEM-1, PSE-4 is very rigid on the ps-ns timescale, although slower mus-ms motions are present for several residues; this is especially true near the active site. However, significant dynamics differences exist between the two homologs for several important residues. Moreover, our data support the presence of a motion of the Omega loop first detected using molecular dynamics simulations on TEM-1. Thus, class A beta-lactamases appear to be a class of highly ordered proteins on the ps-ns timescale despite their efficient catalytic activity and high plasticity toward several different beta-lactam antibiotics. Most importantly, catalytically relevant mus-ms motions are present in the active site, suggesting an important role in catalysis.

Conserved water molecules stabilize the Omega-loop in class A beta-lactamases

A set of 49 high-resolution (<or=2.2 A) structures of the TEM, SHV, and CTX-M class A beta-lactamase families was systematically analyzed to investigate the role of conserved water molecules in the stabilization of the Omega-loop. Overall, 13 water molecules were found to be conserved in at least 45 structures, including two water positions which were found to be conserved in all structures. Of the 13 conserved water molecules, 6 are located at the Omega-loop, forming a dense cluster with hydrogen bonds to residues at the Omega-loop as well as to the rest of the protein. This layer of conserved water molecules is packed between the Omega-loop and the rest of the protein and acts as structural glue, which could reduce the flexibility of the Omega-loop. A correlation between conserved water molecules and conserved protein residues could in general not be detected, with the exception of the conserved water molecules at the Omega-loop. Furthermore, the evolutionary relationship between the three families, derived from the number of conserved water molecules, is similar to the relationship derived from phylogenetic analysis.

Backbone Dynamics of TEM-1 Determined by NMR: Evidence for a Highly Ordered Protein†

Backbone dynamics of TEM-1 beta-lactamase (263 amino acids, 28.9 kDa) were studied by 15N nuclear magnetic resonance relaxation at 11.7, 14.1, and 18.8 T. The high quality of the spectra allowed us to measure the longitudinal relaxation rate (R1), the transverse relaxation rate (R2), and the {1H}-15N NOE for up to 227 of the 250 potentially observable backbone amide groups. The model-free formalism was used to determine internal motional parameters using an axially anisotropic model. TEM-1 exhibits a small prolate axial anisotropy (D(parallel)/D(perpendicular) = 1.23 +/- 0.01) and a global correlation time (tau(m)) of 12.41 +/- 0.01 ns. The unusually high average generalized order parameter (S2) of 0.90 +/- 0.02 indicates that TEM-1 is one of the most ordered proteins studied by liquid-state NMR to date. Although the omega-loop has a high degree of order in the picosecond-to-nanosecond time scale (mean S2 value of 0.90 +/- 0.02), we observed the presence of microsecond-to-millisecond time scale motions for this loop, as for the vicinity of the active site. These motions could be relevant for the catalytic function of TEM-1. Amide exchange experiments were also performed, and several amide groups were not exchanged after 12 days, an indication that global motions in TEM-1 are also very limited. Although detailed dynamics characterization by NMR cannot be readily applied to TEM-1 in the presence of relevant substrates, the unusual picosecond-to-nanosecond dynamics behavior of TEM-1 presented here will be essential to the validation and improvement of future molecular dynamics simulations of TEM-1 in the presence of functionally relevant substrates.