Introducing KICK-MEP: exploring potential energy surfaces in systems with significant non-covalent interactions.

CONTEXT: Exploring potential energy surfaces (PES) is fundamental in computational chemistry, as it provides insights into the relationship between molecular energy, geometry, and chemical reactivity. We introduce Kick-MEP, a hybrid method for exploring the PES of atomic and molecular clusters, particularly those dominated by non-covalent interactions. Kick-MEP computes the Coulomb integral between the maximum and minimum electrostatic potential values on a 0.001 a.u. electron density isosurface for two interacting fragments. This approach efficiently estimates interaction energies and selects low-energy configurations at reduced computational cost. Kick-MEP was evaluated on silicon-lithium clusters, water clusters, and thymol encapsulated within Cucurbit[7]uril, consistently identifying the lowest energy structures, including global minima and relevant local minima. METHODS: Kick-MEP generates an initial population of molecular structures using the stochastic Kick algorithm, which combines two molecular fragments (A and B). The molecular electrostatic potential (MEP) values on a 0.001 a.u. electron density isosurface for each fragment are used to compute the Coulomb integral between them. Structures with the lowest Coulomb integral are selected and refined through gradient-based optimization and DFT calculations at the PBE0-D3/Def2-TZVP level. Molecular docking simulations for the thymol-Cucurbit[7]uril complex using AutoDock Vina were performed for benchmarking. Kick-MEP was validated across different molecular systems, demonstrating its effectiveness in identifying the lowest energy structures, including global minima and relevant local minima, while maintaining a low computational cost.

From Proteome to Potential Drugs: Integration of Subtractive Proteomics and Ensemble Docking for Drug Repurposing against Pseudomonas aeruginosa RND Superfamily Proteins.

Pseudomonas aeruginosa (P. aeruginosa) poses a significant threat as a nosocomial pathogen due to its robust resistance mechanisms and virulence factors. This study integrates subtractive proteomics and ensemble docking to identify and characterize essential proteins in P. aeruginosa, aiming to discover therapeutic targets and repurpose commercial existing drugs. Using subtractive proteomics, we refined the dataset to discard redundant proteins and minimize potential cross-interactions with human proteins and the microbiome proteins. We identified 12 key proteins, including a histidine kinase and members of the RND efflux pump family, known for their roles in antibiotic resistance, virulence, and antigenicity. Predictive modeling of the three-dimensional structures of these RND proteins and subsequent molecular ensemble-docking simulations led to the identification of MK-3207, R-428, and Suramin as promising inhibitor candidates. These compounds demonstrated high binding affinities and effective inhibition across multiple metrics. Further refinement using non-covalent interaction index methods provided deeper insights into the electronic effects in protein-ligand interactions, with Suramin exhibiting superior binding energies, suggesting its broad-spectrum inhibitory potential. Our findings confirm the critical role of RND efflux pumps in antibiotic resistance and suggest that MK-3207, R-428, and Suramin could be effectively repurposed to target these proteins. This approach highlights the potential of drug repurposing as a viable strategy to combat P. aeruginosa infections.

Increased Absorption of Thyroxine in a Murine Model of Hypothyroidism Using Water/CO2 Nanobubbles.

Thyroxine (T4) is a drug extensively utilized for the treatment of hypothyroidism. However, the oral absorption of T4 presents certain limitations. This research investigates the efficacy of CO2 nanobubbles in water as a potential oral carrier for T4 administration to C57BL/6 hypothyroid mice. Following 18 h of fasting, the formulation was administered to the mice, demonstrating that the combination of CO2 nanobubbles and T4 enhanced the drug's absorption in blood serum by approximately 40%. To comprehend this observation at a molecular level, we explored the interaction mechanism through which T4 engages with the CO2 nanobubbles, employing molecular simulations, semi-empirical quantum mechanics, and PMF calculations. Our simulations revealed a high affinity of T4 for the water-gas interface, driven by additive interactions between the hydrophobic region of T4 and the gas phase and electrostatic interactions of the polar groups of T4 with water at the water-gas interface. Concurrently, we observed that at the water-gas interface, the cluster of T4 formed in the water region disassembles, contributing to the drug's bioavailability. Furthermore, we examined how the gas within the nanobubbles aids in facilitating the drug's translocation through cell membranes. This research contributes to a deeper understanding of the role of CO2 nanobubbles in drug absorption and subsequent release into the bloodstream. The findings suggest that utilizing CO2 nanobubbles could enhance T4 bioavailability and cell permeability, leading to more efficient transport into cells. Additional research opens the possibility of employing lower concentrations of this class of drugs, thereby potentially reducing the associated side effects due to poor absorption.

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Li6 E5 Li6 : Tetrel Sandwich Complexes with 10-π-Electrons.

When (4n +2) π-electrons are located in single planar ring, it conventionally qualifies as aromatic. According Hückel's rule, systems possessing ten π-electrons should be aromatic. Herein we report a series of D5h  Li6 E5 Li6 sandwich structures, representing the first global minima featuring ten π-electrons E5 10- ring (E=Si-Pb). However, these π-electrons localize as five π-lone-pairs rather than delocalized orbitals. The high symmetry structure achieved is a direct consequence of σ-aromaticity, particularly favored in elements from Si to Pb, resulting in a pronounced diatropic ring current flow that contributes to the enhanced stability of these systems.

Screening of the antileishmanial and antiplasmodial potential of synthetic 2-arylquinoline analogs.

In this study, six analogs of 2-arylquinoline were synthesized and evaluated for their in vitro and in vivo antiplasmodial and leishmanicidal activity. At a later stage, hemolytic activity and druggability were tested in vitro and in silico, respectively, observing as a result: firstly, compounds showed half-maximal effective concentration (EC50) values between 3.6 and 19.3 µM. Likewise, a treatment using the compounds 4a-f caused improvement in most of the treated hamsters and cured some of them. Regarding the antiplasmodial activity, the compounds showed moderate to high activity, although they did not show hemolytic activity. Furthermore, 4e and 4f compounds were not able to control P. berghei infection when administered to animal models. Molecular dynamic simulations, molecular docking and ligand binding affinity indicate good characteristics of the studied compounds, which are expected to be active. And lastly, the compounds are absorbable at the hematoencephalic barrier but not in the gastrointestinal tract. In summary, ADMET properties suggest that these molecules may be used as a safe treatment against Leishmania.

Antispasmodic Effect of Valeriana pilosa Root Essential Oil and Potential Mechanisms of Action: Ex Vivo and In Silico Studies.

Infusions of Valeriana pilosa are commonly used in Peruvian folk medicine for treating gastrointestinal disorders. This study aimed to investigate the spasmolytic and antispasmodic effects of Valeriana pilosa essential oil (VPEO) on rat ileum. The basal tone of ileal sections decreased in response to accumulative concentrations of VPEO. Moreover, ileal sections precontracted with acetylcholine (ACh), potassium chloride (KCl), or barium chloride (BaCl2) were relaxed in response to VPEO by a mechanism that depended on atropine, hyoscine butylbromide, solifenacin, and verapamil, but not glibenclamide. The results showed that VPEO produced a relaxant effect by inhibiting muscarinic receptors and blocking calcium channels, with no apparent effect on the opening of potassium channels. In addition, molecular docking was employed to evaluate VPEO constituents that could inhibit intestinal contractile activity. The study showed that α-cubebene, ß-patchoulene, ß-bourbonene, ß-caryophyllene, α-guaiene, γ-muurolene, valencene, eremophyllene, and δ-cadinene displayed the highest docking scores on muscarinic acetylcholine receptors and voltage-gated calcium channels, which may antagonize M2 and/or M3 muscarinic acetylcholine receptors and block voltage-gated calcium channels. In summary, VPEO has both spasmolytic and antispasmodic effects. It may block muscarinic receptors and calcium channels, thus providing a scientific basis for its traditional use for gastrointestinal disorders.

Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach.

Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.

Revisiting the potential-energy surface of CnBe3n+2H2n+22+ (n = 2-4) clusters: are planar pentacoordinate carbon structures the global minima?

Using various exploration strategies, in this study, we investigated the potential energy surfaces (PES) of CBe5H5+ and CnBe3n+2H2n+22+ (n = 2-4) clusters. Previous studies proposed that the planar pentacoordinate carbons (ppCs) were the global minima of these clusters. However, our study identified new putative global minima and competitive isomers, refuting some previous assignments. We employed several methods, including evolutive-inspired stochastic approaches guided by "chemical criteria", and ab initio molecular dynamics simulations at elevated temperatures. Our results showed that the size of the scanned population significantly affected the evolutive method and that constrained or guided procedures showed an advantage in identifying better minima for larger systems. This study demonstrated that using multiple complementary strategies can result in a wider variety of minima in a given energy range. Our findings provide valuable insights into exploring the potential energy surfaces of clusters, mainly medium-sized clusters, which could be the connections between small clusters and nanomaterials.

Discovery of New 2-Phenylamino-3-acyl-1,4-naphthoquinones as Inhibitors of Cancer Cells Proliferation: Searching for Intra-Cellular Targets Playing a Role in Cancer Cells Survival.

A series of 2-phenylamino-3-acyl-1,4-naphtoquinones were evaluated regarding their in vitro antiproliferative activities using DU-145, MCF-7 and T24 cancer cells. Such activities were discussed in terms of molecular descriptors such as half-wave potentials, hydrophobicity and molar refractivity. Compounds 4 and 11 displayed the highest antiproliferative activity against the three cancer cells and were therefore further investigated. The in silico prediction of drug likeness, using pkCSM and SwissADME explorer online, shows that compound 11 is a suitable lead molecule to be developed. Moreover, the expressions of key genes were studied in DU-145 cancer cells. They include genes involved in apoptosis (Bcl-2), tumor metabolism regulation (mTOR), redox homeostasis (GSR), cell cycle regulation (CDC25A), cell cycle progression (TP53), epigenetic (HDAC4), cell-cell communication (CCN2) and inflammatory pathways (TNF). Compound 11 displays an interesting profile because among these genes, mTOR was significantly less expressed as compared to control conditions. Molecular docking shows that compound 11 has good affinity with mTOR, unraveling a potential inhibitory effect on this protein. Due to the key role of mTOR on tumor metabolism, we suggest that impaired DU-145 cells proliferation by compound 11 is caused by a reduced mTOR expression (less mTOR protein) and inhibitory activity on mTOR protein.

On the energetic and magnetic stability of neutral and charged lithium clusters doped with one and two yttrium atoms.

DFT calculations were performed to study the effect on energetic and magnetic stability when clusters with up to 24 lithium atoms were doped with one and two atoms of yttrium. In this, the effect of the charge was considered. As a result, some stable structures were identified as possible magnetic superatoms, among them, the YLi12+ cluster with an icosahedron geometry with a spin magnetic moment of 4 bohr magnetons. The participation of yttrium in the electron density of the unpaired electrons providing magnetism in clusters was corroborated at the level of a density of states (DOS) calculation and a spin density calculation. In particular, in the Y2Li12+ superatom, it was found that the encapsulated yttrium atom participates with 35.02% and the second yttrium atom with 15.04%. These percentages, with a contribution from p orbitals, but to a greater extent by d orbitals. The complementation to these percentages is due to the participation of the s and p orbitals of the lithium atoms. In general, doping with a second yttrium atom allowed to obtain a greater amount of high magnetic moments, and considering charged clusters allowed to obtain also high magnetic moments.

Removal of highly concentrated methylene blue dye by cellulose nanofiber biocomposites.

The contamination of water by dyes in high concentrations is a worldwide concern, and it has prompted the development of efficient, economical, and environmentally friendly materials and technologies for water purification. The hydration and adsorption capacity for methylene blue (MB) in biocomposites (BCs) based on cellulose nanofiber (CNF) (0 to 2 wt%) were studied. BCs were synthesized through a simple and straightforward route and characterized by spectroscopy, microscopic techniques and thermogravimetric analysis, among others. Hydration studies showed that BCs prepared with 2 wt% of CNF can absorb large volumes of water, approximately 2274 % in the case of poly 2-acrylamide-2-methyl-1-propanesulfonic acid (PAMPS)-CNF and 2408 % in poly sodium 4-styrene sulfonate (PSSNa)-CNF. These BCs showed outstanding adsorption capacity for highly concentrated MB solutions (4536 mg g-1 PAMPS-CNF and 11,930 mg g-1 PSSNa-CNF). It was confirmed that the adsorption mechanism is through electrostatic interactions. Finally, BCs showed high MB adsorption efficiency after several sorption-desorption cycles and on a simulated textile effluent. Furthermore, the theoretical results showed a preferential interaction between MB and the semiflexible polymer chains at the lowest energy setting. The development and study of a new adsorbent material with high MB removal performance that is easy to prepare, economical and reusable for potential use in water purification treatments was successfully achieved.

Antioxidant activity and enzymatic of lichen substances: A study based on cyclic voltammetry and theoretical.

The antioxidant activity of nine lichen substances, including methylatrarate (1), methyl haematommate (2), lobaric acid (3), fumarprotocetraric acid (4), sphaerophorin (5), subsphaeric acid (6), diffractaic acid (7), barbatolic acid (8) and salazinic acid (9) has been determined through cyclic voltammetry. The compounds 1-4 presented slopes close to the Nernst constant of 0.059 V, indicating a 2H+/2e- relation between protons and electrons, as long as the compounds 5, 6, 7, 8, and 9 present slopes between 0.037 V and 0.032 V, indicating a 1H+/2e- relation between protons and electrons. These results show a high free radical scavenging activity by means of the release of H+, suggesting an important antioxidant capacity of these molecules. Theoretical calculations of hydrogen bond dissociation enthalpies (BDE), proton affinities (PA), and Proton Transfer (PT) mechanisms, at M06-2x/6-311+G(d,p) level complement the experimental results. Computations support that the best antioxidant activity is obtained for the molecules (3, 4, 5, 6, 7 and 8), that have a carboxylic acid group close to a phenolic hydroxyl group, through hydrogen atomic transfer (HAT) and sequential proton loss electron transfer (SPLET) mechanisms. Additional computations were performed for modelling binding affinity of the lichen substances with CYPs enzymes, mainly CYP1A2, CYP51, and CYP2C9*2 isoforms, showing strong affinity for all the compounds described in this study.

Electronic Transmutation Concept: Is the Inverse Process Possible? An Evaluation of Main Group Compounds.

The electronic transmutation (ET) concept states that when an element with atomic number Z gains an electron, it transmutes into a Z + 1 element, leading to species that possess similar chemical bonding patterns and geometric structures regarding the original (Z + 1) element. In this work, the opposite concept, that is, the inverse ET, is assessed. For this purpose, several main group compounds have been analyzed in terms of the adaptive natural density partitioning. The obtained results suggest that when an atom Z loses an electron, it transmutes into a Z - 1 atom, acquiring its geometrical structure and bonding pattern.

Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach.

The rapid emergence and spread of new variants of coronavirus type 2, as well as the emergence of zoonotic viruses, highlights the need for methodologies that contribute to the search for new pharmacological treatments. In the present work, we searched for new SARS-CoV-2 papain-like protease inhibitors in the PubChem database, which has more than 100 million compounds. Based on the ligand efficacy index obtained by molecular docking, 500 compounds with higher affinity than another experimentally tested inhibitor were selected. Finally, the seven compounds with ADME parameters within the acceptable range for such a drug were selected. Next, molecular dynamics simulation studies at 200 ns, ΔG calculations using molecular mechanics with generalized Born and surface solvation, and quantum mechanical calculations were performed with the selected compounds. Using this in silico protocol, seven papain-like protease inhibitors are proposed: three compounds with similar free energy (D28, D04, and D59) and three compounds with higher binding free energy (D60, D99, and D06) than the experimentally tested inhibitor, plus one compound (D24) that could bind to the ubiquitin-binding region and reduce the effect on the host immune system. The proposed compounds could be used in in vitro assays, and the described protocol could be used for smart drug design.

Valeriana pilosa Roots Essential Oil: Chemical Composition, Antioxidant Activities, and Molecular Docking Studies on Enzymes Involved in Redox Biological Processes.

Valeriana pilosa is usually employed in Peruvian folk medicine in the form of infusion to treat stomach pain, and has antispasmodic, relaxing, sleep-promoting, and sedative properties, as well as is an anti-inflammatory. In this study, Valeriana pilosa essential oil (VPEO) was obtained by hydrodistillation, analyzed by GC and GC/MS, and 47 compounds were identified. Major oil components were α-patchoulene (5.8%), α-humulene (6.1%), seychellene (7.6%), and patchoulol (20.8%). Furthermore, we assessed the in vitro antioxidant activities, molecular docking, and Ligand Efficiency studies on enzymes involved in cellular redox pathways such as CYP2C9, catalase, superoxide dismutase, and xanthine oxidase. Essential oil antioxidant activities were assessed by FRAP, ABTS•+, and DPPH• radical scavenging activity. VPEO displays high antioxidant activity as compared to essential oils of Valeriana jatamansi and Valeriana officinalis oil roots. In addition, molecular docking and ADMET prediction was employed to compare the absorption, metabolism, and toxicity properties of Valeriana pilosa compounds. In the molecular docking studies, limonene, p-cimene, carvone, α-cubebene, cyclosativene, α-guaiene, allo-aromadendrene, valencene, and eremophyllene were the compounds with the best docking score on CYP2C9 and xanthine oxidase. Thus, volatile components of Valeriana pilosa could be associated with the detected antioxidant activity, acting as putative inhibitors of CYP2C9 and xanthine oxidase.

Bowl-shaped Cluster CuB12 - : A Viable Global Minimum with Twofold Aromaticity.

A low-lying structure is revealed for the CuB12 - cluster, which is bowl-shaped. It consists of a triangular CuB2 base and a B10 rim. Molecular dynamics simulations indicates its structural robustness; at an elevated temperature (600 K), the base rotates reversibly within the B10 perimeter. Chemical bonding analysis detects 2σ- and 3π-delocalized bonds, suggesting double aromaticity. This is also confirmed by two diatropic and concentric ring currents under an external magnetic field.

A mutation increases the specificity to plant compounds in an insect chemosensory protein.

Host plant recognition are highly dependent on chemosensory perception, which involves chemosensory proteins (CSPs) that bind key chemical compounds the host plants. In this work, we hypothesize that two closely related aphid taxa, which differ in diet breadth, also differ in their CSPs. We detected a non-synonymous difference (lysine for asparagine) between M. persicae sensu stricto (Mpp) and the subspecies M. p. nicotianae (Mpn) in the sequence of a CSP (CSP5). We modeled in silico the binding capacity of both CSP5s variants with 163 different potential ligands from their host plants (120 unique from tobacco, 29 unique from peach, and 14 common ligands). After docking analysis with all ligands, we selected the three best ligands for each variant to perform molecular dynamics (tobacco: 2-cyclopentene-1,4-dione, salicylaldehyde, and benzoic acid; peach: phenol, valeric acid, and benzonitrile). The binding energy of the MpnCSP5 model to the studied ligands was, in all cases, lower than with the MppCSP5 model. The ligands from the host plants showed more stable binding with MpnCSP5 than with MppCSP5. This result suggests that the set of CSPs studied among M. persicae s. str. and M. p. nicotianae are very similar, but focusing on the CSP5 protein, we found a single key mutation that increases affinities for host compounds for M. p. nicotianae, which might have contributed to the specialization to tobacco. This study provides new insights into an evolutionary trend toward specificity in a binding protein.

In vitro and in silico analysis of galanthine from Zephyranthes carinata as an inhibitor of acetylcholinesterase.

Zephyranthes carinata Herb., a specie of the Amaryllidoideae subfamily, has been reported to have inhibitory activity against acetylcholinesterase. However, scientific evidence related to their bioactive alkaloids has been lacking. Thus, this study describes the isolation of the alkaloids of this plant, and their inhibition of the enzymes acetylcholinesterase (eeAChE) and butyrylcholinesterase (eqBuChE), being galanthine the main component. Additionally, haemanthamine, hamayne, lycoramine, lycorine, tazettine, trisphaeridine and vittatine/crinine were also isolated. The results showed that galanthine has significant activity at low micromolar concentrations for eeAChE (IC50 = 1.96 µg/mL). The in-silico study allowed to establish at a molecular level the high affinity and the way galanthine interacts with the active site of the TcAChE enzyme, information that corroborates the result of the experimental IC50. However, according to molecular dynamics (MD) analysis, it is also suggested that galanthine presents a different inhibition mode that the one observed for galanthamine, by presenting interaction with peripheral anionic binding site of the enzyme, which prevents the entrance and exit of molecules from the active site. Thus, in vitro screening assays plus rapid computer development play an essential role in the search for new cholinesterase inhibitors by identifying unknown bio-interactions between bioactive compounds and biological targets.

Structural Factors That Determine the Activity of the Xenobiotic Reductase B Enzyme from Pseudomonas putida on Nitroaromatic Compounds.

Xenobiotic reductase B (XenB) catalyzes the reduction of the aromatic ring or nitro groups of nitroaromatic compounds with methyl, amino or hydroxyl radicals. This reaction is of biotechnological interest for bioremediation, the reuse of industrial waste or the activation of prodrugs. However, the structural factors that explain the binding of XenB to different substrates are unknown. Molecular dynamics simulations and quantum mechanical calculations were performed to identify the residues involved in the formation and stabilization of the enzyme/substrate complex and to explain the use of different substrates by this enzyme. Our results show that Tyr65 and Tyr335 residues stabilize the ligands through hydrophobic interactions mediated by the aromatic rings of these aminoacids. The higher XenB activity determined with the substrates 1,3,5-trinitrobenzene and 2,4,6-trinitrotoluene is consistent with the lower energy of the highest occupied molecular orbital (LUMO) orbitals and a lower energy of the homo orbital (LUMO), which favors electrophile and nucleophilic activity, respectively. The electrostatic potential maps of these compounds suggest that the bonding requires a large hydrophobic region in the aromatic ring, which is promoted by substituents in ortho and para positions. These results are consistent with experimental data and could be used to propose point mutations that allow this enzyme to process new molecules of biotechnological interest.