Trans-[Cu (quin)2(EtOH)2] compound has been synthesized by Nina Podjed et al. [27] during a study on copper(II) Quinaldinate complexes with several O- and N-donor ligands. Using stirring and a hot water bath, to maintain the reaction mixture in hot water, copper (II) acetate hydrate (100 mg, 0.50 mmol) was dissolved in ethanol (15 mL). Then, 86 mg (0.50 mmol) of quinaldinic acid was added to the mixture. A light turquoise solid formed was filtered out and dried. The turquoise filtrate was kept at ambient conditions in a closed container. Trans-[Cu (quin)2(EtOH)2] was identified in a few needle-shaped crystals that formed within two days.

At the DFT level of theory, the optimal molecular structure of the studied Cu-complex was computed with a mix basis set of 6-311G (d, p) and LANL2DZ. The well-known polarized basis set 6-311G (d, p) has a triply divided valence basis set and a core region of six contracted Gaussian primitives. For the first-row elements, the polarization functions are composed of a single set of five pure d-functions, whereas for hydrogen, a set of three p-type orbitals are added, i.e., feature d functions for heavy atoms plus p functions for hydrogen atoms. The LANL2DZ, commonly referred to as effective core potential, has been successfully used in calculations on transition metal complexes [28, 29]. The Gaussian 09 program was used to accomplish all the calculations [30]. GEN keyword was used to specify the basis set 6-311G (d, p) for C, H, O, N atoms and LANL2DZ effective core potential for Cu atom [31]. The geometrical structural parameters, as well as HOMO& LUMO and MESP graphs, were visualized using the Gauss View 6.0.16 graphical interface (Semichem Inc., Shawnee Mission, KS, 2016) [32].

The ADMET properties, associated with the bioavailability, pharmacokinetics, and physicochemical properties of the compound under study, were predicted and analyzed using the OSIRIS property explorer code (https://www.organic-chemistry.org/prog/peo/) and the online web server Molinspiration (https://www.molinspiration.com/). A set of pharmacokinetic properties were also evaluated using the integrated online service for ADMET properties prediction (ADMET Prediction Service). In this web-based prediction system, artificial neural networks and fragmental descriptions were used to construct a predictive model of the relationship between the structure of organic molecules and their related pharmacokinetic parameters [33, 34].

The molecular docking study was carried out using AutoDock 4.2.6 Software (version 4.2.6, https://autodock.scripps.edu/). [35]. A Protein Data Bank webserver (https://www.rcsb.org/) was used to download the 3D structures of the target proteins Bacillus cereus, Staphylococcus aureus, and Escherichia coli bacteria, with protein data bank (PDB) identifiers 5NCD, 2DHN, and 1WXH, respectively. The DFT-optimized structure was provided as the ligand molecule in the docking simulation. Using the Autodock, the proteins were first prepared by removing water ions, and, then, polar hydrogens and Gasteiger partial charges were added to it. The dimension and x, y, and z coordinates of the grid box were determined from the PDB site records using the Discovery Studio software [36]. The Lamarckian genetic algorithm was used with Genetic Algorithm (GA) runs that were set to 100 to find the ligand’s optimum binding pose. The protein-ligand complex and the hydrogen bond interactions, between the protein and ligand molecule, were visualized using the Molegro Molecular Viewer software [37].

Copper (II) can be coordinated in complexes, by four, five, or six donors, due to its d9 electronic configuration. Quinaldinic acid (known as quinoline-2-carboxylic acid and referred to as Hquin), on the other hand, can coordinate the transition of metal ions because it contains N and O-donor ligands with their functional groups OH and NH${}_2$. The importance of Quinaldinate, as a bidentate chelating ligand (as shown in Fig. 1), owes to its ability to participate in hydrogen bonding [38]. The ability of the OH moiety to release its proton-producing anions, which serve as a bridge or chelating, is the basis for the name “chelating ligands”.

Fig. 2 shows how Quinaldinate and ethanol ligands are arranged in the [Cu (quin)2(EtOH)2] composition. The ligands are arranged in a trans geometry within the coordination environment of the complex. The Jahn-Teller effect induced the deformation of an octahedron defined by the arrangement of the N${}_4$O${}_2$ donor group. Two Quinaldinates coordinate the copper (II) ion through four bonds (two bonds with N and O atoms of each Quinaldinate). Moreover, two ethanol molecules and the copper ion were coordinated along the elongated axis of the octahedron. The term “4 + 2 coordination” is typically used to describe this type of bonding configuration [39].

Fig. 2.

Molecular structure (A) and optimized structure (B) of Trans-[Cu (quin)2(EtOH)2] molecule.

A selection of some basic geometrical parameters is shown in Table 1. The optimized molecular parameters and those included in the reported experimental work have been compared (Supplementary Table 1). Long bond lengths of Cu${}_1$–O${}_3$ and Cu${}_1$–O${}_{31}$ (2.395 Å, 2.394 Å), which constitute the elongation of the octahedron pattern, are in good agreement with their experimental counterpart; while in the horizontal plane, there are short distances between the core Cu${}_1$ ion and the oxygen atoms of the carboxylate groups (Cu${}_1$–O${}_2$ and Cu${}_1$–O${}_{30}$). The Cu–N bond lengths represent a middle value between those found between the copper and the oxygen of the ethanol and carboxylate. The base, consisting of the Cu${}_1$, N${}_6$, N${}_{34}$, O${}_2$, and O${}_{30}$ ions, is planar as indicated by the calculated bond angles N${}_6$–Cu${}_1$–N${}_{34}$ = 179.986° and O${}_2$–Cu${}_1$–O${}_{30}$ = 179.99°, which are approximately equal to the observed one of 180°. The Quinaldinate ligands were demonstrated to be nearly planar, as indicated by the calculated dihedral angle, either between Quinaldinate bicyclic system and the carboxylate moiety or between the bicyclic systems themselves.

The oral bioavailability of a drug is often predicted using drug-likeness assessments. Lipinski’s rule of five was established as a straightforward criterion to exclude molecules that lack the optimum physicochemical characteristics. The parameters addressed by this rule are molecular weight, octanol/water coefficient (logP), hydrogen bond donors (HBD), and hydrogen bond acceptors (HBA) [40]. Later, additional relevant parameters such as polar surface area (PSA) and the number of rotatable bonds (nRotb) were incorporated [41]. The computed molecular properties of the title complex are shown in Table 2 along with the minimum constraints of the rule of five.

Table 2.The predicted Drug-likeness character for the studied molecule.

The results revealed that Lipinski’s rule of five has been violated except for the molecular weight, which barely exceeded the maximum range (by 0.01). The lipophilic nature of a molecule is directly correlated with the c Log p value, and good lipophilicity is indicated as the c Log P number that further decreases below 5. The calculated c Log p value (1.42), is in the range of values 1 to 4, where oral drugs are expected to have the optimum physicochemical features [42]. The reduction in polar surface area is a sign of the high oral bioavailability of drugs. In Table 1, the small PSA (77.54 Å${}^2$) provides a clear indicator of the high oral bioavailability.

It is necessary to estimate the potential toxicity risk for each fragment in the drug candidate molecules. This toxicity prediction determines if the suggested structure will be toxic with regard to the identified risk levels. The estimation of mutagenicity, tumorigenicity, irritant, and reproductive effects was indicated by color coding. Each category is distinguished by a red or green color, with green denoting a low potential for toxicity and red denoting a high toxic risk. The results in Table 1 reveal that the compound under test would be safe and is predicted to exhibit minimal to no toxicity.

Further, pharmacokinetic processes that affect the corresponding bioavailability of the drug were predicted by ADMET Prediction Service [43]. The relation between the structure of organic compounds and their pharmacokinetic properties is established by a color-coded distribution graph that displays the predicted value and its position within a known, tested library of compounds. The optimal pharmacological profile, determined in this regard, is depicted in Fig. 3. The calculated permeability through the blood-brain barrier (BBB) was –0.51, surpassing 79% of the database compounds built for the predictive model, this value is in the range of 0.3 to –1, and it indicates a moderate level of access to the central nervous system [44]. A high oral bioavailability was anticipated, based on the high human intestinal absorbance (HIA) value ($\ge$80%), which indicates good oral absorption [45]. The disturbance of hERG, when it is altered by specific diseases, small molecules, or drugs, may result in ventricular arrhythmia and sudden death. Therefore, minimizing the risk of hERG inhibition is a primary priority during the design or development of new drugs. In general, more potent inhibitors are available with higher values of pIC50 (pIC50 = –log IC50). The reported value of hERG activity, pIC50 = 3.83, revealed a low inhibition level of the hERG channel, which has a threshold of pIC50 4.70 [46]. The affinity of a compound, for binding to the hERG channel, could be described by the dissociation constant Ki or pKi value where pki = log 1/Ki. As shown in Fig. 2, the proposed compound is categorized as a medium binder based on the observed pKi value (5 $\le$ pKi $\le$ 8) [47].

Fig. 3.

The calculated blood-brain barrier permeability (BBB), human intestinal absorbance (HIA), human Ether-à-go-go-Related Gene (hERG) activity and hERG affinity for the stated compound.

Frontier orbitals, which are defined by the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO), are the major elements in quantum chemistry. HOMO designates the molecular locations that have the highest propensity for electron donation, while the molecular locations with the highest propensity for electron acceptance are indicated as LUMO. The HOMO and LUMO frontier orbitals are important to understand the molecular properties of a molecule, which include its chemical stability and reactivity. Fig. 4 illustrates the graphical representation of HOMO and LUMO, their associated energy values, and the energy gap (Eg), which describes the total reactivity of the molecule. The calculated energy gap of the studied ligand is 3.88 eV, which makes it more stable than the previously reported metal complexes [48, 49, 50]. The HOMO electron density of the stable ligand is mainly observed as anti-bonding orbitals on the Cu ion and the nitrogen and oxygen atoms of the Quinaldinate fragment; whereas the LUMO electron density has a bonding nature that is localized on the $\pi$-electron multiple bonds of the two rings of the Quinaldinate component. HOMO and LUMO molecular orbitals are not, in any way, affected by vertically oriented ethanol groups. Thus, intramolecular charge transfer, between HOMO and LUMO, occurs within the molecule from its donor sites, in the center, to its ends in the same plane. Accordingly, there is a preferred plane for electron transition in the molecule (plane containing the Cu ion and Quinaldinate fragment) rather than the plane that is perpendicular to it.

Fig. 4.

Electron density distributions in HOMO and LUMO frontier orbitals diagram.

The term “molecular electrostatic potential” (or “MEP”) is a crucial reactivity descriptor that is frequently used to characterize the overall electron density surface of a molecule that displays its positive, negative, and neutral electrostatic potential regions. On the MEP surface, negative, neutral, and positive locations are indicated by red, green, and blue colors, respectively. The blue region contains electron-poor sites that are subject to nucleophilic attack, whereas the green areas correspond to neutral sites or zero potential, and the red spot represents electron-rich sites that are subject to electrophilic attack. Electronic charge density, for a drug surface, is used in computer-aided drug design to examine the relationship between that drug’s structure and the associated biological activity. Therefore, by using this information on charge distribution in the compounds, MEP could be used to interpret the hydrogen bonding interactions and biological properties.

Fig. 5 displays the contour map for the electrostatic potential of the title compound. There are only two symmetrically negative regions (red), which basically cover C=O groups and extend slightly to the two oxygen atoms of the ethanol fragment. These groups exhibit high electronegativity, which represents the most reactive component or the optimum sites for an electrophilic attack on the ligand. Therefore, it is expected that the oxygen atoms will be the key sites of interaction between the molecule and the target protein which could lead to biological activity.

Fig. 5.

Molecular electrostatic potential map for Trans-[Cu (quin)2(EtOH)2] compound.

To validate the docking protocol, the ligands Zinc Metalloenzyme, 6-hydroxymethyl-7, 8-dihydropterin, and natural substrate were taken from the X-ray structures of B. cereus protein (5NCD with 2.45 Å resolution), S. aureus protein (2DHN with 2.2 Å resolution), and E. coli protein (1WXH with 1.90 Å resolution) respectively, and they have been re-docked into its protein co-crystallized structure. The calculated root-mean-square deviation (RMSD) between docked and experimental poses for the same order of the three proteins was 1.472, 1.759, and 2.04 Å. These reasonably low values verified that the docking protocol was adequate.

The docked binding energy, inhibition constant, and hydrogen bond for the molecular poses (lowest energy), associated with the interaction of the proteins 5NCD, 2DHN, and 1WXH with the stated copper complex, are shown in Table 3. Also, the binding interactions within the active domain of the enzymes, along with the labeled adjacent amino acid residues, are shown in Figs. 6,7,8. Out of the obtained results, B. cereus (5NCD) bacterium had the best docking pose, with the lowest binding energy and lowest inhibition constant of –9.83 kcal/mol and 0.06261 $\mu$M, respectively. Two hydrogen bonds are observed between MET170 and TRP191 of the 5NCD enzyme with the two oxygen atoms of the ligand. This strong binding affinity made it clear that the proposed ligand was responsible for the high-level inhibition capability of B. cereus bacteria. Regarding its performance against the bacterium S. aureus, the studied copper complex had a higher binding energy (–6.65 kcal/mol) than other copper complexes that had been recently reported against the same type of target protein. The diaquabis pyridine tetrazole copper complex was previously assessed to demonstrate effective inhibition of the S. aureus bacterial strain, and the recorded docking score was –5.8 kcal/mol without any hydrogen bonds [51, 52]. In another study of a new thiazole-derived chalcone and its metal complexes against microbial enzymes, the same result (–5.8 kcal/mol) was obtained by Cu complex against S. aureus (2DHN) [53]. This result revealed that the investigated Cu (II) complex maintains the most stable interaction within the active site pockets of the S. aureus receptor. The compound did not exhibit the same level of efficacy in the study of its activity against E. coli bacteria, as in the cases of B. cereus and S. aureus. The binding energy for 1WXH protein was small (–6.62 kcal/mol) compared to that obtained by copper complexes in references 52 and 53 (–8.2 and –7.6 kcal/mol), respectively.

Fig. 6.

Cu-complex with B. cereus. (A) 3D representation of Cu-complex in the protein cavity. (B) 3D representation of the interactions within the binding pocket. (C) 2D representation of the interactions with amino acid residues.

Fig. 7.

Cu-complex with S. aureus. (A) 3D representation of Cu-complex in the protein cavity. (B) 3D representation of the interactions within the binding pocket. (C) 2D representation of the interactions with amino acid residues.

Fig. 8.

Cu-complex with E. coli. (A) 3D representation of Cu-complex in the protein cavity. (B) 3D representation of the interactions within the binding pocket. (C) 2D representation of the interactions with amino acid residues.