In-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization

Syeda Abida Ejaz; Amna Saeed; Pervez Rashid Birmani; Khadijah Mohammedsalaeh Katubi; Zainab Mufarreh Elqahtani; M S Al-Buriahi; Rabail Ujan; Farhan Siddique; Samia ben Ahmed; Z A Alrowaili

doi:10.1371/journal.pone.0271602

. 2022 Oct 27;17(10):e0271602. doi: 10.1371/journal.pone.0271602

In-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization

Syeda Abida Ejaz ^1,^*, Amna Saeed ¹, Pervez Rashid Birmani ², Khadijah Mohammedsalaeh Katubi ³, Zainab Mufarreh Elqahtani ⁴, M S Al-Buriahi ⁵, Rabail Ujan ⁶, Farhan Siddique ^7,⁸, Samia ben Ahmed ⁹, Z A Alrowaili ¹⁰

Editor: Joazaizulfazli Jamalis¹¹

PMCID: PMC9612481 PMID: 36301939

Abstract

The aberrant expression of aldo keto reductases (AKR1B1 & AKR1B10) has been extensively studied in different types of cancer especially the colon cancer but a very few studies have yet been reported regarding the discovery of inhibitors for the treatment of colon cancer by targeting these isozymes. Therefore, there is a need of selective inhibitors of both targets for the eradication of colon cancer. Currently, the study is focused on the exploration of two quinolone compounds i.e., (S)-(6-Methoxyquinolin-4-yl)[(1S,2R,4S,5R)-5-vinylquinuclidin-2-yl]methanol (Quinidine) and (R)-(6-Methoxyquinolin-4-yl)[(1S,2S,4S,5R)-5-vinylquinuclidin-2-yl]methanol (Quinine) as the potential inhibitors of AKR1B1 and AKR1B10 via detailed in-silico approach. The structural properties including vibrational frequencies, dipole moment, polarizability and the optimization energies were estimated using density functional theory (DFT) calculations; where both compounds were found chemically reactive. After that, the optimized structures were used for the molecular docking studies and here quinidine was found more selective towards AKR1B1 and quinine exhibited maximum inhibition of AKR1B10. The results of molecular docking studies were validated by molecular dynamics simulations which provided the deep insight of stability of protein ligand complex. At the end, the ADMET properties were determined to demonstrate the druglikeness properties of both selected compounds. These findings suggested further exploration of both compounds at molecular level using different in-vivo and in-vitro approaches that will lead to the designing of potential inhibitor of AKR1B1/AKR1B10 for curing colon cancer and related malignancies.

Introduction

Aldo-keto reductases: AKRs are found in nearly all phyla and are primarily monomeric soluble proteins (34–37 kDa) that function as NAD(P)(H) dependent oxidoreductases [1]. Up to date, huge genome data sets has been reported via in-silico approaches and it has been reported that this protein superfamily has 190 identified proteins which are further divided into 16 families [2]. From these sixteen families, the total fifteen members of AKR family have been identified in human which include: AKR1A, AKR1B, AKR1C, AKR1E, AKR6A and AKR7A subfamilies. Among these sub-families, AKR1B has gained a lot of attention because of its extensive role in different types of cancer. The AKR1B subfamily comprises of three isozymes i.e., AKR1B1 (aldose reductase), AKR1B10 (aldose reductase-like protein-1) and AKR1B15 [3, 4] and here we are focused on the exploration of inhibitors of AKR1B1 and AKR1B10. Both are expressed in various organs but majorly in breast, liver, lungs, pancreas and small intestine [5] and share 71% amino acid sequence similarity. Both targets are extensively involved in different types of cancer, especially the colon cancer and lung cancer, where their level is found to be overexpressed [6]. With respect to the other roles of these targeted AKRs, AKR1B10 is involved in maintaining the retinoic acid homeostasis, which is considered as most important factor that promote the cell differentiation [7]. Moreover, AKR1B10 affects the growth of cells and survival through affecting lipid production and membrane function by blocking the ubiquitin-dependent degradation of acetyl-CoA carboxylase [8].

Another isozyme i.e., maintaining the homeostasis of reactive oxygen species (ROS) is the responsibility of AKR1B1 and thus crucial for the regulation of inflammatory transcription factors (TFs). Aberrant expression of AKR1B1 resulted in the activation of various signaling pathways such as NFκB, an ubiquitous transcription factor found in various cancer [9]. On the basis of these facts, it can be observed that both AKR1B10 and AKR1B1 are aberrantly expressed in different cancer so there is a need to explore potential inhibitors of both targets [10].

Among the different type of heterocyclic compounds, quinolone derivatives have been reported as the significant class of compounds with varying biological activities including analgesics, local anesthetic, anti-inflammatory, anti-cancer, pain relief, neuropharmacological, anti-microbial, anti-fungal and also the anti-malarial activities [11–13]. Due to their remarkable chemical and physical properties, quinolone derivatives have been the subject of several structural and theoretical investigations [4]. Many studies have been reported on the quinolone alkaloids as the anticancer agents but very few of them have been reported as the inhibitor of aldo-keto reductases. Scheme 1 shows the already reported aldo-keto reductase inhibitors; 1-oxopyrimido[4,5-c]quinoline-2-acetic acid (IC₅₀ value = <1 mM), 2-aminopyrimido[4,5-c]quinolin-1(2H)-one (IC₅₀ value = <1 mM) and zenarestat exhibited the IC₅₀ value of 44 nM [14, 15].

Here only two quinolones: quinine and quinidine were selected and the study’s objective was to investigate the reactivity and stability of the quinolone derivatives comprehensively as the potential inhibitors of AKR1B1 and AKR1B10. For this, the optimized geometry analysis has been used to interpret the structural information for the compounds where the dipole moment, polarizability and optimization energy of the selected compounds were calculated. Further, molecular docking studies were performed using geometrically optimized compounds against the targets AKR1B1 and AKR1B10 and the results were supported by molecular dynamic simulations. The physicochemical parameters (ADMET properties) were estimated for considering the druglikeness properties of both compounds. The results of the current study are very promising therefore suggested that both compounds can be used in future for the synthesis of more potential inhibitors of the selected targets, for the treatment of respective cancers caused due to over-expression of AKR1B1 and AKR1B10.

Experimental

Density functional theory (DFT)

The Gaussian 09 package (Rev.E.01) [16] with default settings was used for all calculations with B3LYP functional in SVP basis set [17]. Calculating the electronic structure of atoms and molecules is effectively done using this theory. The following information will be determined by the current study i.e., optimized geometric parameters, the frontier molecular orbital (FMO), global and local reactivity descriptors and molecular electrostatic potential (MEP) [18]. Check files were viewed using Guass View 6 [19].

Molecular docking

Preparation of ligand

The selected ligands were obtained from PubChem [20] and their energy minimization was carried out by using chem3D pro 12 [21]. The structures were then saved into desired formats (pdb/ sdf) that were used for further studies.

Preparation of protein

The protein targets AKR1B1, AKR1B10, NF-κB, caspase-3 and cellular tumor antigen P53 were taken from RCSB (PDB) protein data bank with the PDB ID: 6F7R, 4GQG, 1NFI, 2C1E and 4BUZ, respectively [22]. Targeted protein was prepared in both AutoDock [23] and Molecular Operating Environment (MOE) [24], by removing hetatoms and water molecules. Only polar hydrogens were integrated while utilizing both softwares. AutoDock was used to add Kollman charges. MOE was used to perform partial charges and 3D protonation.

Docking protocol

The most important parameter while using AutoDock was setting grid box for active site. It was prepared by using 60 × 60 × 60 dimensions of x, y and z axis, respectively with gap of 0.375 Å and population size was set to 150 with 100 no. of runs. Lamarckian genetic algorithm (LGA) was adopted for docking [25].

In MOE, dummies were created on the amino acids of active pocket. MMFF94x forcefield was loaded to estimate the forces between the atoms [26]. Triangle Matcher algorithm was chosen for docking. Runs were set up to 100. The results of Autodock were visualized in Discovery studio visualizer (version 2020) [27] while the results of MOE were visualized in the window of MOE [28].

Validation

Validation was being processed by calculating RMSD value. The conformation whose RMSD value is ≤ 2 would be considered as the best pose [29]. No other ways for validation are available [30]. Furthermore, co-crystal ligand (NAP) docking was used to evaluate and verify our findings.

Molecular dynamic simulation

For 100 nanoseconds, MD simulations were carried out for both the complexes by using the same protocol as reported earlier [31]. Protein and protein-ligand complexes were prepared and then NPT equilibration were carried out as discussed previously [31]. The complete protocol of MD simulation is given in the supplementary file.

ADMET properties

The different physicochemical properties i.e., absorption, distribution, metabolism, excretion and toxicity are collectively called ADMET properties that are calculated in order to find a compound’s drug-likeness. These all mentioned properties were analyzed as mentioned in our previous article [32]. By the assistance of web server i.e., ADMET lab 2.0, the ADMET characteristics of the quinolone derivatives were estimated [33]. The detailed information about the ADMET properties is given in the supplementary data.

Results and discussion

Density functional theory (DFT)

Molecular geometry

The optimized geometry of quinolone alkaloids which was performed by B3LYP functional methods with basis set of SVP are depicted in Fig 1.

The optimized energies along with their dipole moments and polarizability, obtained by the DFT/B3LYP/SVP method are listed in Table 1.

Table 1. Calculation of energetic parameters and quantum chemical descriptors of quinolone derivatives.

Compound	Gas phase			Solvent Phase (water)
Compound	Optimization energy (hartree)	Polarizability (α) (a.u.)	Dipole moment (Debye)	Optimization energy (hartree)	Polarizability (α) (a.u.)	Dipole moment (Debye)
Quinidine	-1035.784	236.1147	2.4307	-1035.796	318.855	3.054
Quinine	-1035.7845	236.1147	2.4307	-1035.7962	318.8558	3.054

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With reference to bond lengths and bond angles, it was discovered that the geometric structure has an impact on the electrical properties. Using DFT/B3LYP/SVP models, the most stable optimal structural parameters like bond length, bond angle, and dihedral angles were discovered.

The DFTs were used for both optimization in gas phase and solvent phase. The molecular geometry and molecular descriptors were used for the calculation of reactivity, shape and binding properties of the selected molecules.

In addition to this, the dipole moment is a global measure of the accuracy with which the electron density of a polar molecule is computed. Moreover, the dipole moments influence the molecule’s interactions with other molecules and electric fields. It allows the identification and enumeration of intermolecular interactions. Here both compounds showed maximum dipole moments and formed strong bonding and non-bonding interactions with the targeted proteins. In addition, to evaluate the accuracy of a quantum chemical approach, the links between the electronic structures of molecules were established through the electric properties of molecules.

Frontier molecular orbital (FMOs)

The many types of reactions are described and the most reactive position in conjugated systems is understood through the study of molecular orbitals and their energies. Information about a molecule’s biological and chemical activity can be found in the energies of the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and their energy gap. A narrow orbital gap indicated a highly polarizable molecule, which is typically associated with strong chemical reactivity and low kinetic stability. Currently, Quinidine and Quinine are highly polarized and chemically reactive in solvent phase as compared to gas phase. The HOMO, or outer orbital containing electrons, acts as an electron donor, and hence the ionization potential (I) is proportional to the HOMO’s energy. On either hand, LUMO is capable of accepting electrons, and its electron affinity (A) is proportional to its energy [34].

In gas phase, both the compounds Quinidine and Quinine had shown the same energy gap of 0.16 eV. On contrast in solvent phase (water), both the compounds had shown the same energy gap of 0.15 eV. They might have shown the same energy gaps because they are optical isomers of each other. HOMO-LUMO structures of compounds Quinidine and Quinine in the gas phase and solvent phase (water) are depicted in Fig 2.

Global chemical reactivity descriptors

By using energy values of HOMO LUMO, we evaluated the following parameters mentioned in Table 2 by using following equations:

Table 2. Quantum chemical descriptors of quinolone derivatives in gas phase.

Gas phase
Compound	Hardness (η)		Softness (S)	Electronegativity (X)	Chemical Potential (μ)		Electrophilicity Index (ω)
Quinidine	0.078		6.405	0.135	-0.135		0.118
Quinine	0.078		6.405	0.135	-0.135		0.118
Compound	Electrodonating power (ω ^- )			Electroaccepting power (ω ⁺ )			Net Electrophilicity (Δω ^± )
Quinidine	0.195			0.059			0.254
Quinine	0.195			0.059			0.254
Solvent phase (water)
Compound	Hardness (η)	Softness (S)		Electronegativity (X)		Chemical Potential (μ)	Electrophilicity Index (ω)
Quinidine	0.076	6.603		0.140		-0.140	0.129
Quinine	0.076	6.603		0.140		-0.140	0.129
Compound	Electrodonating power (ω ^- )			Electroaccepting power (ω ⁺ )			Net Electrophilicity (Δω ^± )
Quinidine	0.208			0.069			0.277
Quinine	0.208			0.069			0.277

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Hardness: η = 1/2(ELUMO—EHOMO); Softness: S = 1/2η; Electronegativity: χ = -1/2(ELUMO + EHOMO); Chemical potential: μ = —χ; Electrophilicity index: ω = μ/2η

The Compounds with the lowest energy gap are softest, while those with bigger gaps are hard. Here, in the case with Quinidine and Quinine, both showed the same significant values as they are the optical isomers of each other. Thus, both the compounds behave softer in solvent phase as compared to gaseous phase. The highest HOMO energy of a chemical explains why it is one of the strongest electron donors. Compounds in solvent phase with the lowest anticipated LUMO energy are the most effective electron acceptors. The HOMO and LUMO electron orbital energies are related to two parameters: ionization potential (I) and electron affinity (A). The chemical reactivity of substances varies according to their structures. On the basis of these facts it was observed that the compounds were found more reactive in solvent phase than in gas solvent due to their net electrophilicity, which is 0.277 in solvent phase.

Molecular electrostatic potential

The MEP (molecular electrostatic potential) analysis is used to understand the electrophilic and nucleophilic attacking sites of the molecules. To evaluate the distribution of electrical charge, electrostatic potential is frequently used. It is an essential tool to observe specific biological processes and hydrogen bonding interactions as well as electrophilic and nucleophilic attacks on molecules. An understanding of how various geometries interact can be gained from the surface and contours. The electron density and electrostatic potential of the aforementioned compounds are shown in Fig 3. The graphic showed that the electron density is equally dispersed throughout the molecules, according to ESP bar data, negative ESP is only localized to particular regions of a molecule. Because ESP and a molecule’s electronegativity and partial charges are related, this inference is evident. Different colours displayed in the ESP bar represented different electrostatic potential values. Red represents a region of high electrostatic potential, blue a region of high electrostatic potential, and green a zone of zero electrostatic potential [34].

The very negative potential is clearly restricted to the nitrogen and oxygen atoms of the pyrazole ring, as evidenced by this observation. These discoveries are significant for illustrating how electrophilic and nucleophilic forces act upon molecules.

Molecular docking

Molecular docking with AKR1B1

The detailed 3D and 2D binding interactions of AKR1B1 with NAP (co-crystal ligand) is shown below in Fig 4. Hydrogen bond complex was observed by oxygen of trimethyl phosphate, oxygen of benzamide, hydroxyl group of dimethyl tetrahydrofuran-3, 4-diol of NAP with amino acid residue Lys262. One of the hydrogen bond interactions was formed between oxygen of methyl dihydrogen phosphate of NAP and Lys21. Oxygen of trimethyl tetrahydrofuran-3-ol of NAP exhibited hydrogen bonding with Arg268. Hydrogen bond interaction was also formed between hydroxyl group of dimethyl tetrahydrofuran-3, 4-diol of NAP and Arg217. Strong hydrophobic interaction (Pi-sigma) was seen between Pyrimidin-4-amine group of NAP and Leu228. Electrostatic interaction (Pi-cation) was observed by Phosphorous of methyl dihydrogen phosphate of NAP with Asp216. Detailed binding energies of quinolone derivatives with both targets AKR1B1 and AKR1B10 are shown in Table 3.

Table 3. Binding energies of quinolone derivatives with target AKR1B1 and AKR1B10 (kJ/mol).

	Code	AKR1B1		AKR1B10
	Code	MOE	AutoDock	MOE	AutoDock
1.	Quinine	-29.04	-28.56	-31.02	-35.84
2.	Quinidine	-31.08	-38.55	-29.2	-31.04
Co-crystal	NAP	-29.48	-22.92	-27.44	-18.16

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The detailed 3D and 2D binding interactions of AKR1B1 with Quinidine is shown below in Fig 5. The amino acid residues of active site which were involved in hydrogen bonding with quinidine included; Asn272 and Asn216. The hydrogen bonding is of significant importance in protein-ligand interaction to check the inhibitory action. One of the hydrogen bond was created by oxygen of 6-methoxyquinoline of quinidine with Asn272. Other hydrogen bond was formed by hydrogen of 1-(quinolin-4-yl)ethanol of quinidine with Asn216. No other strong Pi-Pi and Pi-cation interactions were observed.

The detailed 3D and 2D binding interactions of AKR1B1 with Quinine is shown in Fig 6. After analyzing the binding interactions, two carbon hydrogen bonds and one Pi-cation interaction were found. The amino acid residues of active site which were involved in carbon hydrogen bond with quinine included; Asn272 and Arg268. One carbon hydrogen bond was formed by pyridine ring of quinidine with Asn272. One carbon hydrogen bond was formed by oxygen of 6-methoxyquinoline with Arg268. One Pi-cation interaction was formed by anisole ring of 6-methoxyquinoline with Arg268. No other strong Pi-Pi interactions were observed.

Molecular docking with AKR1B10

The detailed 2D and 3D binding interactions of AKR1B10 with NAP (co-crystal ligand) is shown above in Fig 7. Hydrogen bond complex was observed between hydrogen of pyrimidine-4-amine of NAP and Pro216. Hydrogen bonding was also observed between oxygen of acetamide of NAP and Lys22. Another hydrogen bond complex was formed by hydroxyl group of dimethyl hydrogen phosphate of NAP with Val265.

The detailed 3D and 2D binding interactions of AKR1B10 with Quinidine is shown in Fig 8. The amino acid residue of active site which was involved in hydrogen bonding with quinidine included; Lys22. This hydrogen bonding is of chief importance in protein-ligand interaction. Observing the binding interactions predicted only one hydrogen bond. This one hydrogen bond was formed by oxygen 6-methoxyquinoline of quinidine with Lys22. Pi-Pi stacked/T-shaped interaction was formed by Tyr210 and Tyr49 amino acids with 6-methoxyquinoline of quinidine. No Pi-cation interaction was shown with quinidine.

The detailed 3D and 2D binding interactions of AKR1B10 with Quinine is shown in Fig 9. The amino acid residues of active site which were involved in hydrogen bonding with quinine included; Lys22, Ser211 and Cys299. This hydrogen bonding is of chief importance in protein-ligand interaction. Analyzing the binding interactions showed three hydrogen bonds. One hydrogen bond was formed by oxygen of 6-methoxyquinoline of quinine with Lys22. Ser211 amino acid formed hydrogen bond with hydrogen of 1-(quinoline-4yl)ethanol of quinine. The third hydrogen bond formed between Cys299 and oxygen of 1-(quinoline-4yl)ethanol of quinine. Pi-Pi stacked/T-shaped interaction was formed by Tyr210 and Tyr49 amino acids with 2,3-dihydropyridine of quinine. No Pi-cation interaction was shown with quinine.

Conclusively, the above detailed results of molecular docking has been shown that binding affinities for the selected compounds (quinine and quinidine) are better (lowest) than that of the co-crystal ligand (NAP). Moreover, scattering of docking scores was seen when repeated docking runs were performed. Under the identical docking techniques, the docking programme provided consistently variable docking results. Massive and bulkier binding sites typically exhibit a high degree of dispersal in terms of docking poses and scores, as there are more conformational positions for the ligand. Thus, AutoDock was determined to be the better software of the two due to its lower dispersion when compared to MOE over repeated docking protocols.

Structure activity relationship of quinolone derivatives

The structure activity relationship of these quinolone derivatives (quinine and quinidine) was studied on the basis of hydrogen bond interactions observed during molecular docking. Against AKR1B1, quinidine was found to be the most potent inhibitor with two hydrogen bonds formation (binding energy = -38.55 kJ/mol). One of the hydrogen bond was formed due to the methoxy substitution at position-6 of quinoline ring of quinidine. Another hydrogen bond formation was observed by ethanol substitution at position-4 of quinoline ring of quinidine. In case of AKR1B1, quinine didn’t form conventional hydrogen bond within the active pocket’s amino acid residues of AKR1B1.

Against AKR1B10, quinine was found to be the most potent inhibitor with three hydrogen bonds formation (binding energy = -35.84 kJ/mol). One of the hydrogen bond was formed due to the methoxy substitution at position-6 of quinoline ring of quinine. Other two hydrogen bonds formation were observed by ethanol substitution at position-4 of quinoline ring of quinine.

However, in case of quinidine one hydrogen bond was formed due to the methoxy substitution at position-6 of quinoline ring within the active pocket’s amino acid residues of AKR1B10.

Molecular docking with nuclear factor kappa B (NF-κB)

In order to authenticate the anti-cancer potential of quinolone alkaloids, molecular docking is expanded with following targets i.e., nuclear factor kappa B (NF-κB), cellular tumor antigen P35 and caspase-3.

The detailed 3D and 2D interactions are shown in Fig 10 below. The amino acid residues of the active pocket of NF-κB are; Arg302, Lys310, Tyr306, His84, Met313, Arg171, Glu86, Ala88 and Lys87. Quinine had shown the minimum binding energy of -25.08 kJ/. The amino acid residue Arg171 was observed to form one hydrogen bond interaction with amine group of quinine whileMet313 and Phe309 were involved in van der Waals interactions.

Detailed binding interactions of quinidine with NF-κB are shown in Fig 11 below. Quinidine had shown the binding energy of -23.60 kJ/mol. One hydrogen bond interaction was formed between amino acid residue Arg302 and amine group of quinidine. Met313 and Phe309 were involved in van der Waals interaction.

Molecular docking with cellular tumor antigen P53

The amino acid residues of the active pocket of cellular tumor antigen P53 are; Leu215, Asp231, Thr26, Pro27, Gly188, Val232, Asp32 and Asn214. Detailed binding interactions of quinidine with cellular tumor antigen P53 are shown in Fig 12 below. Quinidine had shown the minimum binding energy of -20.68 kJ/mol and interacted with two hydrogen bond. Amino acid residue Ala22 was observed in formation of hydrogen bond interaction with amine group of quinidine. Another hydrogen bond interaction was formed between amino acid residue Ser190 and hydroxyl group of quinidine. Phe162, Val193, Leu191, Leu215 and Phe33 were involved in van der Waals interaction.

Detailed binding interactions of quinine with cellular tumor antigen P53 are shown in Fig 13 below. Quinine had shown the binding energy of -21.80 kJ/mol. Pro31, Ala22, Phe33, Phe161, Ile30, Ile100, Val160, Val193, Met71 and Phe162 were involved in van der Waals interaction. Amino acid residues Asp32 and Asp101 were the acidic groups encircled with red colour.

Molecular docking with caspase-3

Fig 14 depicts the detailed 3D and 2D interactions. The amino acid residues of the active pocket of caspase 3 are; Trp206, Ser205, Phe256, His121, Tyr204 and Cys163. Quinine had shown the minimum binding energy of -18.96 kJ/mol and showed one hydrogen bond interaction. Amino acid residue Arg207 was observed to form one hydrogen bond interaction with hydroxyl group of quinine. Phe256 and Phe250 were involved in van der Waals interactions.

Detailed binding interactions of quinidine with caspase-3 are shown in Fig 15 below. Quinidine had shown the binding energy of -20.28 kJ/mol. Amino acid residue Met61 was observed in formation of hydrogen bond interaction with amine group of quinidine. One Pi-aryl interaction was formed by amino acid residue His121 with quinidine. Trp206 and Phe128 were involved in van der Waals interaction.

MD simulations

To calculate the average change in displacement of atoms in regard to a reference frame, Root Mean Square Deviation (RMSD) was used. The value was computed for each and every trajectories frame individually. MD trajectory analysis was also used to determine the root mean square fluctuation (RMSF), and protein–ligand interactions, among other parameters. RMSD for proteins was found as follows: The graphs depicted that how the relative mean squared deviation (RMSD) of a protein was varied over time (left Y-axis). Following the alignment of all protein frames on the reference frame backbone, the RMSD was computed based on atom selection and is shown on the screen.

During the simulation, it is possible to determine the protein’s structure by measuring its relative mean square deviation (RMSD). It is possible to determine if the final variations of the simulation are centered around some type of thermal average structure by doing an RMSD analysis on the data. When it comes to proteins that are both tiny and globular in structure, changes on the order of 1–3 are perfectly suitable. Larger differences, on the other hand, indicate that the protein is experiencing a significant structural change during the simulation. MD simulations that are run for a prolonged period of time reveal considerable structural deviation in the targeted system. Furthermore, it is necessary for attaining equilibrium in RMSD patterns of simulated trajectories. Ligand RMSD is represented on the right Y-axis. The RMSD ligand (right Y-axis) showed the ligand’s stability with respect to the protein and its binding pocket.

The RMSD of ligand heavy atoms was computed and displayed in this manner after a protein-ligand combination is line up on the reference protein backbone, as shown in the Fig 16. In situations where reported values are much greater than the protein’s standard deviation, it is possible to assume that the ligand has dispersed away from its initial binding site, which is consistent with previous findings.

Fig 16 illustrates the evolution of the RMSD for the apoprotein C-alpha atoms over time. The RMSD plot of the AKR1B1-Quinine, AKR1B1-Quinidine; AKR1B10-Quinine, AKR1B10-Quinidine indicates variation but get stability. The RMSD fluctuations for the goal remain within 2.0 throughout simulation, which is acceptable but in case of quinidine it fluctuates above 2.0. Between 50 and 60 ns, its root mean square deviation was slightly larger. It stabilized around 60 ns. The simulation results indicate that the ligands were tightly bound to the receptor’s binding site. Root Mean Square Fluctuation (RMSF) of the C-alpha atoms of AKR1B1-Quinine, Quinidine; AKR1B10-Quinine, Quinidine complexes with time are shown in Fig 17. Low RMSF values of the binding site residues demonstrate the stability of ligand binding to the protein. The RMSF of both the ligands revealed some oscillations which demonstrated their dynamical shift at their binding domain in respective proteins. The atoms of AKR1B10-Quinine and AKR1B1-Quinidine displayed more oscillations.

The alpha helices and beta strands, which are secondary structural elements, are monitored throughout the simulations (SSE). The distribution of SSE by residue index throughout the protein structure is shown in the above graph. The bottom plot displays the SSE assignment for each residue over time and is presented in Figs 18 and 19. The top image shows the SSE composition for each trajectory frame during simulation.

Fig 18 — The colours red and blue represent alpha helices and beta strands, respectively.

Fig 19 — The colours red and blue represent alpha helices and beta strands, respectively.

ADMET properties

LogP is used to determine a compound’s hydrophilicity; if the LogP value is negative, the compound is hydrophilic. In this research, compounds are lipophilic as they have positive value of LogP. Reduced hydrophilicity (higher LogP values) results in insufficient solubility and absorption. The LogS number indicates solubility: the lower the LogS value, the greater the solubility, which increases absorption. For drugs with CNS activity, the optimal lipophilicity for blood–brain barrier penetration is a LogD≤2. A LogD greater than 4 is considered inappropriate for a central nervous system (CNS) medication. Calculation of the topological polar surface area (TPSA) for the purpose of forecasting the oral absorption of drug-like compounds. A greater TPSA value indicates decreased membrane permeability. Thus, a lower TPSA level was acceptable for drug-likeness. The TPSA value should be low for optimal CNS diffusion. A derivative is deemed to be adequately bioavailable if it has a TPSA of 70. The findings indicated better TPSA values. The number of hydrogen bond donors (nHD) is equal to the total of all OHs and NHs, whereas the number of hydrogen bond acceptors (nHA) is equal to the sum of all nitrogen and oxygen atoms with no positive charge. nHA 0–12 and nHD 0–7 are the optimal ranges (Table 4).

Table 4. Physicochemical properties of quinolone derivatives.

PHYSICOCHEMICAL PROPERTIES
	Molecular Weight	Density	nHA	nHD	TPSA	LogS	LogP	LogD
Quinidine	324.18	0.942	4	1	45.59	-2.454	2.996	2.796
Quinine	324.18	0.942	4	1	45.59	-2.852	2.871	2.767

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In terms of absorption and distribution, a high HIA value indicates that the substance will be absorbed more readily from the gastrointestinal system. According to Table 5, derivatives rapidly penetrate the intestinal membrane, increasing the blood plasma concentration. The calculated BBB value indicated that the majority of substances easily pass across the BBB barrier due to their natural lipophilicity. 90% plasma protein binding should be achieved with drugs. It has a poor therapeutic index at higher concentrations. Our compounds demonstrated increased binding to plasma proteins. Almost all substances were found to be both substrate and inhibitor when the efflux via P-glycoprotein (P-gp) was calculated. The volume of distribution (Vd) of a medicine refers to the ratio of its concentration in plasma to its total amount in the body. 0.04-20L/kg is the optimal range. All derivatives chosen are within this ideal range. In vitro human intestinal permeability is calculated using the Caco-2 cell monolayer model as a surrogate. Caco-2 permeability is optimal when it is more than -5.15 log units.

Table 5. ADMET properties of the quinolone derivatives.

ABSORPTION & DISTRIBUTION PROPERTIES
	VOLUME OF DISTRIBUTION (VD)	HUMAN INTESTINAL ABSORPTION (HIA)	CACO-2 PERMEABILITY	BLOOD BRAIN BARRIER (BBB) & BLOOD-PLACENTA BARRIER (BPB)	PLASMA PROTEIN BINDING (PPB)	PGP-INHIBITOR	P-GLYCOPROTEIN SUBSTRATE (PGP-SUBSTRATE)	MDCK PERMEABILITY
Quinidine	2.595	0.00	-4.738	0.573	76.28%	0.998	0.9	1.5e-05
Quinine	2.253	0.01	-4.727	0.923	80.73%	0.999	0.98	1.8e-05
METABOLISM							EXCRETION
	CYP1A2 inhibitor	CYP2C19 Inhibitor	CYP2C9 inhibitor	CYP2D6 inhibitor	CYP3A4 inhibitor		CL	T1/2
Quinidine	0.127	0.082	0.021	0.97	0.19		2.74	0.299
Quinine	0.244	0.088	0.028	0.973	0.32		2.569	0.24
MEDICINAL PROPERTIES			TOXICITY
	Synthetic Accessibility Score	Lipinski Rule	AMES Toxicity	Carcinogenicity	Eye Corrosion	Eye Irritation	Respiratory Toxicity
Quinidine	4.406	Accepted	0.226	0.322	0.003	0.059	0.95
Quinine	4.406	Accepted	0.318	0.616	0.003	0.067	0.954
TOX21 PATHWAY
	NR-AR		NR-AR-LBD		NR-ER		Antioxidant Response Element
Quinidine	0.399		0.005		0.462		0.137
Quinine	0.781		0.02		0.633		0.183

Open in a new tab

MDCK cells are employed to investigate drug efflux and active transport, most commonly via P-glycoprotein efflux. All of our derivatives have a low to moderate MDCK permeability. <2 has a low permeability, 2–20 has a medium permeability, and >20 has a high permeability. There are three levels of drug clearance rates: high (>15), moderate (5–15), and low (less than 5). When it comes to therapeutic characteristics and toxicity, the AMES toxicity test is used to determine whether or not a chemical is mutagenic. None of the derivatives are carcinogenic. The ease with which drug-like molecules can be produced is measured using the synthetic accessibility score (SAscore). SAscore 6 is straightforward to synthesize. All of our derivatives were rather straightforward to produce. Additionally, the toxicity profile of the compounds was investigated. According to toxicity risk assessment, the proposed chemical has a lower toxicity profile. According to the projected results, the compounds were not corrosive or irritating to the eyes. They had a lower carcinogenic and respiratory toxicity profile. Analyzing NR-AR predicts whether a derivative stimulates or inactivates the androgen receptor. Each of our compounds acted as an activator. By analyzing NR-AR-LBD, we may anticipate whether or not the androgen receptor ligand-binding domain is activated. Each of our compounds acted as an activator. Analyzing NR-ER provides insight into whether the estrogen receptor is activated or inactivated. All of our compounds were designed to behave as activators. The antioxidant response element is referred to as SR-ARE. All of our derivatives were SR-ARE activators.

Conclusion

This is the first work to describe quinolone alkaloids as drug candidates for the AKR1B1 and AKR1B10 receptors, which was done using a blend of virtual screening and rationalized insights into the reactivity and stability of complexes gained using rigorous in silico techniques. DFT results showed that they perform substantially better and have reactive characteristic. The docking data from both softwares indicated that our compounds had higher binding energies than the co-crystal ligand (NAP). Additionally, ADMET characteristics indicated that the chosen compounds possessed drug-like effects.

Quinine was shown to be an effective inhibitor of AKR1B10, whilst Quinidine was found to be a more strong inhibitor of AKR1B1. The stability and dynamics of the complexes (Quinine and Quinidine) with AKR1B1 and AKR1B10 in the aqueous medium were ultimately confirmed by the outcomes of MD simulations. With the help of these powerful aldo-keto reductase inhibitors, it may be possible to synthesize potential new pharmaceuticals that cure colon cancers associated with aberrant expression of the AKR1B1 or AKR1B10 protein.

Supporting information

S1 Fig. Protein-ligand (AKR1B1-Quinine, Quinidine; AKR1B10-Quinine, Quinidine) contact histogram (H-bonds, hydrophobic, ionic, water bridges).

(TIF)

Click here for additional data file.^{(459.1KB, tif)}

S2 Fig. Ligand atom interactions with the protein residues.

(AKR1B1-Quinine, Quinidine; AKR1B10-Quinine, Quinidine).

(TIF)

Click here for additional data file.^{(1.5MB, tif)}

S3 Fig. Ligand properties.

(Quinine, Quinidine ligand).

(TIF)

Click here for additional data file.^{(2.6MB, tif)}

S4 Fig. Ligand torsion profile (Quinidine).

(TIF)

Click here for additional data file.^{(1.1MB, tif)}

S5 Fig. Ligand torsion profile (Quinine).

(TIF)

Click here for additional data file.^{(1.1MB, tif)}

Acknowledgments

The author Dr. Khadijah Mohammedsalaeh Katubi is thankful to Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, for facilitating her in research.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

Princess Nourah bint Abdulrahman University Researchers Supporting Project (Grant No. PNURSP2022R26).

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PLoS One. doi: 10.1371/journal.pone.0271602.r001

Decision Letter 0

Joazaizulfazli Jamalis

6 May 2022

PONE-D-22-10464In-silico Investigations of Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and Structural CharacterizationPLOS ONE

Dear Dr. Ejaz,

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: No

Reviewer #4: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: No

Reviewer #4: N/A

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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Reviewer #4: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Referee Report

• Abstract section is very weak and this section should be more specific.

• Why B3LYP/SVP can you explain?

• Introduction section, FMO, MEP, ADMET, drug-likeness of the title molecule should be expanded with new references such as

1- Synthesis, spectroscopic characterization, DFT, molecular docking and in vitro antibacterial potential of novel quinoline derivatives, Journal of Molecular Structure, Volume 1246, 15 December 2021, 131217

2- Quantum computational, Spectroscopic Investigations on N-(2-((2-chloro-4,5-dicyanophenyl)amino)ethyl)-4-methylbenzenesulfonamide by DFT/TD-DFT with Different Solvents, Molecular Docking and Drug-Likeness Researches, Colloids and Surfaces A Physicochemical and Engineering Aspects 638:128311

3- New Heterocyclic Compound as Carbon Steel Corrosion Inhibitor in 1 M H2SO4, High Efficiency at Low Concentration: Experimental and Theoretical Studies https://doi.org/10.1080/01694243.2022.2034588

• The resolutions of the all figures should be increased.

• I can not see the label of Fig. 1 should be corrected.

• Figure 2 should be transparent form and the labels of atoms should be added.

• Figure 3 should be revised.

• Figure 4-9 should be revised very complex.

• The resolutions of the figures should be increased.

• Why these receptors were select for the molecular docking should be clarified.

• Why both Autodock and MOE are needed for calculations?

• If possible experimental activity should be added.

• Conclusion section should be revised.

MINOR REVISION

Reviewer #2: The paper is written in a style that is not typical of well conceived scientific literature. The main drawback of the paper is that in general there are many values calculated, but no discussion of what they mean and how they compare to other materials. Therefore the reader must find a meaning by him/herself of the computed quantities. On the other hand, many times there are explanations about the physical and chemical meanings of some quantities, which are well known in the literature and should not be explained again. A deep revision of the style of the paper is needed.

Some required minor changes are reported in the following:

- Revise the first sentence of the introduction

- Molecular Dynamics simulations: "For 100 nanoseconds, MD simulations were carried out for both the complexes by using the same

protocol as reported earlier". Add a reference

- remove the word "showing" from the legends of the figures

- revise the first sentence after the caption of Figure 1

- In the sentence just before Table 1, which symmetry constraints are used?

- Table 1 seems to indicate that calculations in the solvent phase are performed in water, but the fourth sentence after Table 1 seems to indicate they are performed in methanol.

- Frontier molecular orbitals: "In gas phase, both the compounds Quinidine and Quinine had shown the same energy gap of 0.15 eV. On contrast in solvent phase (water), again both the compounds had shown the same energy gap of 0.15 eV." There is no contrast.

- Third line before Molecular electrostatic potential: was -> were

- MD simulations Line 9: "the protein's structural structure" revise

- rephrase the part "It is critical for your simulation to achieve a point of convergence, as well as for your residual standard deviation (RMSD) to settle at a constant level. It's likely that your system hasn't achieved equilibrium yet, and that your simulation hasn't run for long enough to offer a thorough assessment of the protein's relative molecular weight distribution"

- "Between 50 and 60 Angstroms, its root mean square deviation was slightly larger". Are the authors sure about the units?"

- Revise the first sentence of conclusions

- Rephrase the sentences "To be truthful, it is not viable to repeat computations for the same ligands because the software is quite lengthy. That is why we calculated the RMSD value using the best poses. Additionally, to validate and verify our findings, co-crystal ligand (NAP) was docked alongside ligands."

- The conclusions are not conclusions in the sense that they do not show the main results of the study.

- Quinidine and quinine in Figure 1 look identical. Please, point out the difference.

- Figure S3: labels and numbers superimpose

- Figure S4: label, numbers and plots superimpose

Reviewer #3: This article contains information and investigation of Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10. It is not determined inconsistency for dual publication, research ethics, and publication ethics. I think that this manuscript is appropriate to publish in the PLOS ONE.

Reviewer #4: Dear Authors,

I have reviewed your manuscript, and I am expressing my positive feedback. Your study is interesting for the readers of the PLOS One journal, and the obtained results are promising. However, there is space for improvements, so I am requesting revisions according to the following comments:

• You should elaborate why did you use SVP basis set, when the def2-TZVP is mostly recommended? Your molecular are not large and the def2-TZVP basis set should have been used.

• Why the acronym of density functional theory is “DFTs”?

• In the chapter dealing with the global reactivity properties, you have only provided numerical values of the quantities, without proper scientific discussion. Also, that chapter doesn’t contain equations how these descriptors have been calculated.

• Please elaborate why you have performed calculations in both gas and solvent phases?

• Regarding your calculations, also please insert the information about the convergence criteria for SCF and optimization procedures. If you used the default settings, it is enough to mention that the default settings have been used, but in that case please mention which revision of the Gaussian program was used for calculations.

Once you address all of the above-mentioned comments, I will gladly review your manuscript again.

Best regards

**********

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Reviewer #1: Yes: Yusuf Sert

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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PLoS One. 2022 Oct 27;17(10):e0271602. doi: 10.1371/journal.pone.0271602.r002

Author response to Decision Letter 0

16 May 2022

Reviewer #1: Referee Report

• Abstract section is very weak and this section should be more specific.

Response: The abstract has been revised, as suggested

• Why B3LYP/SVP can you explain?

Response: B3lyp is a functional, which includes exact exchange and GGA corrections in addition to LDA electron-electron and electron-nuclei energy. The weights of the parts were fit to reproduce geometry of a test suite of small molecules. As such use of b3lyp for calculations with heavier atoms is questionable. B3LYP is generally faster than most Post Hartree-Fock techniques and usually yields comparable results. It is also fairly robust for a DFT method. On a more fundamental level, it is not as heavily parameterized as other hybrid functionals, having only 3 where as some have up to 26. Becke's original paper is one of the most cited papers (I think it's number 8) of all time, so B3LYP is well established in the literature and people are less likely to complain about your choice versus a newer functional.

• Introduction section, FMO, MEP, ADMET, drug-likeness of the title molecule should be expanded with new references such as

3- New Heterocyclic Compound as Carbon Steel Corrosion Inhibitor in 1 M H2SO4, High Efficiency at Low Concentration: Experimental and Theoretical Studies https://doi.org/10.1080/01694243.2022.2034588

Response: These references have been incorporated in the revised manuscript

• The resolutions of the all figures should be increased.

Response: The resolution of all figures has been increased, as suggested

• I can not see the label of Fig. 1 should be corrected.

Response: Figure 1 has been revised

• Figure 2 should be transparent form and the labels of atoms should be added.

Response: Figure 2 has been revised

• Figure 3 should be revised.

Response: Figure 3 has been revised

• Figure 4-9 should be revised very complex.

Response: Figures have been revised

• The resolutions of the figures should be increased.

Response: The resolution of all figures has been increased, as suggested

• Why these receptors were select for the molecular docking should be clarified.

Response: As our work aims to identify potent aldo-keto reductase inhibitors that would help to treat the colon cancer, in which the higher levels of AKR1B1 is observed [1]. AKR1B10 was selected just to confirm whether the inhibitor is selective for AKR1B1 or not.

Reference: [1] Taskoparan B, Seza EG, Demirkol S, Tuncer S, Stefek M, Gure AO, Banerjee S, Opposing roles of the aldo-keto reductases AKR1B1 and AKR1B10 in colorectal cancer. Cellular Oncology, 2017:40(6):563-578.

• Why both Autodock and MOE are needed for calculations?

Response: To check the reliability of both softwares. Thus, AutoDock was proved to be the best among the two softwares because it showed less dispersion as compared to MOE on repeated docking protocols.

• If possible experimental activity should be added.

Response: Yes the reviewer is right that computational work required experimental evidence. In our case based on available resources, we have provided the extensive computational work.We have already planned for our upcoming publication and both enzymes are already in purchase demand. All the authors are thankful to the reviewer in advance for considering this study based on computational data. Hope the reviewer will understand our situation.

• Conclusion section should be revised.

Response: Conclusion has been revised

MINOR REVISION

Some required minor changes are reported in the following:

- Revise the first sentence of the introduction

Response: This sentence has been revised

- Molecular Dynamics simulations: "For 100 nanoseconds, MD simulations were carried out for both the complexes by using the same protocol as reported earlier". Add a reference

Response: Thank You for suggesting, the reference was already added but not cited properly. Now it has been corrected

- remove the word "showing" from the legends of the figures

Response: Corrected as suggested

- revise the first sentence after the caption of Figure 1

Response: This sentence has been revised

- In the sentence just before Table 1, which symmetry constraints are used?

Response: It was a typographical mistake, no symmetry constraints were applied

- Table 1 seems to indicate that calculations in the solvent phase are performed in water, but the fourth sentence after Table 1 seems to indicate they are performed in methanol.

Response: It was a typographical mistake which is corrected in revised manuscript. Water was used as a solvent

Response: It was a typographical mistake and has been corrected in revised version

- Third line before Molecular electrostatic potential: was -> were

Response: Corrected as suggested

- MD simulations Line 9: "the protein's structural structure" revise

Response: Corrected

Response: Suggested part has been rephrased in the revised manuscript

- "Between 50 and 60 Angstroms, its root mean square deviation was slightly larger". Are the authors sure about the units?"

Response: You pointed out right, it is a typo mistake because MD trajectory showed slightly higher RMSD pattern during 50 to 60 nanoseconds of simulation time but mistakenly it was written as 50-60 angstrom.

- Revise the first sentence of conclusions

Response: This has been revised

Response: It has been rephrased in revised manuscript

- The conclusions are not conclusions in the sense that they do not show the main results of the study.

Response: The conclusion has been revised, as suggested

- Quinidine and quinine in Figure 1 look identical. Please, point out the difference.

Response: You are right, they look alike in optimized structures because they are isomers.

- Figure S3: labels and numbers superimpose

Response: Figure S3 has been revised

- Figure S4: label, numbers and plots superimpose

Response: Figure S4 has been revised

Reviewer #3:

This article contains information and investigation of Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10. It is not determined inconsistency for dual publication, research ethics, and publication ethics. I think that this manuscript is appropriate to publish in the PLOS ONE.

Reviewer #4: Dear Authors,

• You should elaborate why did you use SVP basis set, when the def2-TZVP is mostly recommended? Your molecular are not large and the def2-TZVP basis set should have been used.

Response: You are correct, def2-TZVP is the most valuable functional but we donot have enough computational resources to test this. For saving the time and computational cost we recommended small SVP basis set.

• Why the acronym of density functional theory is “DFTs”?

Response: This is a mistake; the correct acronym is DFT and replaced/corrected in the updated paper.

Response: Thank you for highlighting this part. We have added the details for these parameters in the revised manuscript.

• Please elaborate why you have performed calculations in both gas and solvent phases?

Response: The minima corresponding to the gas-phase and solution (PCM model) are definitely not the same. If you're a purist, you should redo all the calculations using the PCM model to get new minima (checked by frequency calculations) and treat the gas-phase calculations just as a good starting point for PCM ones. Although structural differences are typically very small, they will have an effect on energies. The investigated compounds are biological compounds that mostly work according to the body's environment. Usually energy of stabilization/equilibrium change by using the gas phase and solution phase. So, we used both the gas phase and solvent environment so that we know about these parameters.

Response: The Gaussian 09 package (Rev.E.01) with default settings was used for all calculations.

Attachment

Submitted filename: Reviewer report.doc

Click here for additional data file.^{(43.5KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0271602.r003

Decision Letter 1

Joazaizulfazli Jamalis

14 Jun 2022

PONE-D-22-10464R1In-silico Investigations of Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and Structural CharacterizationPLOS ONE

Dear Dr. Ejaz,

Please submit your revised manuscript by Jul 29 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Joazaizulfazli Jamalis

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #5: All comments have been addressed

Reviewer #6: (No Response)

Reviewer #7: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #5: Yes

Reviewer #6: Partly

Reviewer #7: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #5: N/A

Reviewer #6: N/A

Reviewer #7: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #5: Yes

Reviewer #6: No

Reviewer #7: No

**********

6. Review Comments to the Author

Reviewer #5: The manuscript is concise, informative, and well written. The authors have thoroughly shed light on the Quinine and Quinidine as potential inhibitors of AKR1B1 and AKR1B10. All the suggestions of the reviewers are appropriately incorporated. Findings of the study favour Quinidine as a better inhibitor of AKR1B1 and Quinine as a plausible inhibitor of AKR1B10.The article is helpful in the concerned field of pharmacopoeia and rational drug design. Therefore, it is recommendable for publication in the journal. However, authors must check once again for typos and grammar mistakes throughout the manuscript.

Reviewer #6: The reviewer comments are attached . Author needs to work on the pointed areas by giving suitable justifications and explanations in the manuscript.

Reviewer #7: Dear editor,

Have a nice day. The authors carried out some In-silico tests for Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10.

The manuscript lacks novelty in some points

1- That the bases to select Quinine and Quinidine as a potential target for AKR1B1 and AKR1B10? Are these compounds have some sort of similarity with the previously reported AKR1B1 and AKR1B10 inhibitors?? If this is the case, confirm the structural similarity.

2- The binding modes of Quinine and Quinidine need more clarification. Compare the binding mode of Quinine and Quinidine with that of co-crystallized ligand.

3- In vitro cytotoxicity of Quinine and Quinidine against cancer cell line is necessary.

4- In vitro enzymatic inhibition is necessary.

**********

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Reviewer #5: Yes: Dr. Mohammad Kalim Ahmad Khan

Reviewer #6: No

Reviewer #7: No

**********

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PLoS One. 2022 Oct 27;17(10):e0271602. doi: 10.1371/journal.pone.0271602.r004

Author response to Decision Letter 1

27 Jun 2022

Response: First of all Thanks to the reviewer for considering the research article. Now all the typos and grammar mistakes have been corrected in the revised manuscript

Reviewer #6: The reviewer comments are attached. Author needs to work on the pointed areas by giving suitable justifications and explanations in the manuscript.

1. The abstract has been revised well. The very first line does not convey the message. Rephrase.

Response: The abstract has been revised as suggested

2. In results and discussion section, the author has given the following paragraph: In gas phase, both the compounds Quinidine and Quinine had shown the same energy gap of 0.15 eV…………. . But have not fully justified the meaning of values or their impact on the reactivity of compounds as inhibitors for the targeted protein. Moreover, I do not find enough explanation in terms of DFT study which indeed is quite powerful tool for giving reliable predictions about structure reactivity pattern besides many other chemical features. In a nutshell, this section seems more like a general theory for DFT rather than case study details and explanations. There is a lot room for improvement in this study.

Response: The required justifications and corrections have be incorporated in the revised manuscript as suggested.

3. The manuscript needs revision in terms of English language and grammar like e.g. In results and discussion section: “Here, is the case with Quinidine and Quinine shows the same significant values as they are the optical isomers of each other.”

In introduction section: “From this study, may the quinolone derivatives be proved lead compounds for the treatment of colon cancer by inhibiting aldo-keto reductase (17).”

Response: All the grammatical errors have been removed from the revised manuscript

4. There is a lack in connection between the generalization for Global chemical reactivity descriptors values and the values obtained for the test molecules with ultimate effect on reactivity with the targeted proteins. Moreover, authors would have considered predicting the SAR for the inhibitor molecules based on different parameters studied by DFT. A lot much can be improved in DFT section. It seems authors are unable to fully explain/justify the results systematically besides having calculation of values for various useful parameters.

Response: It has been revised, as suggested.

5. Authors state in molecular docking section as:” The hydrogen bonding is significant in protein ligand interaction to check the inhibitory action.” That is true but not a universal rule indeed for docked ligands. To rationalize this statement authors, need to justify in their case by giving suitable explanation in terms of structure activity relationship.

Response: The structure activity relationship has been incorporated in the revised manuscript as suggested.

6. Incomplete explanation of the following statement: “LogP is used to determine a compound's hydrophilicity; if the LogP value is negative, the compound is hydrophilic. Compounds are lipophilic.” Which value type indicates lipophilic behavior, not explained.

Response: It has been explained in the revised manuscript

Reviewer #7: Dear editor,

Have a nice day. The authors carried out some In-silico tests for Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10.

The manuscript lacks novelty in some points

Response: The detailed rationale has been incorporated in the revised manuscript as suggested

2- The binding modes of Quinine and Quinidine need more clarification. Compare the binding mode of Quinine and Quinidine with that of co-crystallized ligand.

Response: The suggestion has been addressed in the revised manuscript

3- In vitro cytotoxicity of Quinine and Quinidine against cancer cell line is necessary.

Response: Yes the reviewer is right that computational work required experimental evidence. Our studies are based on available resources. We have put the purchase demand of targeted enzymes and cell lines, and we will consider it in our upcoming publications. Hope the reviewer will understand our situation and accept this appology. However, a detailed insilico studies against cancer markers have been added to support our results.

4- In vitro enzymatic inhibition is necessary.

Response: The reviewer gave a very fruitful suggestion and we agree that without in-vitro justification out results are partially validated. We have provided more in-silico justifications that our compounds have strong anticancer potential. We have put purchase demand of our targeted Enzymes and they are in process. This will take time and at this stage, authors of this study are thankful in advance to the reviewer to consider our justifications of this point. We are working on more compounds and we will incorporate in-vitro studies in our upcoming publication along with the justification of current compounds.

Attachment

Submitted filename: PONE-Reviewer Comments-7th June 2022.docx

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PLoS One. doi: 10.1371/journal.pone.0271602.r005

Decision Letter 2

Joazaizulfazli Jamalis

4 Jul 2022

In-silico Investigations of Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and Structural Characterization

PONE-D-22-10464R2

Dear Dr. Ejaz,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Joazaizulfazli Jamalis

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0271602.r006

Acceptance letter

Joazaizulfazli Jamalis

7 Oct 2022

PONE-D-22-10464R2

In-silico Investigations of Quinine and Quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and Structural Characterization

Dear Dr. Ejaz:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Joazaizulfazli Jamalis

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Protein-ligand (AKR1B1-Quinine, Quinidine; AKR1B10-Quinine, Quinidine) contact histogram (H-bonds, hydrophobic, ionic, water bridges).

(TIF)

Click here for additional data file.^{(459.1KB, tif)}

S2 Fig. Ligand atom interactions with the protein residues.

(AKR1B1-Quinine, Quinidine; AKR1B10-Quinine, Quinidine).

(TIF)

Click here for additional data file.^{(1.5MB, tif)}

S3 Fig. Ligand properties.

(Quinine, Quinidine ligand).

(TIF)

Click here for additional data file.^{(2.6MB, tif)}

S4 Fig. Ligand torsion profile (Quinidine).

(TIF)

Click here for additional data file.^{(1.1MB, tif)}

S5 Fig. Ligand torsion profile (Quinine).

(TIF)

Click here for additional data file.^{(1.1MB, tif)}

Attachment

Submitted filename: Reviewer report.doc

Click here for additional data file.^{(43.5KB, doc)}

Attachment

Submitted filename: PONE-Reviewer Comments-7th June 2022.docx

Click here for additional data file.^{(13.7KB, docx)}

Attachment

Submitted filename: PONE-Reviewer Comments-7th June 2022.docx

Click here for additional data file.^{(18.2KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

In-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization

Syeda Abida Ejaz

Amna Saeed

Pervez Rashid Birmani

Khadijah Mohammedsalaeh Katubi

Zainab Mufarreh Elqahtani

M S Al-Buriahi

Rabail Ujan

Farhan Siddique

Samia ben Ahmed

Z A Alrowaili

Roles

Abstract

Introduction

Scheme 1. Already reported aldo-keto reductase inhibitors [14, 15].

Experimental

Density functional theory (DFT)

Molecular docking

Preparation of ligand

Preparation of protein

Docking protocol

Validation

Molecular dynamic simulation

ADMET properties

Results and discussion

Density functional theory (DFT)

Molecular geometry

Fig 1. Optimized structures of quinolone derivatives.

Table 1. Calculation of energetic parameters and quantum chemical descriptors of quinolone derivatives.

Frontier molecular orbital (FMOs)

Fig 2. HOMO and LUMO structures of quinolone derivatives in gas and solvent (water) phase.

Global chemical reactivity descriptors

Table 2. Quantum chemical descriptors of quinolone derivatives in gas phase.

Molecular electrostatic potential

Fig 3. Molecular electrostatic potential (MEP) structures of quinolone derivatives in gas phase and solvent phase as well.

Molecular docking

Molecular docking with AKR1B1

Fig 4. 3D and 2D interactions of Nap (co-crystal ligand) with active site of AKR1B1.

Table 3. Binding energies of quinolone derivatives with target AKR1B1 and AKR1B10 (kJ/mol).

Fig 5. 3D and 2D interactions of quinidine with active site of AKR1B1.

Fig 6. 3D and 2D interactions of quinine with active site of AKR1B1.

Molecular docking with AKR1B10

Fig 7. 3D and 2D interactions of Nap (co-crystal ligand) with active site of AKR1B10.

Fig 8. 3D and 2D interactions of quinidine with active site of AKR1B10.

Fig 9. 3D and 2D interactions of quinine with active site of AKR1B10.

Structure activity relationship of quinolone derivatives

Molecular docking with nuclear factor kappa B (NF-κB)

Fig 10. 3D and 2D interactions of quinine with active site of nuclear factor kappa B (NF-κB).

Fig 11. 3D and 2D interactions of quinidine with active site of nuclear factor kappa B (NF-κB).

Molecular docking with cellular tumor antigen P53

Fig 12. . 3D and 2D interactions of quinidine with active site of cellular tumor antigen P53.

Fig 13. 3D and 2D interactions of quinine with active site of cellular tumor antigen P53.

Molecular docking with caspase-3

Fig 14. 3D and 2D interactions of quinine with active site of caspase-3.

Fig 15. 3D and 2D interactions of quinidine with active site of caspase-3.

MD simulations

Fig 16. Root mean square deviation (RMSD) of the C-alpha atoms of AKR1B1-Quinine, AKR1B1-Quinidine; AKR1B10-Quinine, AKR1B10-Quinidine complexes with time.

Fig 17. Residue wise Root Mean Square Fluctuation (RMSF) of the C-alpha atoms of AKR1B1-Quinine, Quinidine; AKR1B10-Quinine, Quinidine complexes with time.

Fig 18. AKR1B1, AKR1B10 bound with quinidine secondary structure element distribution by residue index.

Fig 19. AKR1B1, AKR1B10 bound with quinine secondary structure element distribution by residue index.

ADMET properties

Table 4. Physicochemical properties of quinolone derivatives.

Table 5. ADMET properties of the quinolone derivatives.

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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Joazaizulfazli Jamalis

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Joazaizulfazli Jamalis

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