Prediction of Novel Insecticides for Malaria Prevention: Virtual Screening and Molecular Dynamics of AgAChE Inhibitors

Abstract

Background/Objectives: Malaria is a prominent vector-borne disease, with a high mortality rate, particularly in children under five years old. Despite the use of various insecticides for its control, the emergence of resistant mosquitoes poses a significant public health threat. Acetylcholinesterase (AChE) is a crucial enzyme in nerve transmission and a primary target for insecticide development due to its role in preventing repeated nerve impulses. Recent studies have identified difluoromethyl ketone (DFK) as a potent inhibitor of both sensitive and resistant Anopheles gambiae acetylcholinesterase (AgAChE). This study aimed to identify novel AgAChE inhibitors that could be explored for malaria prevention. Methods: We performed a virtual screening on the PubChem database using a pharmacophore model from difluoromethyl ketone-inhibited AgAChE’s crystal structure. The most promising compound was then subjected to molecular docking and dynamics studies with AgAChE to confirm initial findings. ADMET and agrochemical likeness (ag-like) properties were also analyzed to assess its potential as an agrochemical agent. Results: PubChem18463786 was identified as the most suitable compound from the virtual screening. Molecular docking and molecular dynamics studies confirmed its strong interaction with AgAChE. The ADMET and ag-like analyses indicated that PubChem18463786 possesses physicochemical properties suggesting a high probability of non-absorption in humans and meets the criteria for agrochemical similarity. Conclusions: Our findings suggest that PubChem18463786 is a potential AgAChE inhibitor candidate. After validation through in vitro and in vivo experiments, it could be exploited for malaria prevention and serve as a lead compound for the synthesis of new, more effective, and selective agrochemical agents.

1. Introduction

Malaria is a devastating and endemic disease in many developing countries and is transmitted by the mosquito vector Anopheles gambiae. Current vector control measures are threatened by emerging resistance mechanisms [1]. According to the World Health Organization (WHO) global malaria report 2023, there were approximately 249 million cases in 85 malaria-endemic countries and areas (including the territory of French Guiana), an increase of 5 million cases compared to 2021. The main contributing countries to the increase were Pakistan (+2.1 million), Ethiopia (+1.3 million), Nigeria (+1.3 million), Uganda (+597,000) and Papua New Guinea (+423,000) [2]. Malaria transmission is seasonal, with An. gambiae populations expanding during and immediately after a single annual rainy season that runs from June to October. Adults from three members of the An. gambiae complex were present in low numbers during the last three months of the dry season in each of the four regions of a study in Gambia, most of which were found inside households [3]. With the estimated increase in climate temperature in 2025, declared by the WHO to be an aggravating factor of infestations, research aimed at discovering bioactive molecules against An. gambiae has become an increasing priority [2].

Chemical control of vectors such as An. gambiae plays a pivotal role in malaria management and therapy. However, the frequent use of chemicals is hampered by the development of resistance, which has resulted in many losses and problems for humans [4]. Malaria vector control using organophosphates (OPs) and carbamates, the largest groups of insecticides, has been shown to be effective in recent years. OPs are the WHO’s prequalified insecticides and have been most frequently used in the last decade, as evidenced in recent studies [2,5]. Populations of An. Gambiae remain largely sensitive to carbamates and organophosphates; however, reports of resistance to these two classes, which share the same mode of action, are increasing in areas where pyrethroids are being rapidly replaced due to the loss of efficacy [6]. Most chemical insecticides act as acetylcholinesterase (AChE) inhibitors and inhibit vital biochemical processes in mosquitoes. AChE is one of the main enzymes involved in the neurotransmission process in mosquitoes, so insecticides such as organophosphates and carbamates target AChE [4]. Acetylcholine (ACh) is a neurotransmitter catalyzed by the enzyme choline acetyltransferase that binds to ACh receptors (AChR) to transmit neuronal signals. AChE is a serine hydrolase that catalyzes the hydrolysis of ACh, reducing the amount of ACh required for mild neurotransmission in the central nervous system. Inhibition of AChE may increase the amount of ACh in the synaptic cleft and reduce the neurotransmission of signals, leading to death [7].

While chemical insecticides are very effective, they are toxic to many beneficial organisms and pollute the environment. Conversely, difluoromethylketones (DFK) inhibit An. gambiae AChE (AgAChE), both sensitive and resistant, and exhibited strong binding inhibition, with an IC₅₀ of 25.1 ± 1.2 nM [8]. Although smaller-nucleus carbamates and difluoromethylketones are effective AgAChE inhibitors, they also inhibit human AChE (hAChE) through absorption by non-target organisms and therefore lack selectivity for these species [1]. Therefore, this study was designed to identify new potent AgAChE inhibitors with desirable properties.

2. Results

2.1. Ligand-Based Pharmacophore Modeling and Virtual Screening of PubChem Databse

To identify potential inhibitors of the AgAChE enzyme, we screened 499,442,812 conformers of 103,302,052 molecules from the PubChem database using a pharmacophore model. This process resulted in 183 small molecule hits, which were further filtered by docking them to the AgAChE active site using the ChemScore scoring function. We selected 20 compounds (Supplementary Table S2) with the lowest binding energy (ΔG) values as the most promising candidates.

We then analyzed the docking poses and interactions of these compounds with the AgAChE residues. We found that all the compounds shared at least two interactions with the crystallographic ligand, such as pi–pi interactions with the catalytic residues Trp245, Trp393, Phe449, Phe489 and Phe490; hydrogen bonding with Ser280 of the catalytic triad; and van der Waals interactions with Gly278, Gly279, Tyr291, Glu359, Ala361, Gly362, Phe560, Gly601, and Ile604. These interactions suggest that these compounds may bind to the AgAChE site and inhibit its activity, but further validation by molecular dynamics simulations was required and is shown in Section 2.3.

2.2. ADMET Properties

We assessed the ADMET properties of the 20 compounds and DFK using SwissADME and pkCSM servers [9,10]. We used a Boiled-Egg plot (Figure 1) to predict the absorption of the compounds in the human body. The plot shows three regions: yellow (egg yolk), white (egg white), and gray. The yellow region indicates compounds that have a high chance of crossing the blood–brain barrier, the white region indicates compounds that are likely to be absorbed by the gastrointestinal tract, and the gray region indicates compounds that are unlikely to be absorbed at all [11]. We found that PC4, PC5, PC7, PC8, PC9, PC10, PC11, PC12, PC13, PC14, PC15, PC16, PC17, PC18, PC19 and DFK were in the yellow region; PC1, PC2, PC3, and PC20 were in the white region; and PC6 was in the gray region. This result supports our goal of finding a selective compound that has a low probability of absorption in the human body. Therefore, we selected PC6 for further analysis by molecular dynamics simulations.

We used Tice’s rule to evaluate the ag-like profiles of the compounds, which defines specific parameters for agrochemical agents, such as (i) a cLogP between 0 and 5; (ii) a molecular weight between 150 and 500 Da; (iii) a hydrogen bond acceptor count between 1 and 8; (iv) a hydrogen bond donor count ≤ 2; and (v) a rotatable bond count < 12 [12]. This criterion is relevant for determining the absorption properties of agrochemical compounds, especially insecticides. This criterion reflects the complexity of insecticide absorption, which involves intricate plant-insect interactions. Among the different modes of insecticide exposure, such as contact and ingestion, Tice’s rule highlights that insecticides usually have MLogP values between 1.7 and 3.5, with an average of 2.6. PC6 matches this pattern, with an MLogP value of 2.32, within the expected range. PC6 also met the other parameters and was a negative predictor of skin sensitivity. However, this approach fails the hepatotoxicity test and does not cause toxicity in the other tests. Compared with DFK, PC6 is also likely to have low absorption, which is predicted to occur through the blood–brain barrier and through the skin.

Table 1. In silico physicochemical properties. This table presents the in silico physicochemical attributes of PC6 and the reference DFK.

ID	MW 1	MLogP	2 RB	3 HBA	4 HBD	AMES	5 HP	6 SS
DFK	218.24	1.44	5	4	1	No	No	Yes
PC6	414.37	2.32	5	8	2	No	Yes	No

¹ MW = molecular weight (Da), ² RB = rotatable bonds, ³ HBA = hydrogen bond acceptor, ⁴ HBD = hydrogen bond donor, ⁵ HP = hepatotoxicity and ⁶ SS = skin sensitisation.

shows the details of these parameters and toxicity prediction.

2.3. Molecular Dynamics Simulations of AgAChE-Ligands Complexes

We performed molecular dynamics (MD) simulations to investigate the stability of the AgAChE-PC6 complex and compare it with the AgAChE-DFK complex. We evaluated the results using RMSD and RMSF plots, which allowed us to assess the stability and flexibility of the protein–ligand complexes. We also calculated the binding free energy as a key parameter. The Root Mean Square Deviation (RMSD) measures the deviation of the protein from its initial to its final conformation. The RMSD values obtained during the simulations reflect the stability of the protein with respect to its structural conformation. The RMSD plots of the complex simulations showed low RMSD values, indicating that the systems were well equilibrated and stable (Figure 2).

The Root Mean Square Fluctuation (RMSF) identifies the flexible regions in protein- and/or ligand complexes during molecular dynamic simulations. We computed the RMSF values to predict the structural changes in the protein induced by the ligand binding (Figure 2). In all simulations, fluctuations in the more rigid regions did not exceed 0.2 nm, suggesting overall stability. More flexible regions showed higher peaks, corresponding to residues in the ranges 440–450, 540–550, and 647–652, which exhibited lower fluctuations in the presence of PC6. Conversely, fluctuations in the peaks corresponding to residues in the ranges 240–250, 275–280, 490–500, and 640–651 showed higher fluctuations in the presence of PC6, due to the proximity movement of catalytic residues in these regions with the ligand, a movement that did not occur in the apo form, suggesting that interactions did not take place with DFK. We performed an interaction analysis, comparing the duration of interactions during the MD simulations of the complexes with PC6 and DFK (Figure 3). Over time, we observed that in the AgAChE-PC6 complex, the ligand interacted with at least thirteen residues, highlighting hydrogen bonds with Ser360 and Glu486 and pi-stacking interaction with His600, residues from the catalytic triad, in addition to pi-stacking interactions withTrp245, Tyr489, and Phe490 residues from anionic site, with sustained interaction throughout the simulation (Figure 3C,D). The number of hydrogen bond and pi-stacking interactions was higher than in the AgAChE-DFK complex. Meanwhile, in the DFK complex, only three hydrogen bond interactions persisted for more than 10% of the simulation time with Trp245, Trp592 and His600 (Figure 3A,B). Overall, the most stabilized interactions during the simulation time were observed in the AgAChE-PC6 complex, with notable halogen interactions involving the trifluoromethyl group and Cys447.

The interaction affinity is related to the change in the free energy caused by the formation of a complex. This parameter is directly linked to the binding strength of the ligand. It is known that positive ΔG values correspond to unfavorable reactions, while negative values indicate more stable and spontaneous energetically favorable reactions. The total energies are shown in

Table 2. Free energy values mean calculated by MM/PBSA for each complex.

Complex	ΔGMM/PBSA	SD 1
AgAChE1-DFK	−26.38	4.02
AgAChE1-PC6	−29.89	7.15

¹ SD = Standard Deviation.

The AgAChE-PC6 complex has the lowest binding free energy, indicating a more energetically favorable interaction. This value is mainly derived from contributions from hydrophobic interactions, which was also observed in other studies, in which the stability at the catalytic site is generally associated with contributions from ΔE_vdw. Due to the predominance of aromatic rings in the filtered compound, the electronic delocalization near the aromatic catalytic residues also favored this type of contribution to a stronger binding of PC6, potentially stabilizing the ligand in the binding site. Furthermore, Student’s t-test was conducted, showing that the average ΔG_MM/PBSA values are significantly different, indicating that the binding affinity of the AgAChE-PC6 complex is superior to that of the control complex. These results demonstrated the overall stabilization of the AgAChE-PC6 complex and suggest a potential inhibitory effect.

3. Discussion

Using a comprehensive approach, we aimed to identify novel potent inhibitors of Anopheles gambiae acetylcholinesterase (AgAChE), a key vector in malaria transmission. The approach involved pharmacophore modeling, virtual screening, molecular docking, in silico ADMET prediction, and molecular dynamics simulations to evaluate the efficacy and selectivity of candidate compounds.

The increasing number of cases of malaria in 2023 underscore the need for effective alternatives to control the An. gambiae vector [2,13]. Resistance to conventional insecticides, such as organophosphates (OPs) and carbamates, the main insecticides used to control An. gambiae, necessitates innovative and sustainable approaches to overcome the limitations of conventional insecticides [14,15].

In the search for potent and selective inhibitors, difluoromethyl ketones were explored as inhibitors, evaluating the reduction in electrophilicity identified in trifluoromethyl ketones without success in past studies [16,17]. The enzymatic inhibitory activity of fluoroketones with different substituents was also verified, observing that these molecules substituted with azole groups showed steady state inhibition in 10 min with single-digit nanomolar IC₅₀ values [1]. From these results, we applied pharmacophore modeling using the crystal structure of Difluoromethylketone-bound AgAChE to identify novel inhibitors [18]. We used the PubChem database and similarity filters, such as inclusive and exclusive shape constraints, to select promising compounds based on structural features, mainly hydrophobic. Molecular docking studies using GOLD 2021.1.0 software showed that all 20 selected compounds had important and favorable interactions with catalytic residues, as observed by ΔG estimates.

PC6, identified as PubChem-18463786, exhibited desirable physicochemical properties and was aligned with the ag-like parameters of the Tice Criterion, such as an MLogP of 2.32 [12]. The location of PC6 in the gray region of the Boiled-Egg graph, indicating non-absorption, agreed with the objective of this study for non-absorption in human organisms [19]. Moreover, toxicity analysis indicated a negative prediction of skin absorption, in which more negative values of Log Kp implied a lower probability of skin permeation, emphasizing its selective profile, compared to that of DFK [20].

The molecular dynamics simulations provided a more detailed view of the interaction between PC6 and AgAChE. The low RMSD values suggested the stability of the complexes, while the fluctuations in RMSF indicated the flexibility of the bound protein and suggested movements that did not occur before with DFK, indicating specific interactions that stabilize the complex [21]. PC6 demonstrated global stabilization of the AgAChE-PC6 complex, indicating its potential as an inhibitory agent. These results were also supported by the prediction of free binding energy, in which the AgAChE-PC6 complex had a more negative value with higher binding affinity due to the contributions of interactions with residues Trp245, Tyr489, Phe490 [22]. Furthermore, studies have evaluated the inhibitory potential of trifluoromethyl compounds and aliphatic ketones on acetylcholinesterase and butyrylcholinesterase, highlighting (trifluoromethyl)benzaldehyde. However, the quantitative structure–activity analysis targeted action in the human body for other therapies [23,24]. Other experimental studies found that the enzyme inhibitory activity showed lower results in fluoromethyl ketones substituted with isobutyl groups, since alkyl groups undergo time-dependent activity; this group is present in difluoromethyl ketone and, in the design of PC6, was replaced by aromatic groups, a structural advantage that may provide insights into better enzyme inhibition performance [1].

4. Materials and Methods

4.1. Ligand-Based Pharmacophore Modeling and Virtual Screening

Crystalline structures are mandatory for the construction of a pharmacophore model. Therefore, a ligand-based pharmacophore model was created to identify novel AgAChE inhibitors using the difluoromethylketone-bound crystal structure. The experimental structure was downloaded from the RCSB Protein Data Bank (PDB ID: 6ARY). The pharmacophore model was obtained using Pharmit server https://pharmit.csb.pitt.edu/ (accessed on 25 September 2023) [25]. The characteristics of the pharmacophore model are shown in Figure 4. The PubChem database was screened in relation to the pharmacophore model. ‘Inclusive shape constraints’ and ‘Unique’ with tolerance level 1 were applied to include additional matching compounds during the similarity search. The ‘inclusive shape constraint’ ensures that at least one heavy atom of the compound being selected falls into the position aligned with the pharmacophore. The ‘exclusive shape constraint’ ensures that hits that have heavy atom centers in their poses aligned with the pharmacophore within the ‘exclusive shape’ are discarded [18]. Other filters were also applied to identify more ‘agrochemical-likeness’ hits: molecular weight between 150 and 500 Da, number of rotary bonds ≤ 12, polar surface area ≤ 142 Ų, Log P in the range of 0 to 5, number of hydrogen bond acceptors between 1 and 8, and number of hydrogen bond donors ≤ 2. The values of these parameters were chosen according to the rules of similarity with agrochemicals proposed by Tice’s rule [12,26]. The identified hits were then ranked according to the ‘scoring function’ to identify energetically favorable hits. Finally, only the hits with a maximum score of Vina ≤ −4 kcal/mol, a minimized RMSD value in relation to the pose of the original ligand ≤ 2 Å and a single conformer were selected (Supplementary Figure S1).

4.2. Molecular Docking

Molecular redocking and docking studies were conducted using GOLD 2021.1.0 software (Genetic Optimization for Ligand Docking, CCDC Software Ltd., Cambridge, UK). The optimized parameters for the software were determined based on a comprehensive analysis of the enzyme’s 3D structure involving the analysis of (1) the scoring function, (2) the site diameter relative to the crystallographic ligand, (3) the inclusion of structural water, and (4) amino acid flexibility within the site. The configurations were selected based on the lowest Root Mean Square Deviation (RMSD) value and the highest fitness score (Supplementary Table S1). Using these well-defined parameters, docking simulations were carried out for the previously selected molecules, and the ChemScore function was applied as a rescore to calculate relative binding energy data. After this step, we selected only the 20 molecules that had the lowest estimated energy values (assuming that the lower the ΔG value was, the greater the affinity and spontaneity of binding to the receptor). Of these 20 selected molecules, we chose the best pose for each of them, based on the number of interactions and similarity of interactions with the crystallographic ligand (DFK, control).

4.3. In Silico ADMET Prediction

The design of bioactive molecules, such as drugs or agrochemicals, requires the assessment of their absorption, distribution, and metabolism (ADME) properties to determine their physicochemical behavior in the human body. Moreover, the toxicity profile (T) of these molecules and their metabolites is also essential for evaluating their potential adverse effects on biological systems. These profiles allow thorough screening before the synthesis of the candidates. This strategy helps to optimize the molecular structure and reduce unwanted outcomes before proceeding with the synthetic route [19]. Considering the dynamic interaction between pest diversity and environmental factors, we applied a set of rule-based filters to predict the agrochemical likeness (ag-like) of the molecules. These filters include Lipinski’s Rule of Five, which was applied using the SwissADME server [27]. We submitted the SMILES data of the 20 selected molecules for the prediction of their physicochemical properties and for the generation of a Boiled-Egg graph, which provides insight into gastrointestinal absorption and blood–brain barrier permeation [11]. For the subsequent analyses, we selected only the molecule whose expression was predicted to not be absorbed by the human body. In addition, the toxicity prediction was carried out using the pkCSM server https://biosig.lab.uq.edu.au/pkcsm/ (accessed on 12 August 2024), obtaining results for the AMES test, hepatotoxicity, and skin permeability [10].

4.4. Molecular Dynamics Simulations

Following meticulous evaluations, the chosen compound (PC6) and the difluromethylketone underwent molecular docking simulations. From these simulations, the most promising binding pose within the protein complex was identified. Subsequently, all-atom dynamics simulations were performed using the GROMACS 2022.3 package [28]. For the simulations, the CHARMM36 force field, updated as of July 2022, was used [29].

Each complex was solvated inside a dodecahedral box (890.911 nm³) with a three-point water molecule model (TIP3P), allowing for a minimum of 1 nm of marginal distance between the protein and each side of the simulation box. The net charge of the simulation system was neutralized by counter-ions Na⁺ and Cl⁻ that replace water molecules and, to replicate the physiological conditions of the cell environment, we took the concentration to 150 mM [30].

We carried out the simulation in three stages and used a force constant of 1000 kJ·mol⁻¹·nm⁻² to restrain all heavy atoms. The first energy minimization step involved the initial optimization of each system geometry using 5000 iterations (5 ps) with the steepest descent algorithm. The next step involved equilibrating the system in two stages, where the system was relaxed for 100,000 iterations (100 ps) in each stage (with a timestep of 2 fs). We performed the first stage of equilibration under a constant set of particle numbers, volume, and temperature (NVT). Then, we performed the second stage of equilibrium under a constant set of particle number, pressure, and temperature (NPT) at 1 atm using C-rescale barostat and 300 K using the V-rescale thermostat [31,32]. We used the Particle Mesh Ewald (PME) algorithm to calculate the electrostatic interactions, and the bonds were constrained by the LINCS algorithm.

Finally, we performed the production step under constant pressure in triplicate. We calculated comparative data, including the RMSD and RMSF, by analyzing the 200 ns of the MD trajectories using the integrated tools of GROMACS. Ultimately, we estimated the binding free energy between ligands and proteins using the GROMACS module “gmx_MMPBSA” [33,34]. Additionally, an interaction map was generated using in-house Python 3.11 scripts, employing the pandas, matplotlib, and seaborn libraries, along with the Protein-Ligand Interaction Profiler (PLIP) 2.4.0 [35]. PLIP was used to extract molecular interaction data, such as hydrogen bonds and salt bridges, from the simulations. These data were then compiled into a single DataFrame using pandas and visualized through scatter plots where different types of interactions were distinctly colored using matplotlib and seaborn. The PyMOL v.2.5.2 graphical software (SchrödingerTM) was used in generating figures for ligand-protein conformational analysis.

5. Conclusions

Our results indicate that the compound PubChem-18463786 (PC6) emerges as a promising candidate with distinctive features that can significantly contribute to innovative strategies for malaria vector control. The results presented provide a solid basis for future investigations and in vitro and in vivo biological assays to validate the efficacy and safety of these agents. In addition, environmental considerations are essential when assessing the potential impact of AgAChE on the environment, as these considerations could lead to the development of innovative approaches for the effective control of malaria vectors.