Introduction

Among human beings, malaria has been considered to be one of the dreaded diseases among the black race1. It has been reported to affect millions of African people negatively on yearly bases2. According to Plirat et al., this disease has been considered and reported to be a life threatening health issue due to growing resistance to malaria drug-like agents3. The activities of this disease have led to elevated degrees of mortality and morbidity both in tropical and subtropical part of the globe4. In 2021, over 240 million cases of malaria were reported by World health organization and over 0.5 million deaths were reported globally5. Moreover, several efforts have been put in place to reduce the malaria-based-death via the use of net infiltrated with insecticide as well as the use efficient chemical compounds with anti-malaria activities. Yet, the desire to totally eradicate the activities of malaria among human race draws the attention of many researchers globally6,7,8.

Plasmepsins have been reported to be produced as zymogens with great similarity to other proteases. They are lethargic signs of the enzymes that give shield against proteolysis, preserve the inactive state, and stop the substrate from entering the active site. Proplasmepsins of Plasmodium falciparum (Pf) are converted to active proteinases in vivo by a maturase9,10,11. This enzyme has incredibly complex functions that range from hemoglobin dilapidation to processing secretory organelle proteins for invasion, egress, and effector export12.

Small chains of amino acids joined by peptide bonds are known as peptides13. These studied ligands have been reported to be therapeutic compounds with promise, including anticancer, antimalarial agents etc.14. According to several scientists, derivatives of peptides possess improved specificity and selectivity and this has the ability to assist them to fight various diseases15,16,17. Cyclic peptides have been successfully synthesized by many researchers using mechanized methods and their therapeutic values have drawn the attention of many scientists to continue to investigate the activities of peptides18. The reports by various researchers have shown that cyclic peptides have potential ability to act as drug-like agents based on their features such as exceptional low harmfulness, strong specificity and selectivity as well as outstanding scoring19,20. In the design of new efficient drug-like agent, cyclic peptides are crucial target molecules. According to Oyebamiji et al., cyclic peptide possess constrained conformations and this has been reported in various literatures to enhance metabolic steadiness, target affinity and target selectivity20. In this work, addition of electron withdrawing and donating groups is expected to increase human metabolic stability and promote target selection and attraction.

Thus, this work is aimed at identifying the type of interactions involved between the studied ligands and Proplasmepsin IV as well as to investigate the relationship between the calculated descriptors and the calculated binding affinities.

Methodology

Density functional theory calculation

The studied ligands under study were represented in 3D and optimized using the 6-31+G* basis set in Spartan 14 version 1.1.421. The optimization of the studied ligands took place in a vacuum which is necessary for further study. This calculation involved achieving the equilibrium geometry at the ground state from the current geometry, with a neutral charge and no unpaired electrons. The completion of the studied ligand optimization was dependent on the atoms present in the compounds. The calculated features/descriptors were obtained and presented as required. The 2 and 3-dimensional structures of the studied compounds were shown in Table 1.

Table 1 The Pictorial representation of the studied compounds.
Full size table

Induced Fit docking method

The Induced Fit docking method is a computational approach used in molecular docking to predict how a ligand (such as a drug molecule) interacts with a target protein22. Unlike traditional rigid docking, which assumes that both the studied ligand and protein are fixed in shape, the induced fit model accounts for the conformational flexibility of the protein. It proposes that the protein’s structure may adjust upon studied ligand binding, leading to a more accurate prediction of the binding mode and affinity. This method improves the reliability of docking predictions in drug discovery and protein–ligand interactions23.

The Proplasmepsin IV (pdb id: 5JOD) was obtained from the protein data bank. Using the quickPrep tool, the receptor was prepared with appropriate sub tools and saved in .moe format for docking calculations in the molecular operating environment software version 2022.02. Site finder was utilized to locate the binding site, and a suitable site was selected for the docking calculation (Table 2). The treated and prepared protein structure was saved in .moe format before docking calculation using the induced fit method. The results obtained from the docked complexes were expressed in kcal/mol, and the types of interactions involved in the docked complexes were presented and reported.

Table 2 Predicted binding sites located in Proplasmepsin IV.
Full size table

Pharmacokinetic study

The selected compound with highest calculated binding affinity and binding energy via molecular modeling study was examined using Swissadme and ADMETSar software for absorption, distribution, metabolism, excretion and toxicity (ADMET) investigation. The selected compound was subjected to swissadme to convert it to SMILES format before subjecting to ADMETSar and the results obtained were reported in various categories (Physicochemical Property, Medicinal Chemistry, Absorption, Distribution, Metabolism, Excretion, Toxicity, Environmental toxicity, Tox21 pathway, and Toxicophore Rules).

Procedures for molecular dynamics (MD) simulations

Validation of the docking results was done using MD simulations for both the reference drug and the identified hit candidate. Input files for the protein–ligand complexes were prepared using the CHARMM-GUI platform (https://www.charmm-gui.org/), incorporating the protein in PDB format and the studied ligand in MOL2 format. A 10 Å rectangular simulation box was set to ensure accurate molecular interaction modeling. The system was solvated by adding water molecules and ions (0.15 M K+ and Cl-) to neutralize its charge. Parameter files were generated using the AMBER force field (GAFF2), which is compatible with the AMBER ff19SB protein force field, as demonstrated by Owolabi et al.24. The complexes underwent 100,000 steps of energy minimization, followed by a 5-ns (ns) equilibration and a 100-ns production run. The equilibration was conducted under an NVT ensemble, while the production phase used an NPT ensemble, maintaining a stable temperature of 310.15 K. Trajectory analysis was performed using the CPPTRAJ module, and the binding interactions and free energies (ΔGbind) of the protein–ligand complexes were assessed using the Molecular Mechanics Generalized-Boltzmann Surface Area (MMGBSA) method.

Results and discussion

Calculated descriptors from optimized molecules

Frontier molecular orbital (highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO)) have been reported by several scientists to give accurate qualitative information about the molecular excitation properties21. HOMO energy exposed the tenacity of any studied ligands to donate electron to one or more molecules24. Also, according to report by Semire et al., 2012, ligand with highest HOMO value among other molecules under investigation reveal the compound with ability to donate electron to other compound with ability to receive it25. The most energetic electrons in a molecule are thought to be located at the place with the highest electronic density of the highest-occupied molecular orbital (HOMO), making it the most likely location for an electrophile reaction26.

More so, LUMO explains the receptivity of any studied ligands to compound with strength to donate electron and compound with lowest LUMO value among other investigated compounds shows the compound with highest ability to receive electron from ligand with tendency to release it. Thus, as shown in Table 2, methyl (3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-9-(2-(methylthio)ethyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-3-carboxylate (F5) and 2-((3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-3-(methoxycarbonyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-9-yl)acetic acid (F7) proved to possess tendency to react in term of HOMO energy and LUMO energy respectively. The calculated value for HOMO were − 6.89 eV, − 6.57 eV, − 6.80 eV, − 6.50 eV, − 5.86 eV, − 6.90 eV, − 6.88 eV, − 6.92 eV, − 6.43 eV, and − 6.73 eV for F1 to F10 while `the calculated LUMO value were − 2.93 eV for F1, − 2.45 eV for F2, − 2.51 eV for F3, − 3.02 eV for F4, − 3.35 eV for F5, − 2.44 eV for F6, − 3.37 eV for F7, − 2.89 eV for F8, − 2.71 eV for F9, and − 2.96 eV for F10 (Table 3).

Table 3 Calculated descriptors for studied heterocyclic compounds.
Full size table

Moreover, the energy gap exposes the differences between LUMO energy and HOMO energy which therefore depict the stability and chemical reactivity of any molecule27. As reported by various scientists, utmost value of any calculated energy gap proposes that the compound is less reactive and more stable, and vice versa28,29; thus, as shown in Table 3, methyl (3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-9-(2-(methylthio)ethyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-3-carboxylate (F5) exhibited higher reactivity and less stability than other studied compounds. The calculated energy gap for compound F1–F10 were 3.96 eV, 4.12 eV, 4.29 eV, 3.48 eV, 2.51 eV, 4.46 eV, 3.51 eV, 4.03 eV, 3.72 eV and 3.77 eV respectively (Table 4).

Table 4 Predicted HOMO–LUMO overlay for F1-F10.
Full size table

Dipole moments have been considered by several scientists to be an important characteristic that possess ability to explain the chemical and physical behaviors of molecules in many environments28,29. Also, accurate and appropriate calculation of dipole moments of any compound possesses the ability to influence the reactivity of drug-like agents30. The nature and relevance of nonbonding interactions in biochemical interactions between complexes has been observed to contribute 3–5 kJ/mol to the compound-target energy of interactions31 and arbitrary high value of calculated dipole moment could be ascribed to the inconsistent features of any ligand; thus, as shown in Table 3, the calculated dipole moment were within the acceptable range32. More so, other studied descriptors and HOMO–LUMO overlay were reported in Tables 3 and 4 respectively.

Scoring and predicted nonbonding interactions

The role of calculated binding affinity has been observed to be a key to appreciating the intermolecular relationships motivating biological processes and structure–function relationships. Also, it helps in measuring portion of the steps involved in drug discovery which thereby aid drug design that bind their receptors selectively and specifically33. More so, the connectivity and inhibiting activities of the investigated tetra-peptide based ligands and proplasmepsin IV (pdb id: 5JOD) were observed using molecular operating environment software (MOE) (Fig. 1). According to Oke et al.34 highest binding affinity of any ligand has a great connection to lowest scoring (binding affinity value); thus, compound F9 (− 8.61016846 kcal/mol) exhibited greatest potential anti proplasmepsin IV activities than the standard compound and other investigated tetra-peptide based ligands. The calculated binding affinity were − 7.8757596 kcal/mol for F1, − 8.50530529 kcal/mol for F2, − 8.00267982 kcal/mol for F3, − 8.35911751 kcal/mol for F4, − 7.6687603 kcal/mol for F5, − 7.67455482 kcal/mol for F6, − 8.37009716 kcal/mol for F7, − 8.20956707 kcal/mol for F8, − 8.61016846 kcal/mol for F9, and − 7.95467234 kcal/mol for F10 (Table 5).

Fig. 1
figure 1

Pictorial representation of compound F9 docked into the active site of the target.

Full size image
Table 5 Binding affinity (kcal/mol) of studied polypeptides (F1-F10) and reference drug against the target.
Full size table

As shown in Table 6, the interaction of compound F1 with proplasmepsin IV (pdb id: 5JOD) resulted into a single type of interaction. It was observed that among several amino acid residues present in the active site of the target, GLY 78 was discovered to have a favorable interaction with compound F1 with pi-H as the type of bond observed between the complexes. The distance between the GLY78 and the compound F1 was calculated to be 4.05 Å. Also, the connection between compound F2 and the target generated H-donor and H-acceptor as the bond type between F2 and the studied target. The amino acid residues, type of bond and the distance observed between F2—proplasmepsin IV (pdb id: 5JOD) complex were SER 79, H-donor, 3.08 Å; SER79, H-acceptor, 2.73 Å and THR35, H-acceptor and 3.09 Å.

Table 6 Ligand-receptor interacting residues of proplasmepsin IV (pdb id: 5JOD) and polypeptide-based compound (F1-F10).
Full size table

More so, the biological factors (amino acid residues, type of bond and the distance) considered for the docking of the studied complex were ASP 34, H-donor and 3.59 Å for compound F3; GLY 216 Å, PHE 33; H-donor, H-donor; 3.16 Å and 3.16 Å for compound F4; GLU 258, GLU 258, H-donor, H-acceptor, 2.75 Å and 3.01 Å for compound F5; THR 217, H-donor, 2.83 Å for compound F6; ASP 34, ASP 34, GLY 78, SER 79, LEU 14, LEU 14; H-donor, H-donor, H-acceptor, H-acceptor, pi-H, pi-H; 3.59 Å, 2.79 Å, 3.12 Å, 3.29 Å, 4.61 Å, 4.38 Å for compound F7; ASP 34, ASP 34; H-donor, H-donor; 3.56 Å, 3.23 Å for compound F8; ASP 34, LEU 8, ASP 34; H-donor, pi-H, pi-H; 3.35 Å, 3.91 Å, 4.08 Å for compound F9; ASP 34, ASP 34, GLY 78; H-donor, H-donor, H-acceptor; 3.26 Å, 3.11 Å, 3.11 Å for compound F10. According to the report by Lau et al.35 linear tetra peptides proved to be active against microbes but the result presented in this work proved that cyclic tetra peptides proved to be more efficient in inhibition than linear tetra-peptides. Also, Hussain et al.36 reported the antimicrobial activities of 1,4-disubstituted based compound using experimental and insilico approaches and it was discovered that the reports from our study falls in line with the experiment and insilico reports. This showed the proficiency of the studied cyclic tetra-peptides most especially methyl (3S,9S,12S)-9-(4-aminobutyl)-12-(1,3-dioxoisoindolin-2-yl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-3-carboxylate (F9).

More so, it was observed that the compound attached to the parent compound in compound F9 enhanced its flexibility and makes it fit into the active site compared to the reference compound; thus, this made compound F9 to exhibit higher inhibiting activity than the reference compound.

Pharmacokinetics study

In this work, the pharmacokinetics study of the lead compound and the referenced compound (Chloroquine) were examined using ADMETLab software and various ADMET features were obtained. Molecular Weight, Volume, Density, nHA, nHD, nRot, nRing, MaxRing, nHet, fChar, nRig, Flexibility, Stereo Centers, TPSA, logS, logP, and logD were considered for physicochemical property while QED, SAscore, Fsp3, and MCE-18 were investigated for medicinal property. Caco-2 Permeability, MDCK, Permeability, Pgp-inhibitor, Pgp-substrate, HIA, 20%, NPscore, Lipinski Rule, Pfizer Rule, GSK Rule, Golden Triangle, PAINS, ALARM NMR, BMS, Chelator Rule, and F30%. More so, other predicted features using ADMETLab such as toxicity, metabolism, excretion and absorption were considered in this work (Tables 7 and 8). It was observed that the ADMET properties predicted for compound F9 and Chloroquine (Standard) were similar which proved the potential safety of compound F9. This is a proof that compound F9 have the ability to act as drug with fair toxicity report. Also, the toxicity level of compound F9 was compared to report by Dhivya et al.37 and it was observed that the toxicity level for compound F9 has a fair correlation with the toxicity level of the lead compound in Dhivya et al.

Table 7 Pharmacokinetic features for compound F9.
Full size table
Table 8 Pharmacokinetic features for Chloroquine.
Full size table

Exploring stability and binding interactions through MD simulations

The MD simulations were employed to confirm and validate the findings obtained after identifying the reference drug and the F9_complex through molecular docking calculations. To gain deeper insights into the dynamic behavior and stability of these two complexes, their structural variations and fluctuations were meticulously analyzed over a specific period using a range of parameters38, as illustrated in Fig. 2.

Fig. 2
figure 2

The MD simulations of the reference drug and hit drug (F9_complex) over 100 ns production run. (A) RMSD for receptor (B) RMSD for ligand (C) RMSF (D), and Rg calculated for the complexes.

Full size image

Figure 2A depicts the root mean square deviation (RMSD) over 100 ns for two systems: the Reference (black) and F9_Complex (red), reflecting structural deviations of the receptor from its initial conformation. The RMSD of both systems increases sharply within the first 20 ns. The reference system stabilizes around 1.5–2.0 Å after 30 ns, with minor fluctuations, signifying structural stability38. In contrast, the F9_Complex exhibits higher RMSD values (~ 2.0 to 2.5 Å) and increased fluctuations, particularly in the later stages of the simulation, suggesting greater conformational variability. These observations indicate that the F9_Complex undergoes more pronounced structural rearrangements, potentially due to ligand binding or other interactions, which may destabilize the receptor or alter its dynamics. The higher RMSD values in the F9_Complex suggest it adopts a more flexible or less stable conformation compared to the Reference, providing insights into its altered structural behavior. Figure 2B reveals the RMSD of a ligand in two systems. Both systems exhibit fluctuations within the range of approximately 0.0 to 1.2 Å, indicating dynamic movement of the ligand. The reference demonstrates relatively consistent deviations, whereas the F9_Complex shows more pronounced variability, suggesting greater structural flexibility or instability in the latter39. These insights can aid in understanding the interaction dynamics of the ligand with its environment.

Figure 2C presents the root mean square fluctuation (RMSF) per residue for the Reference and F9_Complex systems, reflecting the flexibility of individual residues over the simulation. Both systems exhibit similar fluctuation patterns across most residues, indicating comparable structural dynamics40. However, the F9_Complex shows consistently higher RMSF values at certain regions, particularly around residues 40–50 and 120–130, suggesting increased flexibility in these areas. These regions may correspond to loop or binding sites affected by interactions in the F9_Complex, such as ligand binding or structural modifications, which promote greater mobility. In contrast, the Reference system maintains relatively lower fluctuations, implying greater rigidity and stability in these regions. The observed differences indicate that the F9_Complex undergoes localized flexibility changes, potentially influencing its functional dynamics. This analysis highlights the impact of modifications in the F9_Complex on residue-level flexibility compared to the more stable Reference system. Figure 2D illustrates the radius of gyration (Rg) over 100 ns for two systems, highlighting differences in structural compactness and stability. The Reference system starts with a slightly higher Rg (~ 22 Å) but stabilizes at lower values (~ 18 to 19 Å) after 20 ns, indicating greater structural compactness and reduced fluctuations. In contrast, the F9_complex exhibits higher average Rg values (~ 19 to 21 Å) and more pronounced fluctuations, suggesting a less compact and more dynamic structure. These variations likely stem from interactions in the F9_complex, such as ligand binding or structural modifications, which may destabilize the system or promote an open conformation. The observed trends suggest that the F9_complex is less stable compared to the Reference, reflecting potential structural changes critical to its function. This analysis provides insights into how molecular interactions affect stability and folding dynamics, contributing to the understanding of system behavior.

Table 9 provides a comparative analysis of the MM/GBSA binding free energy calculations for two complexes: the reference drug and the F9_complex. The parameters evaluated include the gas-phase energy (ΔGgas), solvation free energy (ΔGsolv), and the overall binding free energy (ΔGbind) in kcal/mol. The ΔGgas represents the energy contribution from gas-phase interactions, which are stronger for the F9_complex (− 177.69 ± 6.13 kcal/mol) compared to the reference (− 119.48 ± 3.19 kcal/mol). This indicates that the F9_complex exhibits more favorable gas-phase interactions. Conversely, the ΔGsolv, representing the solvation energy, is higher for the F9_complex (163.84 ± 5.54 kcal/mol) than the reference (97.57 ± 2.89 kcal/mol), suggesting the F9_complex experiences greater solvation effects.

Table 9 The MMGBSA solvation model for (a) Reference and (b) F9_Complex in kcal mol-1.
Full size table

The ΔGbind, which reflects the overall binding affinity, shows that the reference drug (− 21.91 ± 1.16 kcal/mol) has a slightly stronger binding affinity compared to the F9_complex (− 13.85 ± 0.72 kcal/mol). These results highlight that while the F9_complex demonstrates stronger gas-phase interactions, the reference drug exhibits a better balance between gas-phase and solvation contributions, resulting in higher binding affinity. Therefore, this work was compared to the report shown in various literatures41,42. This showed that the efficiency of the lead compound has fair correlation with others which therefore exposed that the lead compound has a potential to act as drug-agent.

Conclusion

In conclusion, ten cyclic tetra-peptide derivatives were studied using in silico methods. The 2D and 3D structures were modeled with Chemdraw and Spartan 14, respectively. The compounds were optimized using the 6-31+G* basis set in Spartan 14 software. Inhibition potential against Proplasmepsin IV (PDB ID: 5JOD) was assessed through induced fit docking using the Molecular Operating Environment (MOE) software. The binding site of Proplasmepsin IV was identified using the Site Finder tool in MOE prior to docking calculations. Additionally, the pharmacokinetic properties of the lead compound and the standard were evaluated with the ADMETSar software.

The optimization of the compounds revealed that methyl (3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-9-(2-(methylthio)ethyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-3-carboxylate (F5) and 2-((3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-3-(methoxycarbonyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-9-yl)acetic acid (F7) exhibited higher reactivity than the other compounds, as indicated by their HOMO and LUMO energies. F5 also showed higher reactivity and lower stability compared to the other compounds, as evidenced by its energy gap. Furthermore, compound F9 demonstrated higher inhibitory activity than the reference drug, Chloroquine, based on binding affinity. Molecular dynamics (MD) simulations over 100 ns further explored the binding affinity between F9 and the reference drug, showing that the reference drug (− 21.91 ± 1.16 kcal/mol) had a slightly stronger binding affinity than the F9_complex (− 13.85 ± 0.72 kcal/mol). The ADMETSar report indicated that compound F9 has the potential to act as a drug agent with acceptable toxicity.

Thus, the findings from Cyclic Tetra-Peptides (CTPs) as potential antimalarial agents provide crucial insights into how modifications can enhance their drug-likeness and therapeutic potential. Understanding the binding interactions between CTPs and malaria-specific targets, enables the identification of key structural features responsible for their efficacy. This knowledge can inform the design of modified derivatives with improved binding affinity and selectivity. Furthermore, modifications to enhance the pharmacokinetic properties of CTPs, such as increasing stability against enzymatic degradation, improving membrane permeability, and optimizing solubility, can make them more suitable for clinical use. Additionally, insights gained from structure–activity relationship (SAR) studies allow for the fine-tuning of CTP derivatives to improve their potency and minimize toxicity, addressing the challenge of balancing efficacy and safety. By applying these findings, researchers can develop more effective, targeted, and durable antimalarial treatments, potentially overcoming issues of resistance and broadening therapeutic options for malaria.