Active compound profiling in fraction 49 of a secondary metabolite extract from Streptomyces hygroscopicus subsp. Hygroscopicus as antimalaria drug using LCMS, molecular docking, and molecular dynamics

doi:10.21203/rs.3.rs-5318778/v1

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Research Article

Active compound profiling in fraction 49 of a secondary metabolite extract from Streptomyces hygroscopicus subsp. Hygroscopicus as antimalaria drug using LCMS, molecular docking, and molecular dynamics

https://doi.org/10.21203/rs.3.rs-5318778/v1

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Background

Antimalarial resistance in Indonesia is caused by mutations at the drug target sites and biochemical changes in drug receptors. Previous research indicates that only fraction 49 of Streptomyces hygroscopicus Subsp. Hygroscopicus (S. hygroscopicus) is effective as an antimalarial agent in in vitro tests.

Methods

The active compounds of secondary metabolites from S. hygroscopicus were identified using LCMS. The binding of compounds to target proteins (PfK13, PfPM2, and PfAMA-1) underwent molecular dynamic simulations to estimate the stability and flexibility of the binding through the dynamic structure of the molecules.

Results

The LCMS analysis identified four compounds with the fastest retention times (indicating increased non-polarity): Dibutyl phthalate, Dihydroyashabushiketol, Dibenzylamine, and Sedanolide. These compounds meet drug-likeness criteria (Lipinski’s rule and Veber’s rule) and exhibit binding affinity values similar to the control for each target protein. Hydrogen and hydrophobic bonds formed between the compounds and targets show similarities with the bonds formed between the target proteins and their respective control ligands. The stability and flexibility of the molecular structures of the active compounds were assessed using molecular dynamics. Dihydroyashabushiketol exhibited the highest binding affinity and demonstrated a stable and flexible structure toward the target proteins PfK13 and PfAMA-1.

Conclusion

Dihydroyashabushiketol exhibits the highest binding affinity values in almost all target proteins compared to the other compounds, suggesting that Dihydroyashabushiketol has the potential as an antimalarial agent. The compound demonstrates a stable and flexible structure towards the target proteins PfK13 and PfAMA-1, but not towards the PfPM2 protein.

Antimalaria

Fraction 49

Streptomyces hygroscopicus

Liquid Chromatography Mass Spectrometry (LCMS)

Molecular docking

Molecular dynamic

Malaria is an infectious disease that remains a focal point of the Sustainable Development Goals (SDGs) until 2030. In endemic areas, this disease can be fatal and cause anemia. Indonesia, as a malaria-prone country, reported 399,666 confirmed cases in 2022, with P. falciparum and P. vivax as the main species. Enhancing control efforts remains crucial program to achieve the malaria reduction targets [1].

Resistance of Plasmodium falciparum to several antimalarial drugs continues to pose a threat to malaria elimination programs, causing increased morbidity and mortality. Since 2011, Indonesia has transitioned to Dihydroartemisinin-piperaquine (DHP) as the first-line antimalarial drug regimen. Despite reports of it resistance in some regions, as of now, resistance to artemisinin has not been detected in Indonesia [2].

Artemisinin resistance has been associated with mutations in the Plasmodium falciparum Kelch 13 gene (PfK13), raising concerns in global efforts for malaria treatment and elimination. The PfK13 gene, which plays a key role in the parasite's asexual reproduction, is currently the subject of extensive investigation [3–4]. Resistance to piperaquine also occurs with the amplification of the Plasmodium falciparum Plasmepsin II gene (PfPM2), which is involved in hemoglobin degradation. This is linked to a decrease in susceptibility to piperaquine and resistance to dihydroartemisinin in vitro [5]. The apical membrane antigen of Plasmodium falciparum (PfAMA-1) plays a role in the invasion of red blood cells. Although its potential as a target for antimalarial drugs has been described, further research and experimental validation are needed to understand the role of PfAMA-1 in drug resistance mechanisms [6].

The use of natural substances for disease therapy has been a focus of research in recent years due to their ability to generate specific biological activities and their potential as pharmaceutical agents [7]. Microbial metabolites, including those derived from Streptomyces hygroscopicus Subsp. Hygroscopicus (S. hygroscopicus), have been identified as a potential source for discovering new therapeutic compounds [8]. S. hygroscopicus, a Gram-positive soil bacterium forming filaments, produces eponemycin, a proteasome inhibitor. Further research by Fitri et al. [9] indicates that three bioactive secondary metabolites from Streptomyces hygroscopicus, when administered in vivo, exhibit potential as antimalarial agents by reducing parasitemia density in mice infected with Plasmodium berghei (P. berghei).

The process of identifying compounds in the metabolites of S. hygroscopicus involves extraction, fractionation, and purification steps. The use of Mass Spectrometry (MS) is crucial in identifying the chemical composition of compounds, providing the ability to profile thousands of molecules without labeling, with high mass resolution and accuracy [10]. The importance of metabolite profiling in this stage often leads to the combination of MS with chromatographic technologies, particularly Liquid Chromatography (LC), which is suitable for non-volatile compounds. LC can be adapted for various molecules, including secondary metabolites such as alkaloids, flavonoids, isoprenes, glucosinates, oxylipins, pigments, and saponins [11].

Computational systems, particularly in Structure-Based Drug Design (SBDD), employ methods such as molecular docking and classical Molecular Dynamics (MD) simulations. Molecular docking is utilized to design inhibitor molecules by predicting binding modes in protein-receptor and ligand-molecule complexes [12–13]. MD simulations take into account the structural flexibility of the drug-target, overcoming limitations associated with static drug design and restricted ligand docking calculations [14]. In the context of antimalarial research, these techniques can be applied to drug discovery with a focus on proteins involved in antimalarial resistance, providing a dynamic and accurate approach to drug design.

In a previous study testing the effectiveness of fraction 49 showed potential as an effective antimalarial during in vitro assays [15]. Therefore, to address antimalarial drug resistance, further research is needed to examine the active compounds within Fraction 49 of the metabolite extract from S. hygroscopicus as antimalarial agents, along with an analysis of their binding. This will be accomplished through LCMS analysis and in silico methods.

Our earlier study involved the extraction and fractionation of secondary metabolites from Streptomyces hygroscopicus subsp. hygroscopicus. Bacterial inoculation was accomplished using 1000 mL of liquid International Streptomyces Project 4 (ISP4) medium with a pH of 5.0–7.0, and fermentation was carried out by combining the ISP4 medium with the bacterial inoculum The fractionation is carried out utilizing the BUCHI Reveleris® PREP Purification System Flash column chromatography.

2.1 Identification of Secondary Metabolites from Streptomyces hygroscopicus Subsp. Hygroscopicus using LCMS

The identification of compound of secondary metabolites from Streptomyces hygroscopicus was conducted using LCMS at the Central Life Sciences Laboratory of Universitas Brawijaya. The LC system employed was the Thermo Scientific Dionex Ultimate 3000 RSLCnano with an HPLC column, Hypersil GOLD (50 x 1 mm, 1.9 µm particle size), column temperature set at 30°C, analytical flow rate at 40 µL/minute, a runtime of 30 minutes, and a sample injection volume of 0.2 mL. High-Resolution Mass Spectrometry was performed using the Thermo Scientific Q Exactive Mass Spectrometry detector with a resolution of 70,000. Data were acquired based on MS2 at a resolution of 17,500, with a runtime of 30 minutes, and the results were expressed in either positive or negative polarity. The mobile phase used consisted of Solution A, a mixture of water and 0.1% ammonium formate, and Solution B, comprising 0.1% formic acid and acetonitrile. The LCMS employed a reverse column using a polar solvent, allowing non-polar compounds, which are suitable drug candidates, to elute faster. The results were analyzed using the Compound Discoverer application with mzCloud MS/MS Library.

2.2 Analysis of Pharmacokinetic Profile

The pharmacokinetic profile includes Absorption, Distribution, Metabolism, and Excretion (ADME), as well as toxicity (T). The identification of pharmacokinetics involves Lipinski’s Rule of Five to distinguish between drug-like and non-drug-like molecules. Additionally, screening for the bioavailability of active compounds is performed based on Veber’s rules and Egan’s rules. The pharmacokinetic profile is obtained using the SwissADME website by entering the SMILES formula of the compound.

2.3 Preparation of Ligand and Target Protein Structures

The target protein database was obtained through literature studies. Target protein screening was conducted by searching for keywords such as "Dibutyl phthalate," "Dihydroyashabushiketol," "Dibenzylamine," "Sedanolide," "Plasmodium," "Malaria," "Antimalaria," "target protein," "resistance," and "molecular docking." In this research, PfK13 (PDB ID: 4YY8), PfPM2 (PDB ID: 1LF4), and PfAMA-1 (PDB ID: 4R1B) were used as target proteins. The three-dimensional (3D) structures of the target proteins were obtained from the Protein Data Bank and optimized using PyMOL 2.5.2. Ligand structures were identified from derivatives of S. hygroscopicus through LC/MS. The identified derivative compounds were searched in PubChem: Dibutyl phthalate (PubChem ID: 3026), Dihydroyashabushiketol (PubChem ID: 10265808), Dibenzylamine (PubChem ID: 7656), and Sedanolide (PubChem ID: 5018391). Information about control ligands for each target protein was sought from the Protein Data Bank (RSCB) and literature reviews. If no database was found, structure generation was performed using MarvinSketch.

2.4 Reverse Molecular Docking Studies

Reverse molecular docking was performed using PyRx 0.9.5 software based on a grid box control system, utilizing the original ligands of the target proteins and calculating grid energy. The reverse molecular docking was conducted among the ligands (Dibutyl phthalate, Dihydroyashabushiketol, Dibenzylamine, Sedanolide), control ligands, and target proteins (PfK13, PfPM2, and PfAMA-1). The grid box was positioned around the binding site of the target protein. For the PfK13 target, the dimensions of the grid box were generated by setting the number of points in the x, y, and z directions to 11 x 11 x 8, with the center of the grid box at (-16.3303, -31.0358, 14.9277), and an exhaustiveness of 8. For the PfPM2 target, the dimensions were generated with 20 x 15 x 18 points, the center at (-4.3484, -6.6081, 36.0706), and an exhaustiveness of 8. Meanwhile, for the PfAMA-1 target, the dimensions were generated with 10 x 10 x 10 points, the center at (-22.3033, 31.9615, 105.7381), and an exhaustiveness of 8. To ensure accuracy, the docking process was repeated three to five times for each target protein with control ligands and each active compound. Subsequently, the average binding affinity value was calculated. The binding affinity of S. hygroscopicus ligands was compared with control ligands for each target protein.

2.5 Visualization of Binding Analysis and Protein-Ligand Interactions

The binding affinity values between the active compounds and proteins were compared with the control ligands and visualized using LigPlot 2.2.8. The visualization process aimed to assess the consistency between the formed residues and the resulting binding affinity values (Supplementary table). The analysis proceeded by evaluating the similarity of the binding between the active compounds and the target proteins, as well as the control ligands and the target proteins. This was done to identify the binding locations of the active compounds with the target proteins.

2.6 Molecular Dynamics Analysis

Molecular Dynamics (MD) of the protein-ligand complex was examined using YASARA (version 19.14.12) and compared with its control ligand complex. The Amber ff14sb force field was applied under the following conditions: temperature of 298 °K, pressure of 1 Barr, pH of 7.4, NaCl concentration of 0.9%, water density of 0.997, and a simulation time of 100 nanoseconds (ns). The results obtained from the GROMACS application include Root Mean Standard Deviation (RMSD) and Root Mean Square Fluctuation (RMSF).

3.1 Identification of Fraction 49 Content Using LCMS

Based on the LCMS analysis, 18 compounds were identified in fraction 49. The identified compounds underwent screening using PubChem and Way To Drug Activity to search for active compounds with the potential for antimalarial activity. Through this search, several active compounds were identified that exhibited potential antimalarial activity (Table 1). The analysis results include active compounds identified based on their molecular weight and fragment peaks at specific Retention Times (RT). Compounds that elute faster are considered more non-polar, especially when using a reverse column. This information helps in characterizing the polarity and behavior of compounds, as those with faster elution times in a reverse column are indicative of being more non-polar in nature. In this study, four compounds with the fastest retention times were used: Dibutyl phthalate, Dihydroyashabushiketol, Dibenzylamine, and Sedanolide.

3.2 Prediction of Pharmacokinetic Profiles of Active Compounds

The analysis was carried out by inputting the SMILES formulas of the four active compounds into a platform that provides ADMET prediction services, specifically the SwissADME website. Subsequently, an analysis was performed, revealing the pharmacokinetic profiles of these active compounds (Tabel 2).

3.3 Reverse molecular Docking Analysis of Active Compound Fraction 49 Metabolite Extract of S. hygroscopicus

Based on the results of reverse molecular docking, it was found that the binding affinity of Artemisinin-PfK13 was more negative compared to the binding affinity of Artemisinin with the active compound Fraction 49. Likewise, the binding affinity of Piperaquine-PfPM2 and Artemisinin-PfAMA-1 had a more negative value compared to the compound Fraction 49. So, the binding between the control and the control ligand was stronger than the control bond with the active compound Fraction 49 (Table 3).

Table 1

The results of fraction 49 secondary metabolites *S. hygroscopicus* identification using LCMS
Retention time (min)	Compound	Molecular formula	Molecular weight (m/z)
0.90	Dibenzylamine	C₁₄H₁₅N	197.27
0.91	(5S)-5-hydroxy-1,7-diphenylheptan 3-one (Dihydroyashabushiketol)	C₁₉H₂₂O₂	282.4
14.66	Palmitic Acid	C₁₆H₃₂O₂	256.42
17.43	Dibutyl phthalate	C₁₆H₂₂O₄	278.34
20.62	Sedanolide	C₁₂H₁₈O₂	194.27
20.62	Acetophenone	C₈H₈O	120.15
20.65	Eicosapentaenoic acid	C₂₀H₃₀O₂	302.5
21.48	Oleyl anilide	C₂₄H₃₉NO	357.6
21.65	Oleamide	C₁₈H₃₅NO	281.5
21.80	Hexadecanamide	C₁₆H₃₃NO	255.44
21.99	Oleolyl ethanolamide	C₂₀H₃₉NO₂	325.5
22.05	Methanandamide	C₂₃H₃₉NO₂	361.6
22.67	1-Stearoylglycerol	C₂₁H₄₂O₄	358.6
22.95	Bis(2-ethylhexyl) phthalate	C₂₄H₃₈O₄	390.6
23.33	Stearoyl Ethanolamide	C₂₀H₄₁NO₂	327.5
23.88	Stearamide	C₁₈H₃₇NO	283.5
25.61	Erucamide	C₂₂H₄₃NO	337.6
26.44	Hexamethylenetetramine	C₆H₁₂N₄	140.19

Table 2

Prediction of pharmacokinetic profile of active compound fraction 49
	Dibutyl phthalate	Dihydroyashabushiketol	Dibenzylamine	Sedanolide
Physicochemical Properties
Molecular weight (g/mol)	278.34	282.38	197.28	194.27
Rotatable bonds	10	8	4	3
H-bond acceptor	4	2	1	2
H-bond donor	0	1	1	0
TPSA	52.60 Å²	37.30 Å²	12.03 Å²	26.30 Å²
Lipofilisitas
cLogP	3.69	3.62	2.98	2.87

Table 3

Reverse molecular docking result of active compound fraction 49
PDB ID	Protein target	Control ligand	Active compound	Binding affinity (Kcal / mol)
				Control	Compound
4YY8	PfK13	Artemisinin	Dibutyl phthalate	-8.1	-6.4
			Dihydroyashabushiketol		-7.4
			Dibenzylamine		-6.4
			Sedanolide		-6.2
1LF4	PfPM2	Piperaquine	Dibutyl phthalate	-8.9	-5.8
			Dihydroyashabushiketol		-7.1
			Dibenzylamine		-6.3
			Sedanolide		-6.0
4R1B	PfAMA-1	Artemisinin	Dibutyl phthalate	-6.8	-5.4
			Dihydroyashabushiketol		-5.6
			Dibenzylamine		-5.7
			Sedanolide		-5.7

3.4 Binding Interaction of Active Compound Fraction 49 to Protein Targets

The analysis of the binding interactions between the active compounds with the target protein was performed using the LigPlot software. The interactions obtained involve hydrophobic binding interactions (Fig. 1). Dibutyl phthalate has 1 hydrogen bond with Phe483(A), similar to the hydrophobic bond in the control ligand with PfK13. It forms 11 hydrophobic bonds, with 6 of them (His719(A), Tyr482(A), Ser720(A), Gly718(A), Ser624(A), Gly625(A)) being identical to both hydrogen and hydrophobic bonds in the control ligand with PfK13. For PfPM2, Dibutyl phthalate has 2 interacting hydrogen bonds (Arg307(A), similar to the hydrophobic bond in the control ligand) and forms 8 hydrophobic bonds, with 5 of them (Tyr272(A), Val160(A), Gln275(A), Glu271(A), His276(A)) being similar to the hydrophobic bonds in the control ligand with PfPM2. For PfAMA-1, Dibutyl phthalate has no hydrogen bonds, matching the control ligand, but has 9 hydrophobic bonds, with 7 of them (Lys364(A), Tyr390(A), Arg143(A), Pro350(A), Gln349(A), Glu365(A), Glu361(A)) being similar to the hydrophobic bonds in the control ligand with PfAMA-1. These interactions suggest the potential of Dibutyl phthalate as an antimalarial drug, especially against PfK13 and PfPM2.

Dihydroyashabushiketol has 2 hydrogen bonds (Gly533(A), Ser485(A)) identical to the hydrophobic bond of the control ligand on PfK13. It forms 12 hydrophobic bonds, with 7 of them (Ala578(A), Gly625(A), Tyr482(A), His719(A), Gly718(A), Phe483(A), Ser720(A)) being similar to the control ligand on PfK13. For PfPM2, Dihydroyashabushiketol has 3 hydrogen bonds (Arg307(A), Tyr272(A), Gln275(A)) similar to the hydrophobic bonds in the control ligand, and has 7 hydrophobic bonds, with 3 of them (His276(A), Val160(A), Glu271(A)) being similar to the hydrophobic bonds in the control ligand with PfPM2. For PfAMA-1, Dihydroyashabushiketol has 2 hydrogen bonds (Arg143(A), Lys364(A)) similar to the hydrophobic bonds in the control ligand, and has 6 hydrophobic bonds, with 5 of them (Tyr390(A), Pro350(A), Lys351(A), Glu361(A), Glu365(A), Gln349(A)) being similar to the hydrophobic bonds in the control ligand with PfAMA-1. These interactions indicate the potential of Dihydroyashabushiketol as an antimalarial drug, especially against PfK13, PfPM2, and PfAMA-1.

Dibenzylamine has 2 hydrogen bonds (Ser720(A), Phe483(A)) identical to the hydrophobic bond of the control ligand with PfK13. It has 9 hydrophobic bonds, with 5 of them (His719(A), Ala578(A), Gly625(A), Gly718(A), Tyr482(A)) being similar to both hydrogen and hydrophobic bonds in the control ligand with PfK13. For PfPM2, Dibenzylamine has 1 hydrogen bond (Glu271(A)) similar to the hydrophobic bond in the control ligand and forms 7 hydrophobic bonds, with 4 of them (Gln275(A), Val160(A), Arg307(A), Tyr272(A)) being similar to the hydrophobic bonds in the control ligand with PfPM2. For PfAMA-1, Dibenzylamine has no hydrogen bonds, similar to its control ligand, but has 7 hydrophobic bonds (Glu365(A), Tyr390(A), Gln349(A), Lys364(A), Arg143(A), Pro350(A), Glu361(A)) similar to the hydrophobic bonds in the control ligand with PfAMA-1. These interactions suggest that Dibenzylamine has the potential to bind with PfK13, PfPM2, and PfAMA-1 as an antimalarial drug.

Sedanolide has 1 hydrogen bond that does not share similarity with the control ligand with PfK13. It has 11 hydrophobic bonds, with 7 of them (Gly625(A), Ala578(A), His719(A), Phe483(A), Gly718(A), Tyr482(A), Ser720(A)) being similar to both hydrogen and hydrophobic bonds in the control ligand with PfK13. For PfPM2, Sedanolide has 1 hydrogen bond (Arg307(A)) similar to the hydrophobic bond in the control ligand and forms 4 hydrophobic bonds, with 3 of them (Tyr272(A), Val160(A), Glu271(A)) being similar to the hydrophobic bonds in the control ligand with PfPM2. For PfAMA-1, Sedanolide has no hydrogen bonds, similar to its control ligand, but has eight hydrophobic bonds, with 7 of them (Tyr390(A), Lys364(A), Arg143(A), Pro350(A), Glu365(A), Gln349(A), Glu361(A)) similar to the hydrophobic bonds in the control ligand with PfAMA-1.

3.5 Molecular Dynamic Analysis

The results of molecular dynamics simulations require analysis, such as RMSD and RMSF analysis. The RMSD values for Dihydroyashabushiketol-PfK13 were lower (mean 2.49 Å) compared to the control ligand (mean 4.51 Å). Dihydroyashabushiketol-PfK13 exhibited a stable graph during the simulation, indicating that the protein could maintain its position well over 100 ns. The RMSD simulation for Dihydroyashabushiketol-PfPM2 showed an unstable graph with higher values (mean 3.07 Å) compared to its control (mean 2.22 Å). Meanwhile, the RMSD simulation for Dihydroyashabushiketol-PfAMA-1 (mean 2.18 Å) was similar to its control ligand (mean 2.06 Å), refer to Fig. 2. Therefore, Dihydroyashabushiketol has the potential for better binding stability with the PfK13 target protein and the same binding stability as its control with the PfAMA-1 target protein. However, the RMSD for Dihydroyashabushiketol-PfPM2 indicates an unstable binding.

The binding of the compound Dihydroyashabushiketol with PfK13, PfPM2, and PfAMA-1, it was found that there were low fluctuations in the same amino acids as the control, indicating that this ligand has the potential to act as an antagonist against each target protein (Fig. 3).

The average binding energy value of Dihydroyashabushiketol-PfK13 (6,115 Kcal/mol) is higher than PfK13-Artemisinin (1,968 Kcal/mol). The average binding energy value of Dihydroyashabushiketol-PfPM2 (-8,301 Kcal/mol) is higher than PfPM2-Piperaquine (-131,021 Kcal/mol). Meanwhile, the average binding energy value of Dihydroyashabushiketol-PfAMA-1 (-102,734 Kcal/mol) is lower than PfAMA-1-Artemisinin (-87,622 Kcal/mol) (Fig. 4). This shows that the affinity and stability of the Dihydroyashabushiketol ligand towards PfAMA-1 is better than the Artemisinin ligand (control).

4.1 LCMS Analysis

The process of identifying active compounds in the fraction of secondary metabolites from S. hygroscopicus involves LCMS analysis. The results of the analysis include active compounds identified based on their molecular weight and fragment peaks at specific retention time. The identification process is influenced by the completeness of the reference data source [16].

Dibutyl phthalate (C₁₆H₂₂O₄), a phthalate ester, has garnered attention in drug research due to its structural similarity to antimalarial compounds [17]. Research suggests that Dibutyl phthalate has the potential as a GSK-3 (Glycogen synthase kinase 3) inhibitor, with a more selective mechanism. However, its effectiveness as an antimalarial agent related to PfGSK-3 suppression needs further understanding [17]. Other studies also indicate that Dibutyl phthalate in H11809 exhibits in vitro anti-plasmodial activity and enhances Ser 9 phosphorylation of GSK3β. These findings suggest a potential contribution of Dibutyl phthalate to the observed antimalarial effects for H11809 [18]. Nevertheless, further approaches are needed to comprehensively understand the antimalarial mechanism of action of Dibutyl phthalate.

Dihydroyashabushiketol (C₁₉H₂₂O₂), a quinine derivative, is an antimalarial compound found in the bark of Cinchona. As part of the Cinchona alkaloid mixture, this compound has been used for centuries in the treatment of malaria. Similar to quinine, dihydroyashabushiketol disrupts the life cycle of malaria parasites, particularly in their replication within red blood cells. Although quinine has been replaced by more effective antimalarial drugs, such as artemisinin-based therapy, dihydroyashabushiketol is still employed in specific regions or cases where other drugs may be less effective.Previous research has revealed that diarylheptanoid compounds from Alnus species, including platyphyllenone, alusenone, yashabushiketo, dihydroyashabushiketol, hirustenone, and hirsutanonol, possess important pharmacological properties. The chloroform extract from the leaves of A. nepalensis, containing diarylheptanoids, exhibited anti-malarial activity. This extract could reduce parasitemia and inflammatory mediators involved in malaria pathogenesis (TNF-α, IFN-γ, and IL-6) [19].

Dibenzylamine (C₁₄H₁₅N) is an organic amine compound commonly used in organic synthesis and has various applications depending on specific industries and chemical processes. While it is not associated with antimalarial properties, this compound shares similarities with triazole molecules that exhibit antimicrobial, antioxidant, anticancer, antiviral, anti-HIV, antituberculosis, and anticancer activities [20–21]. Research indicates its anticonvulsant properties through the inadvertent inhibition of voltage at the N-methyl-D-aspartate (NMDA) receptor [22–23]. Further studies suggest its potential as an anti-obesity compound derived from the fermentation of Clitoria ternatea kombucha [24–25].

Sedanolide (C₁₂H₁₈O₂) is a natural compound found in celery seed oil and celery fruit extracts. It has been used in herbal formulations to address inflammatory conditions such as gout and rheumatism. This compound is non-toxic and exhibits limited bioactivity against cell oxidation in in vitro tests [26]. Sedanolide is a dominant component in celery fruit extracts, showing significant antibacterial activity against various bacteria, including Staphylococcus aureus and Bacillus species. Additionally, sedanolide is also found in coriander essential oil, exhibiting strong antioxidant, antimicrobial, and anti-inflammatory properties [27]. Further research suggests that Bifidobacterium longum R0175 can stimulate the production of sedanolide, making it a promising probiotic for addressing liver diseases [28].

4.2 Prediction of Pharmacokinetic Profile

The pharmacokinetic profile that needs attention includes ADME and then toxicity. ADME explains how the body processes a drug when it is in the body [29–30]. Drug-likeness is determined based on the chemical structure and physicochemical properties. ADME predictions can provide information about oral bioavailability, cell permeability, metabolism, elimination, and toxicity, which are characteristics of the pharmacokinetics and pharmacodynamics of a drug molecule [31].

In addition to ADME, the evaluation of drug-likeness and the oral bioavailability value of the test compound also play a crucial role in drug development. Analysis of drug-like properties is based on Lipinski's rule of five. which includes criteria such as molecular weight of the compound less than 500 mg/dL, lipophilicity (clogP) less than 5, the number of hydrogen bond donors less than 5, and the number of hydrogen bond acceptors less than 10. A compound is predicted to have good absorption and permeability if two or more of these criteria are met [32]. To evaluate oral bioavailability, Veber’s Rule can be used with criteria of the number of Rotatable Bonds (ROTB) < 10 and Topological Polar Surface Area (TPSA) < 140 Å2 [33]. In this study, all four active compounds in fraction 49 meet all Lipinski and Veber's Rule criteria (Table 2).

4.3 Reverse Molecular Docking

The method employed is reverse molecular docking, predicting the interaction between a single ligand, namely the active compound, with multiple target proteins. Reverse Molecular Docking is performed on the active compound with control ligands against the target proteins PfK13, PfPM2, and PfAMA-1. Binding affinity reflects the ability of a compound to bind to a target protein. The strength of this binding is influenced by free binding energy, surface interactions, and intermolecular interactions [34]. Free binding energy describes the stability and spontaneity of binding; lower energy values indicate stable binding and significant biological activity potential. Binding stability can be considered high if the binding affinity value is < -6.0 kcal/mol [35]. The compounds Dibutyl phthalate, Dihydroyashabushiketol, Dibenzylamine, and Sedanolide, each with their respective control ligands, were paired and analyzed by comparing their binding affinities to the target proteins.

4.4 Interaction of Active Compounds in Fraction 49 with Target Proteins

In drug design, understanding molecular interactions, particularly hydrophobic interactions and hydrogen bonding, is crucial for optimizing binding affinity with the target. Hydrophobic interactions arise from the tendency of nonpolar molecules to reduce contact with water, and optimizing these interactions can enhance the binding affinity of ligands to the target. Achieving a more stable complex involves selecting or designing drug candidates with hydrophobic groups on the protein surface [36]. Hydrogen bonding plays a vital role in facilitating binding between proteins and ligands, stabilizing the interaction between the drug and its target protein, thereby enhancing binding affinity and selectivity. Ligand design processes in drug development generally focus on enhancing hydrophobic interactions to influence the ADMET characteristics of the ligand [37]. Optimization of binding affinity and ligand efficacy, related to hydrophobic interactions, can be achieved by combining them with hydrogen bonding. Increasing the number of hydrophobic atoms in the ligand-target active site can enhance the biological activity of the drug [36]. Interaction of active compounds in fraction 49 with target proteins are presented in Fig. 1.

These interactions suggest that Sedanolide has the potential to bind with PfK13, PfPM2, and PfAMA-1 as an antimalarial drug. Similarities in these interactions can predict the reliability of docking results and the accuracy of these predictions [36]. This implies that these compounds have the ability to bind to the target protein in a manner similar to that of the control ligand, involving hydrogen bonding and hydrophobic interactions with the target protein.

4.5 Molecular Dynamics Analysis

Molecular dynamics simulation is utilized to analyze the interaction between protein-compound complexes selected from docking studies, indicating an optimal level of binding affinity. Ligand RMSD movement is employed to observe how the protein-ligand complexes interact while in motion [38]. RMSD is a parameter of similarity based on the comparison of atomic distances in similar compounds. Sharp fluctuations in the RMSD graph may indicate protein binding breaking at a specific distance, leading to protein denaturation [39]. RMSD values of 2.0–5.0 Å can be accepted for a system to be considered stable [40]. Dihydroyashabushiketol-PfK13 shows a stable graph in the simulation, indicating that the protein can maintain its stable position well over 100 ns. The RMSD simulation for Dihydroyashabushiketol-PfAMA-1 is the same as its control ligand. Thus, Dihydroyashabushiketol has the potential for binding stability similar to its control against the PfAMA-1 target protein. However, the RMSD for Dihydroyashabushiketol-PfPM2 indicates an unstable binding (Fig. 2)

The RMSF values describe the fluctuation or conformational shift of amino acid residues forming the receptor during the simulation process, reflecting the flexibility of these residues [41]. In the binding of Dihydroyashabushiketol with PfK13, PfPM2, and PfAMA-1, low fluctuations were observed in amino acids involved in binding (Fig. 3), suggesting that the ligand has the potential as an antagonist against the target proteins. Negative and relatively small binding energy values indicate that the ligand's conformation formed in the protein-ligand complex is at a stable conformation. Dihydroyashabushiketol forms a more stable conformation, and the non-covalent interaction strength with the PfAMA-1 target is higher, while for the PfK13 and PfPM2 target proteins, the ligand's conformation and non-covalent interaction strength are less favorable (Fig. 4).

Based on this study, it can be concluded that the four compounds with the highest retention time, namely Dibutyl phthalate, Dihydroyashabushiketol, Dibenzylamine, and Sedanolide, are present in the active fraction 49 of the secondary metabolite extract from Streptomyces hygroscopicus Subsp. Hygroscopicus through LCMS analysis and meet drug-likeness criteria. Dihydroyashabushiketol shows the highest binding affinity values in almost all target proteins compared to other compounds, indicating its potential as an antimalarial agent. This compound has a stable and flexible structure towards PfK13 and PfAMA-1 target proteins but not towards PfPM2. Therefore, Dihydroyashabushiketol has the potential as an antimalarial agent, especially against PfK13 and PfAMA-1 targets.

Author contributor

This work was collaboration among all authors. All authors had equal work in designing study outlines. LEF, NW, SAA, and AAF contributed in conceptualization. LEF and NW supervised the research work and contributed in funding acquisition. SAA and AAF contributed in data curation. LEF and SAA wrote the initial draft of the manuscript. LEF, NW, SAA, and AAF edited, reviewed, and revised the manuscript. All authors read and approved the submitted version of the manuscript.

Declaration of competing interest

Authors declare that there are no conflicts of interest and have reviewed and approve of the contents of the manuscript and its conclusions. Best wishes.

Acknowledgments

Authors would like to thank to all members of Malaria Research Group, Faculty of Medicine Universitas Brawijaya especially to Nabila Erina Erwan, MD., M.Biomed and Ajeng Maharani Putri, MD., M. Biomed, Dr. Alfian Wika Cahyono, MD., M.Biomed. who have helped in the research working process. This work was financially supported by Faculty of Medicine Universitas Brawijaya, Indonesia (Grant number 2371/UN10.F08/PN/2022).

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Active compound profiling in fraction 49 of a secondary metabolite extract from Streptomyces hygroscopicus subsp. Hygroscopicus as antimalaria drug using LCMS, molecular docking, and molecular dynamics

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Identification of Secondary Metabolites from Streptomyces hygroscopicus Subsp. Hygroscopicus using LCMS

2.2 Analysis of Pharmacokinetic Profile

2.3 Preparation of Ligand and Target Protein Structures

2.4 Reverse Molecular Docking Studies

2.5 Visualization of Binding Analysis and Protein-Ligand Interactions

2.6 Molecular Dynamics Analysis

3. Results

3.1 Identification of Fraction 49 Content Using LCMS

3.2 Prediction of Pharmacokinetic Profiles of Active Compounds

3.3 Reverse molecular Docking Analysis of Active Compound Fraction 49 Metabolite Extract of S. hygroscopicus

3.4 Binding Interaction of Active Compound Fraction 49 to Protein Targets

3.5 Molecular Dynamic Analysis

4. Discussion

4.1 LCMS Analysis

4.2 Prediction of Pharmacokinetic Profile

4.3 Reverse Molecular Docking

4.4 Interaction of Active Compounds in Fraction 49 with Target Proteins

4.5 Molecular Dynamics Analysis

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Active compound profiling in fraction 49 of a secondary metabolite extract from Streptomyces hygroscopicus subsp. Hygroscopicus as antimalaria drug using LCMS, molecular docking, and molecular dynamics

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Identification of Secondary Metabolites from Streptomyces hygroscopicus Subsp. Hygroscopicus using LCMS

2.2 Analysis of Pharmacokinetic Profile

2.3 Preparation of Ligand and Target Protein Structures

2.4 Reverse Molecular Docking Studies

2.5 Visualization of Binding Analysis and Protein-Ligand Interactions

2.6 Molecular Dynamics Analysis

3. Results

3.1 Identification of Fraction 49 Content Using LCMS

3.2 ​Prediction of Pharmacokinetic Profiles of Active Compounds

3.3 Reverse molecular Docking Analysis of Active Compound Fraction 49 Metabolite Extract of S. hygroscopicus

3.4 Binding Interaction of Active Compound Fraction 49 to Protein Targets

3.5 Molecular Dynamic Analysis

4. Discussion

4.1 LCMS Analysis

4.2 Prediction of Pharmacokinetic Profile

4.3 Reverse Molecular Docking

4.4 Interaction of Active Compounds in Fraction 49 with Target Proteins

4.5 Molecular Dynamics Analysis

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

3.2 Prediction of Pharmacokinetic Profiles of Active Compounds