Antimicrobial Profiling of Piper betle L. and Piper nigrum L. Against Methicillin-Resistant Staphylococcus aureus (MRSA): Integrative Analysis of Bioactive Compounds Based on FT-IR, GC-MS, and Molecular Docking Studies

Abstract

This study explored the antimicrobial potential of Piper betle L. (PBL) and Piper nigrum L. (PNL) extracts against MRSA. Plant parts including stem, leaf, and fruit were extracted using aquadest, methanol, and hexane, resulting in 18 distinct extracts. FT-IR combined with cluster analysis (CA) categorized the extracts, and anti-MRSA activity was assessed through the paper disk diffusion method. The most potent extracts were further analyzed using GC-MS to identify bioactive compounds. Additionally, molecular docking studies were conducted for MRSA protein targets (4DKI, 6H5O, and 4CJN). The hexane extract of PNL and the aqueous extract of PBL fruit showed the strongest inhibitory effects. GC-MS identified piperine (14.22%) and diisooctyl phthalate (14.67%) as major compounds, with piperolein B, piperanine, β-caryophyllene oxide, and α-caryophylladienol as minor compounds in the hexane extract of PNL, while hydroxychavicol (81.89%) and chavibetol (12.01%) were predominant in the aquadest extract of PBL. Molecular docking revealed that piperolein B and piperine had strong binding affinities to MRSA proteins 4DKI, 6H5O, and 4CJN, comparable to ciprofloxacin. In conclusion, this study confirms the potential of PBL and PNL as sources of novel anti-MRSA agents, supporting further research to develop new therapies.

1. Introduction

Methicillin-resistant Staphylococcus aureus (MRSA) is considered a significant global health threat due to the increasing resistance to multiple antibiotics. Commonly colonizing human skin and nasal passages, S. aureus strains have developed resistance to methicillin, rendering traditional antibiotic treatments less effective [1]. MRSA, alongside other antibiotic-resistant pathogens including Pseudomonas aeruginosa and Enterococcus faecium, has contributed to increased morbidity and mortality rates, as well as escalating healthcare costs [2]. Popovich et al. [3] reported the prevalence of MRSA as a leading cause of nosocomial infections worldwide, underscoring the critical need for effective infection control measures. Moreover, infections are frequently associated with severe complications, including necrotizing pneumonia, and endocarditis that can lead to sepsis. The limited therapeutic options for MRSA infections have led to prolonged hospitalization and increased mortality rates [4].

In response to the increasing threat, natural products have been proposed as promising sources of novel anti-MRSA compounds [5]. Species of the Piper genus, such as Piper betle and Piper nigrum, have garnered significant study interest due to their diverse phytochemical constituents and anti-MRSA activity. Both plants are used traditionally in medicinal systems to treat various diseases.

The rich phytochemical profile, which includes alkaloids, flavonoids, and terpenoids, contributes to the antimicrobial potential. Key bioactive compounds identified in the P. betle include eugenol, chavicol, and chavibetol. Eugenol, for instance, is renowned for its potent antiseptic properties and has been reported to exhibit antimicrobial activity [6]. P. nigrum contains piperine, which imparts the characteristic pungent flavor of black pepper. Piperine has broad-spectrum antimicrobial activity against Gram-positive bacteria such as S. aureus [7].

Therefore, P. betle and P. nigrum have significant potential as natural sources for the development of novel antimicrobial agents, particularly against antibiotic resistance. To maximize the anti-MRSA activity of these plants, optimizing the extraction methodologies is crucial for achieving high yields of bioactive compounds [8]. Factors such as solvent type, solid-to-solvent ratio, extraction time, and temperature can significantly influence the efficiency of extracting target compounds. Previous studies have demonstrated that an appropriate extraction method can substantially enhance the bioactive content of plant extracts [9]. For instance, polar solvents, including methanol or ethanol, are often effective for extracting phenolic compounds such as eugenol and flavonoids from the P. betle and P. nigrum [10]. Furthermore, advanced extraction methods, including ultrasound-assisted and microwave-assisted extraction, can improve efficiency and reduce processing time [11]. This implies that optimization of extraction procedures is a crucial initial step in developing natural products using P. betle and P. nigrum as anti-MRSA agents.

Following the acquisition of plant extracts using optimized extraction methods, metabolomic profiling was performed to identify the bioactive compounds responsible for anti-MRSA activity. Fourier Transform Infrared (FTIR) spectroscopy can be used to obtain the chemical profiles of the extracts and provide preliminary information about the presence of major functional groups [12]. Cluster analysis (CA) was applied to group samples based on FTIR spectral similarity, facilitating the identification of samples with high potential anti-MRSA activity. Gas chromatography-mass spectrometry (GC-MS) is a powerful method for the identification and quantification of volatile and semivolatile compounds in plant extracts [13].

Integrating FTIR, CA, and GC-MS is crucial to obtaining a comprehensive metabolomic profile of plant extracts and identifying key bioactive compounds contributing to anti-MRSA activity. The study demonstrates the effectiveness of a multi-technique approach in characterizing plant metabolites and identifying novel bioactive compounds [14]. FTIR spectroscopy is considered a valuable tool for qualitative and quantitative analysis of major components in plant extracts, while GC-MS is the gold standard for the identification and quantification of volatile compounds. Integrating metabolomic data from in silico and in vitro studies is a crucial synergistic method for drug discovery [15].

Following the identification of potential bioactive compounds from the P. betle and P. nigrum through GC-MS, in silico studies, such as molecular docking, can be used to predict the molecular interactions of compounds with MRSA bacteria protein targets. This in silico information is crucial to filter candidate compounds validated through in vitro antibacterial assays using MRSA clinical isolates [16]. By integrating CA of extract variability profiles, in silico metabolomics, and in vitro experimental validation, this study presents an innovative approach for identifying novel bioactive compounds in the P. betle and P. nigrum. The multi-disciplinary approach not only enables accurate identification of potential anti-MRSA agents but also provides insights into the chemical diversity within plant extracts and their correlation with biological activity.

2. Materials and Methods

2.1. Plant Material

Fresh samples of P. betle (green betel) and P. nigrum (black pepper) stems, fruits, and leaves were procured from Puty Village, Bua District, Luwu Regency, and South Sulawesi, Indonesia (coordinates: −3.109000, 120.217502). Botanical authentication of the collected plant materials was rigorously conducted at the Botanical Laboratory, Department of Biology, Mathematics, and Natural Sciences, Universitas Negeri Makassar, Indonesia. This process ensured the accurate identification and verification of plant species used in the study.

2.2. Extraction Methods

The plant materials were dried in an oven (IL80EN) which was maintained at a temperature of 40–50 °C for three days [17]. The dried plant matter was then pulverized using a blender (IC-10B) set at 25.000 rpm and sieved through a 40/60 mesh sieve to obtain a fine powder. The powdered material was subsequently stored in airtight containers for further analysis. Ultrasonic-assisted extraction (BRANSON 1800) was then performed to extract bioactive compounds from the plant materials. For each sample, 20 g was extracted using 200 mL of aquadest, methanol, or hexane in an ultrasonic bath for 30 min, maintaining a solvent-to-solid ratio of 1:10 throughout the process. Following extraction, the mixtures were filtered, and the solvents were evaporated using either a rotary evaporator (BUCHI) or a freeze-dryer (BUCHI) to obtain concentrated or dry extracts, respectively. The extraction yield was determined using Formula (1), as suggested by Yasir et al. [17].

$$\% Yield extract = ({\displaystyle \frac{Dry extract weight }{Weight of dried materials}}) \times 100\%$$

(1)

2.3. FTIR Analysis

FTIR Spectroscopy analysis was conducted on 18 Piper extract varieties. To prepare the samples, 5 mg of each extract was mixed with 0.5 mg of potassium bromide (KBr) and 5 mL of analytical-grade methanol. The mixture was homogenized and dried to form a pellet, which was then analyzed using a Perkin Elmer Spectrum GX FTIR spectrometer (8400S, Waltham, MA, USA). Spectra were collected over a wavelength range of 400–4000 cm⁻¹ with 16 scans per sample.

To identify similarities and differences between the extracts, a chemometric analysis using cluster analysis (CA) was performed with Minitab 18 software. The linkage method was set to complete, the distance measure was absolute correlation, and the number of clusters was set to 6. CA was applied to group the 18 extracts based on spectral profiles. Extracts with a similarity above 80% were considered to have similar chemical characteristics [18]. Based on the CA results, six clusters were identified, and each cluster was selected for anti-MRSA activity testing.

2.4. Anti-Methicillin-Resistant Staphylococcus aureus Activity

The MRSA bacteria used in this study were patient isolates from Hasanuddin University Teaching Hospital with a pronumber code (050402003763271). These isolates have shown significant resistance (p > 0.05) against various antimicrobials, including flomoxef, latamoxef, benzylpenicillin, nafcillin, amoxicillin, amoxicillin/clavulanic acid, ampicillin/sulbactam, carbenicillin, ticarcillin, ticarcillin/clavulanic acid, azlocillin, piperacillin, piperacillin/tazobactam, cloxacillin, dicloxacillin, flucloxacillin, and methicillin. Others include oxacillin mic, oxacillin, cefaclor, cefadroxil, cefixime, cefpodoxime, ceftibuten, cefmenoxime, cefoperazone, cefotaxime, cefoxitin, ceftazidime, ceftizoxime, ceftriaxone, cefepime, cefpirome, doripenem, ertapenem, faropenem, imipenem, and meropenem. Based on the antimicrobial testing conducted on 27 September 2023, the isolates showed sensitivity (p < 0.05) to gentamicin, ciprofloxacin, levofloxacin, moxifloxacin, and Ofloxacin, with the card number (AST-GP67) and lot number (1322462503).

For antimicrobial activity testing, Mueller-Hinton agar (MHA) was added to Petri dishes. A 0.5 mL suspension of MRSA bacteria, prepared to match the McFarland standard of 0.5, was evenly spread across the surface of the MHA using a sterile swab [19]. The Petri dishes were then divided into three sections, one each for negative control, positive control, and test samples. Dimethyl Sulfoxide (DMSO) at 0.05 mL was used as the negative control, while ciprofloxacin (15 μg/disk) represented the positive control. The test samples included the following extracts, which are PNL aquadest stem extract, PNL aquadest leaf extract, PNL aquadest fruit extract, PNL hexane fruit extract, PBL methanol stem extract, and PBL aquadest fruit extract. All samples were tested at a concentration of 200 mg/mL using the paper disk method, in which paper disks were soaked in the samples for 5 min before being placed on the surface of MHA inoculated with MRSA. The Petri dishes were then incubated at 37 °C for 24 h. Subsequently, the inhibition zones (clear zones) around the paper disks were observed and measured using a caliper in millimeters (mm) [20]. The data were then analyzed using analysis of variance (ANOVA) and Tukey’s pairwise comparisons to determine significant differences between treatments. The samples with the highest antimicrobial activity were further analyzed by GC-MS for the characterization of active metabolites with anti-MRSA activity.

2.5. GC-MS Analysis

GC-MS analysis was conducted at the Chemical Engineering Laboratory, Politeknik Negeri Ujung Pandang (PNUP), Makassar City, Indonesia. The test samples exhibiting the highest anti-MRSA activity, namely the PNL hexane fruit extract and the PBL aquadest fruit extract (1 g), were dissolved in 5 mL of 96% ethanol (p.a.). The homogenization process was carried out in an ultrasonic bath for 30 min at 55 °C. Subsequently, the mixture was filtered using Whatman filter paper (No. 42), and the obtained filtrate was injected into a GC-MS Ultra QP 2010 (Shimadzu, Japan) instrument [21].

The chromatographic conditions include injector temperature of 250 °C with a splitless mode, pressure of 76.9 kPa, carrier gas flow rate of 14 mL/min, and split ratio of 1:10. The ion source and interface temperatures were set to 200 °C and 280 °C, respectively, with a solvent cut-off time of 3 min and mass ranges of 10–890 m/z. Moreover, the column was an SH-Rxi-5Sil MS with a length of 30 m and an inner diameter of 0.25 mm. The initial column temperature was set at 70 °C with a hold time of 2 min and then increased at a rate of 10 °C/min until 200 °C was reached. Finally, the temperature was increased further to 280 °C at a rate of 5 °C/min with a hold time of 9 min, resulting in a total analysis time of 36 min. The obtained chromatogram data were analyzed using the NIST 17 and Wiley 9 libraries to identify major compounds in the plant extracts with an area percentage of >1% [22].

2.6. Molecular Docking Studies

2.6.1. Sample Preparation (Virtual Screening)

The identified major compounds were then further analyzed using in silico methods to understand the mechanism of action and interaction of the compounds with MRSA target proteins. The compounds identified through GC-MS analysis of the PNL hexane fruit extract and the PBL aquadest fruit extract were subjected to molecular docking. Initially, the structures of these compounds were searched and downloaded from PubChem (accessed on 9 July 2024, link: https://pubchem.ncbi.nlm.nih.gov). The platform provides detailed information on the compound structures, including SMILES (Simplified Molecular Input Line Entry System) data, which can be used to download the 3D structure in PDF format for docking purposes. The target proteins for molecular docking were obtained from the Protein Data Bank (accessed on 9 July 2024, link: https://www.rcsb.org) in PDF [23]. In this study, the MRSA target proteins 4DKI, 6H5O, and 4CJN were prepared for docking.

2.6.2. Molecular Docking

Ligand optimization was performed using Chimera 1.17.3 (UCSF Chimera; accessed on 13 July 2024, link: https://www.cgl.ucsf.edu/chimera/). PyMOL version 2.5 (accessed on 13 July 2024, link: https://pymol.org/2) was used to correct protein structures, remove ligands, and eliminate water molecules. The docking process was conducted using PyRx AutoDock Vina (accessed on 13 July 2024, link: https://pyrx.sourceforge.io/), which was used to calculate binding affinity, RMSD, amino acid residues, and bond types between the optimized ligands and receptors. Furthermore, the interaction results between the ligand and receptor were visualized using the Biovia Discovery Studio Visualizer (accessed on 13 July 2024, link: https://www.3dsbiovia.com). The ligand with the lowest binding energy or docking score, along with hydrogen-bonding interactions, was selected as the best candidate [24].

3. Results and Discussion

3.1. Extraction Optimization

Table 1. Percentage yield of extracts from different solvents and parts of Piper plants.

Code	Sample	Plant Parts	Solvent	Extract (g)	Yield (%)
PNL	Piper nigrum	Stem	Aquadest	0.83	4.15
			Methanol	1.07	5.35
			Hexane	0.13	0.65
		Leaf	Aquadest	0.63	3.15
			Methanol	0.43	2.15
			Hexane	0.62	3.10
		Fruit	Aquadest	0.33	1.65
			Methanol	1.14	5.70
			Hexane	0.4	2.00
PBL	Piper betle	Stem	Aquadest	0.33	1.65
			Methanol	0.82	4.10
			Hexane	0.11	0.55
		Leaf	Aquadest	0.89	4.45
			Methanol	0.67	3.35
			Hexane	0.09	0.45
		Fruit	Aquadest	1.36	6.80
			Methanol	2.35	11.75
			Hexane	0.54	2.70

Noted: Simplicia was 20 g for all samples.

shows the extraction yields of various extracts from different parts of P. nigrum (PNL) and P. betle (PBL) using different solvents. Based on the results, the influence of the solvent on the extraction yield followed the order methanol > aquadest > hexane, with the plant parts yielding in the order fruit > leaves > stems. For the PNL stem, methanol produced the highest yield of 5.35%, while hexane produced the lowest at 0.65%. For the leaves, methanol and hexane yields were 2.15% and 3.10%, respectively, with aquadest having a slightly higher yield at 3.15%. In terms of fruit, methanol provided the highest yield at 5.70%, and aquadest produced the lowest at 1.65%. For PBL, methanol had the highest stem yield of 4.10%, with hexane producing the lowest at 0.55%. The methanol extract was found to contain the highest amounts of hydroxychavicol, eugenol, and gallic acid. All three compounds were present at low levels in the P. betle leaf hexane extract [25]. The aquadest extract had the highest yield of 4.45%, while hexane had the lowest at 0.45%. Regarding fruit, methanol achieved the highest yield at 11.75%, followed by aquadest at 6.80%, while hexane provided the lowest at 2.70%. In general, methanol resulted in higher extraction yields across different plant parts for both species, especially for fruits. In a study by Rajopadhye et al. [26], black pepper roots were used for Soxhlet extraction with methanol producing a Peperine concentration of 9.56 ± 0.83 mg/g [26]. Hexane generally produced lower yields, while aquadest treatment was moderately effective, particularly for leaves and fruits. This information is crucial for selecting the most efficient solvent based on the desired yield and plant characteristics.

3.2. FTIR Profiling Analysis

Figure 1 shows the FTIR spectral profiles of various active compounds isolated from Piper spp., including P. nigrum (PNL) and P. betle (PBL). The peaks at 3383.14 cm⁻¹ and 3404 cm⁻¹ represent OH stretching bonds, commonly associated with compounds such as piperanine from Piper retrofractum [27]. Furthermore, the peak at 3291.31 cm⁻¹ corresponded to phenolic O–H stretching in hydroxychavicol from PBL [28]. The peak at 3469 cm⁻¹ was related to the stretching bonds of OH groups in piperine from PNL [29]. The range of 2927.94 cm⁻¹ to 2936 cm⁻¹ suggested aliphatic C–H stretching bands associated with compounds such as hydroxychavicol [28] and β-caryophyllene oxide [30]. Moreover, the peak at 2856.58 cm⁻¹ signified aliphatic C–H stretching in piperine and pellitorine [31]. Peaks at 1637.56 cm⁻¹ and 1647.45 cm⁻¹ indicated C=C symmetric aromatic stretching in hydroxychavicol [28] and piperanine [31], while peaks at 1658 cm⁻¹ and 1636 cm⁻¹ represent conjugated diene symmetric stretching in piperanine [27] and piperine [29]. Furthermore, peaks at 1600.92 cm⁻¹ and 1613 cm⁻¹ imply conjugated asymmetric diene stretching associated with piperine [32] and chavibetol [33,34]. The profile also included aromatic C=C stretching at 1582 cm⁻¹ and 1598 cm⁻¹, CH₂ bending at 1446.61 cm⁻¹, and C–O stretching at 1197.79 cm⁻¹, representing various active compounds such as chavibetol [33,34], piperine [29], β-caryophyllene oxide [30], and hydroxychavicol [28].

This FTIR profile provides valuable insights into the functional groups and main components in Piper extracts, which are crucial for chemical characterization and phytochemical studies. CA based on functional groups detailed the hierarchical clustering process, in which observations were sequentially merged into larger clusters according to similarity and distance levels [18]. The analysis initially started with 18 distinct clusters, each representing individual observations or small groups. In the initial step, clusters 9 and 17 merged to form a new cluster (cluster 9) containing two observations, at a high similarity level of 99.8523%. This merging process continued, with clusters 14 and 15 combining into cluster 14 in subsequent steps, progressively reducing the total number of clusters. By the fifth step, clusters 3 and 6 merged into a larger cluster (cluster 3), now comprising four observations. This pattern further led to the aggregation of more clusters into increasingly larger groups, and each step reduced the number. For example, clusters 2 and 7 combined in the 14th step to form cluster 2, which included six observation groups.

In the final partition of the CA in Figure 2, six distinct clusters formed with high similarity levels >80%. Cluster 1 includes observations from the initial analysis step. Cluster 2 combines observations from the PNL methanol stem, PNL aquadest leaf, PBL aquadest stem, and PBL aquadest leaf. Cluster 3 aggregates observations from the PNL hexane stem, PNL hexane leaf, PNL fruit hexane, PBL methanol leaf, PBL hexane leaf, PBL aquadest fruit, and PBL methanol fruit, with Piperine as a significant component, and cluster 4 merged observations from PNL methanol leaf, PBL methanol stem, and PBL hexane fruit. Cluster 5 consolidates observations from clusters 7 and 12, while cluster 6 represents observations from cluster 8. The final grouping reflects the hierarchical relationships among observations, capturing similarities and distances throughout the clustering process [35]. Since clusters with similar functional groups based on FTIR data are likely to have the same pharmacological effects [36], representatives from each cluster were selected for further anti-MRSA activity testing.

This novel approach in natural product study simplifies the screening process by focusing on extracts with similar functional group compounds, thereby facilitating the identification of potential candidates without the need to test each extract individually. The extracts selected for anti-MRSA testing were PNL aquadest stem extract from cluster 1, PNL aquadest leaf extract from cluster 2, PNL fruit hexane extract from cluster 3, PBL methanol stem extract from cluster 4, PBL aquadest fruit extract from cluster 5, and PNL methanol fruit extract from cluster 6.

3.3. Anti-MRSA Activity

Based on

Table 2. Antibacterial activity of Piper extracts against MRSA based on inhibition zone diameter (mm) for different plant parts and solvents.

Sample	Inhibition Zone (mm)	Tukey Simultaneous Tests for Differences in Means						StDev
		Difference in Levels	Difference in Means	SE of Difference	95% CI	T-Value	p-Value
PNL Aquadest Stem Extract	8	PNL Aquadest Leaf Extract—PNL Aquadest Stem Extract	−2.667	0.797	(−5.388, 0.055)	−3.35	0.056	1.000
	10	PNL Aquadest Fruit Extract—PNL Aquadest Stem Extract	4.667	0.797	(1.945, 7.388)	5.86	0.001
	9	PNL Methanol Fruit Extract—PNL Aquadest Stem Extract	5.667	0.797	(2.945, 8.388)	7.11	0.000
PNL Aquadest Leaf Extract	6	PBL Methanol Stem Extract—PNL Aquadest Stem Extract	5.000	0.797	(2.279, 7.721)	6.27	0.000	0.577
	6	PBL Hexane Fruit Extract—PNL Aquadest Stem Extract	10.333	0.797	(7.612, 13.055)	12.97	0.000
	7	Ciprofloxacin— PNL Aquadest Stem Extract	16.667	0.797	(13.945, 19.388)	20.92	0.000
PNL Aquadest Fruit Extract	14	PNL Aquadest Fruit Extract—PNL Aquadest Leaf Extract	7.333	0.797	(4.612, 10.055)	9.20	0.000	0.577
	14	PNL Methanol Fruit Extract—PNL Aquadest Leaf Extract	8.333	0.797	(5.612, 11.055)	10.46	0.000
	13	PBL Methanol Stem Extract—PNL Aquadest Leaf Extract	7.667	0.797	(4.945, 10.388)	9.62	0.000
PNL Methanol Fruit Extract	14	PBL Hexane Fruit Extract—PNL Aquadest Leaf Extract	13.000	0.797	(10.279, 15.721)	16.31	0.000	0.577
	15	Ciprofloxacin— PNL Aquadest Leaf Extract	19.333	0.797	(16.612, 22.055)	24.26	0.000
	15	PNL Methanol Fruit Extract—PNL Aquadest Fruit Extract	1.000	0.797	(−1.721, 3.721)	1.25	0.861
PBL Methanol Stem Extract	14	PBL Methanol Stem Extract—PNL Aquadest Fruit Extract	0.333	0.797	(−2.388, 3.055)	0.42	0.999	0.000
	14	PBL Hexane Fruit Extract—PNL Aquadest Fruit Extract	5.667	0.797	(2.945, 8.388)	7.11	0.000
	14	Ciprofloxacin— PNL Aquadest Fruit Extract	12.000	0.797	(9.279, 14.721)	15.06	0.000
PBL Hexane Fruit Extract	20	PBL Methanol Stem Extract—PNL Methanol Fruit Extract	−0.667	0.797	(−3.388, 2.055)	−0.84	0.976	0.577
	19	PBL Hexane Fruit Extract—PNL Methanol Fruit Extract	4.667	0.797	(1.945, 7.388)	5.86	0.001
	19	Ciprofloxacin— PNL Methanol Fruit Extract	11.000	0.797	(8.279, 13.721)	13.80	0.000
Ciprofloxacin	24	PBL Hexane Fruit Extract—PBL Methanol Stem Extract	5.333	0.797	(2.612, 8.055)	6.69	0.000	2.080
	28	Ciprofloxacin— PBL Methanol Stem Extract	11.667	0.797	(8.945, 14.388)	14.64	0.000
	25	Ciprofloxacin— PBL Hexane Fruit Extract	6.333	0.797	(3.612, 9.055)	7.95	0.000

Note: 95% CI (95% confidence interval); t-value (test statistic value); p-value (probability value); StDev (standard deviation); pooled StDev = 0.975900; R-sq (R-squared) = 98.22%.

, a one-way analysis of variance (ANOVA) was conducted to assess the anti-MRSA activity of seven samples. The ANOVA yielded a highly significant F-value of 122.76 and a p-value of 0.000, indicating notable differences in anti-MRSA activity among the samples at a significance level of 0.05. The samples included six extracts, which are PBL aquadest fruit extract, PNL aquadest leaf extract, PNL aquadest stem extract, PNL hexane fruit extract, PBL methanol stem extract, and ciprofloxacin as a control. The ANOVA provided an adjusted sum of squares of 736.57 and an adjusted mean square of 122.762, resulting in a high R² value of 98.13%. This suggests that nearly all variability in anti-MRSA activity can be attributed to differences among the extracts. Additionally, the antimicrobial effectiveness of PBL was tested against 20 clinical isolates of S. pseudintermedius (10 MSSP and 10 MRSP) using the Kirby-Bauer disk diffusion method with P. betle extract disks at concentrations of 250, 2500, and 5000 µg. The minimum inhibitory concentration (MIC) for all isolates was determined to be 250 µg/mL [37].

Tukey’s pairwise comparisons provided further clarity on the results. Ciprofloxacin exhibited the highest mean anti-MRSA activity at 26 mm, significantly differing from all other extracts at a 99.58% confidence level. The PBL aquadest fruit extract had a mean activity of 19 mm, significantly outperforming the PNL hexane fruit extract (15 mm) and the PNL aquadest leaf extract (6 mm). The PBL methanol stem extract had a mean of 15 mm, showing significant differences compared to some lower-performing extracts, while the PNL aquadest stem extract (9 mm) also exhibited significant differences from the lowest-performing extracts. This study accumulatively reports the novel potential utility of the Curcuma longa L. and PNL extracts synergistically against MRSA infection by interfering with the mechanism of infectious angiogenesis and bactericidal action [38].

3.4. GC-MS Metabolite Characterization

The GC-MS analysis

Table 3. GC-MS analysis of compounds in the hexane extract of Piper nigrum fruit and aquadest extract of Piper betle fruit.

Compounds Detected	Molecular Formula	MW (g/mol)	PubChem (CID)	RT (min)	Area (%)	SI (%)
Piper nigrum hexane fruit extract
β-caryophyllene oxide	C15H24O	220	1742210	13.563	1.01	97
α-caryophylladienol	C15H24O	220	14524923	14.276	1.69	97
Pellitorine	C14H25NO	223	5318516	18.772	5.08	93
n-Hexadecanoic acid	C16H32O2	256	985	19.279	2.51	96
Octadecanoic acid	C18H36O2	284	5281	23.877	1.42	93
Diisooctyl phthalate	C24H38O4	390	33934	31.081	14.67	95
(2E,4E)-N-Isobutylhexadeca-2,4-dienamide	C20H37NO	307	6442402	31.595	1.78	90
Piperanine	C17H21NO3	287	5320618	32.804	4.03	94
Piperine	C17H19NO3	285	638024	37.762	14.22	93
Piperolein B	C21H29NO3	343	21580213	41.639	1.58	93
Piper betle aquadest fruit extract
Chavibetol	C10H12O2	164	596375	10.420	12.01	97
Hydroxychavicol	C9H10O2	150	70775	12.496	81.89	93

Note: MW (molecular weight); PubChem (PubChem compound identifier); RT (retention time); SI (similarity index).

of the Piper plant extracts showed important connections between the identified chemical components and anti-MRSA activity. The aquadest extract of PBL aquadest fruit extract and the PNL hexane fruit extract were subjected to GC profiling to identify the major compounds responsible for anti-MRSA activity. The hexane extract of PNL hexane fruit extract was found to contain piperine, a major component [39] with an area of 14.22% and a similarity index (SI) of 93%. This compound has strong antimicrobial properties and is capable of reducing the secretion of diverse virulence factors from MRSA. Therefore, piperine could be a potential antibiofilm molecule against MRSA-associated biofilm infections [40]. This correlates with the anti-MRSA testing results, where extracts containing piperine showed significant activity against MRSA. Pellitorine, also present in the extract with an area of 5.08% and an SI of 93%, may be able to inhibit bacteria efflux pumps. At 16 µg/mL, pellitorine increased the sensitivity of S. aureus (RN4220) to erythromycin through inhibition of the efflux pumps [41], thereby contributing to antimicrobial activity. Other components such as diisooctyl phthalate and piperanine, though present in significant amounts of 14.67% and 4.03%, respectively, require further investigation to understand specific roles in anti-MRSA activity [42]. For the PBL aquadest fruit extract, hydroxychavicol was identified as the primary compound, making up 81.89% of the extract with an SI of 93%. This suggests that the suggesting that the compound plays a crucial role in the effectiveness of the extract against MRSA.

The PBL leaf extract produced the highest percentage of hydroxychavicol content. Almost up to 90% of the final extract was found to contain hydroxychavicol based on HPLC analysis [43]. This supported the result that extracts from cluster 5, including hydroxychavicol, showed significant anti-MRSA activity. The PBL hydroxychavicol (36.02%) was the major constituent that inhibited the growth and biofilm formation of S. pseudointermedius and MRSP isolated from canine pyoderma in a concentration-dependent manner. Therefore, PBL is a potential candidate for the treatment of MRSP infection and biofilm formation in veterinary medicine [44]. Chavibetol, with an area of 12.01% and SI of 97%, also contributed to the anti-MRSA activity but was less dominant than hydroxychavicol. The PBL leaf extract (200 g) sliced into small pieces and subjected to hydro-distillation for 270 min was found to contain chavibetol (63.78%) [43]. The chemical composition of the ethanolic extract also contained chavibetol (12.03%) [44]. The anti-MRSA activity was correlated with the GC-MS results, demonstrating that extracts containing high concentrations of piperine and hydroxychavicol had stronger antimicrobial effects. This relationship underscores the significance of both compounds in anti-MRSA activity and their potential use in developing effective antimicrobial treatments.

3.5. Molecular Docking Analysis

Table 4. Bond-free energy values and amino acid residues binding to MRSA protein targets (4DKI, 6H5O, and 4CJN).

Compounds	Protein Target	Bond-Free Energy (kcal/mol)	H-Bond Interaction
Ceftobiprole (Native ligand)	4DKI	−9.8	LYS 406, SER 462, ASN 464, GLN 521, THR 600
Ciprofloxacin		−8.3	LYS 406, SER 462, SER 643
β-caryophyllene oxid		−6.4	NI
α-caryophylladienol		−6.4	NI
Diisooctyl phthalate		−7.1	SER 462, TYR 446
Piperanine		−7.6	LYS 406, SER 462
Piperine		−7.7	GLN 521, THR 444, GLY 402, SER 400
Piperolein B		−8.3	LYS 406, SER 462, ASN 464
Chavibetol		−5.7	THR 600, ASN 464, SER 462, LYS 406
Hydroxychavicol		−5.7	SER 462, ASN 464, SER 403
Piperacillin (Native ligand)	6H5O	−9.0	THR 444, SER 598, SER 461, SER 403, LYS 406, ASN 464
Ciprofloxacin		−8.1	LYS 406, SER 403, SER 462, SER 643
β-Caryophyllene oxide		−6.9	ASN 464, LYS 406
α-Caryophylladienol		−7.1	SER 403, SER 462
Diisooctyl phthalate		−6.3	ASN 464, THR 600
Piperanine		−7.3	SER 598, THR 582, GLU 460, ARG 445
Piperine		−7.3	SER 403, GLY 599
Piperolein B		−6.8	ASN 464, SER 598, HIS 583
Chavibetol		−5.4	SER 403, ASN 464, SER 462, THR 600
Hydroxychavicol		−5.6	LYS 406, SER 403, SER 462, GLY 599
(E)-3-(2-(4-cyanostyryl)-4-oxoquinazolin-3(4H)-yl)benzoic acid (Native ligand)	4CJN	−7.2	LYS 273, ALA 276, ASP 295
Ciprofloxacin		−6.2	LYS 273, LYS 316, GLU 294
β-caryophyllene oxide		−5.2	ASN 146
α-caryophylladienol		−5.2	NI
Diisooctyl phthalate		−5.0	LYS 273
Piperanine		−6.0	ASP 295, VAL 277, GLN 292
Piperine		−6.5	ALA 276
Piperolein B		−5.9	NI
Chavibetol		−4.6	ASP 295, GLY 296
Hydroxychavicol		−4.9	ASP 275

Note: “NI” indicates that the amino acid residues did not interact with the MRSA protein target (4DKI, 6H5O, and 4CJN) via H-bond interactions and were not found in the native ligand interactions.

shows the binding free energy values and amino acid residues interacting with the MRSA target proteins 4CJN, 4DKI, and 6H5O [45] for the various tested compounds. These data provide insights into the molecular interactions between these compounds and MRSA target proteins, as well as how the results correlate with anti-MRSA activity assays and chemical profiles of Piper extracts identified through GC-MS.

The native ligand for the 4DKI target protein (Figure 3) has a binding free energy of −9.8 kcal/mol and interacts with the amino acid residues LYS 406, SER 462, ASN 464, GLN 521, and THR 600 through hydrogen bonds [46]. Ciprofloxacin, the positive control, has a binding free energy of −8.3 kcal/mol and interacts with the residues LYS 406, SER 462, and SER 643. The PNL hexane fruit extract, which contains piperine, has a binding free energy of −7.7 kcal/mol and interacts with the residues GLN 521, THR 444, GLY 402, and SER 400, indicating significant potential anti-MRSA activity. Piperine, as the major component with an area of 14.22% in this extract, significantly contributes to the antimicrobial activity. Additionally, Piperolein B (1.5%) showed a binding free energy of −8.3 kcal/mol, equivalent to that of ciprofloxacin, with interactions at residues LYS 406, SER 462, and ASN 464, further enhancing the potential anti-MRSA activity. Piperanine (4.03%) and diisooctyl phthalate (14.67%) also demonstrated significant binding free energies of 7.6 and 7.1 kcal/mol, respectively, with interactions at residues LYS406 and SER462. However, β-caryophyllene oxide and α-caryophylladienol did not show any H-bond interactions with this target protein, suggesting limited anti-MRSA activity.

For the 6H5O target protein (Figure 4), the native ligand has a binding free energy of −9.0 kcal/mol and interacts with the amino acid residues THR 444, SER 598, SER 461, SER 403, LYS 406, and ASN 464 [47]. Ciprofloxacin, as the positive control, has a binding free energy of −8.1 kcal/mol and interacts with LYS 406, SER 403, SER 462, and SER 643. Furthermore, piperine and piperanine from the hexane extract of PNL have a binding free energy of −7.3 kcal/mol and interact with residues SER 403, GLY 599, SER 598, THR 582, GLU 460, and ARG 445, indicating significant anti-MRSA activity supported by the major component diisooctyl phthalate. Minor components such as β-caryophyllene oxide, α-caryophylladienol, and piperolein B also contribute to the anti-MRSA activity of this plant extract, with binding free energies ranging from 6.8 to 7.1 kcal/mol. In the PBL extract, the major compounds hydroxychavicol (81.89%) and chavibetol (12.01%) show binding free energies of 5.4 and 5.6 kcal/mol, respectively, interacting with the residues LYS 406, SER 403, SER 462, GLY 599, ASN 464, and THR 600. This interaction indicates significant anti-MRSA potential, although lower than that of Piperine.

For the 4CJN target protein (Figure 5), the native ligand has a binding free energy of −7.2 kcal/mol and interacts with the residues LYS 273, ALA 276, and ASP 295 [48]. Meanwhile, ciprofloxacin has a binding free energy of −6.2 kcal/mol and interacts with LYS 273, LYS 316, and GLU 294. Piperine in the PNL extract has a binding free energy of −6.5 kcal/mol and a stronger interaction at residue 276 than the positive control. Piperanine shows a favorable binding free energy of −6.0 kcal/mol, with interactions at ASP 295, VAL 277, and GLN 292, supporting the anti-MRSA activity, along with β-caryophyllene oxide and diisooctyl phthalate, although piperolein B does not show an H-bond interaction with this target protein. Meanwhile, in the PBL extract, hydroxyychavicol and chavibetol showed binding free energies of 4.9 and 4.6 kcal/mol, respectively. The extract interacted with residues ASP 275, ASP 295, and GLY 296, indicating a contribution to anti-MRSA activity, although lower compared to PNL. The in vitro assays demonstrated that extracts containing major compounds such as piperine [40], diisooctyl phthalate, and hydroxychavicol [44] from clusters 6 and 5 were significantly effective in inhibiting MRSA growth. These results were consistent with the GC-MS results, which showed high concentrations of piperine (14.22%) and diisooctyl phthalate (14.67%) in the hexane extract of PNL fruit, as well as hydroxychavicol (81.89%) and chavibetol (12.01%) in the aquadest extract of PBL. Both proteins contribute to significant anti-MRSA activity. Docking analysis showed that these compounds strongly bind to key residues in MRSA target proteins, reinforcing their potential as effective antimicrobial agents.

4. Conclusions

This study showed the successful optimization of extraction processes with methanol generally yielding the highest extracts across different parts of both P. betle (PBL) and P. nigrum (PNL), particularly in the fruits. This underscores the suitability of methanol as an extraction solvent. Hexane produced lower yields, while aquadest was moderately effective, especially for leaves and fruits. This optimization is crucial for selecting the most efficient solvent based on the desired yield and plant characteristics. The results demonstrated the significant potential of PBL and PNL as promising anti-MRSA agent sources. By using comprehensive FTIR spectroscopy combined with CA and GC-MS, several bioactive compounds were identified, including piperine, diisooctyl phthalate, and hydroxychavicol. These compounds showed strong binding affinities with key MRSA protein targets (4DKI, 6H5O, and 4CJN), as evidenced by molecular docking studies, with a strong correlation between in silico and in vitro results, confirming the effectiveness in inhibiting MRSA growth. The integration of in silico docking studies with in vitro assays provided a robust framework for identifying and validating potential anti-MRSA agents. Piperine, particularly as a major compound in PNL, demonstrated substantial antimicrobial activity, while hydroxychavicol in PBL showed significant inhibitory effects against MRSA. These results emphasize the importance of using a combined approach in drug discovery for identifying new antimicrobial agents. Further investigations are needed into Piper species as natural sources of novel anti-MRSA compounds with potential for development into effective treatments against resistant bacteria strains.