Lablab purpureus bioactive fractions as potent anticancer agents: an in vitro and in Silico study

Abstract

Background

Plant-based bioactive compounds are essential for the discovery of drugs, especially in cancer research, because they include a wide range of chemicals and are very specific to certain types of cells. In earlier research, Lablab purpureus (LP), a legume rich in nutrients and with ethnomedicinal significance, showed encouraging antioxidant and anticancer potential. However, the specific bioactive components that cause these effects are still unknown. The objective of this study was to use a bioactivity-guided approach to isolate and characterise cytotoxic fractions from LP seed methanol extract, followed by in vitro and in silico evaluation.

Methods

Column chromatography and liquid-liquid partitioning were used to separate the fractions from the LP seed methanol extract. DPPH, FRAP, and ABTS assays were performed to determine the individual fractions with high antioxidant activity. Lablab Purpureus Methanol seed extract Methanol fraction (LPMMF 1.5), was hence subjected to MTT assay against MCF-7 and A549 cells for 24, 48, and 72 h. Further, dual AO/EtBr staining was performed to evaluate apoptosis induction. FTIR and HR-LCMS analyses were performed to identify the phytocompounds in LPMMF 1.5. The identified compounds were subjected to ADME screening, molecular docking, MM-GBSA, 500 ns molecular dynamics simulations, per-residue energy decomposition, and metadynamics to evaluate their stability in binding with cancer-specific targets (PARP1, CDK2, and CCNB1).

Results

LPMMF 1.5 had the most antioxidant activity and the strongest cytotoxicity of the tested fractions, with IC₅₀ values of 36.96 µg/ml (MCF-7) and 76.14 µg/ml (A549) at 72 h. AO/EtBr staining revealed the induction of apoptosis in treated MCF-7 and A549 cells. Picroside-1 and other bioactives were identified by HR-LCMS. In silico studies revealed that Picroside-1 formed very stable complexes with PARP1, CDK2, and CCNB1, and the docking scores of Picroside-1 were found to be better than those of standard inhibitors. MD simulations and MM-GBSA confirmed that the binding was stable and that important residues were involved. Metadynamics provided additional validation of the binding energetics.

Conclusions

This study identified LPMMF 1.5 as a bioactive fraction with significant antioxidant and anticancer activity. Picroside-1 emerged as a key compound, exhibiting strong affinity and stable interactions with cancer-relevant targets. These findings support its potential development as a plant-derived anticancer lead candidate.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03904-2.

Background

Natural compounds derived from plants are crucial resources for pharmacological discovery, making significant contributions to primary health and medical research [1]. Remarkably, over half of the prescription cancer pharmacotherapies originate from plant-based natural substances, which are essential in the battle against cancer [2]. Specifically, in oncology, owing to their varied chemical compositions and biological functions, bioactive molecules are useful as they aid in the advancement of novel and comprehensive cancer-treating approaches, providing abundant benefits, including structural complexity and evolutionary refinement for biological purposes [3, 4]. Furthermore, natural products often possess a multitude of bioactive compounds that exhibit greater chemical diversity than traditional synthetic small molecule libraries, thereby increasing the probability of uncovering cancer-fighting agents [5].

Lablab purpureus (LP) is a nutritionally significant and most diverse domesticated legume species, renowned for its versatile and multifunctional utilities. It has been utilized in traditional medicinal practices to cure gastrointestinal disorders and other ailments. Its flowers were used in Ayurveda to treat uterine inflammation and promote menstruation, while its seeds were used as remedies for cholera, diarrhoea, and food poisoning. Packed with fiber, proteins, carbs, and minerals, it promotes digestive health and is a cheap staple food in southern India [6]. It can also be used as cattle feed. Despite its advantages, the crop is still underutilized because of its limited palatability and anti-nutritional qualities, even though its bioactive compounds have encouraging biomedical potential. Flavonoids (luteolin, apigenin, genistein), isoflavones, lectins (FRIL, dolichin), alkaloids, phenolics, saponins (lablabosides A–F), terpenoids, are all found in this plant. Numerous pharmacological effects are attributed to these bioactives. According to pharmacological studies, L. purpureus has potent anti-inflammatory, anti-bacterial, antifungal, antiviral, anti-diabetic, hepatoprotective, hypolipidemic, anti-anemic, anticoagulant, and insecticidal qualities. It is noteworthy for its anticancer potential, as compounds like genistein, lectins, brassinolides, and spermidine/spermine induce apoptosis, inhibit angiogenesis, modulate metastasis, and regulate multiple signaling pathways (e.g., NF-κB, STAT3, TGF-β, VEGF). Pharmacological research, phytochemical characterization, and ethnobotanical data point to L. purpureus as a valuable but underutilized crop with substantial biomedical potential. The development of affordable, plant-based treatments for cancer, infections, and chronic illnesses may result from additional mechanistic research and clinical validation [6].

Oxidative stress, often associated with the onset, progression, and resistance to treatment of cancer, is characterized by an imbalance between the generation of reactive oxygen species (ROS) and the body’s antioxidant defenses. Excess ROS not only cause lipid peroxidation, protein oxidation, and DNA damage (mutations, strand breaks), but they also change redox-sensitive signaling pathways that control cell survival, proliferation, and apoptosis, such as NF-κB, MAPK, and p53. Antioxidants reduce oxidative DNA damage and pro-tumorigenic signaling, which mitigates these effects by scavenging ROS and restoring redox homeostasis. Importantly, it has been shown that a range of antioxidants found in natural products can either directly block ROS-induced genomic instability that encourages tumor growth or (ii) change the redox balance of cancer cells to specifically induce apoptosis in malignantly transformed cells. ROS refers to a group of unstable, reactive, partially reduced oxygen derivatives, including singlet oxygen, hypochlorous acid, hydrogen peroxide, the superoxide anion, and the hydroxyl radical [56–58]. In both healthy and malignant cells, these serve as second messengers in cell signaling and are vital for a number of biological functions. ROS function as signaling transducers at low to moderate concentrations, promoting angiogenesis, drug resistance, invasion, migration, and proliferation of cancer cells (58–60). In other words, the homeostasis of cancer cells, which is involved in the development of cellular processes like proliferation, differentiation, migration, and cell death, depends on sufficient levels of ROS. High ROS levels, on the other hand, damage cancer cells and ultimately lead to cell death (61). Numerous studies have shown that immunotherapies and other anticancer treatments that alter ROS levels exhibit encouraging in vitro and in vivo outcomes. ROS are produced by the majority of chemotherapeutic agents, and many of them can change the redox homeostasis of cancer cells [56]. Anthracyclines (doxorubicin, epirubicin, and daunorubicin), alkylating agents, platinum coordination complexes (cisplatin, carboplatin, and oxaliplatin), and camptothecins (topotecan and irinotecan) are the major drugs that increase ROS in cancer cells (62). Two main causes of the increase in ROS in response to chemotherapeutic agents are the production of ROS in the mitochondria and the suppression of the cellular antioxidant system [56]. For instance, one of the most popular and successful chemotherapeutic agents for a variety of cancers, cisplatin, causes mitochondria-dependent ROS, which leads to nuclear DNA damage and ultimately cell death. The production of ROS linked to cisplatin is caused by a direct impact on mitochondrial DNA, which hinders the production of electron transport chain proteins (63).

Previous studies on LP phytochemicals extracted from different parts of the plant have demonstrated the antioxidant and cytotoxic potential of crude extracts [6]. Previously, we have ascertained the potent antioxidant, anti-inflammatory, and anticancer efficacy of the methanolic crude extract of LP seeds (LPM) against MCF-7 (breast) and A549 (lung) cancer cells. LPM was shown to cause apoptosis, and S phase, and G₂/M phase cell cycle arrest in MCF-7 and A549 cells, respectively [7]. In addition, LP seeds and their copper oxide nanoparticles have been identified to have antiviral activity against bovine alpha herpes virus [8]. A specific bioactive substance called genistein, a soy isoflavonoid, has also been found in LP. It has been shown to have anticancer properties and is presently undergoing clinical trials to determine its anti-breast cancer effectiveness [9]. However, the exact bioactive compound(s) responsible for the anticancer activities of the L. purpureus is/are yet to be identified.

Bioactivity-guided fractionation techniques play an essential role in natural product discovery because they allow the separation of the bioactive fraction from complex mixtures, including plant, fungal, or marine extracts. Specifically, in the framework of antitumour drug design research, these approaches aim at uncovering candidate therapeutic agents by establishing a relationship between the structure of a chemical compound and biological action [10, 11]. As previously determined, LPM was found to demonstrate potent anticancer activity against MCF-7 and A549 cells; hence, in this study, the bioactivity-directed fractionation of the LPM crude extract was performed to identify the most potent bioactive fraction, and its anticancer activity was validated on the same MCF-7 and A549 cell lines.

Methods

Extraction

Lablab purpureus (LP) pods were purchased as a single batch from a single local vendor in the Mysore district of Karnataka and authenticated at the Indian Council of Agricultural Research – Indian Institute of Horticulture Research, Bangalore, India (Supplementary Fig. 1). The seeds were separated from the pods by hand and washed with tap water before drying in the shade for ten days, and pulverised with a mixer grinder. For the extraction, 60 g of seed powder was processed with 300 ml of methanol for 4 h at 50 °C using the Soxhlet method [7, 12]. The extracted phytochemicals were filtered (with Whatman filter paper No. 1), and the filtrates were concentrated using rotary evaporation, followed by air-drying. The respective extract was labelled LP Methanol extract (LPM).

Liquid-Liquid (L-L) partition chromatography

500 mg of Lablab purpureus methanol seed extract (LPM) was dissolved in 10 ml of methanol and subjected to separation with the liquid-liquid partition chromatography using methanol, chloroform, and hexane solvents [13]. The solvent-soluble fractions were collected, air-dried, and labelled as LPMMF (LPM Methanol Fraction), LPMCF (LPM Chloroform Fraction), and LPMHF (LPM Hexane Fraction), respectively.

TLC

Silica gel 60 Å F₂₅₄ TLC plates with a thickness of 250 μm were used for the TLC analysis. 1.5 µl of LPMMF, LPMCF, and LPMHF samples dissolved in methanol (10 mg/ml) were spotted onto the TLC plate and allowed to dry. A mixture of chloroform and methanol in a 9.5:0.5 ratio was used as the mobile solvent system. The spot end of the TLC plate was later dipped into the mobile phase. The plate was removed after the run and allowed to dry. The spots were visualized using visible light, short-wave ultraviolet (254 nm), and long-wave ultraviolet (366 nm) light. The R_f value was calculated using:

Column chromatography

500 µg of LPMMF, the methanol fraction obtained after L-L extraction, was dissolved in 3 mL of chloroform. The sample was subjected to column chromatography using a glass column (length 60 cm x 1.5 cm) comprising 25 g of silica gel (60–120 mesh). The sample was eluted with 50 ml of a mixture of eluents (methanol and chloroform) in a gradient of concentrations, with a gradual decrease in chloroform and an increase in methanol concentration (Fig. 1). 5 ml fractions were collected continuously. The flow rate was set to 1 ml/minute. The fractions were labelled LPMMF 1.1 to LPMMF 1.10 and so on till LPMMF 7.1 to LPMMF 7.10, as per the order of the elute obtained.

Fig. 1.

Schematic representation of the order of eluents added and elutes collected in the separation of LPMMF compounds using silica gel column chromatography

Determination of the bioactivity of the obtained fractions

The collected fractions were initially pooled and tested together for DPPH free radical scavenging activity. The pooled samples and their constituents are listed in Table 1.

Table 1.

Column chromatography fraction samples were pooled for preliminary evaluation of antioxidant activity

Samples pooled	Given pool code
LPMMF 1.1 to LPMMF 1.10	LPMMF 1
LPMMF 2.1 to LPMMF 2.10	LPMMF 2
LPMMF 3.1 to LPMMF 3.10	LPMMF 3
LPMMF 4.1 to LPMMF 4.10	LPMMF 4
LPMMF 5.1 to LPMMF 5.10	LPMMF 5
LPMMF 6.1 to LPMMF 6.10	LPMMF 6
LPMMF 7.1 to LPMMF 7.10	LPMMF 7

1. 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay: DPPH radical scavenging activity was evaluated using the methodology outlined by Aksoy et al. (2013) [14]. The antiradical activity was quantified by the reduction in absorbance at 517 nm of a coloured methanolic DPPH solution, induced by the sample. 1.6 ml of each sample pool was incubated with 2.4 ml of DPPH solution (0.1 mM in methanol) for 30 min at room temperature in the absence of light, following which the absorbance was measured at 517 nm. Ascorbic acid (1000 µg/ml) served as the positive control, and all the samples were analysed in triplicate. The percentage of DPPH radical scavenging activity was calculated using the following formula:

   

   Where A0 is the absorbance of the control and A1 is the absorbance of the extracts/standard.

   The FRAP assay was used to further analyze the fraction pool with the highest free radical scavenging activity and determine its total phenolic content.

• 2.Ferric reducing antioxidant power (FRAP) assay: The FRAP assay was conducted in accordance with Benzie and Strain’s (1999) protocol [15]. A freshly prepared FRAP reagent comprising 2.5 ml of 10 mM 2,4,6-trypyridyl-s-triazine heated to 50 °C for 5 min, 2.5 ml of 20 mM FeCl₃ solution, and 25 ml of 300 mM acetate buffer at pH 3.6 was used in the assay. The samples (LPMMF pools at a 1 mg/ml concentration) or ferrous sulfate standards at different concentrations (200, 400, 600, 800, and 1000 µg/ml) were incubated with 190 µl of the reagent for 30 min in a darkened room. A ferrous sulphate standard curve was used to convert the absorbance, which was measured at 593 nm, to FRAP units.
• 3.Assessment of total phenol content (TPC): TPC was measured using the Folin-Ciocalteu (FC) method, which was standardized to microtiter plate volumes [16]. 150 µl of 10% FC reagent was combined with 30 µl of the fraction that had been dissolved in 95% methanol. This mixture was incubated in the dark for one hour before 120 µl of a 6% sodium bicarbonate solution was added. The absorbance was measured at 760 nm. The standard used was gallic acid, and the standard curve that was produced was given as milligrams of gallic acid equivalent (GAE) per gram of dry extract.

  The pool demonstrating the highest free radical scavenging activity, ferric oxide reducing potential, and TPC was selected, which was found to be LPMMF 1 in this case, for individual testing of the fractions. Antioxidant activity evaluation assays (DPPH, FRAP, and ABTS assays) were performed on the individual fractions present within the LPMMF 1 pool to identify the most antioxidant fraction.

• 4.
  ABTS radical cation assay: The free radical scavenging activity of samples was quantified using the ABTS radical cation assay [17]. A solution of 7 mM ABTS in water was added to 2.45 mM potassium persulfate in a 1:1 ratio and kept at room temperature, in the absence of light, to produce the ABTS cation radicals. The solution, after 12 h, was subsequently diluted with methanol to achieve an absorbance of 0.7 at 734 nm. Following this, 10 µl of the samples, LPMMF 1.5, and LPMMF 1.8 (62.5 µg/ml − 1000 µg/ml) were incubated with 200 µl of ABTS working solution for 30 min in darkness. Trolox (62.5 µg/ml − 1000 µg/ml) served as the positive control, while water was used as the blank. Absorbance was quantified at 734 nm. All the samples underwent testing in triplicate, and the % scavenging activity was calculated using:

  

  Where A0 is the absorbance of the control and A1 is the absorbance of the extracts/standard.

  LPMMF 1.5 had the highest antioxidant activity and was taken up for qualitative analysis to ascertain its phytochemical constituents. All assays were performed in triplicate.

• 5.Biochemical evaluation: The LPMMF 1.5 fraction was subjected to various qualitative assays to understand its possible phytochemical makeup, as follows:

• 0.2 ml of the LPMMF 1.5 fraction was mixed with a few drops of Molisch’s reagent, and 0.2 ml of sulfuric acid was added along the test tube’s side to check for the presence of carbohydrates [18].

• 2 ml of water and 0.2 ml of the LPMMF 1.5 fraction were incubated for 10 min in a water bath. After filtering, a 10% ferric chloride solution was added to the mixture to determine whether tannins were present [19].

• One drop of 5% ferric chloride and 3 ml of glacial acetic acid were combined with 2 ml of the LPMMF 1.5 fraction. To check for glycosides, 0.5 ml of concentrated sulfuric acid was then added along the side of the test tube [20].

• To test for flavonoids, 1.5 ml of the fraction was combined with magnesium and heated. Five to six drops of concentrated hydrochloric acid were then added to the mixture [21].

• A 100 ml solution containing 1.27 g of iodine, 2 g of potassium iodide, and distilled water was made in order to check for alkaloids. The sample was mixed with the resulting solution [22].

• In test tubes, 1 ml of the extracts and 3 ml of distilled water were combined to check for saponins. These tubes were firmly sealed, shaken vigorously for a few minutes, and left undisturbed for 30 min to observe froth formation [23].

• 5 ml of Fehling’s solutions 1 and 2 were added to 2 ml of the extracts, and the mixture was boiled to check for reducing sugars [24].

• A few drops of both 3% copper sulphate and sodium hydroxide (10%) were added to 1 ml of the extract, to test for proteins [25].

• 0.2 ml of the extract was combined with 4 ml of 50% Folin-Ciocalteu reagent and a few drops of a 10% sodium carbonate solution in order to test for phenols. For 30 min, the mixture was incubated at room temperature [26].

TLC: As previously described, TLC was used to determine the purity of the phytochemicals extracted into the LPMMF 1.5 fraction.

MTT assay: The effects of LPMMF 1.5 fraction and positive controls Cisplatin and Doxorubicin on the viability of the MCF-7 (breast cancer) and A549 (lung cancer) cell lines, respectively (procured from NCCS, Pune), were assessed as per Wahab et al. (2021) [27, 28]. Separately, 1 × 10⁴ MCF-7 and A549 cells were seeded into each well of 96-well plates, which were then incubated at 37 °C with 5% CO₂. Following 48 h incubation, the cells were incubated for 24 h after being treated with different concentrations of the test sample and standards. After discarding the treatment-containing media, 200 µl of MTT reagent was added, and the mixture was incubated for 4 h to allow for the formation of formazan crystals. It was then rinsed with PBS. After carefully removing the MTT reagent, 200 µl of DMSO was added to dissolve the formazan crystals that had formed. At 570 nm, absorbance was measured. Additionally, the samples underwent triplicate testing for 48- and 72-h treatments. The percentage inhibition was determined using the formula.

Where A0 is the absorbance of the control and A1 is the absorbance of the sample.

In addition, the effect of LPMMF 1.5 on L929 (mouse fibroblast) and HEK-293 (Human embryonic kidney 293) cells was also ascertained at 24 h, 48 h, and 72 h treatment using MTT assay.

Dual staining with acridine orange (AO) and ethidium bromide (EtBr) to detect apoptosis

According to Zhang et al. (1998), AO and EtBr staining were used to detect apoptosis [29]. The IC₅₀ values of the samples (LPMMF 1.5: 36.96 µg/ml, and Cisplatin positive control: 4.149 µg/ml for MCF-7 and LPMMF 1.5: 76.14 µg/ml, and Doxorubicin positive control: 6.02 µg/ml for A549, respectively) were added to the MCF-7 and A549 cells, which were then incubated for 72 h at 37 °C. As controls, untreated cells were used. Post-incubation, PBS was used to wash the wells, and the AO and EtBr stain mixture (1:1 ratio by volume) was added, and the mixture was further incubated at room temperature for 10 min and observed under the blue filter of a fluorescent microscope. The assay was performed in triplicate.

Chemical characterization of the LPMMF 1.5 fraction

• 6.FTIR: DMSO served as the diluent for sample preparation at a 1 mg/ml concentration. FTIR spectra were recorded in the range 4000–400 cm^− 1 on a Shimadzu FTIR-8400 S Spectrometer.
• 7.HR-LCMS: The chemical profiling of sample LPMMF 1.5 was conducted using Liquid Chromatography-High Resolution Mass Spectrometry. The analysis was performed using an Exion LC2.0 system coupled with a SCIEX 6600 + TTOF mass spectrometer. The chromatographic separation was performed with a C-18 column and a gradient elution program with mobile phase A (comprising 0.1% formic acid in water) and mobile phase B (comprising 0.1% formic acid in acetonitrile). Methanol served as the diluent for sample preparation at a concentration of 2 mg/ml, with an injection volume of 10 µl. The analysis was performed in both positive and negative ionization modes under identical source temperature settings of 500 °C. Collision energies were set to 25 V for positive mode and 20 V for negative mode. The TOF MS range was 50–1500 m/z for positive mode and 50–1250 m/z for negative mode, with corresponding MS/MS ranges of 30–1500 m/z and 30–1250 m/z. Data acquisition was carried out using the SWATH© software, and processing was performed with SCIEX software in conjunction with the NIST 2017 library. The gradient program featured an initial flow rate of 0.200 µl/min with a composition of 95% A and 5% B for 2 min, transitioning to 15% A and 85% B by 8 min, and then 10% A and 90% B from 18 to 20 min. The program reverted to the initial composition of 95% A and 5% B by 20.05 min, which was maintained until 21 min.

In silico analysis of identified phytochemicals against cancer-specific proteins

• 8.Molecular docking and dynamics simulations of phytochemical-protein interactions, energy decomposition, trajectory, and metadynamics analysis: The identified phytochemicals were further subjected to ADME property prediction using the Qikprop application in Schrodinger [30]. Phytochemicals identified using HR-LCMS, with adequate ADME properties were subjected to molecular docking against the proteins, cell division protein kinase 2 (CDK2: PDB 3IG7, 1.80 Å, chain A, sequence length 299 residues), poly [ADP-ribose] polymerase 1 (PARP1: PDB 7KK2, 1.70 Å, chain A, sequence length 352 residues), and cyclin B1 (CCNB1: PDB: 4Y72, chain B, sequence length of 273, resolution of 2.30 Å). In addition to the selected phytochemical ligands, the standard pre-bound ligands in the protein 3IG7 and 4Y72 (EFP and LZ9, respectively) were used for comparison, as well as the standard drug Talazoparib for 7KK2. The selected proteins and ligands were prepared, and receptor grids were generated around the active sites. Further, the ligands were docked against the proteins with the Schrodinger glide suite (v 14.2) [31–40]. Protein-ligand complexes with the highest number of interactions and lowest glide gscore energies were further validated with molecular dynamics (MD) simulations. The protein-ligand complexes were submerged in orthorhombic simulation boxes filled with a TIP3P solvent system, and the OPLS5 force field was applied. The temperature and pressure were set to 300 K and 1.01325 bar, respectively, and the total simulation duration was 500 ns. Additionally, thermal MM/GBSA was employed to investigate the energy stability of all the complexes. The MMGBSA was executed utilizing a script to compute the binding free energy of the complexes. Every fiftieth frame of 1000 frames produced from the molecular dynamics simulation was utilized to determine the binding energy. Employing the OPLS4 force field and the VSGB 2.1 solvent model, the binding energies of ligands were computed using the formula provided below.

  DGbind = E_complex(minimized)−[E_ligand(minimized) + E_receptor(minimized)]

  Where E_complex represents the total free energy of the protein–ligand complex, while E_ligand and E_receptor denote the free energies of the ligand and receptor in a solvent, respectively.

  Per-residue decomposition was conducted to quantitatively analyze the energetic contributions of each amino acid, encompassing both backbone and sidechain assessments. The study of binding energy decomposition was essential for outlining the significance made by every residue to the ligand.

• 9.Metadynamics analysis: The equilibration stage for the metadynamics simulations followed the same protocol as described for the MD simulations. Metadynamics production runs were performed for 50 ns each to calculate the binding free energies of the ligands bound to their respective proteins. The collective variables included the distance and the centre of mass between the ligand and the amino acid residues involved in key interactions. A Gaussian hill width of 0.05 Å was maintained consistently across all runs. The simulations employed the NPT ensemble, with key parameters including a Gaussian hill height of 0.03 Kcal/mol deposited at intervals of 0.09 ps. The temperature was maintained at 300 K using the Nose-Hoover chain thermostat with a relaxation time of 1 ps, and the pressure was stabilized at 1.01325 bar using the Martyna-Tobias-Klein barostat with a relaxation time of 2 ps in isotropic coupling mode. Electrostatics were treated using a Coulombic short-range cutoff method with a radius of 9 Å to ensure accurate energy calculations [41].
• 10.PASS prediction: QSAR analysis was performed to predict the relationship between the ligand structure and biological activity. The PASS-Way2Drug server (https://www.way2drug.com/PASSOnline/predict.php) was used to perform the PASS prediction (Prediction of Activity Spectra for Substances) on the chosen ligands using canonical smiles retrieved from the PubChem server. Pa, or probability of being active, and Pi, or probability of being inactive, were the formats in which the results were obtained. A higher likelihood of the compound’s biological activity was indicated by the Pa being filtered to be greater than 0.3. Both potential biological activities and potential negative effects of the chosen ligand were anticipated by the PASS prediction study [36].

Statistical analysis

All experiments were conducted in triplicate, and the results were expressed as the mean of 3 independent experiments ± SD, which were calculated using GraphPad Prism version 10. The results were subjected to two-way ANOVA to analyse the differences in treatment (of sample, and positive controls) and no treatment, wherever applicable. Tukey’s post hoc test was used after ANOVA and the “p” value of < 0.05 was considered significant.

Results

Crude exaction

Extraction of phytochemicals in methanol solvent provided an overall yield of 6.25%.

Liquid–liquid partition chromatography obtained 3 fractions

LPM extract (20 g) was subjected to L-L partition chromatography to separate the phytochemical mixture with methanol, chloroform, and hexane solvents based on the polarity. Methanol, chloroform, and hexane soluble fractions were separated using a separating funnel. The total yields of phytochemicals extracted into each solvent are as mentioned in Table 2. Most polar phytochemicals were extracted into the methanol solvent while the partially polar phytochemicals were derived into chloroform solvent.

Table 2.

Yield of liquid-liquid partition chromatography

Solvent used	Given name	Yield (g)
Methanol	LPMMF	12.67
Chloroform	LPMCF	4.72
Hexane	LPMHF	2.61

TLC chromatogram of obtained Liquid-Liquid partition fractions

Thin-layer chromatography (TLC) analysis of the methanol fraction, chloroform fraction, and hexane fraction was performed, and the results were detected under visible light, short-wave UV light (254 nm), and long-wave UV light (366 nm). The chromatogram showed distinct spots corresponding to different compounds in each fraction. Two spots were observed in the LPMMF fraction, while only 1 spot was observed in both LPMCF and LPMHF fractions. Observation of the TLC chromatogram of L-L fractions revealed a better separation of compounds into the LPMMF fraction compared to LPMCF and LPMHF (Fig. 2; Table 3).

Fig. 2.

Thin layer chromatogram showing separation of compounds in LPMMF, LPMCF, and LPMHF at visible light, shortwave UV (254 nm), and longwave UV (366 nm). The chromatogram shows clearer separation of compounds in LPMMF as compared to LPMCF and LPMHF

Table 3.

TLC characteristics of Liquid-liquid fractions

Sample	Number of observed spots at 366 nm	Spot Number	Rf value
LPMMF	2	1	0.48
		2	1
LPMCF	1	1	1
LPMHF	1	1	1

Column chromatography was performed to obtain fractions of the extract

To further isolate and purify the phytochemicals separated in the LPMMF fraction, column chromatography was performed using silica gel as the stationary phase. A total of 70 fractions were collected sequentially based on the elution order, and each fraction was labelled accordingly. The yield of phytochemicals in each fraction was calculated and is presented in Supplementary Table 1.

LPMMF 1.5 fraction exhibited the highest antioxidant activity

Preliminary evaluation of the antioxidant potency of the pooled fractions was evaluated using the DPPH assay. Among these pools, LPMMF 1 and LPMMF 2 pools exhibited the highest DPPH free radical scavenging activity with 62.7 ± 0.89% and 42.37 ± 1.02% DPPH radical scavenging activity, respectively (Fig. 3a). Based on these results, both pools were further analysed using the FRAP assay, where LPMMF 1 exhibited the highest FRAP value (400.95 ± 1.05 µmol Fe²⁺ equivalents per gram of fraction) compared to LPMMF 2 (246.24 ± 0.68 µmol Fe²⁺ equivalents per gram of fraction) (Fig. 3b and c). Further, the TPC of the fraction pools was measured using a gallic acid standard curve. LPMMF 1 and LPMMF 2 were found to have 0.0256 mg and 0.0172 mg Gallic acid Equivalent per gram of dry fraction, respectively. Based on its higher phenolic content and antioxidant activity, the LPMMF 1 pool was selected for further evaluation of its constituent fractions with antioxidant assays.

Fig. 3.

LPMMF fractions exhibited antioxidant activity amongst the tested pools. (a) DPPH radical scavenging activity of LPMMF pools (b), FRAP assay of LPMMF pools (c), and Standard curve of ferrous sulphate for determination of FRAP values

Fractions LPMMF 1.1-LPMMF 1.10 (from LPMMF 1 pool) were assessed for their free radical scavenging activity and ferric ion-reducing ability. Among these, LPMMF 1.5 showed the highest free radical scavenging, ferric ion reducing ability (77 ± 1.41% DPPH radical inhibition, 580 ± 2 µmol Fe²⁺ equivalents per gram of LPMMF 1.5, respectively), followed by LPMMF 1.8 (68.5 ± 0.707% inhibition and 470 ± 2 µmol Fe²⁺ equivalents per gram of LPMMF 1.8) (Fig. 4a, b). Hence, ABTS scavenging activity was determined only for the LPMMF 1.5 and LPMMF 1.8 fractions. Among the tested samples, Ascorbic acid standard exhibited 88.15% scavenging activity at 1000 µg/ml. In comparison, LPMMF 1.5 and LPMMF 1.8 displayed moderate scavenging activities, with 72 ± 0.76% and 63.83 ± 0.35% inhibition at 1000 µg/ml, respectively. A concentration-dependent increase in radical scavenging activity was observed for all tested samples. LPMMF 1.5 consistently showed higher activity than LPMMF 1.8 across all concentrations. However, both extracts had lower activity than the ascorbic acid positive control. Notably, the radical scavenging activity of LPMMF 1.5 increased significantly from 24.86% (62.5 µg/ml) to 72% (1000 µg/ml), while LPMMF 1.8 exhibited a similar trend, reaching 63.83% at 1000 µg/ml (Fig. 4c). Further qualitative phytochemical analysis of LPMMF 1.5 fraction revealed the presence of major phytochemical groups, including glycosides, cardiac glycosides, carbohydrates, phenols, steroids, and terpenoids, highlighting potential bioactive properties (Table 4).

Fig. 4.

LPMMF 1.5 exhibited the highest antioxidant activity among the tested LPMMF 1 pool fractions. (a) DPPH radical scavenging activity of LPMMF fractions 1–10 compared with Ascorbic acid positive control. (b) FRAP assay of LPMMF fractions 1–10 with Ascorbic acid positive control. (c) ABTS assay of fractions LPMMG 1.5 and LPMMF 1.8 with Ascorbic acid positive control displayed concentration-dependent antioxidant activity

Table 4.

Phytochemical constituents of LPMMF 1.5 fraction

Test	LPMMF 1.5
Glycosides	+
Cardiac glycosides	+
Phenols	+
Terpenoids	+
Steroids	+
Carbohydrates	+
Alkaloids	-
Tannins	-
Saponins	-

TLC chromatogram of LPMMF 1.5

The chromatogram revealed distinct bands, indicating the separation of phytochemical constituents in the LPMMF 1.5 fraction. 2 distinct spots were observed under the longwave UV light (R_f 0.48 and 1, respectively) (Fig. 5). The R_f values were consistent with those observed in the parent LPMMF liquid-liquid fraction, suggesting retention of crucial bioactive compounds.

Fig. 5.

Thin-layer chromatogram showing the separation of compounds into LPMMF 1.5 fraction at visible light, short-wave UV (254 nm), and long-wave UV (366 nm)

LPMMF 1.5 exhibited concentration- and time-dependent cytotoxicity on MCF-7 and A549 cell lines, but did not show cytotoxicity on normal mouse fibroblast and human embryonic kidney cells: MTT assay was performed on MCF-7 and A549 cell lines treated with LPMMF 1.5 fraction in multiple concentrations ranging from 6.25 to 250 µg/ml. At all timepoints of incubation (24, 48, and 72 h), LPMMF 1.5 showed concentration-dependent cellular growth inhibition of both cell lines. For the highest treated concentration of 250 µg/ml, 50 ± 1.41% inhibition of cell viability was observed at 24 h, cell cytotoxicity increased to 56.67 ± 0.66% at 48 h, and 70.48 ± 0.22% at 72 h in MCF-7 cell lines (Figs. 6 and 7).

Fig. 6.

LPMMF 1.5 showed potent cytotoxic activity against MCF-7. A concentration-dependent increase in the % inhibition of LPMMF 1.5-treated MCF-7 cells was observed. At the highest concentration of 250 µg/ml, LPMMF 1.5 showed 50.85% inhibition, which increased to 56.67% at 48 h and was the highest at 72 h, with 70.48% inhibition. The IC₅₀ was calculated to be 188.3, 127.6, and 36.96 µg/ml at 24 h, 48 h, and 72 h time points, respectively. Results are denoted as Mean ± SD. Two-way ANOVA and Tukey’s multiple comparisons tests were made to analyse the data. ***p < 0.001 compared to the control groups

Fig. 7.

Photomicrographs of MCF-7 untreated control cells, and cells treated with Cisplatin and LPMMF 1.5 at 24, 48, and 72 h timepoints. All experiments were performed in triplicate. The arrows point towards the observed morphological changes in the cells

The cytotoxicity of Cisplatin on MCF-7 cells was evaluated at varying concentrations (6.25–100 µg/ml) over 24, 48, and 72 h. A concentration-dependent increase in cell inhibition was observed across all time points. At 24 h, Cisplatin exhibited modest cytotoxicity, with 21.09% inhibition of cell growth at 6.25 µg/ml and reaching 73.47% inhibition at 100 µg/ml concentration (IC₅₀ = 26.87 µg/ml). At 48 h, the cytotoxicity increased, with 32.41% inhibition at 6.25 µg/ml and 82.20% inhibition at 100 µg/ml (IC₅₀ = 14.10 µg/ml). By the 72nd hour, Cisplatin demonstrated a pronounced cytotoxic effect, with 55.65% inhibition at 6.25 µg/ml and 86.26% inhibition at 100 µg/ml (IC₅₀ = 4.15 µg/ml) (Figs. 7 and 8).

Fig. 8.

Cisplatin demonstrated a concentration- and time-dependent cytotoxicity towards MCF-7 cells. Inhibition of MCF-7 cells by the positive control Cisplatin showed a concentration-dependent increase. At the highest concentration of 100 µg/ml, Cisplatin showed 73.47% inhibition, which increased to 82.19% at 48 h and was the highest at 72 h, with 86.26% inhibition. All experiments were executed in triplicate, and results represented as Mean ± SD. Two-way ANOVA and Tukey’s multiple comparisons test were performed to analyse data. ***p < 0.001 compared to the control groups

Meanwhile, for the A549 cell line, 45 ± 2.16%, 57.37 ± 0.74% and 63.67 ± 1.03% inhibition of cell viability was observed at 24, 48, and 72 h, respectively, at 250 µg/ml concentration. Further, microscopic observation of treated cells, made using an inverted microscope, revealed morphological changes at the lowest concentration of 12.5 µg/ml in both cell lines. Interestingly, both MCF-7 and A549 cell lines exposed to LPMMF 1.5 for the duration of 72 h showed the greatest inhibition amongst all treated groups (Figs. 9 and 10).

Fig. 9.

LPMMF 1.5 showed cytotoxicity towards A549 cells. % inhibition of A549 cells by LPMMF 1.5 showed a concentration-dependent increase. At the highest concentration of 250 µg/ml, LPMMF 1.5 showed 45.17% inhibition, which increased to 57.37% at 48 h and was the highest at 72 h, 63.67% inhibition. The IC₅₀ was calculated to be 250.9, 114.9, and 76.14 µg/ml at 24 h, 48 h, and 72 h timepoints, respectively. All experiments were completed in triplicate and represented as Mean ± SD. Two-way ANOVA and Tukey’s multiple comparisons test were performed to analyse the data. ***p < 0.001 compared to the control groups

Fig. 10.

Photomicrographs of A549 untreated cell control and treated with Doxorubicin and LPMMF 1.5 at 24, 48, and 72 h timepoints. The arrows point towards the cells that have undergone morphological changes post-treatment

Similarly, the cytotoxic activity of Doxorubicin on A549 cells was assessed at various concentrations (6.25–100 µg/ml) over 24, 48, and 72 h. The results demonstrated both concentration- and time-dependent increase in cell inhibition. At 24 h, Doxorubicin showed significant cytotoxicity, with 40.94% inhibition at 6.25 µg/ml and 81.93% inhibition at 100 µg/ml (IC₅₀ = 10.48 µg/ml). At 48 h, the cytotoxic effect further intensified, with 45.45% inhibition at 6.25 µg/ml and 84.7% inhibition at 100 µg/ml (IC₅₀ = 8.68 µg/mL). By 72 h, Doxorubicin exhibited its most potent cytotoxic effect, with 51.55% inhibition at 6.25 µg/ml and 88.55% inhibition at 100 µg/ml (IC₅₀ = 6.02 µg/ml) (Figs. 10 and 11).

Fig. 11.

% inhibition of A549 cells by positive control Doxorubicin showed a concentration- and time-dependent increase. At the highest 100 µg/ml concentration, Doxorubicin showed 81.92% inhibition, which increased to 84.69% at 48 h and to 88.55% at 72 h. All experiments were carried out in triplicate, representing the results as Mean ± SD. Two-way ANOVA and Tukey’s multiple comparisons test were used to analyse the data. ***p < 0.001 compared to the control groups

To understand the cytotoxicity of LPMMF 1.5 on the normal cells, and thereby ascertain its localized effect and selectivity towards cancer cells, the fraction was treated onto L929 normal mouse fibroblast and HEK-293 cells for 24, 48, and 72 h. both the cell lines were exposed to various concentrations of the LPMMF 1.5 fraction (12.5 to 250 µg/ml), and subjected to MTT assay. Observations of the MTT assay suggested that the exposure of LPMMF 1.5 showed a concentration- and time-dependent inhibition of normal mouse fibroblast cell line, with less than 20% inhibition at the highest concentration (250 µg/ml) at 24 h of incubation, 26.12% inhibition at 48 h and less than 30% inhibition at 72 h exposure (Fig. 12), indicating that LPMMF 1.5 showed selective cytotoxicity towards cancer cells.

Fig. 12.

LPMMF 1.5 showed no significant growth inhibition of normal mouse fibroblast L929 cells. (a) Photomicrographs of L929 untreated control and LPMMF 1.5-treated cells at 24, 48, and 72 h timepoints. (b) % inhibition of L929 cells by LPMMF 1.5 showed a concentration- and time-dependent increase. (c) The IC₅₀ was calculated to be 2.51, 2.42, and 1.19 mg/ml at 24 h, 48 h, and 72 h timepoints, respectively. Two-way ANOVA and Tukey’s multiple comparisons test were executed to analyse the data. n = 3, Mean ± SD, p value of < 0.05 was significant, *p < 0.005, **p < 0.002, ***p < 0.001 and ns = not significant

Similar observations were made in the HEK-293 cells treated with the LPMMF 1.5 test sample, dependent mainly on the concentration and exposure durations. At 24 h, the percentage inhibition of HEK-293 cells remained relatively low, with 1.75% inhibition at 12.5 µg/ml, gradually increasing to 20.35% at the highest concentration (250 µg/ml). Meanwhile, after 48 h of incubation, the inhibition ranged from 2.20% at 12.5 µg/ml to 21.77% at 250 µg/ml. With 72 h incubation, a further increase in inhibition was observed, reaching 25.17% at 250 µg/ml (Fig. 13).

Fig. 13.

LPMMF 1.5 showed no significant growth inhibition of normal HEK-293 cells. (a) Photomicrographs of HEK-293 untreated control and LPMMF 1.5-treated cells at 24, 48, and 72 h timepoints. (b) The inhibition of HEK-293 cells by LPMMF 1.5 showed a concentration-dependent rise in percentage. (c) The IC₅₀ was calculated to be 3.3, 3.4, and 4.2 mg/ml at 24 h, 48 h, and 72 h timepoints, respectively. The inhibition of HEK-293-293 cells by LPMMF 1.5 was concentration- and time-dependent; however, less than 30% cytotoxicity was observed after 72 h incubation. Two-way ANOVA and Tukey’s multiple comparisons test were performed to analyse data. n = 3, Mean ± SD, p value of < 0.05 was significant, ***p < 0.001 and ns = not significant

A dual staining assay confirmed the apoptosis-inducing potential of LPMMF 1.5 in both MCF-7 and A549 cells

72 h-treated MCF-7 and A549 cells were stained with AO and EtBr to observe apoptosis features. While the healthy, viable cells retained the AO stain and appeared fluorescent green, the apoptotic cells, characterized by condensed chromatin, incorporated the EtBr stain and appeared fluorescent orange. Additionally, cells in the late stages of apoptosis or undergoing necrosis appeared yellow due to overlapping AO and EtBr staining. A significant number of orange and yellow-stained cells were observed, indicating extensive treatment-mediated apoptosis (Fig. 14).

Fig. 14.

Dual staining with AO and EtBr confirmed the apoptosis induction potential of LPMMF 1.5 in both MCF-7 and A549 cells. (a) As the dual staining assay indicated, LPMMF 1.5 showed MCF-7 cell apoptosis at an IC50 concentration of 36.96 µg/ml, compared to Cisplatin at a 4.149 µg/ml concentration. (b) LPMMF 1.5 showed A549 cell apoptosis at an IC₅₀ concentration of 76.14 µg/ml compared to Doxorubicin at a 2.8 µg/ml concentration. The control group contained living cells stained in bright green. Cells in early apoptotic and apoptotic necrotic stages were stained in yellow and orange, respectively, and cells in late apoptotic stages were stained in orange or red

Chemical characterization of LPMMF 1.5 fraction led to the detection of many phytochemicals

FTIR: LPMMF 1.5 fraction was characterized using FTIR, ¹H NMR, and ¹³C NMR spectroscopy. The FTIR spectrum displayed multiple characteristic absorption bands, indicating a wide range of functional groups across the mixture (Fig. 15). A broad band at 3369 cm⁻¹ suggests the presence of O–H or N–H groups, while strong absorptions at 2919 and 1650 cm⁻¹ correspond to aliphatic C–H and carbonyl (C = O) stretching, respectively. The band at 2130 cm⁻¹ may reflect the presence of nitriles or alkynes, and several peaks between 1400 and 1000 cm⁻¹ indicate C–H bending, C–O, and C–N stretching. The complex fingerprint region (below 1000 cm⁻¹) supports the presence of multiple structurally diverse compounds.

Fig. 15.

FTIR spectrum of LPMMF 1.5 fraction

HR-LCMS: Library hits with library score ≥ 70, mass error between − 20 and 20 ppm, and area exceeding 10⁴ were filtered. After applying these filters, 70 compounds were identified with high confidence. Table 5 provides the list of compounds identified using HR-LCMS in both positive and negative modes.

Table 5.

HR-LCMS identified compounds from positive and negative mode analysis of the LPMMF 1.5 fraction

Library Hit	Library Score	Mass Error (ppm)	Area
DL-Arginine	92.9	-3.7	6.76E + 04
Normeperidine	98.7	6.5	1.17E + 06
Heptenophos	81.1	-10	2.11E + 06
Luteolinidin cation	84.6	2.9	7.99E + 04
Venlafaxine	77.5	17.2	3.02E + 05
Elaidic acid	92.2	12.6	2.12E + 06
Methylcodeine	93.6	-4.9	1.17E + 07
Carbocyclic thromboxane A2	100	-10.8	6.41E + 04
14(15)-Epoxy-5Z,8Z,11Z-eicosatrienoic acid, methyl ester	76.7	15.9	8.91E + 04
Monolinolenin (9c,12c,15c)	88	19.2	2.88E + 04
Previtamin D3	94.5	14.5	1.28E + 06
3,5,3’,5’-Tetra-tert-butyldiphenoquinone	90.4	18.1	7.21E + 04
15(R)-17-Phenyltrinorprostaglandin F2α	98.9	-15.1	2.03E + 05
Tianeptine	87.3	-11.7	4.19E + 06
Picroside-1	72.5	10	6.75E + 04
1-Palmitoyl-sn-glycero-3-phosphocholine	86	4.9	2.87E + 05
1-O-Hexadecyl-2-O-methyl-sn-glyceryl-3-phosphorylcholine	77	4	3.60E + 07
15 S-Hydroxy-5Z,8Z,11Z,13E-eicosatetraene-(2-biotinyl)hydrazide	96.4	6.6	5.90E + 06
3-Aminopentanoic acid	92.6	9.9	5.75E + 04
Benzoic acid	84	6.8	6.97E + 04
2,4-Dimethylphenol	86.5	-16.7	6.78E + 04
(R)-3-Amino-4-methylpentanoic acid	90.4	4.4	2.26E + 05
6-Aminocaproic acid	93.1	4.4	2.26E + 05
L-Leucine	88.7	4.4	2.26E + 05
DL-β-Leucine	92.8	4.4	2.26E + 05
6-Aminocaproic acid	93.1	4.4	2.26E + 05
3-Aminohexanoic acid	83.8	4.4	2.26E + 05
(+)-2-Hydroxyisocaproic acid	97.2	10.6	2.29E + 04
2-Ethyl-2-hydroxybutyric acid	96.8	10.6	2.29E + 04
(.+-.)-2-Hydroxyisocaproic acid	97.2	10.6	2.29E + 04
4-Hydroxybenzoic acid	100	6.2	7.02E + 04
Salicylic acid	100	6.2	7.02E + 04
4-Hydroxybenzoic acid	100	6.2	7.02E + 04
3-Hydroxybenzoic acid	100	6.2	7.02E + 04
D-(+)-Arabitol	85.8	5.8	6.78E + 04
L-Arabinitol	85.5	5.8	6.78E + 04
Xylitol	84	5.8	6.78E + 04
Adonitol	88	5.8	6.78E + 04
3,3-Dimethylglutaric acid	95.2	10.2	2.88E + 04
DL-Phenylalanine	87.3	9.7	4.95E + 04
L-Phenylalanine	86.7	9.7	4.95E + 04
1,2-Benzenedicarboxylic acid	95.9	-17.5	2.52E + 04
3-Bromophenol	79.1	18.9	1.30E + 04
N-Acetyl-D-norleucine	97.3	9.6	3.44E + 04
Cyclohexanesulfamic acid	77.2	-15.2	3.01E + 04
Dulcitol	95.7	7	1.71E + 05
D-Sorbitol	92	7	1.71E + 05
Citric acid	99.3	8.1	1.97E + 04
Undecanedioic acid	80.4	15.5	1.77E + 04
12-Hydroxydodecanoic acid	81.2	5.1	2.69E + 04
12(13)-Epoxy-9Z-octadecenoic acid	92.3	10.6	4.90E + 06
13R-Hydroxy-9Z,11E-octadecadienoic acid	96.2	10.6	4.90E + 06
13 S-Hydroxy-9Z,11E-octadecadienoic acid	95.8	10.6	4.90E + 06
13-Hydroxy-9Z,11E-octadecadienoic acid	88	10.6	4.90E + 06
9-Hydroperoxy-10E,12Z-octadecadienoic acid	78.9	-10.8	3.17E + 05
5 S-Hydroperoxy-6E,8Z,11Z,14Z-eicosatetraenoic acid	84.3	5.7	1.13E + 04
Melibiose	88.7	8.4	1.37E + 07
Sucrose	95.4	8.4	1.37E + 07
Galactinol	87.5	8.4	1.37E + 07
D-Turanose	91.6	8.4	1.37E + 07
D-(+)-Trehalose	88.7	8.4	1.37E + 07
4-O-β-Galactopyranosyl-D-mannopyranose	85.9	8.4	1.37E + 07
2.alpha.-Mannobiose	73.8	8.4	1.37E + 07
Sucrose	95.4	8.4	1.37E + 07
α, β-Trehalose	92.2	8.4	1.37E + 07
Sucrose	95.4	-17.6	1.28E + 07
Lactitol	91.2	-14.5	6.52E + 05
α,α’-Trehalose 6-phosphate	91.1	-3	1.56E + 05
1-Palmitoyl-2-hydroxy-sn-glycero-3-phospho-(1’-rac-glycerol)	92.6	-18.7	1.27E + 06

The total ion chromatograms (TIC) of the LPMMF 1.5 sample analysed are attached as Supplementary Figs. 2 and 3. Together, these spectroscopic results confirmed that LPMMF 1.5 is a heterogeneous mixture comprising multiple components with varied functional groups, including alcohols, amides, alkanes, aromatics, and carbonyl-containing structures.

Picroside-1 formed the most stable complexes with cancer-specific target proteins

The obtained list of compounds from HR-LCMS was subsequently subjected to ADME analysis to evaluate their drug-likeness properties (Table 6). Only compounds that adhered to Lipinski’s Rule of Five were selected for further analysis. Of all the compounds obtained, only 61 compounds were found to adhere to Lipinski’s Rule of Five. Molecules, Previtamin D3, Dulcitol, D-Sorbitol, and DL-Arginine had at least 1 violation of the rule of 5, while α,α.‘-Trehalose 6-phosphate, 2α-Mannobiose, 4-O-β-Galactopyranosyl-D-mannopyranose, D-Trehalose, D-Turanose, Galactinol, Sucrose, and Melibiose showed 2 violations. Luteolinidin cation, 14 (15)-Epoxy-5Z,8Z,11Z-eicosatrienoic acid, methyl ester, 3,5,3’,5’-Tetra-tert-butyldiphenoquinone, 15(R)-17-Phenyltrinorprostaglandin F2α, 15 S-Hydroxy-5Z,8Z,11Z,13E-eicosatetraene-(2-biotinyl) hydrazide, Xylitol, and 12 (13)-Epoxy-9Z-octadecenoic acid failed to produce any ADME predictions.

Table 6.

ADME prediction of identified compounds

Title	No of Violations to Lipinski’s Rule Of Five	Human Oral Absorption (%)	QPlogPo/w	No. of Acceptor Hydrogen bonds	No. of Donor Hydrogen bonds	Molecular Weight
Normeperidine	0	93.59	2.44	3.50	1.00	233.31
Tianeptine	0	55.17	1.48	8.00	2.00	436.95
Monolinolenin	0	100.00	4.87	5.00	2.00	352.51
Methylcodeine	0	94.98	1.84	5.20	0.00	313.39
Venlafaxine	0	100.00	3.40	3.50	1.00	277.40
Heptenophos	0	100.00	1.87	5.00	0.00	250.61
Adonitol	0	55.04	-1.77	8.50	5.00	152.14
3,3-Dimethylglutaric acid	0	48.90	0.47	4.00	2.00	160.16
1,2-Benzenedicarboxylic acid	0	50.37	0.76	4.00	2.00	166.13
3-Bromophenol	0	100.00	2.29	0.75	1.00	173.00
N-Acetyl-D-norleucine	0	64.05	0.88	3.75	1.25	173.21
Cyclohexanesulfamic acid	0	67.70	0.66	4.50	2.00	179.23
Citric acid	0	15.59	-0.03	5.75	3.00	192.12
12-Hydroxydodecanoic acid	0	75.22	2.63	3.70	2.00	216.32
9-Hydroperoxy-10E,12Z-octadecadienoic acid	0	80.12	3.69	5.40	2.00	312.44
5 S-Hydroperoxy-6E,8Z,11Z,14Z-eicosatetraenoic acid	0	89.27	4.20	5.40	2.00	336.47
Salicylic acid	0	76.60	2.25	1.75	1.00	138.12
3-Hydroxybenzoic acid	0	63.98	0.57	2.75	2.00	138.12
4-Hydroxybenzoic acid	0	63.96	0.57	2.75	2.00	138.12
2,4-Dimethylphenol	0	100.00	2.22	0.75	1.00	122.16
13-Hydroxy-9Z,11E-octadecadienoic acid	0	89.63	4.70	3.70	2.00	296.44
13 S-Hydroxy-9Z,11E-octadecadienoic acid	0	89.63	4.70	3.70	2.00	296.44
13R-Hydroxy-9Z,11E-octadecadienoic acid	0	89.71	4.70	3.70	2.00	296.44
Undecanedioic acid	0	53.78	2.325	4.00	2.00	216.27
DL-Phenylalanine	0	46.31	-1.16	3.00	3.00	165.19
L-Phenylalanine	0	46.26	-1.16	3.00	3.00	165.19
Adonitol	0	55.04	-1.77	8.50	5.00	152.14
L-Arabinitol	0	52.98	-1.83	8.50	5.00	152.14
D-Arabitol	0	52.97	-1.83	8.50	5.00	152.14
2-Ethyl-2-hydroxybutyric acid	0	76.78	1.10	2.75	2.00	132.59
-2-Hydroxyisocaproic acid	0	68.61	0.50	3.70	2.00	132.15
3-Aminohexanoic acid	0	44.07	-1.33	2.00	2.00	131.17
6-Aminocaproic acid	0	37.93	-1.67	3.00	3.00	131.17
DL-β-Leucine	0	44.90	-1.40	2.00	2.00	131.17
L-Leucine	0	45.55	-1.52	3.00	3.00	131.17
3-Amino-4-methylpentanoic acid	0	44.90	-1.40	2.00	2.00	131.17
Benzoic acid	0	80.77	1.86	2.00	1.00	122.12
3-Aminopentanoic acid	0	42.49	-1.59	2.00	2.00	117.14
Picroside-1	0	44.88	4.21	10.00	4.00	492.47
Elaidic acid	1	87.87	5.388	2.00	1.00	282.46
Previtamin D3	1	100.00	6.805	1.70	1.00	384.64
Carbocyclic thromboxane A2	1	88.18	5.162	3.70	2.00	348.52
Dulcitol	1	26.83	-3.09	10.20	6.00	182.17
D-Sorbitol	1	25.03	-3.08	10.20	6.00	182.17
DL-Arginine	1	0.00	-3.51	5.00	7.00	174.20
Lactitol	1	0.00	2.12	9.00	5.00	344.31
α,α’-Trehalose 6-phosphate	2	0.00	-4.13	22.00	9.00	422.27
2α-Mannobiose	2	0.00	-3.79	18.70	8.00	342.29
4-O-β-Galactopyranosyl-D-mannopyranose	2	3.25	-3.49	18.70	8.00	342.29
D-Trehalose	2	0.00	-3.91	18.70	8.00	342.29
D-Turanose	2	0.00	-3.62	18.00	7.00	342.29
Galactinol	2	0.00	-3.87	18.70	9.00	342.29
Sucrose	2	0.00	-3.69	16.80	8.00	342.29
Melibiose	2	0.00	-3.88	18.70	8.00	342.29

These 61 compounds were then subjected to molecular docking studies to investigate their binding affinities and interactions with the selected protein targets. All ligands docked successfully with the selected proteins. The molecular docking study revealed significant binding interactions of ligands with the respective protein targets. For the 7KK2 protein, compound Picroside-1 exhibited peak binding affinity with a docking score of -8.3 Kcal/mol, forming 6 hydrogen bonds (H-bonds, HB) with residues TYR896, ARG878, ASP770, ASN868, SER864, and one π-π stacking interaction with TYR896 (Fig. 16a). Lactitol formed 10 H-Bonds with GLY888, MET890, TYR907, HIS862, SER864, and ASP770, ASP766 and one π-π interaction with TYR896, with glide gscore of -7.86 Kcal/mol (Fig. 16c). Talazoparib, the reference compound, showed a binding affinity of -5.786 Kcal/mol, forming two H-bonds with ARG878 and ASP766 and two π-π stacking interactions with TYR896 and TYR889 (Fig. 16e). With the 7KK2 protein, the molecular docking validation by superposition of ligands prior to and after molecular docking, revealed RMSD values to be 1.41 Å for Picroside-1, 0.76 Å for Lactitol, and 1.63 Å for the Talazoparib reference ligand (Fig. 16b, d, f).

Fig. 16.

Molecular docking of the 7KK2 protein with ligands Picroside-1, Lactitol, and Talazoparib revealed successful docking interactions. The 7KK2 protein was found to interact with (a) Picroside-1, (c) lactitol, and (e) Talazoparib through hydrogen bonds and π-π stacking. Before and after the docking process of (b) Picroside-1, (d) Lactitol, and (f) Talazoparib, the conformations of the ligands were superposed. Prior to docking, the green ligand displays its conformation, and following docking, the blue ligand displays its conformation

For the 3IG7 protein, compound Picroside-1 showed high binding affinity of -9.366 Kcal/mol, interacting through 6 H-bonds with residues ASN132, ILE10, LYS89, ASP86, LEU83, and one π-π stacking interaction with PHE80 (Fig. 17a). Lactitol formed 6 hydrogen bonds involving amino acids LYS33, ASP86, and ASP154, with a glide gscore of − 8.56 Kcal/mol (Fig. 17c). EFP reference ligand exhibited the strongest binding affinity among all ligands with a docking score of -9.589 Kcal/mol, forming three H-bonds with LYS33 and LEU83 (Fig. 17e). The molecular docking validation by superposition of ligands prior to and after molecular docking revealed RMSD values to be 1.69 Å for Picroside-1, 1.63 Å for Lactitol, and 0.96 Å for the EFP reference ligand with the 3IG7 protein (Fig. 17b, d, f).

Fig. 17.

Picroside-1, lactitol, and EFP interactions with the 3IG7 protein were successfully docked with interactions, as demonstrated by the 2D visualization. The 3IG7 protein was found to interact with (a) Picroside-1, (c) lactitol, and (e) EFP through hydrogen bonds (HB) and π-π stacking. Before and after the docking process of (b) Picroside-1, (d) Lactitol, and (f) EFP, the conformational superposition of the ligands. Prior to docking, the green ligand displays its conformation, and following docking, the blue ligand displays its conformation

For the 4Y72 protein, compound Picroside-1 again exhibited strong binding with a score of -8.027 Kcal/mol, forming five H-bonds with ASP230, SER227, LEU300, TYR222, and MET225 (Fig. 18a). However, Lactitol exhibited the lowest glide score − 8.74 Kcal/mol, demonstrating 10 hydrogen bonds involving amino acids SER227, ASP230, GLU181, LEU300, GLN184, VAL186, VAL336, and THR329 (Fig. 18c). LZ9 displayed a weaker interaction with a score of -6.825 Kcal/mol, forming only one H-bond with LEU300 (Fig. 18e). With the 4Y72 protein, the molecular docking validation by superposition of ligands prior to and after molecular docking, revealed RMSD values to be 1.28 Å for Picroside-1, 0.65 Å for Lactitol, and 1.53 Å for LZ9 reference ligand (Fig. 18b, d, f).

Fig. 18.

The 2D visualization of the 4Y72 protein’s interaction with Picroside-1, Lactitol, and LZ9 showed effective docking with interactions. The 4Y72 protein was found to form hydrogen bonds (HB) with (a) picroside-1, (c) lactitol, and (e) LZ9. Before and after the docking process, the ligand conformational superposition of (b) Picroside-1, (d) Lactitol, and (f) LZ9. The green ligand exhibits its conformation prior to docking, while the blue ligand does so after docking

Molecular dynamics simulations (MDS) lasting 500 ns each were used to evaluate the structural stability, binding interactions, and dynamic behaviour of the 7KK2-Picroside-1, 7KK2-Talazoparib, and 7KK2-Lactitol complexes. Each system was built using the OPLS5 force field and solvated with approximately 13,000 water molecules to ensure physiological relevance. Upon analysis, the backbone RMSD of 7KK2 in all three complexes remained within 1.0 Å to 3.0 Å, suggesting structural stability throughout the simulations. However, subtle variations were observed, with the 7KK2-Picroside-1 complex showing RMSD fluctuations from 1.2 Å to 2.8 Å, stabilizing after 100 ns, the 7KK2-Lactitol complex showing similar RMSD behaviour, maintaining stability early in the simulation with fluctuations between 1.0 Å and 3.0 Å, and the 7KK2-Talazoparib complex exhibiting a slightly lower fluctuation range of 1.2 Å to 2.7 Å, indicating a highly stable protein-ligand system (Fig. 19a-c). RMSF evaluation showed that flexible loop regions exhibited the highest deviations, while α-helices and β-strands remained structurally stable (Fig. 19d-f).

Fig. 19.

The protein, Ligand (PL) RMSD values observed during the 500ns trajectory for (a) 7KK2-Picroside-1 complex, (b) 7KK2-Lactitol complex, and (c) 7KK2-Talazoarib complex. And the RMSF of the ligands (d) Picroside-1, (e) Lactitol, and (f) Talazoparib in complex with 7KK2

In the 7KK2-Picroside-1 complex, ARG878 exhibited a combination of backbone donor, backbone acceptor, and side-chain donor interactions between the ligand and protein, maintained for 95% and 78% of the trajectory, with an energy contribution of -7.34 Kcal/mol. TYR896 functioned predominantly as a backbone donor, contributing to 98% of the simulation with an interaction energy of -5.77 Kcal/mol. Water-mediated interactions were significant, with ASP770 forming water bridges for 95% of the trajectory and ASP766 contributing for 47% of the simulation (Fig. 20a, b). The 7KK2-Lactitol complex demonstrated strong binding stability, with HIS862 (ΔGbind of -5.83 Kcal/mol), TYR907 (ΔGbind of -8.17 Kcal/mol), and TYR896 (ΔGbind of -7.12 Kcal/mol) contributing significantly to the ligand’s binding free energy. Hydrophobic contacts and water-mediated bridges further strengthened ligand accommodation within the active site (Fig. 20c, d). In the 7KK2-Talazoparib complex, ARG878 maintained backbone donor interactions for 81% of the trajectory, contributing ΔGbind of -2.69 Kcal/mol, while TYR896 played a key role as a backbone donor for 99% of the simulation with an energy contribution of -5.63 Kcal/mol. Water bridges were prominently formed by ASP770 (85%, ΔGbind of 1.12 Kcal/mol) and GLY863 (84%, ΔGbind of 0.74 Kcal/mol). Additionally, HIS862 exhibited hydrophobic π-π stacking interactions with the ligand for 80% of the trajectory, contributing − 4.65 Kcal/mol (Fig. 20e, f) .

Fig. 20.

The protein-ligand (PL) contacts observed and their timeline representation in the molecular dynamic simulation trajectory for (a, b) 7KK2-Picroside-1 complex, (c, d) 7KK2-Lactitol complex, and (e, f) 7KK2-Talazoarib complex

The protein structure in the 7KK2-Picroside-1 complex contributed to an overall percentage of 51.18% secondary structural elements (SSEs) with 30.85% helices and 20.33% strands, confirming that 7KK2 maintained a well-preserved fold. In comparison, while the protein in the 7KK2-Lactitol complex maintained 30.75% helices and 18.92% strands, resulting in 49.67% SSEs, the 7KK2 protein in the 7KK2-Talazoparib complex had a similar composition with 30.85% helices and 19.13% strands, totalling 49.98% SSEs. The ligand RMSD in 7KK2-Picroside-1 fluctuated between 0.8 Å and 3.2 Å, stabilizing between 1.6 Å and 2.8 Å during the final 200 ns. The 7KK2-Lactitol complex exhibited stable ligand RMSD behaviour, indicating that Lactitol remained within the binding site without significant displacement. 7KK2-Talazoparib showed a much more constrained fluctuation range between 0.5 Å and 0.75 Å, suggesting a highly stable binding conformation. The radius of gyration (rGyr) values further supported these observations, with 7KK2-Picroside-1 fluctuating between 4.2 Å and 5.1 Å. 7KK2-Lactitol also displayed a stable rGyr, confirming a well-folded structure, and 7KK2-Talazoparib maintained a more compact range of 3.8 Å to 3.9 Å. Talazoparib exhibited the highest stability with the lowest ligand RMSD (0.5 Å–0.75 Å), a compact rGyr (3.8 Å–3.9 Å), and minimal solvent exposure (25 Ų–75 Ų), meanwhile, Picroside-1, demonstrated strong binding interactions, showed slightly higher fluctuations in ligand RMSD (0.8 Å–3.2 Å) and SASA (40 Ų–120 Ų), indicating moderate solvent exposure. Lactitol, on the other hand, showed strong binding contributions from HIS862, TYR907, and TYR896, but exhibited moderate stability compared to Talazoparib (Fig. 21a-c).

Fig. 21.

The ligand properties, including Ligand RMSD, rGyr, Intra-hydrogen bonds, MSA, SASA, and PSA of (a) Picroside-1 and (b) Lactitol, and (c) Talazoparib when in complex with the 7KK2 protein

Molecular dynamics simulations were conducted for the 3IG7-Picroside-1, 3IG7-Lactitol, and 3IG7-EFP complexes. Each system was built using the OPLS5 force field, with 3IG7-Picroside-1 consisting of 36,093 atoms and 10,391 water molecules, 3IG7-Lactitol containing 36,072 atoms and 10,395 water molecules, and 3IG7-EFP composed of 36,116 atoms and 10,404 water molecules.

The RMSD analysis revealed that the protein RMSD of 3IG7-Picroside-1 increased from an initial value of 1.2 Å and fluctuated between 2.0 Å and 2.8 Å throughout the 500 ns trajectory, indicating well-maintained structural stability with minor conformational adjustments (Fig. 22a). The protein RMSD in the 3IG7-Lactitol complex showed well-preserved structural integrity, rising from an initial 1.5 Å and stabilizing between 2.2 Å and 3.1 Å (Fig. 22b). The protein RMSD for the 3IG7-EFP complex varied from 2.4 Å to 3.6 Å, remaining steady between 2.4 Å and 3.2 Å throughout the 0–300 ns trajectory, before increasing to 2.8 Å to 3.6 Å in the final 200 ns (Fig. 22c). A persistent binding mode was suggested by the Picroside-1 RMSD, which fluctuated between 1.2 Å and 3.6 Å but stabilised in the 1.6 Å to 3.6 Å range over the last 100 ns (Fig. 22d), while consistent ligand binding was indicated by the Lactitol ligand RMSD, which varied between 1.2 Å and 2.8 Å before stabilising after 50 ns in the range of 2.4 Å to 3.2 Å (Fig. 22e). The EFP ligand RMSD varied between 1.5 Å and 4.0 Å, stabilizing between 2.5 Å and 3.0 Å from 100 to 200 ns before fluctuating again in the 1.5 Å to 4.0 Å range during the final 100 ns (Fig. 22f).

Fig. 22.

The protein, Ligand (PL) RMSD values observed during the 500 ns trajectory for (a) 3IG7-Picroside-1 complex, (b) 3IG7-Lactitol complex, and (c) 3IG7-EFP complex. And the RMSF of the ligands (d) Picroside-1, (e) Lactitol, and (f) EFP in complex with 3IG7

The secondary structure analysis indicated that 3IG7-Picroside-1 maintained 27.41% helices and 14.78% strands, with a total of 42.18% secondary structural elements (SSEs), suggesting good structural integrity. 3IG7-Lactitol maintained 27.03% helices and 13.51% strands, with a total SSE content of 40.55%, confirming overall stability. The 3IG7-EFP complex exhibited 27.39% helices and 14.63% strands, with a total SSE content of 42.02%, indicating that the protein conformation was preserved across all complexes. Protein-ligand interaction analysis revealed key stabilizing contacts. In 3IG7-Picroside-1, hydrogen bonds were consistently formed with TYR15, LEU83, ASP86, GLY16, LYS89, and GLN131. LEU83 acted as a backbone donor in 93% of the simulation (ΔGbind of -2.88 Kcal/mol), while ASP86 was a side-chain acceptor in 77% of the trajectory (ΔGbind of -1.17 Kcal/mol). Hydrophobic stabilization was observed with PHE80 via π-π stacking interactions (Fig. 23a, b). In the 3IG7-Lactitol complex, hydrogen bonds were observed with GLN131, ASP86, and LYS89, where GLN131 consistently interacted throughout the simulation, and ASP86 and LYS89 played stabilizing roles. Hydrophobic interactions with LEU83 and PHE80, including π-π stacking with PHE80, further enhanced ligand stability. Water bridges with GLY16 and TYR15 reinforced binding stability. Energy contribution analysis revealed − 5.54 Kcal/mol from GLN85 (Fig. 23c, d). For the 3IG7-EFP complex, LEU83 was a critical interaction site, forming backbone acceptor and donor interactions for 99% and 98% of the trajectory, respectively (ΔGbind of -4.09 Kcal/mol). ASP145 acted as a backbone donor for 75% of the simulation (ΔGbind of -2.99 Kcal/mol), while hydrophobic stabilization by PHE80 (π-π stacking, ΔGbind of -4.18 Kcal/mol) was crucial in maintaining ligand binding (Fig. 23e, f).

Fig. 23.

The protein-ligand (PL) contacts observed and their timeline representation in the molecular dynamic simulation trajectory for (a, b) 3IG7-Picroside-1 complex, (c, d) 3IG7-Lactitol complex, and (e, f) 3IG7-EFP complex

Ligand stability and compactness were assessed using RMSD, rGyr, MSA, SASA, and PSA. In 3IG7-Picroside-1, ligand RMSD stabilized between 0.8 Å and 2.4 Å after initial adjustments, with rGyr ranging from 4.5 Å to 5.5 Å, indicating a compact molecular structure with minimal conformational changes. MSA was 390–435 Ų, providing insights into ligand spatial occupation. SASA varied between 50 Ų and 150 Ų, suggesting moderate solvent exposure, while PSA remained stable at 260–300 Ų, indicating strong hydrogen bonding potential. The ligand maintained 1–2 intra-hydrogen bonds, confirming internal stability (Fig. 24a). In 3IG7-Lactitol, ligand RMSD remained stable within 1.2 Å to 2.8 Å, with rGyr ranging from 4.8 Å to 5.6 Å, confirming a compact structure. MSA was 380–420 Ų, while SASA fluctuated between 60 Ų and 170 Ų, suggesting moderate solvent exposure. The PSA remained stable between 270 Ų and 310 Ų, highlighting strong hydrogen bonding potential. The ligand maintained 1–3 intra-hydrogen bonds, reinforcing its internal stability and rigid conformation (Fig. 24b). In the 3IG7-EFP complex, rGyr predominantly ranged between 4.5 Å and 6.0 Å, with the ligand adopting a more compact structure towards the end of the simulation. SASA fluctuated between 50 Ų and 200 Ų, while PSA values ranged between 135 Ų and 352 Ų. MM-GBSA calculations provided insights into binding affinity (Fig. 24c). For 3IG7-Picroside-1, key contributors to binding energy were LEU83, ASP86, and GLN131. In 3IG7-Lactitol, the binding energy contributions were primarily from GLN131, ASP86, and LYS89, with additional stabilization provided by hydrophobic contacts. For 3IG7-EFP, LEU83, ASP145, and PHE80 played a significant role in ligand stability. Overall, the results indicated that Picroside-1 maintained the most stable binding interactions, followed by Lactitol, while EFP exhibited slightly higher fluctuations but retained key stabilizing interactions within the binding pocket.

Fig. 24.

The ligand properties, including Ligand RMSD, rGyr, Intra-hydrogen bonds, MSA, SASA, and PSA of (a) Picroside-1 and (b) Lactitol, and (c) EFP when in complex with the 3IG7 protein

The 4Y72-Picroside-1 complex was built using the OPLS5 force field, consisting of 34,855 atoms and 10,157 water molecules. The 4Y72-Lactitol complex, composed of 10,159 water molecules and 34,845 atoms, was constructed in a similar manner using the OPLS5 force field. The OPLS5 force field was also used to parameterize the 4Y72-LZ9 complex, which had 10,157 water molecules and 34,829 atoms.

Throughout the 0–300 ns trajectory, the 4Y72-Picroside-1 complex’s protein backbone RMSD varied between 1.2 Å and 2.4 Å. It stabilized between 2.4 Å and 3.2 Å over 400–500 ns after slightly increasing to 1.6 Å–2.8 Å between 300 and 400 ns, suggesting that the protein had reached equilibrium (Fig. 25a). The RMSD in the 4Y72-Lactitol complex showed a similar stabilization trend, ranging from 1.2 Å to 3.0 Å from 0 to 200 ns, increasing marginally to 1.5 Å–3.4 Å through 200–350 ns, and stabilizing at 2.6 Å–3.5 Å in the final stage of 350–500 ns (Fig. 25b). The protein stabilized after initial fluctuations, as the 4Y72-LZ9 complex showed an RMSD range of 1.2 Å–3.2 Å from 0 to 200 ns, followed by stabilizing between 1.6 Å and 2.8 Å beyond 200 ns (Fig. 25c). Throughout the simulation, the ligand RMSD in the 4Y72-Picroside-1 complex stayed constant between 2.0 Å and 2.4 Å, suggesting reliable binding in the active site (Fig. 25d). Additionally, the ligand RMSD in the 4Y72-Lactitol complex showed little variation beyond 300 ns and stayed between 1.0 Å and 2.8 Å, indicating excellent preservation inside the binding pocket (Fig. 25e). However, over the course of the trajectory, the ligand RMSD for the 4Y72-LZ9 complex varied more, ranging from 0.8 Å to 3.6 Å. For the first 300 ns, it stabilized between 0.8 Å and 2.8 Å, but during the next 300–500 ns, it fluctuated between 1.2 Å and 3.6 Å, suggesting a comparatively dynamic binding mode (Fig. 25f). The 4Y72 protein in the 4Y72-Picroside-1 complex exhibited 56.95% helices and showed no β-strands, according to the secondary structure analysis, indicating a primarily helical structure that held steady throughout the simulation. Similarly, in the 4Y72-Lactitol complex, the protein retained 57% helices with no β-strands, confirming its structural integrity. In the 4Y72-LZ9 complex, comparable results were observed, with 56.53% helices and no β-strands, further supporting the structural consistency of the protein.

Fig. 25.

The protein, Ligand (PL) RMSD values observed during the 500ns trajectory for (a) 4Y72-Picroside-1 complex, (b) 4Y72-Lactitol complex, and (c) 4Y72-LZ9 complex. And the RMSF of the ligands (d) Picroside-1, (e) Lactitol, and (f) LZ9 in complex with 4Y72

Key hydrogen bonding interactions in the 4Y72-Picroside-1 complex were observed with GLN184, which acted as a side-chain acceptor for 98% of the trajectory (-5.2 Kcal/mol contribution), SER227, serving as a side-chain donor in 94% of the simulation (the strongest observed interaction with − 7.07 Kcal/mol ΔGbind contribution), and ARG298, functioning as a backbone donor in 69% of the trajectory (ΔGbind contribution of -3.03 Kcal/mol). Hydrophobic interactions were involved with PRO340, which contributed − 5.63 Kcal/mol, and water bridges were formed with GLU220, establishing water-mediated interactions for 59% of the simulation (0.39 Kcal/mol contribution) (Fig. 26a, b). In the 4Y72-Lactitol complex, hydrogen bonds were predominantly formed with GLN184 (96%, ΔGbind of -6.18 Kcal/mol) and SER227 (92%, ΔGbind of -7.11 Kcal/mol), with significant hydrophobic interactions involving PRO340 (ΔGbind of -5.62 Kcal/mol). Water bridges were observed with GLU220, contributing 61% to the trajectory (Fig. 26c, d). In the 4Y72-LZ9 complex, hydrogen bonding interactions were observed with GLN184 (93%, -4.28 Kcal/mol), hydrophobic contacts with PRO340 (ΔGbind of -4.72 Kcal/mol), and water bridges with ASP230, forming water-mediated interactions in 36% of the trajectory (Fig. 26e and f).

Fig. 26.

The protein-ligand (PL) contacts observed and their timeline representation in the molecular dynamic simulation trajectory for (a, b) 4Y72-Picroside-1 complex, (c, d) 4Y72-Lactitol complex, and (e, f) 4Y72-LZ9 complex

For ligand compactness and surface properties, the 4Y72-Picroside-1 complex exhibited ligand RMSD stabilization within a narrow range of 1.2 Å to 1.8 Å, highlighting consistent positioning in the binding site. The rGyr remained between 4.8 Å and 5.2 Å, indicating a stable and compact structure. A single intramolecular hydrogen bond was observed, supporting internal stability. The MSA ranged from 405 Ų to 420 Ų, while the SASA varied between 20 Ų and 60 Ų, indicating moderate solvent exposure. The PSA remained between 255 Ų and 300 Ų, showcasing a significant contribution from polar atoms (Fig. 27a). In the 4Y72-Lactitol complex, ligand rGyr ranged from 4.7 Å to 5.3 Å, demonstrating a compact structure, with MSA ranging from 410 Ų to 425 Ų, SASA between 22 Ų and 65 Ų, and PSA values between 260 Ų and 310 Ų, all of which indicated strong binding and moderate solvent exposure (Fig. 27b). The 4Y72-LZ9 complex displayed an even more stable ligand RMSD range of 0.4 Å to 1.2 Å, with rGyr values between 3.9 Å and 4.35 Å, suggesting a highly compact structure. No intramolecular hydrogen bonds were observed, indicating a relatively flexible internal conformation. The MSA was measured between 294 Ų and 312 Ų, with SASA ranging from 15 Ų to 45 Ų, indicating minimal solvent exposure, while PSA values remained between 128 Ų and 144 Ų, further confirming the compact nature of the ligand (Fig. 27c).

Fig. 27.

The ligand properties, including Ligand RMSD, rGyr, Intra-hydrogen bonds, MSA, SASA, and PSA of (a) Picroside-1 and (b) Lactitol, and (c) LZ9 when in complex with the 4Y72 protein

In addition to these parameters, the binding free energy values of each complex at every 25 ns interval were ascertained (Figure). The MM-GBSA binding free energy calculations provide insights into the strength and stability of interactions between a protein and its ligands, which are critical for understanding their potential as inhibitors or modulators in a biological system. Lower (more negative) binding free energy values indicate stronger and more stable interactions, suggesting that the ligand has a higher affinity for the protein. Among the complexes, 4Y72-Picroside-1 exhibited the most favourable binding energy, with the lowest value of -105.68 Kcal/mol observed at 25 ns, indicating strong interactions. 4Y72-Lactitol ranged from − 71.79 Kcal/mol to -90.59 Kcal/mol. In comparison, 4Y72-LZ9 showed moderately stable binding energies, ranging from − 61 to -79 Kcal/mol across the simulation. The 7KK2-Picroside-1 complex displayed less favourable binding energies, with values consistently higher than − 56 Kcal/mol. Complex 7KK2-Lactitol ranged from − 41.36 Kcal/mol to -55.81 Kcal/mol, while 7KK2-Talazoparib exhibited significant fluctuations, suggesting weaker and less stable interactions. For 3IG7-Picroside-1, the binding energies were consistently strong, ranging from − 78 to -90 Kcal/mol, demonstrating stable binding throughout the simulation. 3IG7-Lactitol ranged from − 21.21 to -49.71 Kcal/mol. On the other hand, 3IG7-EFP showed moderate binding, with energies slightly less favourable than those of 3IG7-Picroside-1. Picroside-1 consistently showed stable binding free energy values across all 3 studied proteins (Fig. 28).

Fig. 28.

Binding free Energy, as calculated by thermal MMGBSA, of all the complexes studied. Snapshots were generated every 50th trajectory frame

Metadynamics analysis identified Picroside-1 to be a stable ligand that bound to the chosen proteins

The Free Energy Surface (FES) is a crucial output of metadynamics simulations, representing the free energy landscape of a system as a function of selected collective variables. In ligand binding and unbinding studies, the FES provides insights into the most stable binding states, the energy barriers between different conformations, and the pathway for ligand dissociation. Energy minima, or wells, correspond to the most stable ligand binding positions—the deeper the well, the more stable the interaction. The energy needed for a ligand to change binding modes or completely dissociate is represented by energy barriers, which show up as peaks between minima. Therefore, the FES produced by these simulations provides crucial information about the dissociation pathways, binding strength, and ligand stability.

The binding stability and unbinding mechanisms of three ligands—Picroside-1, EFP, and lactitol—with the 3IG7 protein were examined using metadynamics simulations over a 50 ns time span (Fig. 29). Two major energy minima at roughly 5.5 Å and 8 Å were seen in the FES of Picroside-1, indicating two stable binding conformations. While the free energy increased dramatically beyond 9 Å, indicating a significant energetic barrier to unbinding, the global minimum, approximately − 14 Kcal/mol, indicated moderate stability. This implied that Picroside-1 might undergo conformational transitions prior to total dissociation, even though it could bind steadily. Lactitol, on the other hand, showed a global minimum of about − 100 Kcal/mol, indicating stronger binding. From 5 Å to 15 Å, the stable binding region spanned, indicating favourable interactions over a sizable range. Lactitol could switch between multiple binding modes or interact flexibly within the binding pocket before fully dissociating, as indicated by the steep energy rise that occurred beyond 20 Å, which signalled the transition to an unbound state. With a deep free energy minimum of roughly − 160 Kcal/mol, EFP showed the best binding stability. Throughout a wide range (10–12 Å), the energy stayed comparatively constant, indicating robust and long-lasting interactions with the protein. A significant unbinding barrier was indicated by a sharp increase in energy above 14 Å, indicating that EFP is extremely resistant to dissociation once bound. EFP showed the highest binding stability among the three ligands in 3IG7, as indicated by its high energy barrier for unbinding and deep free energy minimum. Picroside-1 demonstrated a moderate binding strength with multiple stable states and a lower unbinding barrier, while lactitol came next, displaying strong but flexible interactions. These variations in the free energy landscapes indicated that Picroside-1 could move between several binding states prior to unbinding, while EFP was the least likely to dissociate.

Fig. 29.

Metadynamics analysis of each of the complexes studied to understand the binding and unbinding energy of the ligands

Similarly, over a 50 ns simulation period, metadynamics simulations were conducted to examine the binding stability and unbinding mechanisms of Talazoparib, lactitol, and picroside-1 with the 7KK2 protein (Fig. 29). A stable binding conformation was indicated by the deep free energy minimum of about − 120 Kcal/mol in the FES of Picroside-1. Picroside-1 maintained stable interactions while permitting a certain amount of flexibility within the binding pocket, according to the collective variable’s smooth energy transition. A moderate barrier to unbinding was suggested by the slow energy increase above a threshold, suggesting that even though the ligand and 7KK2 form a stable complex, the ligand may change conformations before completely dissociating. A comparable binding free energy minimum of roughly − 120 Kcal/mol was shown by lactitol. This ligand may have more flexibility within the binding pocket or adopt several stable conformations prior to unbinding, as indicated by the free energy landscape’s more gradual decline. Lactitol is more likely to dissociate than the other two ligands, according to the comparatively lower energy barrier. With a global free energy minimum of roughly − 250 Kcal/mol, Talazoparib showed the best binding stability. At about 22 Å, the FES showed a significant energy increase, indicating a significant unbinding barrier. This implied that, in comparison to lactitol and picroside-1, Talazoparib had a substantially lower propensity to dissociate after binding to 7KK2. Talazoparib established strong and long-lasting interactions within the binding site, as demonstrated by the steep free energy profile. Comparing the three ligands bound to 7KK2, Talazoparib exhibited the highest binding stability, followed by Picroside-1 and Lactitol.

Metadynamics simulations were also conducted to examine the binding stability and unbinding mechanisms of Picroside-1, Lactitol, and LZ9 with the 4Y72 protein over a 50 ns simulation period (Fig. 29). The FES of Picroside-1 exhibited a deep free energy minimum of approximately − 80 Kcal/mol, indicating a stable binding conformation. The relatively smooth energy landscape along the collective variable suggested that Picroside-1 maintained stable interactions within the binding pocket while allowing for some conformational flexibility. Although the ligand was stable, it might change conformations before completely dissociating, as indicated by the slow rise in energy above a particular threshold, which suggested a moderate barrier to unbinding. With a binding free energy minimum of roughly − 120 Kcal/mol, lactitol demonstrated a higher binding affinity than Picroside-1. The free energy landscape exhibited a more gradual descent, indicating that Lactitol might experience greater flexibility within the binding pocket or multiple stable conformations before unbinding. Out of the three ligands, LZ9 showed the highest binding stability, with a global free energy minimum of about − 120 Kcal/mol. The FES profile showed a sharp increase in energy beyond 17.5 Å along CV1, indicating a significant unbinding barrier, suggesting that LZ9 was much less likely to dissociate once it bound to 4Y72 than Picroside-1 and Lactitol. The steep free energy profile confirmed that LZ9 formed deep and persistent interactions within the binding site. Lactitol exhibited moderate stability, characterized by a well-defined binding pocket with a discernible unbinding barrier. Picroside-1 showed the best binding affinity, characterized by an efficient free energy minimum and a less flexible binding interaction, indicating a lower possibility of detachment.

Picroside-1 was predicted to have pharmacological activities

In silico predictions using the PASS online tool revealed a diverse range of potential pharmacological activities for Picroside-1 (Table 7), with 17 distinct activities showing a high probability of activity (Pa > 0.7). Notably, Picroside-1 exhibited strong anti-inflammatory potential (Pa = 0.885), along with predicted antiprotozoal activity against Leishmania species (Pa = 0.855) and antinociceptive effects (Pa = 0.788), suggesting a possible role in managing both inflammatory and infectious conditions. Additionally, it showed hepatoprotective potential (Pa = 0.808) and was predicted to be effective in treating hepatic disorders (Pa = 0.809), supporting previously reported liver-protecting properties of the compound. Other notable activities include chemopreventive (Pa = 0.716), antifungal (Pa = 0.715), and antineoplastic (Pa = 0.733) properties, indicating its potential as a broad-spectrum bioactive compound. The high probability scores for several enzyme inhibition and receptor modulation functions, such as CDP-glycerol glycerophosphotransferase inhibition (Pa = 0.882) and membrane integrity agonism (Pa = 0.874), further highlight Picroside-1 as a promising candidate for multi-target drug development.

Table 7.

Predicted Pharmacological activities of picroside-1

Predicted activity	Pa	Pi
Anti-inflammatory	0.885	0.005
CDP-glycerol glycerophosphotransferase inhibitor	0.882	0.013
Membrane integrity agonist	0.874	0.018
Antiprotozoal (Leishmania)	0.855	0.004
Hepatic disorders treatment	0.809	0.004
Hepatoprotectant	0.808	0.004
Benzoate-CoA ligase inhibitor	0.804	0.016
Antinociceptive	0.788	0.002
CYP2A11 substrate	0.76	0.004
Glyceryl-ether monooxygenase inhibitor	0.754	0.007
Anaphylatoxin receptor antagonist	0.744	0.018
Membrane permeability inhibitor	0.737	0.025
Antineoplastic	0.733	0.021
Dermatologic	0.726	0.006
Chemopreventive	0.716	0.006
Antifungal	0.715	0.009
Lactase inhibitor	0.71	0.006

Adverse effect prediction analysis of Picroside-1 revealed two endpoints with a high probability of occurrence, each displaying a Pa (probability of activity) greater than 0.7, indicating a significant likelihood of associated toxicological effects. The compound exhibited a Pa of 0.783 for general toxicity and 0.720 for inflammation, with corresponding Pi (probability of inactivity) values of 0.030 and 0.023, respectively. These findings suggest that Picroside-1 may pose notable risks of toxic and pro-inflammatory responses, which should be carefully considered in the context of its pharmacological application.

The MS/MS spectrum of Picroside-1 as obtained in HR-LCMS is attached as Supplementary Fig. 4.

Discussion

Historically, medicinal plants and their phytoconstituents have been the source of numerous drugs for treating various diseases, including cancer. Currently, the chemotherapeutic options for breast and lung cancers are of minimal success with the existing drugs, mainly owing to the evolution of resistant cancers and the toxicity of the existing anticancer drugs [42]. Lablab purpureus (LP), an edible bean, has traditionally been of gastronomic and medicinal value in South and Southeast Asian regions. L. purpureus has been utilised in traditional medicinal practices to treat a variety of gastrointestinal as well as uterine disorders. Globally, research conducted on the evaluation of anticancer activities of L. purpureus has focused only on assessing the in vitro cytotoxicity of crude extract on various cancer cell lines, while the exact phytocompounds and the molecular mechanisms responsible for this have not been looked into. In addition, there are little to no evidence about the antiproliferative activity of the edible L. purpureus seeds. Addressing these lacunae, the bioactivity-mediated fractionation of L. purpureus seeds was performed to identify the most potent anti-cancer fraction and validate its potential anticancer activity.

Previously, the bioactivity-mediated fractionation of many plants has been reported, performed using a number of techniques, including column chromatography to identify fractions with anticancer effects [43–46]. In the current study, liquid-liquid partition chromatography was employed to separate LPM into three solvent fractions using methanol, chloroform, and hexane, based on polarity. The methanol fraction yielded the highest amount, indicating that most polar phytochemicals were extracted into this fraction, while LPMCF and LPMHF contained partially polar and non-polar compounds, respectively. TLC analysis revealed better compound separation in LPMMF, with two distinct spots under UV light (R_f values 0.48 and 1.0). In contrast, LPMCF and LPMHF displayed only one major spot, suggesting a lower complexity of phytochemical constituents. These results indicated that LPMMF retained the most diverse range of bioactive compounds, making it a promising candidate for further purification and bioactivity evaluation. Hence, to further refine LPMMF, column chromatography was performed, leading to the isolation of 70 individual fractions. The fractionation process aimed to segregate phytochemicals based on their differential solubility and affinity to the stationary phase. Among the obtained fractions, LPMMF 1.5 emerged as a promising bioactive fraction, with a high yield (583.6 mg) and potent antioxidant properties. LPMMF 1.5 displayed the strongest antioxidant activity (77% DPPH inhibition, 580 FRAP value). The ABTS assay further confirmed that LPMMF 1.5 exhibited antioxidant activity, reinforcing its potential as a key bioactive fraction. Phytochemical analysis of LPMMF 1.5 established the presence of phenols, glycosides, cardiac glycosides, steroids, and terpenoids. TLC analysis of LPMMF 1.5 established the presence of multiple bioactive compounds, as evidenced by distinct bands under visible and UV light. The observed R_f values (0.48 and 1.0) were consistent with those detected in the parent LPMMF fraction, indicating the retention of key bioactive constituents during the fractionation process.

Sipahli et al. (2023) also evaluated the in vitro anticancer and apoptotic effect of low-molecular-weight peptides derived from Lablab purpureus (L.) Sweet seeds using a comprehensive workflow involving enzymatic hydrolysis with pepsin, ultrafiltration, and subsequent purification. The 3 kDa fraction showed the strongest cytotoxic potential against cancer cell lines, with IC₅₀ values of 13.6 µg/ml for A549 and 20.48 µg/ml for MCF-7. The peptide’s selective toxicity was highlighted by the HEK-293 cells, which showed a significantly higher IC₅₀ value of 145.1 µg/ml. This fraction was chosen for additional fractionation due to its outstanding efficiency. Upon RP-HPLC purification, Fraction 2 was identified as the most bioactive sub-fraction. This purified peptide displayed IC₅₀ values of 141.8 µg/ml (A549), 250 µg/ml (MCF-7), and 179 µg/ml (HEK-293), which, although higher than the original 3 kDa fraction, still indicated targeted activity towards cancer cells over healthy ones. The fraction’s apoptotic effect was extensively characterized using Annexin V-PI staining, which revealed that 85.4% of A549 and 89.6% of MCF-7 cells underwent early apoptosis, while 93.9% of HEK-293 cells showed early apoptosis with 0.3% remaining viable. These results suggested that while the peptide fraction does affect non-cancerous cells, its primary apoptotic activity was directed at the cancerous cells [47].

Unlike previous investigations focused on proteins, the present study considered evaluating the secondary metabolites of LP. The cytotoxic potential of LPMMF 1.5 on MCF-7 and A549 cancer cell lines was determined using the MTT assay over different time points, of 24, 48, and 72 h, at varying concentrations ranging from 12.5 to 250 µg/ml. The results indicated concentration- and time-dependent inhibition of cancer cell viability in both the breast and lung cancer cell lines. Notably, at 250 µg/ml, MCF-7 cells exhibited 50 ± 1.41% inhibition at 24 h, increasing to 56.67 ± 0.66% at 48 h and peaking at 70.48 ± 0.22% inhibition after 72 h of incubation. Likewise, A549 cells showed 45 ± 2.16% inhibition at 24 h, 57.37 ± 0.74% at 48 h, and 63.67 ± 1.03% at 72 h. Significant morphological changes, including cell shrinkage, membrane blebbing, and the presence of necrotic debris, were evident in the treated cells, along with the observed cytotoxicity, even at the lowest treatment concentration (12.5 µg/ml). These alterations implied that LPMMF 1.5 triggered apoptotic pathways in MCF-7 and A549 cells, which intensified with time. Standard chemotherapeutic agents, Doxorubicin for A549 cells and Cisplatin for MCF-7 cells, were used to compare the efficacy of LPMMF 1.5. With IC50 values of 26.87 µg/ml at 24 h, 14.10 µg/ml at 48 h, and 4.15 µg/ml at 72 h, the cytotoxic effects of Cisplatin on MCF-7 cells increased over time. With an IC₅₀ of 10.48 µg/ml at 24 h, 8.68 µg/ml at 48 h, and 6.02 µg/ml at 72 h, Doxorubicin also demonstrated strong cytotoxic effects on A549 cells. Comparable cytotoxicity was only shown by LPMMF 1.5 at concentrations greater than 250 µg/ml. However, LPMMF 1.5’s time-dependent rise in cytotoxicity was comparable to that of Doxorubicin and Cisplatin in each cell line, confirming its effectiveness against cancer cells.

To determine the apoptotic effects of various plant extracts, AO and EtBr staining is widely used [48, 49]. Through the use of AO and EtBr dual staining, the proapoptotic properties of LPMMF 1.5 in both cells have been confirmed in this investigation. It was highly probable that LPMMF 1.5 caused apoptosis-mediated cytotoxicity in both tested cell lines because a considerable percentage of orange and yellow-stained cells were seen in treated samples when compared to controls. This effect was comparable to Cisplatin (for MCF-7) and Doxorubicin (for A549), indicating its potential as an alternative therapeutic agent. A proposed mechanism of action of the antioxidant and anticancer action of LPMMF 1.5 on cancer cells has been summarized in Fig. 30.

Fig. 30.

Schematic representation of the experimental workflow and the proposed mechanism underlying the anticancer action of LPMMF 1.5

Purwanti et al. (2022) described the occurrence of over 60 bioactives in the ethanol extract of LP, which was assessed using HR-LCMS. Among them, phenolic acids and flavonoids were predominant, with notable compounds including ferulic acid, coumaric acid, daidzein, rutin, and isoquercetin [50]. To investigate anti-diabetic potential, molecular docking was employed, targeting key diabetes-related enzymes: α-amylase, DPP4, and PTP1B. Fourteen compounds demonstrated strong binding affinities with these enzymes. Ursolic acid showed the highest inhibition of α-amylase with a binding energy of − 10.1 Kcal/mol. 19-nortestosterone exhibited strong affinity for PTP1B (–9.3 Kcal/mol), while rutin bound effectively to both α-amylase (–8.8 Kcal/mol) and DPP4 (–9.1 Kcal/mol). Compounds such as 19-Norandrostenedione, 19-Nortestosterone, Icariside B1, Ilicic Acid, and Psilostachyin B exhibited favourable physicochemical properties [50]. We have found similar compounds reported in the LPMMF 1.5 fraction of the seed methanolic extracts. Nonetheless, there exists no in silico analysis of LP phytochemicals to evaluate their anticancer properties. Filling the gaps in the availability of computational evaluation of the LP anticancer activity, we have identified LP phytochemicals using HR-LCMS analysis and subjected them to in silico analysis to validate our experimental observations and to elucidate the possible mechanisms of anticancer action. The ADME analysis ensured the selection of drug-like compounds based on Lipinski’s Rule of Five, filtering out molecules with unfavourable pharmacokinetic properties [51]. The molecular docking study provided an initial understanding of ligand-protein interactions by evaluating binding affinities and interaction mechanisms. The 93 molecules that adhered to Lipinski’s rule of 5 were subjected to molecular docking against three different cancer-specific protein targets. Cyclin B1, which plays a major role in mitotic progression and is often overexpressed in cancers such as breast, lung and colorectal, making it a promising cancer therapeutic target. The cell cycle’s transition from the G2 phase to mitosis is regulated by cyclin B1 (G2/mitotic-specific cyclin B1, PDB: 4Y72). Its role in apoptosis and its interactions with apoptotic signalling pathways have long been associated with its function in cell cycle regulation [52]. An established target in anticancer treatment, poly (ADP-ribose) polymerase 1 (PARP1, PDB: 7KK2) is an essential enzyme in DNA repair, as PARP1 inhibition damages DNA, it can cause cancer cells to die [53]. Since cell division protein kinase 2 (CDK2, PDB: 3IG7) plays a critical role in controlling cell division, especially at the G₁/S phase transition, and its dysregulation has been connected to the emergence of several cancers, blocking CDK2 with small molecule inhibitors may prevent tumour growth by impeding the progression of the cell cycle [54]. Picroside-1 showed the highest binding affinity among the tested ligands for a variety of proteins, especially for 7KK2 (-8.3 Kcal/mol), 3IG7 (-9.366 Kcal/mol), and 4Y72 (-8.027 Kcal/mol). Multiple hydrogen bonds, π-π stacking interactions, and salt bridges stabilised these strong binding interactions. This implied that ligands derived from nature might provide better pharmacokinetic characteristics as well as competitive binding qualities. The proteins’ binding pockets contained numerous hydrogen bonds and hydrophobic interactions, suggesting a high probability of stable complex formation and calling for additional investigation through molecular dynamics simulations.

The structural stability and dynamic behaviour of the aforementioned docked ligand-protein complexes were evaluated using molecular dynamics simulations (MDS) over a 500-nanosecond (ns) trajectory. All complexes retained structural stability, as indicated by the RMSD analysis, and protein RMSD values, which are a measure of binding stability, consistently fell between 1.0 Å and 3.6 Å. While the 7KK2-Talazoparib complex displayed the lowest fluctuation range (1.2–2.7 Å), suggesting a highly stable binding mode, the 7KK2-Picroside-1 complex displayed early fluctuations (1.2–2.8 Å) prior to stabilisation. Talazoparib had the most restricted movement (0.5–0.75 Å), according to the ligand RMSD analysis, indicating strong binding stability. On the other hand, lactitol and picroside-1 exhibited mild oscillations but remained within the active site, indicating stable yet adaptable interactions. According to RMSD analysis, the Picroside-1 complex for the 3IG7 protein exhibited stable interactions over time, fluctuating between 2.0 Å and 2.8 Å. Although lactitol fluctuated a little more (from 2.4 Å to 3.2 Å), it was still tightly bound in the active site. A more flexible binding conformation was indicated by EFP’s slightly greater variation (1.5 Å to 4.0 Å). According to the ligand RMSD trends, EFP showed moderate flexibility, while Picroside-1 showed the most stable binding, followed by Lactitol. Picroside-1 had the strongest binding interactions, according to binding free energy calculations, followed by lactitol and the prebound ligand EFP. With an RMSD range of 2.4 Å to 3.2 Å, the MDS results for 4Y72 showed that Picroside-1 stayed consistently stable, stabilising even more after 400 ns. While lactitol fluctuated between 2.6 and 3.5 Å, the prebound reference ligand LZ9 showed the greatest degree of fluctuation (1.2 to 3.6 Å), indicating a more dynamic binding mode. With only slight variations throughout the trajectory, ligand RMSD values showed that Picroside-1 and lactitol maintained dependable binding interactions. A well-preserved conformation was confirmed by the secondary structure analysis, which revealed that 4Y72-Picroside-1 retained 56.95% of its helices without any β-strands. Understanding the strength and stability of interactions between proteins and ligands is essential for comprehending their potential as inhibitors or modulators in biological systems. This information was obtained through the use of Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) binding free energy calculations. Stronger and more stable interactions were indicated by lower binding free energy values. The most favourable binding energy was displayed by 4Y72-Picroside-1, whereas the interactions between 7KK2-Picroside-1 and 3IG7-Picroside-1 were weaker and less stable. While 4Y72-Lactitol had binding energies ranging from − 71.79 Kcal/mol to -90.59 Kcal/mol, 7KK2-Lactitol and 3IG7-Lactitol had moderate binding energies.

To assess the ligands’ binding stability and unbinding mechanisms, metadynamics simulations were run for 50 ns. Important information about the most stable binding conformations and possible ligand dissociation pathways was obtained from the free energy surface analysis. Because of their deep free energy minima and high barriers to unbinding, Talazoparib (for 7KK2), LZ9 (for 4Y72), and EFP (for 3IG7) showed the most stable binding among the control/reference ligands. Picroside-1 and lactitol were the non-control ligands that continuously demonstrated robust and stable binding to a variety of targets. Two major energy minima were observed by picroside-1 with the 3IG7 protein, indicating two stable binding conformations. With a global minimum of about − 100 Kcal/mol, EFP showed stronger binding. With a deep free energy minimum of roughly − 160 Kcal/mol, EFP showed the best binding stability. Likewise, a stable binding conformation with a deep free energy minimum of roughly − 120 Kcal/mol was demonstrated by picroside-1, lactitol, and Talazoparib with the 7KK2 protein. With a global free energy minimum of roughly − 250 Kcal/mol, Talazoparib showed the best binding stability. Likewise, with the 4Y72 protein, Picroside-1 exhibited moderate stability with a well-defined binding pocket and an unbinding barrier, while Lactitol demonstrated the best binding affinity with an efficient free energy minimum and a less flexible binding interaction, suggesting a lower chance of detachment [55, 56].

In addition, Picroside-1 was predicted to have a variety of other pharmacological activities, using PASS prediction. Picroside-1’s anticancer activity against triple-negative breast cancer (MDA-MB-231) cells has previously been determined through in vitro investigations. The IC₅₀ value for cell viability assays was 95.3 µM, indicating a concentration-dependent decline. According to DNA fragmentation and Annexin V-PI flow cytometric analyses, apoptosis analysis showed a 20% increase in the early apoptotic phase at concentrations of 0, 50, 75, and 100 µM. Picroside-1 treatment was found to decrease the mitochondrial membrane potential and cause cell cycle arrest in the G₀/G₁ phase [57]. Similarly, it was reported with significant cytotoxicity and anti-invasion activity towards MCF-7 cells with a decrease in the activity of Matrix Metalloproteins, collagenases, and gelatinases, inhibiting breast cancer progression [58]. However, no anticancer activity evaluations of lactitol have been reported thus far. The findings of this study suggest that Picroside-1 could serve as a capable prime compound for further anticancer drug advancement. The observed high binding affinities, stable interactions, and favourable binding free energies indicated their potential to act as inhibitors or modulators for their respective protein targets.

Nonetheless, the evaluation of the mechanistic pathways of anticancer effect of LPMMF 1.5, along with further purification of the LPMMF 1.5 fraction, followed by in vitro and in vivo anticancer evaluation of Picroside-1, is essential to validate the observations made during the in silico studies.

Conclusion

This study successfully used a bioactivity-guided technique to separate and isolate the cytotoxic fractions from the methanol extract of Lablab purpureus seeds. The methanol fraction LPMMF 1.5 had a strong ability to fight free radicals and potent dose- and time-dependent cytotoxicity against MCF-7 and A549 cancer cell lines, and caused apoptosis. Chemical profiling showed the presence of multiple bioactive phytochemicals, including Picroside-1, which had strong binding affinity and stable interactions with cancer-specific targets PARP1, CDK2, and CCNB1 in molecular docking and simulation studies. Further mechanistic evaluation of the anticancer effect of LPMMF 1.5 as well as in vitro evaluation of the anticancer effect of Picroside-1 could lead to the development of plant-based cancer-fighting drugs.

Author contributions

S.S.B contributed to the Methodology, Writing - original draft, Formal analysis, Interpretation of the Data, Investigation, Visualization. S.J contributed to Writing- review & editing, Formal analysis, Investigation. S.R.S contributed to Conceptualization, Supervision, Writing - original draft, Formal analysis, and Investigation. R.D. and P.M contributed to Investigation, Writing- review & editing, Formal analysis and Visualization. S.F.A, H.M and G.I.H contributed to the Writing- review & editing, Formal analysis, Funding acquisition, Resources. S.P.K and C.S were responsible for Supervision, Writing- review & editing, Resources, and Project administration. S.K.P contributed to the Conceptualization, Design, Supervision, Interpretation of the Data, Funding acquisition, Resources, Validation, Software, Project administration. All authors read and approved the final manuscript.

Funding

This research was supported by the Ongoing Research Funding Program (ORF-2025-1037) at King Saud University in Riyadh, Saudi Arabia.

Data availability.

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Declarations

Ethics approval and consent to participate

Not applicable as the article does not contain any studies with human participants or animals performed by any of the authors. The human cancer cell lines- MCF-7 and A549 were procured from National Centre for Cell Science (NCCS), Pune, India. These are commercially available cell lines and do not require ethics committee approval. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

All authors have reviewed the manuscript and agreed to publication.

Informed consent

Not applicable.

Competing interests

The authors declare no competing interests.