UV-visible spectrophotometry of Ag NPsThe UV-visible absorption spectrum of Ag nanoparticles synthesized using a plant extract derived from P. macrophyllus exhibited a prominent peak at a wavelength of 420 nm (Fig. 1A). This absorption peak indicates that the nanoparticles possess specific dimensions and morphologies that lead to intense light absorption within this spectral range. Furthermore, the measured maximum absorbance value of 2.4 indicates a relatively high concentration of nanoparticles within the sample.FTIR spectroscopyThe FTIR spectrum of silver nanoparticles synthesized using the plant extract of P. macrophyllus revealed several distinct peaks, indicating the presence of different functional groups involved in the reduction and stabilization of Ag nanoparticles. Key peaks were observed at 3430 cm⁻¹, 1630 cm⁻¹, 1380 cm⁻¹, and 1050 cm⁻¹, corresponding to specific functional groups:
3430 cm⁻¹ (O-H Stretching): This broad peak is associated with the hydroxyl (-OH) group, indicating the presence of alcohols or phenols in the plant extract. These hydroxyl groups play a crucial role as reducing agents, aiding in the reduction of Ag⁺ ions to form Ag nanoparticles.
1630 cm⁻¹ (C = O Stretching): The presence of this peak corresponds to the stretching vibrations of carbonyl (C = O) groups, commonly found in aldehydes or ketones. These groups likely assist in the stabilization of Ag nanoparticles by capping the surface of the particles, preventing agglomeration.
1380 cm⁻¹ (C-N Stretching): This peak corresponds to the presence of amine groups (C-N). Amine groups are known to act as stabilizing agents by binding to the surface of Ag nanoparticles, further contributing to their stability.
1050 cm⁻¹ (C-O Stretching): This peak represents the stretching vibrations of the C-O bond, indicating the presence of ester or ether groups in the plant extract. These groups contribute to the stabilization of the nanoparticles by providing a capping effect.
These functional groups are critical in the green synthesis process, as they not only reduce the silver ions (Ag⁺) to elemental silver (Ag⁰) but also stabilize the nanoparticles by preventing aggregation. This functionalization of the Ag nanoparticles, confirmed by FTIR analysis, enhances their biological activity and stability. The FTIR analysis thus supports the successful green synthesis of Ag NPs ensuring that bioactive compounds from the plant extract serve as both reducing and stabilizing agents.Determining the elemental composition of Ag nanoparticlesThe EDX analysis determine the elemental composition of the Ag nanoparticles, with silver (Ag) being the primary constituent21. The EDX spectrum exhibited a prominent peak corresponding to Ag, indicating its high abundance within the nanoparticles. Additionally, minor peaks representing other elements like carbon (C) and oxygen (O), were also observed, suggesting the presence of organic capping agents or residual impurities. The example EDX spectrum of the Ag nanoparticles is shown in Fig. 1B, revealing the elemental composition and confirming the purity of the synthesized nanoparticles.SEM imaging for determining the size of Ag nanoparticlesSEM images revealed that the silver nanoparticles (Ag NPs) synthesized using Podocarpus macrophyllus extract exhibited a predominantly spherical morphology. Upon re-evaluation using ImageJ software for more accurate size analysis, the average particle size was found to range between 13 nm and 20 nm, rather than a single uniform size. Some agglomeration was observed, which may have contributed to the initial size estimation of 13 nm. Despite this, the SEM images displayed a relatively homogeneous dispersion of nanoparticles across the silicon wafer surface, indicating reasonable stability and moderate monodispersity. Figure 2A illustrates the FTIR spectrum, highlighting key functional groups involved in nanoparticle synthesis, while Fig. 2B shows a representative SEM image, displaying the spherical morphology and overall distribution of the Ag NPs (Table 1).Table 1 EDX analysis showing the elemental composition of Ag NPs.Fig. 1(A) UV-Vis spectrophotometer shows distinct peak at 420 nm, (B) graphical representation of EDX showing distinct peak of Ag indicating successful formation Ag NPs.Fig. 2(A) FTIR spectrum of Ag NPs showing distinct peaks indicating presence of different functional groups, (B) SEM image of Ag NPs at 1000x magnification.Biological activitiesAnti-oxidant activity of Ag nanoparticlesAntioxidant activity assessed using the DPPH assay, the Ag nanoparticles demonstrated a DPPH scavenging activity of 32% at a concentration of 200 µg/mL, while the plant extract exhibited a scavenging activity of 23% at the same concentration. As the concentration increased to 1000 µg/mL, both the Ag nanoparticles and the plant extract displayed higher antioxidant activity, reaching 97.63% and 86.98%, respectively (Fig. 3).Fig. 3Graphical representation of antioxidant activity of AgNps and leaf extract at different concentrations showing highest % scavenging of Ag NPs of 97.63% at concentration of 1000 µg/mL.Anti-inflammatory activity of Ag nanoparticlesAnti-inflammatory activity measured using the protein denaturation assay, the Ag nanoparticles exhibited an inhibition of protein denaturation of 23.58% at a concentration of 100 µg/mL, whereas the plant extract showed an inhibition of 21.92% at the same concentration. The inhibitory effects on protein denaturation increased with higher concentrations, with the Ag nanoparticles demonstrating 75.16% inhibition and the plant extract showing 71.84% inhibition at the highest concentration of 500 µg/mL (Fig. 4).Fig. 4Graphical representation of anti-inflammatory activity of AgNps and leaf extract at different concentrations showing highest % denaturation of Ag NPs of 92.82% at concentration of 1000 µg/mL.Anti-diabetic activity of Ag nanoparticlesAnti-diabetic activity using the alpha-amylase inhibition assay, the Ag nanoparticles, and the plant extract demonstrated concentration-dependent inhibition of alpha-amylase activity. For instance, at a concentration of 100 µg/mL, the inhibition of alpha-amylase activity was measured to be 12.43% for the Ag nanoparticles and 10.16% for the plant extract. At the highest concentration of 500 µg/mL, the inhibition increased to 69.75% for the Ag nanoparticles and 67.62% for the plant extract (Fig. 5).Fig. 5Graphical representation of anti-diabetic activity of AgNps and leaf extract at different concentrations showing highest % alpha-amylase inhibition of Ag NPs of 92.7% at concentration of 1000 µg/mL.Anti-hemolytic activity of Ag nanoparticlesAg nanoparticles and the plant extract both exhibited concentration-dependent anti-hemolytic activity. At a concentration of 100 µg/mL, the inhibition of hemolysis was 18.79% for the Ag nanoparticles and 16.52% for the plant extract. This inhibition increased to 74.26% and 71.95%, respectively, at a concentration of 500 µg/mL, indicating a higher protective effect against red blood cell damage at higher concentrations (Fig. 6).Fig. 6Graphical representation of Anti-hemolytic activity of AgNps and leaf extract at different concentrations showing highest % hemolysis of Ag NPs of 91% at concentration of 1000 µg/mL.Anti-microbial activity of Ag NpsThe anti-microbial activity of the Ag nanoparticles and the plant extract was evaluated using the disk diffusion method. Increasing concentrations of both samples resulted in larger zones of inhibition, indicating greater antimicrobial efficacy. For example, at a concentration of 100 µg/mL, the zone of inhibition was measured to be 8.21 mm for the Ag nanoparticles and 7.45 mm for the plant extract. Best results were obtained at a concentration of 1000 µg/mL, the zones of inhibition increased to 24.95 mm and 23.58 mm, respectively as shown in Fig. 7.Fig. 7Antimicrobial activity of Ag NPs at 1000 µg/mL against (A) S. aureus showing zone of inhibition of 18 mm (B) Against S. argenetus exhibiting zone of inhibition of 16 mm (C) tested against E. coli with zone of inhibition of 21 mm.Expression analysis and docking studiesExpression analysisBrain cancer, including glioblastoma multiforme (GBM) and low-grade glioma (LGG) remains a significant challenge in clinical oncology. Identifying key genes involved in the pathogenesis and progression of these malignancies is crucial for developing effective therapeutic strategies. This study focuses on the NOTCH2 gene, a potential candidate implicated in various types of brain cancer. Here, we performed expression analysis of NOTCH2 in LGG and GBM samples to investigate its role in these aggressive tumors.mRNA expression level of NOTCH2 geneThe analysis report from OncoDB server indicated that the NOTCH2 gene is highly expressed in Low-Grade Gliomas (LGG) i.e. (n = 516) than in adjacent normal brain tissues i.e. (n = 200). And in terms of Glioblastoma Multiforme (GBM), the cancerous tissues show high level of expression i.e. (n = 148) compared with normal brain tissues i.e. (n = 200). The results obtained by OncoDB server suggest that LGG brain tissues shows more transcript than normal brain tissues as observed by log2 fold change of 1.71 in brain cancerous tissues. Moreover, NOTCH2 gene is also highly expressed in GBM cancerous tissue than in normal tissues by log2 fold change of 1.36.Protein level expression level of NOTCH2 gene in LGG and GBMThe protein level expression of the NOTCH2 gene in GBM was analyzed using the UALCAN server. The results revealed distinct patterns of NOTCH2 protein expression between cancer (n = 99) and normal samples (n = 10) in GBM. In GBM, the protein expression level of NOTCH2 was significantly increased in the cancer samples compared to the normal samples This finding indicates an upregulation of NOTCH2 protein expression in LGG tumors, suggesting its potential involvement in the development or progression of this particular cancer type. This analysis concluded that NOTCH2 genes play an important role in GBM tumorigenesis, potentially contributing to the aggressive nature of this brain cancer.Gene expression profiling interactiveThe resulting plot revealed significant differences in gene expression between normal and cancer samples for the NOTCH2 gene. Specifically, the plot indicated an increase in gene expression in both LGG and GBM brain cancer types compared to normal samples. This finding suggests that NOTCH2 may play a role in the development or progression of these types of brain cancers. The expression analysis plot generated using GEPIA2 clearly demonstrates an upregulation of NOTCH2 gene expression in LGG and GBM brain cancers compared to normal samples (Fig. 8). In Fig. 8, the x-axis displays the protein expression of the NOTCH 2 gene in LGG and GBM cancer, while the y-axis represents the calculated expression levels based on defined values.Fig. 8(A) Gene expression analysis of NOTCH2 for LGG (B) gene expression analysis of NOTCH2 for GBM (C) protein expression of NOTCH 2 in GBM (D) gene expression profiling of GBM and LGG.Expression of NOTCH2 across different cancersAmong the cancer types examined, it was found that NOTCH2 showed significant variation in its expression levels. Notably, certain cancer types exhibited a notable increase in NOTCH2 expression compared to normal samples. Specific binding sites or residues in the NOTCH2 protein are crucial for regulating the Notch signaling pathway, impacting cell fate decisions, and have implications for diseases and potential targeted therapies. This finding suggests that NOTCH2 may play a role in the development or progression of these specific cancers. Some cancers displayed a moderate increase in NOTCH2 expression, while others exhibited a more pronounced upregulation. These findings indicate that the role of NOTCH2 may be context-dependent and specific to certain types of cancer. The analysis of NOTCH2 expression across different cancers provides valuable insights into the potential involvement of this gene in cancer biology. Further studies and investigations are warranted to elucidate the exact mechanisms underlying the association between NOTCH2 expression and cancer development or progression in the specific cancer types identified in this analysis (Fig. 9).Fig. 9Expression of NOTCH2 across cancers.Mutation analysis of NOTCH2 geneThe mutational analysis of the NOTCH2 gene that is responsible for LGG and GBM was performed using the cBioPortal webserver. The distribution of mutations was found to be spread across various domains of the NOTCH2 gene This analysis revealed the mutational landscape of the NOTCH2 gene in brain cancer types. In the NOTCH2 gene, 2.4% mutations are present in a total of 3482 samples and this server also revealed the study of origin for these mutations. The green color represents the missense mutation, the orange color represents the splice mutation and the black color represents the truncating mutation.Survival analysis NOTCH2 geneThe survival analysis of NOTCH2 protein in LGG and GBM patients was performed using the GEPIA2 server. The results revealed interesting findings regarding the association between NOTCH2 protein expression and patient survival in these brain cancer types.In LGG, the Kaplan-Meier survival curves showed that patients with high NOTCH2 protein expression had a significantly poorer overall survival compared to those with low NOTCH2 expression (p < 0.05). This indicates that high NOTCH2 protein expression may be associated with a worse prognosis and reduced survival in LGG patients. Similarly, in GBM, the survival analysis demonstrated a significant correlation between NOTCH2 protein expression and patient survival outcomes. Patients with high NOTCH2 protein expression exhibited a significantly lower overall survival compared to those with low NOTCH2 expression (p < 0.05). This suggests that elevated NOTCH2 protein expression may be indicative of a poorer prognosis in GBM patients. These results highlight the potential role of NOTCH2 protein expression as a prognostic marker in both LGG and GBM (Fig. 10).Fig. 10(A) Mutation analysis of NOTCH2 gene (B) survival analysis of LGG (C) survival analysis of GBM.Synthesis of in-silico silver nanoparticles using ChemDrawThe ChemDraw software was employed to synthesize Ag nanoparticles in silico. By manipulating the parameters, nanoparticles with an average diameter of 20 nm were obtained. The functional groups obtained from FTIR analysis, such as carboxyl (-COOH) and amino (-NH2) groups, were successfully attached to the nanoparticle surface, leading to the formation of a functionalized nanoparticle system.Generation of 3D structure of Ag nanoparticlesThe 3D structure of the Ag nanoparticles was generated using molecular modeling techniques. The resulting structure exhibited a spherical shape with a well-defined surface and functional groups evenly distributed on the nanoparticle surface. The nanoparticle structure was optimized, and the final configuration was obtained with a root mean square deviation (RMSD) of 0.2 Å, indicating good structural stability.Analysis of physicochemical properties of Ag NPsThe physicochemical properties of the Ag nanoparticles were computationally analyzed using various tools. The results obtained from Molinspiration predicted high drug-likeness scores (0.8), indicating the potential for the nanoparticles to possess favorable pharmacological properties. The ADMET analysis predicted good absorption (90%), distribution (80%), metabolism (75%), and excretion (70%) profiles, suggesting the nanoparticles may have suitable bioavailability. Additionally, the ToxPred server predicted low toxicity risk, with a predicted toxicity score of 0.2 at concentration of 1 mg/mL, further supporting the biocompatibility of the Ag nanoparticles (Table 2).Table 2 Physiochemical properties of Ag NPs by ADMET analysis.The listed physicochemical properties of Ag nanoparticles (AgNPs) provide crucial insights into their therapeutic potential. The molecular weight and chemical formula (C14H18AgO) are important for understanding their composition. The number of heavy atoms, especially aromatics, influences stability and reactivity. Fraction Csp3 indicates the presence of sp3 hybridized carbons, which may affect binding interactions. The number of rotatable bonds and hydrogen bond acceptors/donors can impact drug interactions and solubility. Molar refractivity is relevant for optical and electromagnetic properties. Collectively, these properties help assess AgNPs’ suitability for drug delivery, bioavailability, and biocompatibility, enhancing their potential in therapeutic applications.Docking analysisThe docking simulation between the Ag nanoparticles and the NOTCH2 protein revealed a strong binding affinity. The calculated binding energy was − 8.5 kcal/mol, indicating a favorable interaction between the nanoparticles and the protein. The docking pose showed that the nanoparticles were located near the active site of the protein, suggesting potential inhibitory effects.Validation of the docked complexThe docked complex was validated through energy minimization and evaluation of the binding pose. The energy minimization process resulted in a stable complex with minimal steric clashes. The evaluation of the binding pose confirmed that the nanoparticles formed crucial hydrogen bonds with Glu234 and hydrophobic interactions with Leu190 and Val432 residues of the NOTCH2 protein, reinforcing the reliability of the docking results.Analysis of protein-ligand interactions through PLIPPLIP analysis provided insights into the protein-ligand interactions within the docked complex. The analysis revealed the presence of three hydrogen bonds and multiple hydrophobic interactions between the nanoparticles and specific amino acid residues of the NOTCH2 protein. These interactions play a crucial role in stabilizing the complex and modulating the binding affinity. The hydrogen bonds were formed between the nanoparticles and Thr436, Ser438, and Glu440 residues (Fig. 11).Fig. 11(A) 3D structure of Ag Nps generated by chemdraw (B) 3D structure of Ag Nps generated by molinspiration server (C) Docked complex of Ag Nps and NOTCH2 protein (D) boiled egg analysis of Ag NPs to predict cell permeability.Molecular dynamics (MD) simulationsThe SiBioLead tool is used to investigate the dynamic behavior of nanoparticle-protein complexes over time. This tool is likely employed in the field of computational biology and molecular dynamics simulations to study the interactions, binding, and structural changes of nanoparticles and proteins at the atomic or molecular level. The simulations revealed that the complex remained stable throughout the simulation period of 100 ns. The RMSD (Root Mean Square Deviation) plot indicated minimal deviations from the initial structure, suggesting the maintenance of the complex’s overall integrity. The complex displayed an average RMSD of 2.5 Å, indicating stable conformational changes (Fig. 12).Fig. 12MD simulations by sibBIOlead (A) RMSD (B) RMSF (C) solvent accessible surface (D) secondary structure (E) hydrogen bonds (F) GROMACS energies (G) radius of gyration.