Network pharmacology and molecular docking reveal antiviral mechanisms of silver nanoparticles synthesized by Oscillatoria sp. against HCV pathogenesis

Abstract

This study explored the green synthesis of silver nanoparticles (AgNPs) using eco-friendly aqueous and methanolic extracts from Oscillatoria sp. algae. Characterization techniques (Fourier transform infrared spectroscopy, Transmission electron microscopy, Dynamic light scattering technique, and Zeta potential) confirmed the successful synthesis and stability of the AgNPs. Untargeted metabolic profiling via LC–MS tentatively identified 22 potential metabolites (1–22) in the extracts, primarily glycolipids, macrolides, fatty acids, and cyclic polyketides. Notably, the methanolic extract and its derived AgNPs exhibited potent antiviral activity against the Hepatitis C Virus (HCV) in HuH7.5 cells. The AgNPs displayed a significantly lower IC₅₀ (0.0705 µg/mL) than the raw extract (20.325 µg/mL). Meanwhile, formulations showed little effect on normal cell lines (HuH7, THLE2). Network pharmacology analysis revealed the therapeutic potential of Oscillatoria sp. against HCV by targeting key genes. Subsequent molecular docking simulations identified o-anisic acid methyl ester (2), oscillaginin B (11), and methyl-11-octadecenoate (14) as promising antiviral candidates. These compounds exhibited strong binding affinities (binding energy < -5.0 kcal/mol) with Albumin (ALB) and Cluster of Differentiation 4 (CD4), suggesting their potential role in modulating critical pathways for viral infection and liver function. The increased antiviral effect of the AgNPs compared to crude extracts can be attributed to their ability to enhance the delivery of metabolites and improve their bioavailability. Additionally, the strong interactions between the identified metabolites and ALB and CD4 indicate a modulation of host–virus interactions, as well as key pathways involved in HCV entry and liver function. This provides a mechanistic explanation for the observed therapeutic potential. Overall, this work highlights the potential of Oscillatoria sp. metabolites and their biogenic AgNPs for combating HCV and paves the way for discovering novel antiviral agents from natural sources.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-26310-8.

Introduction

Hepatitis C virus (HCV) is a small virus and is considered an enveloped virus. The genus Hepatitis Virus belongs to the Flaviviridae family. HCV causes Hepatitis C, a liver infection that often progresses to chronic hepatitis, liver cancer, and cirrhosis. It can also be said that it is a serious and long-term disease, as it poses a threat to public health and threatens the lives of thousands around the world. The World Health Organization reports that there are 1.5 million new cases each year with HCV, and approximately 58 million people around the world are infected with chronic infection, and the rate is constantly increasing ^1–4. Current treatment primarily involves direct-acting antivirals (DAAs) like NS3/4A, NS5A, and NS5B inhibitors, which have demonstrated high cure rates; however, challenges such as high cost, limited access, and potential resistance highlight the ongoing need for new antiviral strategies ⁵. Therefore, to meet the WHO’s HCV elimination goals, efforts must focus on discovering effective and safe treatment ^6–9. Promising strategies, such as nanotechnology, are being explored to enhance treatment effectiveness ^10,11 and find alternative therapeutic approaches that are effective and sustainable, such as utilizing cyanobacteria rich in bioactive compounds with demonstrated antiviral properties ¹².

There are many diverse genera of blue-green algae (cyanobacteria), including Oscillatoria, which are considered filamentous blue-green algae and were named because of their oscillating movement. Members of the genus Oscillatoria are considered an excellent source for producing many active substances such as food additives, vitamins, minerals, anti-cancer, and antioxidants ^13–19. They are also usually found at the bottom of lakes, rivers, and ponds. They can also grow and float on the water’s surface, forming dense surface blooms ^14,20.

They are rich in biologically active secondary metabolites, exhibiting antibacterial, antioxidant ²¹, anticancer ^15,17, antiviral, and antifungal ¹⁶ properties. However, their antiviral activity still requires further research ²². Additionally, their secondary metabolites are used as reducing agents in nanoparticle formation ^18,23. Oscillatoria sp. has also demonstrated antiviral activity against Rotavirus, Coxsackievirus B3 ^24–26, and the influenza A virus ^22,27,28. This prior antiviral activity provides the rationale for selecting Oscillatoria sp. in this study. Despite the presence of antiviral compounds, their efficacy against viruses remains an area requiring further investigation.

Furthermore, the biological activity of Oscillatoria sp.’s secondary compounds has significantly advanced the development of green synthesis methods for nanoparticles. These environmentally friendly, cost-effective, and efficient methods address traditional physical and chemical synthesis limitations ^{18,23,29–32}.

Nanoparticles (NPs) were synthesized in several ways, including the old traditional physical and chemical methods. Still, with the development of research and the efforts made to reduce environmental pollution and toxic waste, these methods are considered unsafe, as they have many disadvantages, including the toxic materials used, the lack of stability of the metabolites produced, and the lack of efficiency, high cost, complex steps, and environmental pollution ^33–36. So, it was necessary to search for other nano-synthesis methods, and the biosynthesis of NPs has received significant interest. This is because they are environmentally friendly, low-cost, effective, and easy to apply, and do not require special conditions to create them. Because of this, we consider it an excellent alternative to physical and chemical methods ^37–45.

Silver NPs (AgNPs) have achieved prominence in various fields, including cosmetics, food industries, medical devices, and electronics, due to their chemical, physical, and safety properties ^33,46–48. Their application in biology, medicine, and drug manufacturing has increased, with research highlighting their antifungal, anti-inflammatory, antiangiogenic, antiplatelet, and anticancer activities ^49–55. They have also been extensively studied for developing antiviral therapeutics using various mechanisms to affect the viral lifecycle, such as entry and replication within cells or enhancing the efficacy of existing antiviral drugs ^{33,39,56–61}.

Recent studies have demonstrated that AgNPs possess antiviral effects against various viruses, such as influenza A, HBV, human parainfluenza, herpes, human immunodeficiency, herpes simplex, respiratory syncytial, poliovirus, dengue, enterovirus, coronavirus, and HCV ^57,62,63. Previous research has highlighted a scarcity of studies explicitly examining the antiviral effects of AgNPs on HCV ^64–70.

This study proposes a novel approach utilizing the bioactive potential of Oscillatoria sp. combined with the proven antiviral properties of silver nanoparticles (AgNPs). Oscillatoria sp. was chosen because it contains vast biologically active compounds, including antioxidants and antiviral compounds, and is amenable to sustainable cultivation. This positions it as a promising source for environmentally friendly NPs synthesis ^18,25,71, as demonstrated by the evidence shown earlier, and many previous studies have proven. Furthermore, AgNPs were selected due to their direct antiviral action, effectively targeting viruses during the initial stages of host-cell interaction. Their adaptable antiviral effectiveness qualifies them for use in antivirus therapy ⁷².

However, there are existing research gaps concerning the impact of AgNPs synthesized from Oscillatoria sp. on HCV, and the biocompatibility of these nanoparticles remains unexplored, mainly ^73–75. Furthermore, HCV does not fully explain the complete metabolic profile of Oscillatoria sp. and identifies specific compounds responsible for antiviral activity. Moreover, the need for effective and affordable treatments for HCV is becoming urgent. This study investigates Oscillatoria sp. as a sustainable source of antiviral drugs that could become a promising approach, especially in resource-limited settings. Providing a pathway for developing innovative drugs reduces reliance on conventional chemical synthesis and minimizes environmental impact ^76–78. This research aims to combine sustainable industrial innovation with public health requirements. This presents a unique opportunity for our study to contribute meaningfully to this field. Therefore, this research synthesized AgNPs using a biomass extract from Oscillatoria sp. Additionally, its effectiveness and activity on HCV were studied, and LC–MS was utilized for metabolic profiling and molecular docking investigations. Hypothesis: This study suggests that integrating the bioactive metabolites from Oscillatoria sp. with AgNPs will enhance antiviral activity through various mechanisms. It is anticipated that this combination will improve the cellular absorption of active compounds, provide stability to unstable metabolites, and simultaneously target different viral proteins or stages of replication, resulting in greater effectiveness than the crude extract by itself.

Results

LC–MS and metabolic profiling

About 22 metabolites were annotated in previous reports and natural products databases. All identified metabolites, either in positive or negative mode, were previously isolated from Oscillatoria sp. The chemical structures of the identified compounds from the Oscillatoria sp. extract are depicted in Fig. 1. Additionally, the molecular formula, molecular weight, chemical class, exact mass difference, peak area, and the previously reported Oscillatoria sp. source were summarized for each compound in (Supplementary Table S1).

Fig. 1.

Chemical structures of identified compounds in Oscillatoria sp. extract as shown in LC–MS total ion chromatogram.

Characterization of the synthesized AgNPs

TEM characterization of the synthesized AgNPs

The analysis using TEM revealed that the AgNPs are spherical nanoparticles. The methanolic and aqueous nanoparticles had an average size ranging between 16.63 nm and 17.39 nm, as depicted in Fig. 2, respectively. Furthermore, a histogram depicting the size distribution is also presented in Fig. 2.

Fig. 2.

TEM analysis reveals the shape and size of AgNPs synthesized from (A) methanolic and (B) aqueous extracts of Oscillatoria sp. The histogram illustrates the size distribution of the synthesized AgNPs from (C) methanolic and (D) aqueous extracts of Oscillatoria sp.

UV–visible and FTIR characterization of the synthesized AgNPs

The UV–Vis spectrum of the reaction medium was analyzed within the wavelength range of 200 to 800 nm to track the formation of AgNPs. An absorbance peak was identified at 423 nm for the methanolic extract and at 416 nm for the aqueous extract Fig. 3.

Fig. 3.

Spectral analysis using UV–Vis for the biosynthesis of AgNPs of Oscillatoria methanolic extract and aqueous extract (A), FTIR spectra of Oscillatoria methanolic extract and aqueous extract (B).

FTIR spectrum Table. 1 and Fig. 3 display distinct major peak positions at 3308.31, 3326.39, 2083.84, 1636.62, 1283.47, 544.01, and 458.19 cm^-1. The broad peaks around 3300 cm^-1 suggest the presence of -OH groups in alcohols or phenols, likely with strong hydrogen bonding and N–H groups. The peak at 1636 cm^-1 corresponds to a carbon–carbon double bond. Finally, the peaks below 550 cm^-1 are characteristic of alkyl halide bonds.

Table 1.

FTIR major peaks and functional groups.

FTIR Peak (cm⁻1)	Functional Group Assignment
3308, 3326	O–H stretching (alcohols/phenols) and N–H stretching, broad due to hydrogen bonding
2083	C≡C stretching (alkynes)
1636	C = C stretching (alkenes)
1283	C–O stretching (alcohols, ethers, esters)
544, 458	Alkyl halide bonds (C–X)

Evaluating zeta potential and particle size

The z-average diameter of Oscillatoria methanolic extract-synthesized AgNPs was found to be 471.4 nm and a polydispersity index (PDI) of 0.468. For Oscillatoria aqueous extract-synthesized AgNPs, the z-average diameter was 262.5 nm and a PDI of 0.552. Figure 4 and Table 2 summarize the different characteristics of Oscillatoria methanolic and aqueous extract AgNPs.

Fig. 4.

DLS measurement of synthesized AgNPs (A) Oscillatoria methanolic extract AgNPs, and (B) Oscillatoria aqueous extract AgNPs.

Table 2.

Size, PDI, and Zeta Potential of Oscillatoria methanolic and aqueous extract AgNPs.

Name	Size (nm)	PDI	Zeta potential (mV)
Oscillatoria methanolic extract AgNPs	471.4	0.468	-35.7
Oscillatoria aqueous extract AgNPs	262.5	0.552	-26.8

Antiviral results

First, to assess the safety of the synthesized AgNPs, their cytotoxicity was evaluated on two cell lines: HuH7 and THLE2. Hence, the results in Table 3 confirmed that it exhibited low cytotoxicity within the safe range for normal cells ^79,80, as AgNPs of Oscillatoria methanolic extract outperformed Asunaprevir. Antiviral results of the synthesized AgNPs in HuH7 and THLE2 cell lines at different concentrations are shown in (Supplementary Table S21).

Table 3.

The cytotoxicity of Oscillatoria methanolic extract and Oscillatoria methanolic extract of AgNPs on two normal cell lines (HuH7, THLE2). Statistical evaluation was conducted using a one-way ANOVA; groups with different letters are significantly different at p ≤ 0.01 based on Tukey’s HSD test.

Sample	IC50 µg/mL
	HuH7	THLE2
Methanolic extract of Oscillatoria	61.12 ± 3.44a	217 ± 7.33a
AgNPs of Oscillatoria methanolic extract	131.5 ± 8.83b	388.3 ± 13.1a
Asunaprevir	68.85 ± 3.86a	118.5 ± 4.2a

The antiviral activity was assessed using the MTT antiviral assay on HuH7.5 cells. The methanolic extract showed modest antiviral activity with an IC₅₀ of 20.325 µg/mL. In contrast, the AgNPs derived from the methanolic extract exhibited potent antiviral activity, with an IC₅₀ of 0.0705 µg/mL, comparable to the well-established direct-acting antiviral agent Asunaprevir (IC₅₀ = 0.0695 µg/mL). These results are shown in Table 4, and the results of Oscillatoria methanolic extract and Oscillatoria methanolic extract of AgNPs against HCV at different concentrations are presented in (Supplementary Table S22). Importantly, this represents a ~ 288-fold improvement in potency over the crude methanolic extract, emphasizing the significant enhancement achieved through nanoparticle formulation. Cytotoxicity on HuH7 and THLE2 cells, assessed by MTT assay, confirmed low toxicity, and selectivity indices (SI = CC50/IC50) ranged from ~ 3–11 for the crude extract and ~ 1,866–5,508 for AgNPs, highlighting a dramatic increase in antiviral selectivity.

Table 4.

Antiviral results of Oscillatoria methanolic extract and Oscillatoria methanolic extract of AgNPs against HCV. Statistical evaluation was performed using a one-way ANOVA; groups displaying different letters significantly differed at p ≤ 0.01 based on Tukey’s HSD test.

Sample	Cells	IC50 µg/mL
Methanolic extract of Oscillatoria	HuH7.5	20.325 ± 0.78b
AgNPs of Oscillatoria methanolic extract		0.0705 ± 0.0027 a
Asunaprevir		0.0695 ± 0.0004 a

Molecular docking studies of identified metabolites toward EGFR (mapping hepatic target protein networks)

Predictions of biological activities

In-silico methodologies have been increasingly integrated with experimental research, enhancing our understanding of complex chemical and biological phenomena. This synergy between computational and experimental approaches has been particularly effective in identifying potential targets of biologically active metabolites and their mechanisms of action. Our research utilized computational tools to analyze key components of Oscillatoria sp. extract, focusing on its potential anti-HCV activities and effects on related hepatic disorders, such as hepatocellular carcinoma and fibrosis. A thorough literature review indicated that specific bioactive metabolites in the Oscillatoria sp. extract are reputed to possess hepatoprotective properties (refer to Supplementary Table S2). To expand upon this knowledge, we conducted statistical screening on metabolites with unknown activities, explicitly targeting HCV-related hepatic disorders. These metabolites were processed through the PASS Online web server to predict their efficacy as anti-HCV agents and/or hepatic protectants. The detailed results of this analysis are presented in (Supplementary Table S3).

Therapeutic targets for HCV-related hepatic disorders

A dataset comprising 140 target proteins associated with HCV-related hepatic disorders was systematically compiled from two authoritative databases: The National Center for Biotechnology Information’s Gene Expression Omnibus (NCBI-GEO) and the Pharmacogenomics Knowledgebase (PharmGKB). These proteins have been identified and validated as therapeutic targets in the treatment and management of hepatic disorders related to HCV infection (see Supplementary Table S4). In the pursuit of identifying therapeutic targets for anti-HCV and hepatoprotective phytochemicals, the PharmMapper server was utilized ⁸¹. This platform employs a pharmacophore mapping approach to predict potential targets for specific metabolites. The study focused on several metabolites: o-Anisic acid methyl ester (2), Oscillaginin B (11), Methyl-11-octadecenoate (14), Dimorphecolic acid (15), 1-O-palmitoyl-3-O-(a-D-galactosyl)-sn-glycerol (16), 18:1,16:0 Sulfoglycolipid (Diglycolipid 22) (20), and 18:3,16:0 Sulfoglycolipid (21). For each metabolite, PharmMapper predicted 300 protein targets, detailed in (Supplementary Materials: Tables S5–S11). A Venn diagram was constructed to identify overlapping proteins between the metabolite-related and disease-related targets. This analysis resulted in the identification of 1021 unique potential target proteins across the seven metabolites. Further scrutiny of the Venn diagram revealed 72 proteins potentially related to hepatic disorders associated with HCV infection Fig. 5. These proteins were selected for subsequent analyses (see Supplementary Table S12–S18).

Fig. 5.

Shared targets between disease and metabolites. This Venn diagram illustrates the overlap between HCV-related hepatic disorder target proteins and the predicted targets of bioactive metabolites from Oscillatoria sp. extract. A total of 1021 unique potential targets were identified across the metabolites, with 72 proteins specifically linked to hepatic disorders.

PPI network development

The STRING database version 12.0 (https://string-db.org/cgi/input?sessionId=barlI0uOHF46) was used to analyze the identified proteins for the construction of the primary Protein–Protein Interaction (PPI) networks, aiming to elucidate their direct and functional partnerships. Subsequently, Cytoscape software version 3.10.1 was used to visualize the resulting PPI network diagram. Cytoscape’s network analyzer was used for a comprehensive protein interaction network encompassing 149 nodes and 906 interactions. This network, visualized in Fig. 6, had an average node connectivity of 12.02.

Fig. 6.

PPI network of targets for key active metabolites (2, 11, 14, 16, 20, and 21) of Oscillatoria sp. extract. The PPI network, constructed using STRING and visualized in Cytoscape, depicts 149 nodes and 906 interactions among proteins targeted by Oscillatoria sp. metabolites. Proteins with higher connectivity (hubs) are crucial in viral pathogenesis and hepatic function regulation.

Enrichment analysis of gene ontology

The Gene Ontology (GO) enrichment analysis of the overlapping proteins was performed utilizing ShinyGO v0.80 to determine their biological attributes. This analysis revealed a total of 439 BP terms, 51 CC terms, and 50 MF terms (p-value < 0.05). The results are documented in (Supplementary Table S19). From the BP category, notable processes include “Stress-induced regulation of RNA polymerase II-dependent transcription,” which is critical in the cellular stress response, and “Modulation by virus of host process,” indicating a virus’s influence on host cellular mechanisms. Similarly, “miRNA-regulated gene silencing” and “Regulation of production of miRNAs involved in gene silencing by miRNA” suggest a role for microRNAs in the gene regulatory networks affected by HCV. The “Host cell nucleus” and “Transcription regulator complex” are prominent in the CC category, suggesting that nuclear events and transcriptional regulation are significantly impacted during HCV-related hepatic disorders. “Protein kinase complex” and “Cyclin-dependent protein kinase holoenzyme complex” suggest key protein phosphorylation and cell cycle regulation involvement. For the MF category, enriched terms such as “Nuclear receptor activity” and “Protein tyrosine kinase activity” point to the participation of specific receptor-mediated pathways and phosphorylation events. “Heat shock protein binding” and “Hsp90 protein binding” indicate a stress response, while “Adenylyl ribonucleotide binding” and “Phosphatidylinositol 3-kinase binding” are indicative of energy metabolism and signaling pathways. The visualizations in Fig. 7 represent the enriched GO terms for BP, CC, and MF categories.

Fig. 7.

Gene Ontology (GO) Enrichment Analysis of Target Proteins This bubble plot visualization categorizes the enriched GO terms into three groups: biological processes (BP), cellular components (CC), and molecular functions (MF). Key enriched pathways include viral life cycle, miRNA-mediated gene silencing, and protein kinase activity, suggesting the involvement of these targets in HCV pathogenesis and immune response modulation.

Analysis of enriched KEGG pathways

The KEGG enrichment analysis is vital for mapping protein targets onto their corresponding molecular pathways. This analysis has elucidated potential pathways implicated in the activity of protein targets associated with selected metabolites and delineated the processes entwined in the HCV life cycle (https://www.genome.jp/dbget-bin/www_bget?pathway:hsa05160), as depicted in Figs. 8 and 9. The integration of these results reveals a multifaceted landscape of molecular interactions (see Supplementary Table S20). It underscores the intricate network through which HCV manipulates host cellular mechanisms for replication and propagation while concurrently evading the host immune response. The pathways highlighted through this analysis provide insights into the virus’s pathogenicity and offer potential therapeutic avenues for intervention. By mapping the protein targets to their respective pathways, we gain a more coherent understanding of the biological processes disrupted by HCV and the possible points of interruption by the selected metabolites, which might be leveraged to hinder the virus’s lifecycle and mitigate the progression of related hepatic disorders.

Fig. 8.

KEGG Pathway barplot of our target gene. This bar plot represents the most significantly enriched KEGG pathways associated with the identified target proteins. Key pathways include Viral carcinogenesis, pathways in cancer, and cell cycle, highlighting the critical roles of these proteins in viral replication and liver disease progression ^82–84.

Fig. 9.

KEGG Pathway Map of the Hepatitis C Virus (HCV) Life Cycle This pathway diagram maps the molecular interactions between HCV and host cellular mechanisms, detailing viral entry, replication, and immune evasion strategies. The identified target proteins are positioned within relevant pathways, demonstrating their potential as intervention points for antiviral drug development ^82–84.

Identification of hub genes

Within the constructed Protein–Protein Interaction (PPI) network, comprising 149 nodes and 906 edges, the nodes with the highest degree of connectivity were prioritized to identify key hub genes. Utilizing the CytoHubba plugin, a tool specifically designed for such analyses within the Cytoscape environment, the top 10 hub genes were discerned based on their connectivity metrics. These central hub genes are as follows: Albumin (ALB), Heat Shock Protein 90 Alpha Family Class B Member 1 (HSP90AB1), Epidermal Growth Factor Receptor (EGFR), Cyclin D1 (CCND1), Cluster of Differentiation 4 (CD4), Triosephosphate Isomerase 1 (TPI1), Cyclin-Dependent Kinase 2 (CDK2), Glutamic-Oxaloacetic Transaminase 2 (GOT2), Histone Cluster 3, Subunit B (H3-3B), and Estrogen Receptor 1 (ESR1). These genes, due to their significant connectivity within the PPI network, are postulated to play pivotal roles in the biological processes related to HCV infection and its consequent hepatic disorders Fig. 10.

Fig. 10.

Top 10 hub genes; node color intensity indicates connection strength. This network visualization highlights the top 10 hub genes, including ALB, HSP90AB1, EGFR, CCND1, and CD4, which play significant roles in viral pathogenesis, immune response, and liver function. Nodes with stronger connectivity (red/orange color) indicate higher involvement in HCV-related disorders, making them prime candidates for therapeutic targeting.

The top 10 nodes in the PPI network, along with their topological parameters and target information, are summarized in Table 5. Closeness is defined as the mean distance from a node to all other nodes, signifying the node’s proximity to the rest of the network. ‘Maximum Neighborhood Component’ (MNC) measures the impact of a node based on the connectivity of its immediate neighbors, assessing the node’s importance via the largest connected component when the node is excluded. ‘Edge Percolated Component’ (EPC) evaluates network robustness, calculating the fraction of nodes in the largest connected component after sequential edge removals, thereby reflecting the node’s contribution to network integrity.

Table 5.

The top 10 hub genes results were calculated by 3 different measures.

No	Name	Gene	Closeness	MNC	EPC
1	Albumin	ALB	95.58333333	54	32.878
2	Heat Shock Protein HSP 90-beta	HSP90AB1	93.16666667	48	31.11
3	Epidermal Growth Factor Receptor	EGFR	95.91666667	55	32.59
4	Cyclin D1	CCND1	90.41666667	46	31.632
5	CD4 receptor	CD4	84.75	35	28.254
6	triosephosphate isomerase 1	TPI1	82.58333333	33	28.55
7	Cyclin-Dependent Kinase 2	CDK2	85.08333333	37	29.473
8	Mitochondrial Aspartate Aminotransferase	GOT2	83.08333333	33	27.537
9	Histone H3	H33B	82.91666667	32	28.529
10	Estrogen Receptor 1	ESR1	87.25	40	30.529

In summary, the network analysis, which is based on seven filtered metabolites from Oscillatoria sp. extract, reveals specific targeting of distinct proteins, as illustrated in Fig. 11. This network showcases the intricate biochemical interactions between these metabolites and their protein targets. Such interactions indicate a targeted therapeutic approach, offering valuable insights into the potential mechanisms of action of the Oscillatoria sp. extract in a biological context.

Fig. 11.

Relationship Analysis between 7 Phytochemical Metabolites from Oscillatoria sp. extract and their hub target genes. This network diagram illustrates the interaction between seven key metabolites from Oscillatoria sp. extract and the hub genes identified in the PPI network. The connections indicate potential binding interactions, providing a mechanistic insight into the antiviral effects of these bioactive compounds.

Molecular docking

Albumin (ALB) and CD4, key proteins identified through PPI network analysis and targeted screening, were selected for molecular docking studies. This investigation involved three specific metabolites: o-Anisic acid methyl ester (2), Oscillaginin B (11), and Methyl-11-octadecenoate (14). Molecular docking was used to investigate these metabolites’ binding affinities, stability, and free energy interactions with the active sites of ALB (PDB ID: 4L8U) and CD4 (PDB ID: 2NY1). The results are detailed in Table 6. Besides, Figs. 12 and 13 also indicate their strong interactions characterized by a network of hydrogen bonds and hydrophobic interactions.

Table 6.

Docking Scores of three phytochemical metabolites from Oscillatoria sp. extract with two target genes in HCV-related hepatic disorders.

Metabolite	Binding energy score (S) (kcal/mol)
	ALB	CD4
o-Anisic acid methyl ester (2)	-5.52	-6.61
Oscillaginin B (11)	-6.22	-7.01
Methyl-11-octadecenoate (14)	-5.8	-5.46

Fig. 12.

3D and 2D Molecular Docking Visualizations of ALB with (A) Anisic Acid Methyl Ester, (B) Oscillaginin B, and (C) Methyl-11-Octadecenoate. This figure presents 3D and 2D docking visualizations of o-Anisic acid methyl ester, Oscillaginin B, and Methyl-11-octadecenoate with ALB (PDB ID: 4L8U). The interactions include hydrogen bonds, hydrophobic interactions, and electrostatic forces, confirming strong binding affinity. These results suggest that these metabolites may enhance AgNPs’ stability and antiviral efficacy by interacting with ALB.

Fig. 13.

3D and 2D Molecular Docking Visualizations of CD4 with (A) Anisic Acid Methyl Ester, (B) Oscillaginin B, and (C) Methyl-11-Octadecenoate. Docking analyses of the metabolites with CD4 (PDB ID: 2NY1). The docking studies indicate strong molecular interactions, which could inhibit HCV entry and immune evasion mechanisms. These findings further support the potential of functionalized AgNPs in targeting viral infection pathways.

Discussion

The UV–Vis spectroscopy results provided evidence supporting the successful synthesis of AgNPs. The observed spectral band fell within the characteristic range for silver nanoparticles ^69,85. This is due to the Surface Plasmon Resonance (SPR) effect, a collective oscillation of conduction electrons in the AgNPs ^86–88.

FTIR spectroscopy has emerged as a critical technique for investigating the interactions between biomolecules and metal particles, particularly in nanoparticle synthesis and stabilization. This technique allows us to identify biomolecules that stabilize metal nanoparticles and characterize the chemical composition of their surface ⁸⁹. The FTIR results shown in Fig. 2 revealed the presence of several functional groups that likely contributed to the bioreduction of Ag + ions. For example, the peak at 3308.31 and 3326.39 cm^-1 corresponds to the O–H stretching of phenols, and the peak at 1636.62 cm^-1 corresponds to a carbon–carbon double bond. Furthermore, the peak observed at 2083.84 cm^-1 suggests a potential occurrence of alkyne C≡C stretching or the presence of cumulated double bonds. The peak found at 1283.47 cm^-1 may signify C-O stretching, while the peaks at 544.01 and 458.19 cm^-1 are associated with alkyl halide bonds ^90,91. The observed peaks are primarily associated with biomolecules such as terpenoids, flavonoids, glycosides, phenols, and tannins. Functional groups like ketones, aldehydes, and carboxylic acids in these biomolecules promote bioreduction by providing electrons to Ag⁺ ions, thereby converting them to Ag⁰. For instance, the hydroxyl groups present in phenols can transfer electrons to Ag⁺ ions. Specifically, the -OH groups in phenols act as electron donors, facilitating the reduction process. These peaks suggest that phenolic compounds within the Oscillatoria sp. extract likely act as reducing agents^69,85,92. These functional groups and phenolic compounds enhance the extract’s reducing capacity⁹³. Additionally, these functional groups act as capping agents, stabilizing the AgNPs. They do it through electrostatic or coordinate bonds between the functional groups and the AgNP’s surface. For instance, C = C bonds typical of unsaturated compounds, such as flavonoids and terpenoids, may interact through coordination bonding with the AgNPs’ surface for stabilization. Likewise, -OH groups can provide stabilization through electrostatic interactions ⁹⁴. These functional groups’ presence enhances the NPs’ stability by preventing their aggregation and clustering. The capping and efficient stabilization of the synthesized nanoparticles are attributed to these biomolecules ⁹⁵.

The presence of these different functional groups suggests potential biological applications of the synthesized AgNPs⁹⁶. For example, the -OH and N–H groups can form hydrogen bonds with proteins and cell membranes, which is crucial for a wide range of biological reactions. They have also been shown to interact with the lipid bilayer of bacterial cell membranes, producing reactive oxygen species (ROS), leading to membrane damage due to oxidative stress ^97,98.

Furthermore, the results of Zeta potential measurements and PDI confirmed the stability of the nanoparticles. The negative surface charge of the nanoparticles promotes electrostatic repulsion, preventing aggregation ^85,99. Moreover, the PDI value is within the acceptable range ^100–102. indicating a wider size distribution while still ensuring acceptable homogeneity ¹⁰³. Besides, AgNPs from the methanolic extract of Oscillatoria sp. show higher stability than the aqueous extract of Oscillatoria sp. This may be due to the higher concentration of stabilizing biomolecules extracted by methanol, a better solvent for non-polar molecules like terpenoids and flavonoids ^104,105. According to standard zeta potential measurements ¹⁰⁶. The DLS analysis showed the size distribution. These findings suggest that the methanolic extract yielded larger, more stable nanoparticles with a slightly narrower size distribution. Additionally, TEM observations confirmed the spherical morphology and core size of AgNPs, as supported by previously reported studies ¹⁰⁷. The discrepancy between TEM and DLS sizes is expected, as TEM measures the dimensions of dry particles, whereas DLS reflects the hydrodynamic sizes in solution, including the hydration layer and possible aggregation ¹⁰⁸.

Based on the Antiviral results, Table 3 confirmed that it is perfectly safe for normal cells, as AgNPs of Oscillatoria methanolic extract outperformed Asunaprevir. Hence, the results obtained in Table 4 showed a remarkable convergence between the IC₅₀ results for each AgNP of Oscillatoria methanolic extract and Asunaprevir, which makes it a new candidate in the treatment of HCV. In that, the IC₅₀ result for AgNPs of Oscillatoria methanolic extract is much better than the IC₅₀ result for Oscillatoria methanolic extract. This significant increase in antiviral activity points to several possible mechanisms. The greater surface area of silver nanoparticles may allow for a higher concentration of active compounds to interact with viral targets ¹⁰⁵. The enhanced cellular uptake, facilitated by the small size of AgNPs, likely contributes to higher intracellular concentrations ^109–112. Additionally, the observed differences in IC₅₀ values strongly suggest this enhanced uptake. The synergistic effect between silver nanoparticles and the biomolecules of the extract may also contribute to the improved activity, as evidenced by the variety of functional groups shown in FTIR ¹¹³. These factors collectively explain why AgNP synthesis may enhance bioactivity compared to the crude extract.

AgNPs of Oscillatoria extract demonstrated potent anti-HCV activity significantly outperforming Amphimedon (IC₅₀ 0.11 & 2.38 µg/mL) and H. opuntia (IC₅₀ 7.14 µg/mL) extracts^114,115. This proves the higher efficacy of Oscillatoria-derived AgNPs. While silver nanoparticles have demonstrated efficacy against various viruses, including HSV-1, HAVE-10, and Cox B4^69,116, research specifically targeting HCV remains limited. These findings, therefore, contribute a valuable study to the field. This proves the effectiveness of AgNPs of Oscillatoria methanolic extract against HCV as a novel potential therapeutic strategy for HCV. Several studies have reported the synthesis of AgNPs using various plant sources, emphasizing the role of natural biomolecules in their formation and bioactivity. These studies indicate that green-synthesized AgNPs possess diverse bioactivities, including antidiabetic, anticancer, and antimicrobial effects ^{44,88,117–121}. This, in turn, supports our results, which also highlight that AgNPs from Oscillatoria demonstrate strong antiviral efficacy against HCV.

As mentioned in the results of LC–MS and metabolic profiling, Various chemical classes were detected, including macrolides (4), fatty acids (4), glycolipids (4), cyclic polyketides (2), and macrolides (2), in addition to other miscellaneous metabolites. Secondary metabolites are crucial in environmental interactions and have gained significant attention for their biomedical potential. These metabolites demonstrate extensive antimicrobial, antioxidant, and anti-inflammatory properties ^122,123. Focusing on biological activity against viruses, it was found in the macrolides to which metabolites 12 (Acutiphycin) and 13 (30-Methyloscillatoxin D) belong, particularly in respiratory viral infections such as SARS-CoV ^124–126, and it was also found that glycolipid metabolites, including 16 (1-O-palmitoyl-3-O-(alpha-D-galactosyl)-sn-glycerol), 20 (18:1,16:0 Sulfoglycolipid (Diglycolipid 22)), 21 (18:3,16:0 Sulfoglycolipid) and 22 (Sulfonoquinovosyl dipalmitoyl glyceride) are active against HCV ¹²⁷, HSV-1 ¹²⁸, and others ²⁵. In addition, biological activity against viruses was also recorded for metabolites 3 (Methyl laurate), 7 (Dasycarpidan-1-methanol, acetate (ester)), 14 (Methyl-11-octadecenoate), and 19 (1,2-benzenedicarboxylic acid mono (2-ethylhexyl) ester) against coxsackievirus B3 and rotavirus ¹²⁹ and dengue-2 virus ¹³⁰.

Our findings in Network pharmacology and molecular docking analyses demonstrate a high likelihood of specific metabolites exhibiting anti-HCV and/or hepatoprotective activities. These metabolites include o-Anisic acid methyl ester (2), Oscillaginin B (11), Methyl-11-octadecenoate (14), Dimorphecolic acid (15), 1-O-palmitoyl-3-O-(α-D-galactosyl)-sn-glycerol (16), 18:1, 16:0 Sulfoglycolipid (Diglycolipid 22) (20), and 18:3,16:0 Sulfoglycolipid (21), each demonstrating a probability of action (Pa) greater than 0.5..

Besides, the top 10 hub genes identified within the PPI network each play a distinct role in the context of HCV infection and associated hepatic disorders. Albumin (ALB) is key for maintaining the blood’s osmotic balance and indicative of liver function ¹³¹. Heat Shock Protein 90 (HSP90AB1) assists in protein folding, potentially influencing HCV replication. EGFR is involved in cell proliferation, impacting liver regeneration post-infection ¹³². Cyclin D1 (CCND1) controls cell cycle progression, with aberrations linked to hepatocellular carcinoma ¹³³. CD4 is crucial for immune response modulation, critical in viral infections ^134,135. Triosephosphate Isomerase 1 (TPI1) has a role in energy metabolism, which can be disrupted by HCV ¹³⁶. Cyclin-dependent kinase 2 (CDK2) is also involved in cell cycle regulation, and its interaction with viral proteins may lead to liver disease ¹³⁷. Glutamic-Oxaloacetic Transaminase 2 (GOT2) reflects changes in amino acid metabolism during HCV infection ¹³⁸. Histone Cluster 3, Subunit B (H3-3B) influences gene expression and can affect the cellular response to infection, while Estrogen Receptor 1 (ESR1) may explain gender differences in disease progression ¹³⁹.

Collectively, these genes provide a snapshot of the complex host-virus interactions and are potential targets for therapeutic strategies. Finally, the results in Table 6 indicate that certain metabolites exhibited strong binding affinities to target proteins. Such results indicate a strong inclination for these metabolites to bind to the surface of AgNPs through comparable interactions. The addition of such metabolites would enhance antiviral efficacy by improving the adhesion of AgNPs onto viral targets, cellular uptake, and stability of AgNPs, likely by synergism. Strong binding affinity for major proteins suggests that the metabolites are biologically interactive and, when used to coat AgNPs, could enhance the ability of the NPs to target the proteins effectively ^140,141. Notably, all three metabolites demonstrated significant stabilizing effects on ALB and CD4, stabilizing the target proteins. In conclusion, their integration could enhance viral inhibition through improved cellular uptake, stronger protein interactions, and better bioavailability.

Materials and methods

Microalgae cultures

The Oscillatoria sp. blue-green algae was cultivated in a controlled environment using a BG11 medium. A 100 mL starter culture was transferred to 2-L flasks containing a sterile medium. These cultures were incubated under specific conditions: a temperature of 30 ± 2 °C, a 10-h light cycle followed by a 14-h dark period, and an atmosphere enriched with 3% CO2. Sterilized air was continuously pumped into the cultures to promote growth. After 14 days, the algal biomass was harvested, dried, and weighed¹⁴².

Preparation of aqueous and methanolic extracts

Two extracts were prepared: aqueous and methanolic. To obtain the aqueous extract, 10 g of dried Oscillatoria powder was added to 100 mL of distilled water (1:10 w/v) and stirred for 48 h at room temperature. The methanolic extract was prepared using 0.1 g of dried Oscillatoria powder added to 100 mL of 99% methanol (0.1% w/v), and stirred for 48 h at room temperature. Both extracts were then used in further steps. After extraction, both solutions were subjected to filtration and stored at 4 °C under sterile conditions until they were directly used for nanoparticle synthesis.

Nanoparticles synthesis

AgNPs were synthesized using Oscillatoria extract in two different solvents: aqueous and methanolic. For the aqueous method, 5 mL of a 10% aqueous Oscillatoria extract was added to 10 mL of 1 mM silver nitrate solution at pH 7.0 and heated in a water bath at 60 °C for 10 min with continuous stirring. In the methanolic method, 1 mL of a 0.1% methanolic Oscillatoria extract was mixed with 1 mL of dimethyl sulfoxide (DMSO) and then added to 10 mL of 1 mM silver nitrate solution at room temperature.

The initial indication of AgNPs formation was a color change in aqueous and methanolic extracts, turning them reddish-brown. And then confirmed by UV–Vis spectroscopy. The AgNPs were then characterized ^87,143,144.

Characterization of the synthesized AgNPs

Transmission electron microscopy (TEM) analysis

A droplet of the synthesized AgNPs was applied to the coated carbon grids (CCG) and left to dry as the water evaporated at room temperature. Electron micrographs were captured using the JEOL JEM-1010 transmission electron microscope operating at 80 kV ¹⁴⁵.

Analysis using a UV–visible spectrometer and Fourier transform infrared spectroscopy

The synthesis of AgNPs was monitored using a double-beam V-630 spectrophotometer (Jasco, Japan) to record the UV–Vis spectrum of the reaction mixture across a wavelength range of 200 to 800 nm ⁶⁹. Fourier Transform Infrared Spectroscopy (FTIR) analysis, using a Bruker ALPHA II spectrometer, was employed to characterize the functional groups on the AgNPs surface ⁶⁹.

Dynamic light scattering (DLS) analysis

The particle size distribution of the nanoparticles was analyzed using a Zeta-sizer Nano ZS (Malvern Instruments). Measurements were conducted in a disposable cell at 25 °C, and the data were processed using Zeta-sizer 7.01 software ⁶⁹.

Antiviral assay

Cell culture protocol

Huh7.5 purchased from Sigma chemicals were cultured in DMEM supplemented with 10% FBS and 1% penicillin–streptomycin at 37 °C in a 5% CO2 incubator, as in previous studies ¹⁴⁶.

HCV infectious production

The Huh7.5 cell monolayer was rinsed two times with PBS, treated with trypsin, and then centrifuged at 850 rpm for 3 min ¹⁴⁷. The cells were resuspended in 1 mL of Opti-MEM (Invitrogen), then centrifuged at 1,000 rpm, and subsequently resuspended in 360 µL of Cytomix (120 mM KCl2, 0.15 mM CaCl2, 10 mM K2HPO4, 25 mM HEPES, 2 mM EDTA, and 5 mM MgCl2; pH 7.6) that contained 2 mM ATP and 5 mM glutathione. The cells were combined with 10 µg of Jc1 RNA and subjected to electroporation using the Gene Pulser Xcell system (Bio-Rad Laboratories, USA) in a cuvette with a 4-mm gap. The cell culture supernatant containing the newly generated HCV particles was collected four days after electroporation. This supernatant was filtered through a syringe filter with a 0.45 µm pore size to eliminate cellular debris. The infection was carried out at a multiplicity of infection (MOI) of 0.5, and cells were incubated for 72 h during the antiviral assay. Asunaprevir was used as a positive control to validate the assay, which means that, on average, each Huh7.5 cell was exposed to 0.5 infectious HCV particles ¹⁴⁶.

Analyzing metabolic profiles and identifying peaks in LC–MS chromatograms

Following our previous LC–MS analysis of crude natural extract ¹⁴⁸, the Acquity Ultra Performance Liquid Chromatography system was linked to a Synapt G2 HDMS quadrupole time-of-flight hybrid mass spectrometer (Waters, Milford, CT, USA) for the metabolomic study. The chromatographic separation was performed using a BEH C18 column (Waters, Milford, CT, USA), which featured a diameter of 2.1 mm, a length of 10 cm, and a particle size of 1.7 μm. It was connected to a guard column (2.1 × 5 mm, 1.7 μm). An acetonitrile–water system underwent a gradient elution at a flow rate of 0.3 mL/min, starting at 5% acetonitrile and ending at 100% acetonitrile. Finally, the column was cleaned using water that contained 0.1% formic acid (v/v). The temperature of the column was set to 40 °C. The raw data obtained were transformed into sliced files for positive and negative ionization utilizing mass spectrometry conversion software (Supplementary Figs. S1 and S2). The files were subjected to a series of data processing steps using MZmine 2.10, a data mining software developed by the Okinawa Institute of Science and Technology Graduate University in Japan. These steps included peak picking, alignment, deisotoping, deconvolution, and formula prediction. Using the MarinLit database, the metabolites’ approximate m/z, mass error < m/z 0.003, retention time, isotope distribution, and fragmentation pattern were determined ^88,149.

Anti-cancer-based in silico studies

Forecasting molecular targets associated with metabolites

The PASS Online system (https://www.way2drug.com/passonline/predict.php) operates by quantitatively categorizing the activity of metabolites. It assesses the likelihood of each metabolite being biologically active, using a prediction accuracy rate of around 95%. This high level of precision is a testament to the tool’s robust algorithm and extensive dataset ¹⁵⁰.

For our study, we uploaded the chemical structures of molecules numbered 1–22 to the PASS Online platform in the form of MOL files. Each metabolite was analyzed individually, and the resultant activity predictions are comprehensively documented. According to the PASS Online criteria, a metabolite is deemed likely to exhibit the predicted biological activity in experimental settings if its probability of activity (Pa) > 0.5. It is important to note that the PASS Online tool relies solely on chemical metabolites’ two-dimensional (2D) structure for its predictions. This reliance on 2D structural data means the tool does not account for the three-dimensional (3D) conformational nuances and interactions that can significantly influence a molecule’s biological activity. As a result, the predictions provided by PASS are approximate and should be interpreted cautiously. While the tool offers valuable insights, particularly in the preliminary stages of research, it is crucial to understand that its predictions are based on structural similarities with known metabolites and do not encompass the full complexity of molecular interactions in biological systems. Therefore, PASS predictions serve as a guide rather than definitive conclusions ¹⁵¹.

Pharmacophore-based prediction of potential protein targets for filtered metabolites

The potential protein targets of the annotated metabolites in our study were identified using the PharmMapper server (https://www.lilab-ecust.cn/pharmmapper/submitfile.html). PharmMapper represents a sophisticated and updated web-based pharmacophore modeling platform. It employs reverse molecular docking, which inverts the traditional docking process, to predict potential targets for given metabolites. The metabolites’ chemical structures were uploaded to the PharmMapper server in MDL/SDF file format for our analysis. The server’s default settings were utilized for the prediction process. This includes enabling the ‘Generate conformers’ option, with a limit set to generate a maximum of 300 conformations for each metabolite. Additionally, the ‘Energy minimization’ option was activated, ensuring that the conformations used in the docking simulations were at their lowest energy states, which is critical for accurate docking predictions. The database chosen for this pharmacophore mapping task was the “Druggable Pharmacophore Models (v2017, 16,159)”. A comprehensive collection of pharmacophore models representing a wide range of potential drug targets. This database provides a robust framework for identifying likely interactions between our metabolites and various protein targets, thereby offering essential insights into the promising biological activities and mechanisms of action associated with these metabolites ¹⁵².

Exploring data for the identification of disease-associated targets

In our quest to acquire detailed insights into human genes associated with HCV infection, we conducted an exhaustive data-gathering process from two prominent sources. Firstly, we leveraged the Gene Expression Omnibus (GEO) Database, a publicly accessible resource hosted by the National Center for Biotechnology Information (NCBI) in the United States (https://www.ncbi.nlm.nih.gov/guide/genes-expression/). GEO provided a wealth of information on gene expression profiles, allowing us to delve into the transcriptional behavior of genes in the context of hepatitis C infection. Secondly, we harnessed the power of the Pharmacogenomics Knowledgebase (PharmGKB), available at https://www.pharmgkb.org/. In the context of HCV infection, this resource provided invaluable insights into how genetic variations might influence drug efficacy and responses. This multi-pronged approach to data collection ensured a comprehensive and scientifically rigorous exploration of the genetic facets of HCV infection. It not only advances our understanding of the disease but also offers critical insights into potential therapeutic targets and the development of personalized treatment strategies.

Venn diagram analysis for overlapping genes identification

Following the identification of potential targets associated with the disease (HCV-related hepatic disorders) and the bioactive metabolites present in Oscillatoria sp. extract, the overlapping genes between these two entities were identified by creating a Venn diagram. The genes in this intersection represent a crucial subset, signifying the shared genetic components between Oscillatoria sp. extract and the pathophysiology of HCV-related hepatic disorders. Within this intersection, we identify promising candidate genes, which may serve as potential biomarkers for potentially mitigating the progression of hepatic disorders linked to HCV. This strategic approach aligns with uncovering molecular targets within Oscillatoria sp. extract that hold promise in counteracting the pathophysiological mechanisms underlying these hepatic disorders.

Creation of a protein–protein interaction (PPI) network

After identifying the genes common to both datasets, these genes were further analyzed using the STRING database ¹⁵³ to facilitate the generation of a PPI network. The significance of PPIs lies in their remarkable specificity, adaptability, and versatility, making them pivotal in understanding biological processes. A combined score threshold of 0.4 was applied to evaluate overlapping target functional interactions among themselves. Subsequently, the generated PPI network underwent analysis with Cytoscape version 3.8 ¹⁵⁴. Within this network, our focus shifted to identifying hub genes—significantly interconnected nodes interacting with other proteins. Hub genes are acknowledged as vital elements of the PPI network, playing a crucial role in its integrity and stability. They often hold key roles in essential biological processes and pathways. In this study, hub genes were selected using degree-based methods available in CytoHubba, a Cytoscape plugin that provides insights into the central players within the PPI network. This comprehensive approach enhances our understanding of network topology and the pivotal genes orchestrating intricate interactions.

Analysis of gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment

To gain insights into the molecular functions (MF), biological processes (BP), cellular components (CC), and essential signaling pathways, GO and pathway enrichment analyses associated with the identified genes were conducted ^155–157. The BP, CC, and MF categories were utilized to elucidate the specific biological processes, subcellular locations, and precise molecular activities and interactions, respectively, in which a protein or gene plays a role. For this purpose, ShinyGO (http://bioinformatics.sdstate.edu/go/) is 0.8 with FDR < 0.05. The SRplot tool (https://www.bioinformatics.com.cn/en) was used to represent the enrichment bubble plot analysis findings visually. This specialized tool facilitated the creation of informative visual representations that elucidated the molecular interactions and processes associated with the genes relevant to HCV-related hepatic disorders.

Exploration of molecular docking

Molecular docking analysis was employed to verify the precision of our network pharmacology predictions. This technique allows for the designation of probable drug combinations with synergistic effects. Discovery Studio Client version 16.1.0.15350 was used for the docking analysis ¹⁵⁸. The predicted X-ray crystal structures of hub proteins were docked with active ingredients. Utilizing the RCSB Protein Data (http://www.rcsb.org/) to obtain Protein crystal structures. Before molecular docking, the input files were meticulously prepared. Protein preparation involves removing water molecules and small ligands, adding polar hydrogens, and assigning charges. Ligands were minimized using a universal force field, saved in PDB format, added polar hydrogens, and adjusted to account for rotatable bonds. The active site for the ligands was selected based on a thorough literature review and designated as the center of the active grid. The size of the grid box was chosen to include all atoms of the ligands. Following that, the molecular docking procedure was started, and the protein–ligand arrangement with the lowest binding energy was chosen. This rigorous molecular docking analysis was a critical step in validating the potential interactions between our identified targets and metabolites, ultimately aiding in exploring promising drug combinations for disease treatment.

Statistical analysis

Data from the Antiviral assay were analyzed using one-way ANOVA in Microsoft Excel, followed by Tukey’s Honestly Significant Difference (HSD) post-hoc test¹⁵⁹.

Conclusions

This study successfully demonstrated the green synthesis of AgNPs with antiviral activity against HCV using Oscillatoria sp with an IC₅₀ of 0.0705612 µg/mL. The biosynthesized AgNPs exhibited a small size and good stability. Our findings not only highlight the potential of Oscillatoria sp. for nanomaterial production but also unveil its antiviral properties. Network pharmacology and molecular docking analyses revealed specific biomolecules from the algae that target key genes and proteins involved in HCV pathogenesis. O-anisic acid methyl ester, Oscillaginin B, and Methyl-11-octadecenoate displayed strong binding affinities for albumin (ALB) and CD4. In particular, O-Anisic acid methyl ester had binding affinities of -5.52 kcal/mol for ALB and -6.61 kcal/mol for CD4. Oscillaginin B showed affinities of -6.22 kcal/mol for ALB and -7.01 kcal/mol for CD4, whereas Methyl-11-octadecenoate demonstrated affinities of -5.80 kcal/mol for ALB and -5.46 kcal/mol for CD4. These interactions suggest that these biomolecules could potentially modulate immune response, viral entry, and liver function. Network pharmacology and molecular docking have offered insights into how Oscillatoria metabolites interact with proteins related to the hepatitis C virus (HCV). However, experimental validation of these targets is still needed. This study has certain limitations. While this study established excellent in vitro antiviral activity, in vivo validation is essential to assess therapeutic potential. Further studies must aim to elucidate the novel mechanisms of action of the compounds and AgNPs, including targeted molecular pathway analysis. Evaluating the synergy with existing antiviral therapies and conducting thorough toxicity and biocompatibility assessments in appropriate models are essential for successful translation to clinical practice. This research paves the way for developing novel, natural, product-based therapies for HCV and related liver complications by providing a deeper understanding of the underlying mechanisms of action.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization, I.B.M.I., K.N.M.E.; methodology, L.A., K.N.M.E., K.A.Y., A. Z., E.A., U.R.A., S.F.G.; investigation, H.O. K., L. A., I.B.M.I., K.N.M.E., U.R.A., and K.A.Y.; data curation, L.A., .O. K., K.N.M.E., H.A.A., M.A.A.A., A.Z., S.F.G., K.A.Y. and S.P.P.; writing—original draft preparation K.N.M.E. writing—review and editing, H.O. K., L. A., K.N.M.E., H.A.A., E.A., A.Z., S.F.G., and M.A.A.A., S.P.P., and K.A.Y; visualization, L.A., I.B.M.I., U.R.A., M.A.A.A.; supervision, I.B.M.I., K.N.M.E.; project administration, E.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Ongoing Research Funding Program-Research Chairs, (ORF-RC-2025–4500), King Saud University, Riyadh, Saudi Arabia. This work was supported by the United Arab Emirates University (UAEU) Strategic Research Program 2024 grant (proposal number 3702; fund code 12R310) for H.O.K.

Data availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Not applicable.

Consent for publication

Not applicable.