Comparative in-silico analysis of enzymatic azo dye degradation using laccase,tyrosinase,and peroxidase from Grifola frondosa

Abstract

Azo dyes, widely used in the textile industry, are toxic, persistent, and resistant to natural degradation, causing significant water pollution and disrupting aquatic ecosystems. This study investigates the enzymatic biodegradation of these pollutants using laccase, tyrosinase, and peroxidase MSP1 from Grifola frondosa. These enzymes, known for their broad substrate specificity, can catalyze the breakdown of complex aromatic structures, making them promising candidates for eco-friendly azo dye removal. Protein sequences were retrieved from NCBI, and physicochemical analysis confirmed structural stability (instability indices <40). Molecular docking with 14 azo dyes identified Congo red as the best-binding substrate, with docking scores of −10.1 (laccase), −10.6 (MSP1), and −11.3 (tyrosinase). Interaction analysis revealed hydrogen bonds and van der Waals forces (1.75–5.42 Å) supporting effective binding. Protein–protein interaction scores were high (0.82, 0.88, and 0.84), and STRING co-expression analysis indicated gene clusters involved in azo dye metabolism. These results suggest that combining these enzymes could synergistically enhance azo dye degradation. Future work should focus on enzyme engineering, experimental validation, and scale-up for industrial wastewater treatment.

Keywords

comparative analysis azo dyes biodegradation molecular docking

Introduction

Numerous sectors, including textiles, paint, ink, plastics, and cosmetics, employ various dyes. During their production and usage, some of these dyes are lost, often resulting in environmental concerns.¹ In recent times, many nations have implemented stricter regulations regarding the discharge of colored wastewater that contains dyes. Azo dyes are the most widely manufactured chemically synthesized dyestuffs.² These dyes, a class of synthetic compounds primarily used in commercial applications, are aromatic molecules containing one or more azo (N=N) groups.³ An estimated 1.5 million tons of dyes were produced globally in 2020, with azo dyes comprising more than half of this amount. Azo dyes represent the most significant category of artificial colorants. Azo compounds are extensively utilized across various industries, including paper printing, food, textiles, and cosmetics.⁴

Every day, millions of liters of wastewater from textile mills are discharged into public drains, which eventually empty into rivers as untreated effluents. A large portion of this wastewater, particularly that containing azo dyes, demonstrates resistance to degradation.⁵ Due to their xenobiotic nature and structural stability, reactive azo dyes are resistant and cannot be fully degraded by conventional wastewater treatment methods, which include chemical treatment, photolysis, and activated sludge. Consequently, these dyes are released into the environment as colored wastewater. The toxicity of these dyes poses serious risks, potentially leading to acute effects on exposed organisms.⁶

Numerous approaches have been developed for the reduction of azo dyes. These include physical and chemical methods such as chemical flocculation, activated carbon adsorption, filtration, and specific coagulation techniques. Advanced processes such as reverse osmosis, nanofiltration, and multiple-effect evaporators (MEEs) have also been proven to be effective, although they are often expensive.⁷ Moreover, there have also been multiple studies on the application of ionizing radiations to decolorize and degrade the water-contaminating dyes. In research, electron beam radiation has been used to decolorize and degrade various dyes, crystal violet being the representative cationic triphenylmethane dye. The influence of chelating agents, as well as inorganic anions, was explored on the dyes’ degradation, along with total organic carbon elimination. The degradation pathway included primarily N-demethylation, triphenylmethane chromophore cleavage, aromatic product ring breakage and oxidation to carboxylic acid, and mineralization to CO₂ and H₂O.⁸

Biotreatment represents a more cost-effective and environmentally sustainable alternative for azo dye degradation.⁹ Various microorganisms, including yeasts, fungi, and bacteria, have been reported to degrade azo dyes. However, despite the structural diversity of dye molecules, only a limited number of enzymes are capable of breaking them down effectively.¹⁰ Among the most promising enzymes for the enzymatic remediation of azo dyes are laccases, azo reductases, and tyrosinase.¹¹Grifola frondosa, commonly known as the “Maitake mushroom,” produces multicopper oxidase enzymes such as laccase, peroxidase, and tyrosinase. These enzymes can oxidize a wide range of substrates, including phenolic compounds, amines, and even certain inorganic molecules.¹²

In this study, we used computational techniques to evaluate the degradation potential of Grifola frondosa’s laccase, peroxidase, and tyrosinase in breaking down azo dyes. Laccase catalyzes the oxidation of azo dyes by removing an electron from the dye’s phenolic or amine group. This leads to the cleavage of the azo bond (–N=N–), producing aromatic amines or other less-toxic intermediates. Peroxidase, another member of the oxidoreductase enzyme family, facilitates the oxidation of various organic and inorganic substrates by reacting with hydrogen peroxide and similar compounds.¹³ Due to their broad catalytic activity, peroxidases can be applied in chemical synthesis, degradation, and the removal of both phenolic compounds and peroxides. Tyrosinase catalyzes the oxidation of phenolic compounds, generating reactive intermediates that promote the cleavage of azo linkages, thereby decolorizing the dye.¹⁴

Recent studies support the role of these enzymes in dye degradation. Therefore, the primary objective of this study was to compare the biodegradation capability of laccase, peroxidase, and tyrosinase in the breakdown of azo dyes using computational approaches.

Methodology

Selection of Enzymes for Comparative Analysis

After identifying the fungal strain, its azo dye-degrading capacity was assessed. Three enzymes—laccase, peroxidase MSP1, and tyrosinase—from Grifola frondosa were selected, and their FASTA sequences were obtained from the NCBI protein database.

Physicochemical Property Forecasting

The physicochemical properties of the enzymes were analyzed using the ExPASy ProtParam, which calculates parameters such as molecular weight, theoretical pI, amino acid composition, half-life, instability index, aliphatic index, and GRAVY.¹⁵

Homology Modeling of Proteins

Homology modeling is a computational approach used to construct reliable three-dimensional structures of a query protein using the known structures of template proteins.¹⁶ The 3D structures of the three enzymes from Grifola frondosa—laccase, dye-decolorizing peroxidase MSP1, and tyrosinase—were modeled using the SWISS-MODEL by uploading the FASTA sequence of each enzyme.

Validation of 3D Structures of Proteins

Model quality was evaluated using PROCHECK and ERRAT via the SAVES server. Ramachandran plots were used to assess residue distribution, and ERRAT was used to identify potential error-prone regions.¹⁷

Selection and Toxicological Analysis of Azo Dyes

Fourteen azo dye structures were selected from the PubChem database, along with their molecular formulas and PubChem identification numbers. Toxicity was predicted using CompTox Chemicals Dashboard, including carcinogenicity, genotoxicity, and the potential for skin or eye irritation.¹⁸

Binding Site Prediction of Enzymes

The active sites of the enzymes were predicted using Discovery Studio¹⁹ to determine potential dye-binding regions and characterize ligand–residue interactions.

Comparative Molecular Docking Analysis

To evaluate the molecular interactions between the selected azo dyes and the three enzymes, docking analysis was performed using PyRx.²⁰ Dye structures obtained in SDF format were converted to PDB, ligand energies were minimized, and docked to active sites were defined by grid parameters.

Molecular Interaction Analysis of the Docked Complexes

Discovery Studio Visualizer was used to analyze the top protein–ligand complexes based on binding energies. This tool identified the types of bonds and interactions formed between amino acid residues and ligands.²¹

Protein–Protein Interaction Analysis

The STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database was used to perform protein–protein interaction analysis. This analysis was used to identify functional associations of the target enzymes, generating high-confidence interaction networks.²¹

Co-expression of Genes

Co-expression analysis of genes encoding laccase, tyrosinase, and dye-degrading peroxidase MSP1 was also performed using the STRING database. The co-expression data for the selected enzymes were analyzed to identify gene clusters potentially involved in azo dye degradation.²³

Flow diagram of the complete process

Results

Selection of Enzymes for Comparative Analysis

The selected enzyme sequences were obtained from the National Center for Biotechnology Information (NCBI) database. The three enzymes chosen for this study were laccase, peroxidase, and tyrosinase. The retrieval process involved querying the NCBI GenBank and Protein databases for sequences corresponding to these enzymes from Grifola frondosa. The accession numbers of laccase, peroxidase, and tyrosinase are BBB87418, OBZ73081, and BBD78621, respectively.

Physicochemical Properties of Enzymes

The physicochemical properties of laccase, peroxidase, and tyrosinase were determined via the Expasy ProtParam tool. Their theoretical isoelectric points (pIs) were discovered to be 4.47, 6.84, and 5.83, respectively. Their molecular weights were reported to be 56214.68 Da, 65919.84 Da, and 67691.00 Da, respectively. Subsequently, the stability index was computed and discovered to be less than 40, signifying the enzymes’ stability. When the GRAVY (Grand Average of Hydropathicity) was calculated, a negative value was found, showing that the enzymes are hydrophilic. Other parameters were also predicted, given below in Tables 1 –3, respectively, for laccase, peroxidase, and tyrosinase.

Table 1.

Predicted physicochemical properties of laccase from Grifola frondosa using the ExPASy ProtParam tool. (A) Amino acid composition, (B) physicochemical parameters including molecular weight, isoelectric point (pI), and hydropathicity, and (C) atomic composition.

Table 2.

Predicted physicochemical properties of dye-decolorizing peroxidase MSP1 from Grifola frondosa using the ExPASy ProtParam tool. (A) Amino acid composition, (B) physicochemical parameters including molecular weight, isoelectric point (pI), and hydropathicity, and (C) atomic composition.

Table 3.

Predicted physicochemical properties of tyrosinase from Grifola frondosa using the ExPASy ProtParam tool. (A) Amino acid composition, (B) physicochemical parameters including molecular weight, isoelectric point (pI), and hydropathicity, and (C) atomic composition.

Homology Modeling of Proteins

Homology modeling of laccase, peroxidase MSP1, and tyrosinase was performed through SWISS-Model-Expasy, and the model with a high percent identity with the template of each enzyme was downloaded for further analysis. Figures 1 –3 show the three-dimensional structure of each enzyme.

Figure 1.

Predicted 3D structure of laccase from Grifola frondosa, modeled using the SWISS-MODEL server.

Figure 2.

Predicted 3D structure of dye-decolorizing peroxidase MSP1 from Grifola frondosa, modeled using the SWISS-MODEL server.

Figure 3.

Predicted 3D structure of tyrosinase from Grifola frondosa, modeled using the SWISS-MODEL server.

Validation of 3D Structures of Proteins

Ramachandran plot results predicted that laccase, peroxidase MSP1, and tyrosinase had 91.7%, 89%, and 88.8% amino acid residues in the favored region. Moreover, the ERRAT graph showed laccase, peroxidase MSP1, and tyrosinase had 91.2%, 88.5%, and 95.5% residues in the non-error region. All these results supported the accuracy of computationally built protein structures. Figures 4 –6 present the results of the RAMACHANDRAN plot for each enzyme, respectively.

Figure 4.

Ramachandran plot of the modeled laccase enzyme, used for structural validation, showing the distribution of amino acids in favored, additional allowed, and disallowed regions.

Figure 5.

Ramachandran plot of the modeled peroxidase MSP1 enzyme, used for structural validation, showing the distribution of amino acids in favored, additional allowed, and disallowed regions.

Figure 6.

Ramachandran plot of the modeled tyrosinase enzyme, used for structural validation, showing the distribution of amino acids in favored, additional allowed, and disallowed regions.

Retrieval of Pollutant Structure and Toxicology Analysis

The PubChem database was used to obtain 14 3D structures of azo dyes as SDF files. Table 4 provides the names, structures, and molecular formulas, along with the PubChem ID of target pollutants and the hazard type of the specific pollutant.

Table 4.

Details of the pollutants of interest, including their chemical names, 3D structures, molecular weights, corresponding PubChem IDs, and toxicological assessment results.

The toxicological assessment of 14 selected azo dyes using the CompTox tool revealed that 12 azo-based compounds were identified as carcinogenic, meaning they have the potential to cause cancer by promoting abnormal cell growth. Additionally, seven were found to be genotoxic, indicating their capacity to damage genetic material such as DNA, which can lead to mutations or other genetic disorders. These findings suggest that a significant proportion of the azo dyes exhibit hazardous properties, underscoring the need for further evaluation and careful consideration of their safety, particularly if they are intended for therapeutic or industrial use. The hazard type of each compound is mentioned in the following table.

Binding Site Prediction of Enzymes

The binding site prediction of an enzyme is crucial in molecular docking analysis because it enables the site-specific docking among proteins and ligands. Binding site pockets of laccase, peroxidase MSP1, and tyrosinase of Grifola frondosa were visualized by Discovery Studio for analysis of binding residues as shown in Figure 7. Table 5 shows the three active site residues along with their XYZ dimensions and point counts of each protein.

Figure 7.

Active sites predicted by Discovery Studio for (1) laccase, (2) peroxidase MSP1, and (3) tyrosinase.

Table 5.

Binding site predictions for each enzyme (laccase, peroxidase MSP1, and tyrosinase) using Discovery Studio, with their respective XYZ dimensions and point counts.

Sr. no#	Enzymes	Sites	Dimensions XYZ	Point count
1	Laccase	Site 1	10.554000 −11.673959 −7.740901	1,804
		Site 2	1.804000 2.576041 13.009099	861
		Site 3	17.554000 −0.423959 7.259099	643
2	Peroxidase msp1	Site 1	16.779103 −0.985000 −2.685696	7,409
		Site 2	−12.970897 −4.235000 −8.185696	6,434
		Site 3	25.029103 −2.985000 −4.435696	1,856
3	Tyrosinase	Site 1	9.778812 7.935147 12.315968	11,506
		Site 2	14.778812 15.685147 −7.184032	2,084
		Site 3	−10.971188 −3.064853 16.065968	1,118

Molecular docking analysis

Molecular docking was performed between 14 selected azo-based compounds and each enzyme on PyRx. The grid box was adjusted on the proteins according to the active sites for better docking poses. The binding affinities were shown to be above −6 kcal/mol for all 14 ligands, predicting the good catalyzing ability of enzymes on azo dyes. This indicates that either one enzyme or a combination of all can be beneficial in removing textile dyes from the environment. The values of binding affinities (kcal/mol) of the protein–ligand complexes are shown in Table 6.

Table 6.

Binding affinities (in kcal/mol) of laccase, peroxidase MSP1, and tyrosinase with 14 target ligands.

Sr. no#	Compounds	Binding affinities (kcal/mol)
Sr. no#	Compounds	Laccase	Peroxidase MSP1	Tyrosinase
1	Congo red	−10.1	−10.6	−11.3
2	Mordant black 11	−9.4	−10.5	−8.6
3	Indigocarmine	−9.3	−8.3	−9
4	Reactive orange 14	−9.2	−9.9	−8.5
5	Reactive orange 16	−8.6	−8.0	−8.7
6	Acid blue 93	−8.6	−10.1	−8.9
7	Acid yellow 99	−8.2	−7.4	−9.1
8	Metanil yellow	−8.1	−10.5	−7.9
9	2-(4-Hydroxyphenyl azo) benzoic acid	−7.5	−8.3	−6.7
10	Methyl orange	−7.0	−7.8	−7.4
11	Methyl red	−7.0	−7.7	−7.3
12	Basic red 18	−6.9	−6.8	−7
13	Ethyl red	−6.9	−8.0	−7.2
14	Acetosyringone	−5.5	−7.4	−5.8

Molecular Interaction Analysis of the Docked Complexes

The PDB files (protein data bank) were downloaded of the top three docked complexes of laccase, peroxidase MSP1, and tyrosinase enzymes, and Discovery Studio was employed to analyze the interactions between pollutants and each enzyme. The distance between laccase and Congo red, Mordant black 11, and Indigocarmine ranges from 2.18 to 5.22 Å. The distance between peroxidase MSP1 and Congo red, Mordant black 11, and Acid blue 93 ranges from 2.03 to 5.33 Å. The distance between tyrosinase and Congo red, Acid yellow 99, and Indigocarmine ranges from 1.75 to 5.42 Å. As a result, these distances among the two enzymes and three substrates suggest the existence of hydrogen bonds (2.8–3.4 Å) and van der Waals forces (3.8–4.2 Å). The results are shown in Table 7 on laccase, peroxidase, and tyrosinase. Interaction studies of each enzyme and azo dye at the molecular level are shown in Figures 8 –10.

Table 7.

Amino acid residues, distance range, and types of bonds present between each enzyme (laccase, peroxidase MSP1, and tyrosinase) and the pollutants.

Enzymes	Pollutants	Amino acid residues	Distance range (Å)	Type of bond present
Laccase	Congo red	ASP205 LEU195 VAL17 ALA207 PRO144 PRO38 THR164 VAL166 ALA37 ASP163 ALA205 SER204 LEU56	2.18–5.22	Hydrogen bonds/all hydrophobic interactions/van der Waals forces
	Mordant black 11	ILE36 GLY194 PRO38 PRO53 ASN193 LEU195 THR164 THR168 ALA37	1.84–5.21
	Indigocarmine	ALA37 VAL166	1.83–2.51
Peroxidase msp 1	Congo Red	GLN514 ASP500 GLY498 GLY520 LYS523 TYR525 SER560 SER559 ASP500 VAL541 VAL535	2.02–4.91
	Mordant black 11	ARG399 ASP297 GLU465 LYS296 PHE478 PRO461 ILE518 ARG454 ALA448 HIS447	2.49–5.33
	Acid blue 93	PRO481 ILE465 PHE478 GLU465 GLY248 VAL259 SER300 ASP297	2.03–5.11
Tyrosinase	Congo red	ARG442 THR140 PHE439 PRO152 LEU231 VAL229 VAL435 GLY227 ALA153 MET232 LEU490	1.95–5.28
	Acid yellow 99	ALA230 MET232 VAL491 ARG441 GLY227 ARG442 LEU231	2.02–5.25
	Indigocarmine	ASP31 ASN30 ASN148 GLUU28 LEU387 ALA147 ILE402 PRO363	1.75–5.42

Figure 8.

Molecular interaction analysis of laccase with (a) Congo red, (b) Mordant black 11, and (c) Indigocarmine.

Figure 9.

Molecular interaction analysis of peroxidase MSP1 with (a) Congo red, (b) Mordant black 11, and (c) Acid blue 93.

Figure 10.

Molecular interaction analysis of tyrosinase with (a) Congo red, (b) acid yellow 99, and (c) Indigocarmine.

Protein–Protein Interaction Analysis

The construction of protein–protein interaction (PPI) networks using the STRING database provides a visual and analytical framework to understand the relationships between proteins. These networks reveal functionally coherent modules—groups of proteins that collaborate within specific biological processes. In our analysis of enzymes from Grifola frondosa, the PPI network demonstrated high interaction scores among key enzymes involved in azo dye degradation. Laccase, with a confidence score of 0.82, is strongly associated with other enzymes in the degradation pathway. Tyrosinase exhibited a high interaction score of 0.88, reflecting its significant role in hydrolyzing complex molecules involved in the dye degradation process. Additionally, dye-decolorizing peroxidase MSP1 showed an interaction score of 0.84, highlighting its substantial interactions with detoxifying enzymes. These results illustrate the collaborative and coordinated nature of these enzymes working together to neutralize toxic compounds, aligning with the concept of functionally coherent modules within the PPI network (Figures 11 –13).

Figure 11.

Laccase interaction network predicted by String.

Figure 12.

Tyrosinase interaction network predicted by String.

Figure 13.

Dye-decolorizing peroxidase MSP1 Interaction network predicted by String.

Co-expression of Gene

The co-expression analysis of the laccase, tyrosinase, and dye-degrading peroxidase MSP1 genes in Grifola frondosa identified clusters of genes with similar expression patterns. This pattern suggests that these genes are functionally related and may be co-regulated or involved in the same biological pathways. Cluster A displayed strong co-expression among the target genes and associated genes, indicating that these genes likely work together in the enzymatic breakdown of complex azo dye compounds. The consistent expression patterns support the notion that these genes are part of a coordinated effort to degrade azo dyes in the environment. Cluster 2, enriched with gene ontology terms related to azo dye metabolism, highlights its role in the degradation process. The co-expression network visualized in Figures 14 –16 shows the complex interactions between laccase, tyrosinase, and dye-degrading peroxidase MSP1, as well as their connections to other proteins involved in azo dye degradation pathways. This visualization underscores the functional relationships and potential regulatory interactions among these genes.

Figure 14.

Co-expression analysis of the gene encoding laccase in Grifola frondosa.

Figure 15.

Co-expression analysis of the gene encoding tyrosinase in Grifola frondosa.

Figure 16.

Co-expression analysis of the gene encoding dye-decolorizing peroxidase MSP1 in Grifola frondosa.

Discussion

The biodegradation of azo dyes presents a significant environmental challenge due to the stability of these synthetic compounds and their potential toxicity, particularly when discharged into aquatic systems from textile industry effluents.²⁴ Azo dyes, widely used in the textile industry, are notorious for their persistent nature due to the azo bonds linking aromatic rings, which render them resistant to natural degradation processes.²⁵ This persistence can lead to the accumulation of dyes in water bodies, posing severe risks to aquatic life and human health, including toxic, mutagenic, and carcinogenic effects.²⁶ Bioremediation provides an eco-friendly solution to azo dye pollution by using enzymes from microorganisms like fungi and bacteria. It provides a sustainable alternative to conventional chemical treatments, which often involve hazardous substances and generate harmful by-products.²⁷ Enzymatic degradation, by contrast, typically operates under mild conditions and produces fewer by-products, making it a greener option. Additionally, the ability to engineer and optimize these enzymes for specific dye degradation tasks enhances their efficiency and applicability in various industrial and environmental settings.²⁸ The development of enzyme-based treatments represents a promising direction for addressing pollution from the textile industry and other sectors that utilize azo dyes.^29,30

In this study, the retrieved structures of laccase, tyrosinase, and peroxidase from NCBI were analyzed for their physicochemical properties, indicating that all three proteins are stable, as evidenced by their instability indices being less than 40. An instability index below 40 suggests that these proteins have a stable structure, making them suitable for functional applications in environmental bioremediation processes, such as the degradation of azo dyes.³¹ This stability is crucial for the effectiveness of these enzymes in industrial applications, where they are exposed to various environmental conditions. The findings align with previous studies where protein stability was directly linked to enzyme efficiency and longevity during catalytic processes.³² In contrast to previous studies where bacterial enzymes were used, our study focuses on fungal-derived enzymes, which demonstrated greater predicted stability, suggesting their potential suitability for long-term and large-scale applications. Compared to enzymes from Pleurotus sajor-caju, Agaricus bisporus, and bacterial sources reported in earlier studies, G. frondosa enzymes exhibited higher predicted binding affinities, greater structural stability (instability index <40 for all three enzymes), and stronger interaction scores in co-expression networks. These features suggest that G. frondosa enzymes may remain active for longer periods under industrial wastewater conditions, tolerate a wider range of substrates, and operate more efficiently in synergistic combinations, making them promising candidates for scalable azo dye degradation processes.³³

Moreover, molecular docking analysis was performed against 14 azo dyes with laccase, peroxidase (MSP1), and tyrosinase to check their potential against dyes. The results show that the enzymatic degradation of azo dyes by laccase, peroxidase (MSP1), and tyrosinase is characterized by specific molecular interactions that play a crucial role in the breakdown process. The docking analysis revealed that all the proteins—laccase, peroxidase, and tyrosinase—exhibit the highest binding affinity against Congo red dye, with values of −10.1, −10.6, and −11.3, respectively. Toxicology analysis also shows that Congo red dye is carcinogenic and causes toxicological effects. Suryamathi et al. examined the use of tyrosinase from Agaricus bisporus for the degradation of Orange G and Congo red, noting that while tyrosinase alone had moderate activity, its efficiency increased significantly when used alongside laccase. This suggests a synergistic effect where tyrosinase initiates the degradation process, which is then further catalyzed by laccase.³⁴ These results underscore the significance of binding affinity and interaction distances in enzymatic degradation, with the strongest binding energies observed in Congo red dye suggesting higher degradation potential. However, further validation experiments, including control simulations, would be essential to confirm these findings and better understand the exact nature of these molecular interactions.

The interaction analysis of laccase, tyrosinase, and peroxidase MSP1 with azo dyes reveals distinct roles and effectiveness in the degradation process.³⁵ Laccase shows moderate interaction distances (2.18–5.22 Å) with substrates like Congo red, indicating its ability to form essential hydrogen bonds and van der Waals interactions for substrate binding and oxidative cleavage of azo bonds. In a study by Junior et al., the application of laccase from Pleurotus sajor-caju in degrading Acid Black 1 demonstrated over 90% decolorization within 24 h. Our findings suggest that Grifola frondosa laccase may offer comparable or improved activity based on predicted interactions and energy scores, particularly in conjunction with other enzymes. Tyrosinase, with slightly shorter distances (1.75–5.42 Å), engages more closely with dyes such as Acid Yellow 99, suggesting its effectiveness in the initial oxidation steps and generation of reactive intermediates necessary for azo bond cleavage, further highlighted by its strong interaction score of 0.88.³⁶ Peroxidase MSP1, while showing slightly longer interaction distances compared to tyrosinase, demonstrates significant collaboration with other enzymes (interaction score of 0.84), indicating its role in working synergistically to achieve complete degradation and detoxification of azo dyes. The presence of these interactions aligns with studies like those by Bai et al.,³⁷ which demonstrated that peroxidases, including MSP1, play a vital role in breaking down complex dye structures. Overall, while laccase and tyrosinase are pivotal for the initial and intermediate stages of dye degradation, peroxidase MSP1 plays a crucial role in the later stages, ensuring complete breakdown and detoxification, making all three enzymes complementary in bioremediation efforts. The cooperative role of laccase, tyrosinase, and peroxidase in enzymatic degradation suggests that their synergy leads to enhanced degradation efficiency compared to individual enzymes. This synergy allows for a complete and more effective breakdown of azo dyes, with each enzyme contributing in specific stages of the degradation process.

The co-expression analysis of laccase, tyrosinase, and peroxidase genes in Grifola frondosa indicates that these enzymes function within a coordinated metabolic pathway for azo dye degradation rather than acting in isolation. Laccase, tyrosinase, and peroxidase have been individually studied for their roles in bioremediation, but their co-expression and coordinated activity have been gaining attention in recent research.³⁸ The concept of co-expressed gene clusters, where genes encoding these enzymes are upregulated together, indicates a possible regulatory mechanism that ensures these enzymes are available in sufficient quantities and work harmoniously during the degradation process.³⁹ This has been observed in various fungi and bacteria involved in dye degradation, where enzymes are often co-regulated to tackle complex dye structures effectively.⁴⁰ The co-expression network visualization reveals intricate interactions among the laccase, tyrosinase, and peroxidase genes, suggesting that these enzymes are likely regulated together and may work sequentially or synergistically to achieve complete dye degradation. This coordinated activity enhances the potential application of these enzymes in industrial wastewater treatment, ensuring a thorough breakdown of azo dyes. Recent studies have demonstrated that co-expression of these enzymes enhances the overall degradation efficiency.⁴¹ For instance, research by Rane et al. highlighted the importance of enzyme cooperation in the biodegradation of persistent pollutants. Their findings suggest that the simultaneous expression of these enzymes might be essential for breaking down more recalcitrant compounds found in dyes, which require a multifaceted enzymatic approach.⁴²

The comparative analysis in this study indicates that while all three enzymes—laccase, peroxidase, and tyrosinase—can degrade azo dyes, peroxidase from Grifola frondosa shows the highest potential due to its broad substrate range and ability to produce less toxic degradation products. However, a combined enzymatic approach may offer the most effective strategy for the complete breakdown and detoxification of azo dyes. The synergy between laccase, tyrosinase, and peroxidase not only enhances the degradation efficiency compared to individual enzymes but also ensures a more complete and environmentally friendly process. Future research should focus on optimizing the conditions for such synergistic enzyme applications and exploring their potential for large-scale industrial use. Furthermore, validating the results of molecular docking through experimental control simulations could provide more conclusive evidence of their efficacy in enzymatic degradation. This study integrates the in-silico analysis of three enzymes especially from a fungus; however, other past studies focus on only one enzyme or bacteria to degrade dyes.⁴³ Further, the co-expression network analysis and protein–protein interactions data showcase the synergistic action of these enzymes; such analysis has rarely been performed in other studies.^44,45

Conclusion

This study highlights the significant potential of laccase, tyrosinase, and peroxidase MSP1 from Grifola frondosa in the enzymatic degradation of azo dyes. Molecular docking analyses demonstrated strong binding affinities, particularly with Congo red, indicating efficient interaction between these enzymes and various synthetic dyes. Detailed interaction profiling and co-expression network analysis revealed that these enzymes do not function in isolation but operate within a coordinated metabolic system, suggesting a synergistic mechanism in dye degradation. Notably, peroxidase MSP1 exhibited broad substrate specificity, while tyrosinase and laccase contributed to early and intermediate oxidative steps, respectively. This complementary enzymatic activity enhances the overall degradation efficiency and supports their applicability in sustainable wastewater treatment. These findings provide a compelling foundation for enzyme-based bioremediation strategies in industrial settings. Future research should prioritize in vitro validation of these in silico results, optimization of enzyme mixtures for maximum synergistic effect, and the development of scalable bioreactor systems. Additionally, the exploration of enzyme engineering or immobilization techniques may further improve enzyme stability and reusability, enhancing the feasibility of large-scale applications. There is a need for in-vitro and in-silico studies on the degradation ability of such enzymes owing to the fact these recalcitrant dyes need to be eliminated from water systems efficiently. In-silico studies can help in managing time and enhance the experiment success rate as they identify the degradative properties of enzymes and reduce the chances of wrong interpretations.

Footnotes

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-9).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All the data generated in this research work has been included in this manuscript.

ORCID iDs

Muhammad Naveed

Sana Miraj Khan

Muhammad Azeem Iftikhar

Ayesha Saleem

Ayaz Ali Khan

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