Biogenesis, molecular characterization and dye degradation efficacy of alkaline protease from an estuarine associated actinobacterium Streptomyces variabilis using in-silico method

doi:10.21203/rs.3.rs-5308788/v1

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Biogenesis, molecular characterization and dye degradation efficacy of alkaline protease from an estuarine associated actinobacterium Streptomyces variabilis using in-silico method

https://doi.org/10.21203/rs.3.rs-5308788/v1

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In this study, biogenesis, statistical optimization and molecular modeling of alkaline protease from an estuarine associated actinobacterium Streptomyces variabilis was carried out by Box-Behnken design. Initially, the biogenesis of alkaline protease from the selected actinobacterium was attained through submerged condition. Simultaneously, the actinobacterial mediated biogenesis of alkaline protease was statistically optimized through ‘one factor at a time approach’ using Box-Behnken design in a basal medium constitutes 2.5% w/v of NaCl concentration with pH 8.0, temperature 55°C and 2.50% of inoculum size for 94h of incubation. The analysis of variance (ANOVA) exhibited a maximum level of coefficient (R² = 0.9720) with more significance (P < 0.0001). In purification step, the alkaline protease expressed 21.93% of recovery with 2.93 of purification fold at the last stage using Sephadex G-100 chromatography. Followed by, the molecular mass of the enzyme was calculated as 35kDa on 10% of SDS-PAGE. The three dimensional structure of purified alkaline protease was predicted with the encoded total amino acid content 481. The maximum stability range of purified alkaline protease was denoted at pH 8, temperature 60°C and the fermentation medium constituted with 1mM of Mg²⁺, 3.5% of NaCl and 2.5% of casein. The kinetic parameters like K_m and V_max of purified alkaline protease showed 5.158mg/ml and 484.90 ± 2.04µg/min/mg, respectively. Further, the degradation efficacy and the interaction between the alkaline protease as well as dye molecules like acridine orange and erythrosine pink were assessed by in-silico docking method using online Swiss modeling software tool. The decolouration of dyes were evaluated through first order kinetic study with the R² values 0.9987 & 0.9953 respectively. By keeping this view, this study could be validated that the selected actinobacterium is a potent strain for the production of alkaline protease and also used as dye decoulouring agent.

Streptomyces spp.

Alkaline protease

RSM

Dye Degradation

Fermentation medium

Proteases are the third largest group of most commercially significant industrial enzyme and which are served for 65% of total world enzyme sales. They are found in all living forms and actively participated in different metabolic processes. The main usage of proteases is catalyse the hydrolysis of peptide bond in between the amino acids (Allison et al. 2024). It is widely used in various industries like food, pharmaceutical, diagnostic, leather, detergent, tannery, chemical, silk degumming industry and also in waste treatment. Based on the nature of pH and optima range, the protease enzyme can be classified as acidic, neutral and alkaline protease. Among them, alkaline proteases are of great attention due to their maximum proteolytic activity and stability through the alkaline pH range of 8–14. Alkaline proteases have vast variety of applications including biotherapeutic, pharmaceuticals, soy processing, protein / peptide synthesis industries (Hakim et al. 2018; Allison et al. 2024). Alkaline proteases are biosynthesized by different microbial resources like bacteria, actinobacteria, algae, fungi and yeasts. The microbial based biosynthesis of alkaline proteases is more exciting due to their broad biochemical diversity, bioengineering potentiality and relatively inexpensive. The microbial derived proteases served approximately for 45% of total global enzyme market. The majority of industrially important alkaline proteases are biosynthesized by actinobacteria, especially Streptomyces spp., (Mostafa et al. 2019).

The Streptomyces spp., under extreme conditions, have the ability to biosynthesis an extracellular hydrolytic enzyme, alkaline proteases in culture medium which is generally regarded as safe with food and drug administration (Abdelwahed et al. 2014). This organism mediated alkaline proteases are familiar for their catalytic properties under stable conditions at wide ranges of pH and temperature, deciding them suitable usage in numerous industrial sectors (Tran et al. 2022). Streptomyces spp., that have been reported to biosynthesize the alkaline proteases such as S. fungicidicus (Ramesh et al. 2009), S. gulbargensis (Vishalakshi et al. 2009), S. albidoflavus (El-Shafei et al. 2010), S. clavuligerus (Thumar and Singh, 2011), S. griseus (El-Gammal et al. 2012), S. ambofaciens (Abdelwahed et al. 2014)d speibonae (Tran et al. 2022), were mainly depend on different factors like fermentation process, nutritional requirement, physiological and genetic nature etc. Alkaline protease from these Streptomyces strains exhibited different applications in food and pharmaceutical industries, silk gumming, detergents, feather processing, bioremediation and biotransformation (Tran et al. 2022; Allison et al. 2024). Based on these in view, the main purpose of this present study was to screen and optimally biosynthesized thermophilic alkaline protease by an estuarine associated actinobacterium Streptomyces variabilis from southwest coast of India using Box-Behnken design and the usages in feather degradation and blood strain removal have also been determined.

2.1. Determination of proteolytic property of mangrove associated actinobacterium

The enumerated actinobacterial strains were used to determine the proteolytic activity through both qualitative and quantitative methods as follows

2.1.1. Preliminary screening for proteolytic activity

14 actinobacterial strains (MAB1 to MAB14) were enumerated from the sediment sample of estuarine associated environment under laboratory condition using sterile Starch Casein agar (SCA) at the pH range 8 and it was already documented in our previous report (Sanjivkumar et al. 2018). In qualitative analysis of proteolytic activity of all the isolated 14 actinobacterial strains was preliminary screened using sterile Skim Milk agar through confirming a halo zones around the colonies found on the media. From the preliminary screening result, the positive six proteolytic (MAB2, MAB3, MAB7, MAB8, MAB9 and MAB12) actinobacterial strains were selected for further secondary screening process.

2.1.2. Secondary screening and selection of potent proteolytic strain

Before starting the secondary screening experiment, the culture inocula of all the selected six proteolytic actinobacterial strains (MAB2, MAB3, MAB7, MAB8, MAB9 and MAB12) were prepared individually by using sterile Yeast Extract Malt Extract medium (ISP2). In secondary screening, all the selected six strains were assessed individually through quantitatively using sterile skim milk medium (50ml). Followed by, the inoculated individual culture flasks were store in a rotary shaker at 150rpm) for 5 days. After fermentation, the individual culture media were further centrifuged at 10,000rpm for 15min. The proteolytic activity of all the culture free supernatants was determined by assessed through Folin’s Phenol method. Based on the results obtained, an effective proteolytic actinobacterium MAB3 (3143.21IU) was elected as a candidate strain than the rest of organisms and further, it was confirmed as Streptomyces variabilis (KM435070) through 16S rRNA method. The selected actinobacterium was sub-cultured in a sterile ISP2 medium for further studies.

2.1.3. Protease assay

For determination of protease activity, 4µl of individual cell free supernatant (enzyme) was taken in a test tube containing 2ml of phosphate buffer (50mM, pH 7.0) with 20mg/ml of casein as substrate and the reaction mixtures were kept at 70°C for 10 min (Ledoux and Lamy,1986). After incubation, 2ml trichloroacetic acid (0.4M) was added to all the reaction tubes and incubate at 70°C for 25min. Followed by, all the reaction mixtures were centrifuged at 12,000xg for 15min and the respective supernatants were taken in a fresh tube containing 5ml of buffer. Then, 1ml of Folin’s phenol reagent was added and incubate the reaction mixtures at 37°C for 20 min. After incubation, the absorbance of each samples were measured spectroscopically (UV2301II model) at 660nm. One unit of protease activity was determined as the amount of enzyme which released 1µg of tyrosine in the reaction mixture per minute under standard assay conditions.

2.2. Optimization of protease production by S. variabilis through Box-Behnken design (BBD)

In Box-Behnken design (BBD), the biosynthesis of alkaline protease was determined at three variable levels of -1, 0, and + 1 using the four reaction factors viz., pH (A), temperature (B), incubation time (C) and NaCl concentration (D) (Box and Behnken, 1960). The error and sum of squares were evaluated through twenty-nine statistical replicates (run) with the highest production of alkaline protease considered as dependent variable (response) and it was calculated by using a quadratic equation

Y = β₀ +∑ β_nX_n +∑ β_nn X²_n+∑ β_nmX_nX_m

The predicted and experimental values of production of alkaline protease with their coefficient were verified through BBD design using a statistical software “Design-Expert 7.0”.

2.3. Protein estimation

The total protein content of the cell free supernatant was assessed through the methodology of Lowry et al. (1951), respectively compared with a standard Bovine serum albumin (BSA).

2.4. Purification and molecular weight determination of protease enzyme

The produced protease enzyme from the selected actinobacterium S. variabilis was purified through standard procedure of Georis et al. (2000) with slight modification under 4°C. The culture free filtrate obtained from 10–90% of ammonium sulphate fractionation was further dialyzed by using 50mM phosphate buffer (pH 7). Followed by, the dialyzed protein was further loaded in to 3.0 × 40cm size of DEAE cellulose column pre-equilibrated with the same buffer and eluted at 2.0ml/min. The active fractions which exhibited high alkaline protease activity were obtained and subsequently loaded on the Sephadex G -100 column (3.0 × 40cm) equilibrated with 50mM phosphate buffer (pH 7) and eluted with the same buffer at 2.0 ml/min. The active fraction with highest alkaline protease activity was pooled and dialyzed against the same buffer. Further, the molecular weight of the active fraction of purified alkaline protease was determined by 10% SDS-PAGE.

2.5. Amino acid sequencing of alkaline protease

Total amino acid residues of purified alkaline protease enzyme protein from S. variabilis was sequenced through the standard procedure of Altschul et al. (1990). The sequenced amino acid products and its homology nature was determined by using a database programme SWISS-PROT BLAST.

2.6. Molecular modeling

The molecular modeling of purified alkaline protease enzyme protein from S. variabilis was attained with the help of a molecular modeling server SWISS-MODEL. The structural model of the enzyme protein was estimated by Ramachandran plot analysis and verify 3D protein model (Sanjivkumar et al. 2020).

2.7. Characterization of alkaline protease enzyme

The influences of different factors on purified alkaline protease activity were determined through a methodology of Sanjivkumar et al. (2020) by using range of pH (5 to 12), temperature (20 to 90°C), NaCl concentrations (0.5 to 4.0%), 1M of metal ions (Fe³⁺, Zn²⁺, Mg²⁺, Mn²⁺, Ca²⁺, Co²⁺, Cu²⁺ and Lb) and the substrate concentrations from 0.5 to 4.0%. Similarly, the stability of the purified alkaline protease was performed by using the enzyme (1:10 v/v) with 50mM of phosphate buffer at different pH ranges from 5 to 12, temperature (20 to 90°C) and NaCl (0.5 to 4.0%) concentrations at the incubation time for 30, 60 and 90mins.

2.8. Kinetic study

The kinetic parameters of the purified alkaline protease were determined by using stable enzyme concentration of 1.0 ml with various concentrations (0.25 to 10.0mg/ml) of casein as a substrate in 50mM of phosphate buffer (pH 7.0) in different test tubes. The reactions were accompanied at the temperature of 45°C for 5 min. The Lineweaver-Burk plot was attained when 1/v was plotted against 1/s and the K_m and V_max factors were calculated through linear regression method (Lineweaver and Burk, 1934).

2.9. Applications of alkaline protease enzyme

2.9.1. Determination of dye degradation efficacy of alkaline protease

The dyes acridine orange and erythrosine pink used in this study were obtained from Sigma- Aldrich. In degradation study, the rates of dyes degradation were calculated by observing the colour change at maximum absorption (λ max) at 660nm using UV spectrophotometer (Chen et al. 2004). The colour removal efficiency was expressed as percentage ratio based on the following equation

D = I_o - I_f / I_o ×100

2.9.2. In-silico analysis of dye degradation

The degradation efficiency and the interaction between the alkaline protease as well as dyes were assessed through in-silico studies. For that, the enzyme protein PDB model was prepared using Swiss modeling online software tool and the acridine orange and erythrosine structures were prepared using the chimera software tool and protonated to pH-7.2. The protein and dyes interaction binding target site was selected (X = 36.149000, Y = -18.573000, Z = -1.725000) and analysis of docking studies using the Autodock and Autodock vina software tools. Docking analysis was validated and visualized using Discover Studio software tool.

2.10. Assessment of kinetic parameters of dye degradation

The efficiency and feasibility of enzyme mediated dye degradation processes can be characterized by first order kinetic model and the experimental data were analyzed by using the following equation

In C_t=k₁t+In (C₀)

Where, Ct -concentration of dye solution (mgl^− 1), C₀ -initial dye concentration in the solution (mgl^− 1), t- time and k₁- constant rate (min^− 1).

3.1. Assessment of alkaline protease property of mangrove associated actinobacteria

In this investigation, totally 14 estuarine associated actinobacterial strains (MAB1 to MAB14) were selected and which were previously documented in our previous report (Sanjivkumar et al. 2018). All these 14 actinobacterial strains were individually screened for alkaline protease production by both primary and secondary screening methods. Among them, only six (MAB2, MAB3, MAB7, MAB8, MAB9 and MAB12) actinobacterial isolates expressed positive result for protease production on Skim milk agar medium (Table 1). From the six positive isolates, only one strain MAB3 displayed maximum hydrolytic halo zone (22 mm diameter) with the proteolytic activity of 3143.21 IU/ml. The rest of isolates like MAB2, MAB7, MAB8, MAB9 and MAB12 showed the hydrolytic halo zone in between the diameter range of 9 and 20 mm diameter, subsequently the protease activity was attained from 109.86 to 1986.0 IU/ml. From this screening results, the selected isolate MAB3 was elected as an efficient strain for protease production and which was already identified and confirmed as Streptomyces variabilis, used for further studies. In an another report, Sharma and Singh (2016) enumerated the biosynthesis of alkaline protease of 32 different halo-alkaliphilic actinobacteria from marine sediment samples of Gujarat and who were also confirmed that the maximum (2511.9IU/ml) enzyme production was attained from Nocardiopsis dassonvillei (KC119570). Similarly, Mechri et al. (2022) confirmed that an actinobacterium Streptomyces mutabilis (MT704624) isolated from sediment sample of Salt Lake in southern Tunisia expressed highest alkaline protease activity 5400IU/ml. Sarkar and Suthindhira (2020) documented the biosynthesis of alkaline protease of Streptomyces spp., GS-1 from backwaters of Munanbam and Valapad, Kerala exhibited the enzyme activity 3410IU/ml under laboratory condition.

Table 1

Assessment of alkaline protease activity from the selected positive actinobacterial strains
Isolates	Plate assay (zone in mm)	Enzyme activity (IU/ml)
MAB2	20.00 ± 1.44	1986.00 ± 2.62
MAB3	22.00 ± 1.52	3143.21 ± 2.90
MAB7	19.00 ± 1.47	1490.07 ± 2.40
MAB8	09.00 ± 0.62	109.86 ± 1.62
MAB9	15.00 ± 1.38	136.09 ± 1.89
MAB12	16.00 ± 1.40	167.82 ± 2.00
Each value is the Mean ± SD of triplicate analysis

3.2. Statistical optimization of alkaline protease from S. variabilis through Box Behnken design (BBD) and validation of model

The biosynthesis of alkaline protease from S. variabilis was statistically optimized at three coded variable levels of different parameters of pH (A), temperature (B), incubation time (C) and NaCl (D) concentration by BBD design. The resulted experimental and predicted statistical values were built by calculating their polynomial equations and the ANOVA results are represented in Table 2a, b, and c. The equations of regression and integrity of fitness was evaluated. The maximum experimental degree of freedom was assessed with the Adj. R² value of 0.7430. The statistical experiment model with their significance level was studied through ANOVA test. In the present study, the regression of the ANOVA model for alkaline protease production was determined as extremely significant with the F value of 8.58 and the Prob > F (< 0.0001) value is less than 0.931, revealing that the model responded significantly. Likewise, Asitok et al. (2022) documented the optimization of alkaline protease of Stenotrophomonas acidaminiphila through RSM method exhibited that the maximum coefficient of ANOVA (R²: 0.9749) with P < 0.0001 level of significance. Dhavala Swarna and Joel Gnanadoss (2021) performed the statistical optimization of protease biosynthesis from Streptomyces sp. LCJ1A through Central composite design (CCD) at 29 experimental set up runs with five factors. They were also observed that the ANOVA exhibited the highest coefficient (R² 0.912) with the Prob > F (< 0.0001) level of significance.

Table 2

a. Analysis of variance (ANOVA) for the response surface quadratic model for alkaline protease production
Source	Sum of Squares	df	Mean Square	F Value	p-value	Prob > F
Model	14091.07475	14	1006.505339	8.589204	0.0001	significant
A-pH	71.34563333	1	71.34563333	0.608841	0.4482
B-Temperature	0.006533333	1	0.006533333	5.58E-05	0.9941
C-Incubation time	41.7387	1	41.7387	0.356185	0.5602
D-NaCl con.	1.2288	1	1.2288	0.010486	0.9199
AB	0.265225	1	0.265225	0.002263	0.9627
AC	45.4276	1	45.4276	0.387665	0.5435
AD	975.000625	1	975.000625	8.320353	0.0120
BC	16.120225	1	16.120225	0.137565	0.7163
BD	40.3225	1	40.3225	0.3441	0.5668
CD	74.046025	1	74.046025	0.631886	0.4399
A^2	5388.561122	1	5388.561122	45.98431	< 0.0001
B^2	5612.382	1	5612.382	47.89433	< 0.0001
C^2	3548.705514	1	3548.705514	30.28355	< 0.0001
D^2	5409.145986	1	5409.145986	46.15997	< 0.0001
Residual	1640.556533	14	117.1826095	0.97821	0.6371
Lack of Fit	1640.556533	10	164.0556533	1.5639	0.9310	Not significant
Pure Error	0	4	0
Cor Total	15731.63128	28

Table 2

b. Sequential and statistical summary of alkaline protease production
Std. Dev.	Mean	C.V. %	PRESS	R²	Adj R²	Pred R²	Adeq Precision
11.165	105.387	10.594	961.049	0.972	0.743	0.293	8.754

Table 2

b. Sequential and statistical summary of alkaline protease production
Std. Dev.	Mean	C.V. %	PRESS	R²	Adj R²	Pred R²	Adeq Precision
11.165	105.387	10.594	961.049	0.972	0.743	0.293	8.754

In this study, the production of alkaline protease from S. variabilis and its fermentation condition was statistically optimized through BBD design at 29 experimental set up runs with four factors. The BBD design displayed 2.93 fold increases in level of alkaline protease production by the actinobacterial strain with the production medium have the NaCl concentration of 2.5% w/v, pH 8, incubation temperature 55°C and 94h of incubation time. The ANOVA expressed a maximum coefficient level (R² 0.972) with P < 0.0001level of significance (Fig. 1). Similar to that of the current investigation, Lario et al. (2020) determined the optimization of protease from Rhodotorula mucilaginosa through RSM with the maximum level of protease biosynthesis (441.5U/ml) expressed at the incubation time of 96h and temperature 20°C. Matrawy et al. (2023) assessed the optimization of alkaline protease production from Lysinibacillus sphaericus through BBD method. They were also documented that the maximum enzyme production was observed at pH 7.8, temperature 25°C, yeast extract (0.446%w/v), (NH₄)₂SO₄ (0.339%w/v) and the model coefficient R² value of 0.93 with P < 0.0001level of significance.

3.3. Purification and molecular weight determination of alkaline protease

In this investigation, the purification profile of alkaline protease from the actinobacterial culture free supernatant was performed through the standard methodology and the results are displayed in Table 3. Which showed that the highest protease activity 3143.21IU/ml was observed in crude enzyme, subsequently it was gently decreased up to 689.16IU/ml in the last stage of purification step by using Sephadex G-100 column. Likewise, the huge amount (120.80mg/ml) of protein was found to be in crude enzyme and it was gradually reduced to 9.04mg/ml in the final step of purification. In DEAE-cellulose chromatography, the 27th fraction of enzyme (Fig. 2a) showed the highest peak range with the total activity of 1401IU, protein content 26.80mg/ml and 52.27IU/mg of specific activity. Followed by, the Sephadex-G-100 chromatography displayed the maximum peak range at 22nd fraction (Fig. 2b) with the total protein content of 9.04mg/ml, total enzyme activity of 689.16IU and 76.22IU/mg of specific activity. The final step of purification profile revealed 21.92% yield with the purification fold of 2.93 fold. Similarly, Nageswara et al. (2019) documented the purification profile of alkaline metallo protease of S. hydrogenans (MGS13) using Sephadex G-100 column chromatography expressed the highest protein content of 370.25mg/ml, specific activity of 50U/mg with the purification 12 fold and 34% of yield. In an another report, the maximum purification fold (36.43fold), total activity (3312IU), specific activity (85.14IU/mg) and the purification yield (60.85%) of alkaline protease of S. flavogriseus (AB723782) were described by Mostafa et al. (2022).

Table 3

Purification process of alkaline protease from the selected actinobacterium *S. variabilis*
Purification steps	Total protein (mg/ml)	Total activity (IU)	Specific activity (IU/mg)	Purification (fold)	Recovery (%)
Crude enzyme	120.80 ± 2.10	3143.21 ± 4.10	26.01 ± 1.52	1.00 ± 0.000	100.00 ± 1.67
Ammonium sulphate fractionation	70.31 ± 2.00	2438.06 ± 3.08	34.67 ± 1.62	1.33 ± 0.009	77.56 ± 1.70
Dialysis	58.03 ± 1.52	2082.00 ± 2.14	48.03 ± 1.50	1.85 ± 0.029	66.24 ± 1.62
DEAE- cellulose	26.80 ± 1.14	1401.00 ± 2.00	52.27 ± 1.56	2.01 ± 0.016	44.57 ± 1.69
Sephadex G-100	9.04 ± 0.92	689.16 ± 1.59	76.22 ± 1.90	2.93 ± 0.024	21.92 ± 1.58
Each value is the Mean ± SD of triplicate analysis

In this study, the purified alkaline protease of S. variabilis with their molecular mass of excised band was recorded as 35kDa on 10% of SDS-PAGE (Fig. 3). Likewise, El-Hadedy et al. (2023) documented that the purified alkaline protease of S. flavogriseus ADEM7 exhibited the maximum specific activity of 550 U/mg with the molecular mass of 37kDa on 10% of SDS-PAGE.

Lario et al. (2020) assessed that the molecular mass of purified alkaline protease of Rhodotorula mucilaginosa as 34.5kDa. Another study by Dhayalan et al. (2022), denoted the molecular mass of purified alkaline protease of B. thuringiensis was found to be 27kDa with the purification yield of 50.72%. Asitok et al. (2022) reported the purification profile of alkaline protease from S. acidaminiphila with the purification yield of 73.87% and the purification fold (52.55fold). They were also observed the molecular weight of the purified alkaline protease was found to be 45.7kDa.

3.4. Amino acid sequencing

The total amino acid residues of the purified alkaline protease of S. variabilis was analyzed and the result is represented in Fig. 4. It expressed that the protein of alkaline protease (APP) showed 482 residues of amino acids with 90% of sequence similarity to that of Streptomyces rimosus alkaline protease. In an another report, Rohamare et al. (2015) described the total amino acid 411 residues of alkaline serine protease of Nocardiopsis sp. NCIM 5124 expressed 90% similarities to N. dassonivillie. Xin et al. (2015) documented that the total amino acid residues 392 of Streptomyces sp. M30 with the protein mass of 39.4kDa showed 89% similarity to that of alkaline protease enzyme protein of Streptomyces sp. AA0539 through Edman degradation method. Kitadokoro et al. (1993) studied the total amino acid residues of proteinase from S. fradiae ATCC 14544 through amino acid sequencing method. Who also observed that the total 357 amino acid residues had 82% homology similarity to Streptomyces griseus.

3.5. Homology modeling of alkaline protease

The homology model of high quality three dimensional (3D) ribbon structure of purified alkaline protease of S. variabilis was achieved by using SWISS-MODEL server, subsequently the Ramachandran plot was prepared through PROCHECK program (Fig. 5). The validated model executed that the total (482) amino acid codes in the most favored region 90.4%, codes in additional allowed area 9.6% and 0 residue in the disallowed area with good stereochemical structural quality (Fig. 5). This refined model indicated that the backbone phi (φ) and psi (ψ) angles occupy reasonable perfect position in the 3D structure (Table 4). Likewise, El-Hadedy et al. (2023) studied the molecular structural modeling of alkaline protease protein of S. flavogriseus ADEM7 exhibited 92.8% residues in the favored area and 0 residue in the region of disallowed area. Mushtaq et al. (2023) predicted three dimensional structure (3D) of alkaline protease of Bacillus sp. HM49 with 90.6% residues in the most favoured regions, 8.9% residues in additional allowed regions and 0 residue in the disallowed area region. Mechri et al. (2022) assessed the homology modeling of serine protease from S. mutabilis TN-X30 expressed 93.0% of residues in the favored area, 9.10% residues in the additional allowed region with 0 residue in the disallowed region.

Table 4

Statistics of the 3D model of alkaline protease from the Ramachandran plot
Ramachandran plot statistics	Alkaline protease
Residues in most favoured regions	310	90.4%
Residues in additional allowed regions	33	09.6%
Residues in generously allowed regions	0.0	0.00%
Residues in disallowed regions	0.0	0.00%
Number of non glycine and non proline residues	343	100%
Number of end residues	2
Number of glycine residues	48
Number of proline residues	38
Total residues	431

3.6. Characterization of alkaline protease

The purified alkaline protease from the chosen actinobacterial strain was further characterized by the influences of various factors such as pH, temperature and NaCl concentrations and their results are displayed in Table 5. It showed that the maximum protease activity exhibited at pH 9 (130.7IU/ml), temperature 50°C (120.6IU/ml) and 3.0% of NaCl concentration (110.2IU/ml). However, the increasing level of the above parameters, the enzyme activity of alkaline protease was gradually decreased. Likewise, Sharma and Singh (2016) examined that the purified alkaline protease of Nocardiopsis dassonvillei exhibited maximum enzyme activity at pH 9 (251.19IU/ml), 30°C temperature (231.90IU/ml) and 177.78IU/ml for 96h of incubation time. In an another report, the highest protease activity (441.5IU/ml) of Rhodotorula mucilaginosa was denoted at pH 6, temperature 20°C, casein peptone (10g/l) as substrate with 96h of incubation time by Lario et al. (2020). Mostafa et al. (2022) pointed out the purified alkaline protease from S. flavogriseus showed the highest enzyme (312.0 IU) activity at the temperature 65°C and pH 11.5.

Table 5

**Influences of different pH, temperature, NaCl concentrations, substrate concentrations and different metal ions on alkaline protease activity** Each value is the Mean ± SD of triplicate analysis
pH	Alkaline protease activity (IU/ml)	Temperature (°C)	Alkaline protease activity (IU/ml)	NaCl concentration (%)	Alkaline protease activity (IU/ml)	Substrate concentration (%)	Alkaline protease activity (IU/ml)	Metal ions (1M)	Alkaline protease activity (IU/ml)
5	48.90 ± 1.52	10	38.72 ± 1.48	0.5	39.76 ± 1.48	0.5	43.91 ± 1.52	Fe³⁺	64.92 ± 1.72
6	76.40 ± 1.56	20	59.16 ± 1.56	1.0	54.70 ± 1.56	1.0	60.82 ± 1.50	Zn²⁺	89.38 ± 2.00
7	97.60 ± 1.72	30	91.27 ± 1.84	1.5	68.36 ± 1.70	1.5	91.74 ± 1.79	Mg²⁺	129.0 ± 2.10
8	119.3 ± 2.00	40	120.6 ± 2.00	2.0	81.39 ± 1.64	2.0	107.4 ± 1.96	Mn²⁺	67.26 ± 1.69
9	130.7 ± 2.12	50	106.5 ± 1.92	2.5	95.16 ± 1.78	2.5	147.2 ± 2.09	Ca²⁺	81.43 ± 1.90
10	108.3 ± 1.80	60	79.17 ± 1.80	3.0	110.2 ± 2.10	3.0	113.3 ± 1.72	Co²⁺	41.73 ± 1.56
11	83.71 ± 1.64	70	48.81 ± 1.56	3.5	89.28 ± 1.90	3.5	85.62 ± 1.64	Cu²⁺	56.21 ± 1.50
12	69.82 ± 1.50	80	20.04 ± 1.32	4.0	68.19 ± 1.59	4.0	61.94 ± 1.48	Lb	21.37 ± 1.22

Further in the present investigation, the influences of different factors such as pH, temperature and NaCl concentrations on the stability of alkaline protease of S. variabilis was assessed and the results were represented in the Fig. 6. Which expressed that the maximum relative activity of 95.47% at pH 9, 88.67% at 60°C temperature and 84.76% at 3.0% of NaCl concentration respectively, within the incubation period 60min. However further increasing the incubation time, the stability of the purified alkaline protease was gradually decreased. Similarly, Asitok et al. (2022) determined the highest relative activity of alkaline protease from S. acidaminiphila exhibited at pH 11.5 (92.0%), temperature 75°C (79.0%), and 2.5% of NaCl concentration (91.98%) respectively within the incubation period of 75min. Xin et al. (2015) achieved the maximum relative activity of alkaline protease of Streptomyces sp. M30 showed 90.0% at pH 9 and temperature 70°C for 48h of incubation time. Darwesh et al. (2019) observed that the maximum relative activity (90.0%) of alkaline protease of S. viridis (HWG550) denoted at pH 8, temperature 50°C for 30min of incubation time.

The presence of metal atoms such as Fe³⁺, Zn²⁺, Mg²⁺, Mn²⁺, Ca²⁺, Cu²⁺ etc., in the production medium may influences the biosynthesis of alkaline protease from S. variabilis under fermentation process. The maximum enzyme activity (129.09IU/ml) revealed that the influence of Mg²⁺ in the production medium than the rest of other tested metal ions such as Fe³⁺ (64.92IU/ml), Zn²⁺ (89.38IU/ml), Mn²⁺ (67.26IU/ml), Ca²⁺ (81.43IU/ml), Co²⁺ (41.73IU/ml) and Cu²⁺ (56.21IU/ml). The lowest enzyme activity (21.37IU/ml) was observed in the fermentation medium supplemented with 1M of Lb. Subsequently, the influence of casein concentration on alkaline protease activity was performed, which exhibited that the maximum enzyme activity of 147.29IU/ml was denoted at 2.5% of casein concentration (Table 5). In an another study, Xin et al. (2015) described the metal ions Mn²⁺ and Cu²⁺ expressed the maximum alkaline protease activity 113 and 147IU/ml by Streptomyces sp. M30. Sharma and Singh (2016) portrayed the maximum alkaline protease activity of N. dassonvillei 177.68IU/ml observed against K₂HPO₄ under fermentation process.

3.7. Kinetic study

The kinetic parameters such as K_m and V_max values of purified alkaline protease from S. variabilis were assessed through the method of Lineweaver-Burk (LB) plot as 5.158 ± 0.12mg/ml and 484.90 ± 2.04µg/min/mg, respectively using casein as substrate with the R² value of 0.9958 (Fig. 7). Similarly, the kinetic factors like K_m (1.00mg/ml) and V_max (319.0µg/min/mg) values of purified metalloprotease from S. hydrogenans (MGS13) using casein as substrate were reported by Nageswara et al. (2019). In an another report, Xin et al. (2015) documented the kinetic parameters like K_m (35.7mg/ml) and V_max (5x10⁴µg/min/mg) values of purified alkaline protease of Streptomyces sp. M30 using LB plot method. Parthasarathy and Joel Gnanadoss (2020) attained the respective kinetic values of V_max and K_m values of 500mM/min/mg and 73.5mg/ml of purified alkaline protease of Streptomyces sp. LCJI2A employing casein as substrate.

3.8. Analysis of in-silico molecular docking of enzyme-dye interaction

Molecular docking analysis revealed that the average binding energies between the alkaline protease enzyme protein and acridine orange, erythrosine were − 6.3 kcal/mol and − 7.7 kcal/mol, respectively. These amino acids are involved in the binding and degradation of dyes in Fig. 8. and Table 6. A stronger binding energy was associated with the binding between the protein and compound. According to the docking results, the strongest and weakest bindings of alkaline protease enzyme protein were observed with acridine orange and erythrosine dye, respectively. According to our experiment findings, the strongest binding interaction between alkaline protease enzyme protein and acridine orange, erythrosine was responsible for a higher degradation of dyes. In accordance with these, Sarkar et al. (2020) documented that the laccase (-8.3 kcal/mol) and azoreductase (-7.3 kcal/mol) from Thermus thermophilus HB27 and Bacillus sp. B29 effectively showed azo dye degradation under laboratory condition. They were also observed the in-silico molecular assessment of enzymatic activity on dye degradation resulting into co-expressive interactions of dye mostly with the unrecognized enzyme proteins. In an another study, Rajalakshmi Sridharan et al. (2021) determined the dye degradation efficacy of laccase enzyme from alkaliphile Pseudomonas mendocina through in-vitro conditions and who also observed that the interaction of enzyme protein with azo dyes using in silico method to predict the binding energy with reactive red (− 7.19kcal/mol), brown (− 8.57kcal/mol) and black (− 9.17kcal/mol) azo dyes, respectively. The degradation efficiency of laccase on bromophenol blue (100%) with the binding energy of -7.7 kcal/mol as well as Malachite green − 7.8 kcal/mol, respectively showed with the degradation rate of 100 and 68% were observed by Dasgupta et al. (2020).

Table 6

*In-silico* analysis of acridine orange and erythrosine pink dyes with alkaline protease enzyme interaction for dye degradation
Enzyme	Dye Name	Binding Energy Score (kcal/mol)	Amino Acid	Distance	Type of Interaction	Type of Bond
Alkaline Protease Enzyme	Acridine orange	-6.3	GLU352	3.73507	Hydrogen Bond	Carbon Hydrogen Bond
			PRO353	3.58977	Hydrogen Bond	Carbon Hydrogen Bond
			GLY100	3.5849	Hydrogen Bond	Carbon Hydrogen Bond
			GLN155	3.54634	Hydrogen Bond	Carbon Hydrogen Bond
			TYR109	3.53726	Hydrogen Bond	Carbon Hydrogen Bond
			TYR362	5.41655	Hydrophobic	Pi-Pi T-shaped
			ALA358	4.23395	Hydrophobic	Alkyl
			PRO357	4.48676	Hydrophobic	Alkyl
			PRO357	4.96903	Hydrophobic	Alkyl
			HIS464	5.15924	Hydrophobic	Pi-Alkyl
	Erythrosine Pink	-7.7	SER105	2.80508	Hydrogen Bond	Conventional Hydrogen Bond
			TYR362	2.85215	Hydrogen Bond	Conventional Hydrogen Bond
			GLY136	3.41882	Hydrogen Bond	Carbon Hydrogen Bond
			TYR362	3.8616	Hydrogen Bond	Pi-Donor Hydrogen Bond
			PRO357	4.40531	Hydrophobic	Alkyl
			PRO357	4.60858	Hydrophobic	Alkyl
			PRO48	5.41957	Hydrophobic	Alkyl
			PRO108	4.41168	Hydrophobic	Alkyl
			PRO48	5.17725	Hydrophobic	Pi-Alkyl
			PRO108	4.82739	Hydrophobic	Pi-Alkyl
			ALA137	5.33491	Hydrophobic	Pi-Alkyl
			PRO48	3.94894	Hydrophobic	Pi-Alkyl
			PRO108	4.9585	Hydrophobic	Pi-Alkyl

3.9. Assessment of first order kinetics of alkaline protease mediated degradation of acridine orange and erythrosine pink dyes

The degradation efficiency of alkaline protease on acridine orange and erythrosine pink dye was determined through first order kinetic method (Fig. 9a & b) and the resulted parameters were highlighted in the Table 7. It expressed that the k₁ (0.0861 and 0.1893 g/ml) and k_cal (1.8251 and 2.1371g/ml) values, respectively with the R² values of 0.9987 and 0.9953. Likewise, Yayci et al. (2022) reported that the first order kinetic model for the degradation ability of enzyme DyP-type peroxidases of S. chartreusis NRRL 3882KM on azo dye under laboratory condition expressed the parameter values of k₁ 0.0981g/ml and k_cal 2.67s^− 1 with the R² values of 0.9876. In an another study, Rajalakshmi Sridharan et al. (2021) documented the first order kinetic model for the dye degradation efficacy of laccase of P. mendocina on reactive black showed of k₁ 0.0972 g/ml and k_cal 2.47s^− 1 with the R² values of 0.9786.

Table 7

Assessment of first order kinetics of alkaline protease mediated degradation of acridine orange and erythrosine pink dyes
Kinetic model	Constant	Acridine orange (--g/ml)	Erythirosine pink (--g/ml)
First order	k₁ (min.)	0.0861	0.1893
	C₀ (cal.)	1.8251	2.1371
	R²	0.9987	0.9953

From this study, it could be concluded that the actinobacterial strain S. variabilis from an estuarine environment produced alkaline protease through submerged fermentation process using skim milk medium, simultaneously the biosynthesized enzyme was optimized by using the following parameters such as pH, temperature, NaCl concentrations, inoculum size and incubation time through BBD model. The calculated design of alkaline protease is effectively sensible and gives much more information about enzyme - substrate interactions. Further, the enzyme was purified through standard methodologies and the purified enzyme from the optimized medium expressed highest stability and activity, could abled to degrade acridine orange and erythrosine pink dyes effectively.

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Conflict of interest:

The authors declare that we have no competing interest for the work described in this manuscript.

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Author Contribution

Sanjivkumar Muthusamy: Conceptualization, Investigation, Methodology, Software, Original draft writing. Pandiselvi Balamurugan: Data curation, Software validation, Visualization. Silambarasan Tamilselvan: Project administration, Supervision, Writing – review & editing.

Acknowledgements

The authors are gratefully acknowledging the Management and the Principal of K.R. College for their moral support.

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Biogenesis, molecular characterization and dye degradation efficacy of alkaline protease from an estuarine associated actinobacterium Streptomyces variabilis using in-silico method

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Determination of proteolytic property of mangrove associated actinobacterium

2.1.1. Preliminary screening for proteolytic activity

2.1.2. Secondary screening and selection of potent proteolytic strain

2.1.3. Protease assay

2.2. Optimization of protease production by S. variabilis through Box-Behnken design (BBD)

2.3. Protein estimation

2.4. Purification and molecular weight determination of protease enzyme

2.5. Amino acid sequencing of alkaline protease

2.6. Molecular modeling

2.7. Characterization of alkaline protease enzyme

2.8. Kinetic study

2.9. Applications of alkaline protease enzyme

2.9.1. Determination of dye degradation efficacy of alkaline protease

2.9.2. In-silico analysis of dye degradation

2.10. Assessment of kinetic parameters of dye degradation

3. Results and Discussion

3.1. Assessment of alkaline protease property of mangrove associated actinobacteria

3.3. Purification and molecular weight determination of alkaline protease

3.4. Amino acid sequencing

3.5. Homology modeling of alkaline protease

3.6. Characterization of alkaline protease

3.7. Kinetic study

3.8. Analysis of in-silico molecular docking of enzyme-dye interaction

4. Conclusion

Declarations

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Acknowledgements

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