A strategic approach of enzyme engineering by attribute ranking and enzyme immobilization on zinc oxide nanoparticles to attain thermostability in mesophilic Bacillus subtilis lipase for detergent formulation

Article abstract of DOI:10.1016/j.ijbiomac.2019.06.042

The present study envisaged rationalized protein engineering approach to attain thermostability in a mesophilic Bacillus subtilis lipase. Contributing amino acids for thermostability were analyzed from homologous thermophilic-mesophilic protein dataset th

Full text of DOI:10.1016/j.ijbiomac.2019.06.042

Accepted Manuscript
A strategic approach of enzyme engineering by attribute ranking
and enzyme immobilization on zinc oxide nanoparticles to attain
thermostability in mesophilic Bacillus subtilis lipase for detergent
formulation
Mohd Faheem Khan, Debasree Kundu, Chinmay Hazra, Sanjukta
Patra
PII:
S0141-8130(19)31788-X
DOI:
https://doi.org/10.1016/j.ijbiomac.2019.06.042
BIOMAC 12548
Reference:
To appear in:
International Journal of Biological Macromolecules
Received date:
Revised date:
Accepted date:
9 March 2019
6 June 2019
7 June 2019
Please cite this article as: M.F. Khan, D. Kundu, C. Hazra, et al., A strategic approach
of enzyme engineering by attribute ranking and enzyme immobilization on zinc oxide
nanoparticles to attain thermostability in mesophilic Bacillus subtilis lipase for detergent
formulation, International Journal of Biological Macromolecules, https://doi.org/10.1016/
j.ijbiomac.2019.06.042
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ACCEPTED MANUSCRIPT
A strategic approach of enzyme engineering by attribute ranking and enzyme
immobilization on zinc oxide nanoparticles to attain thermostability in mesophilic Bacillus
subtilis lipase for detergent formulation
1
1
2
1*
Mohd Faheem Khan , Debasree Kundu , Chinmay Hazra and Sanjukta Patra
1
Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati,
Guwahati-781039, Assam, India
2
Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302,
West Bengal, India
*
Corresponding author
Phone: [+91-361-258-2213]
Fax: [+91-361-258-2249]
E-mail: sanjukta@iitg.ac.in
1

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Abstract
The present study envisaged rationalized protein engineering approach to attain thermostability
in a mesophilic Bacillus subtilis lipase. Contributing amino acids for thermostability were
analyzed from homologous thermophilic-mesophilic protein dataset through relative abundance
and generated ranking model. Analyses divulged priority of charged amino acids for
thermostability. Ranking model was used to predict thermostabilizing mutations. Three lipase
mutants, bsl_the1 (V149K, Q150E), bsl_the2 (F41K, W42E, V149K, Q150E) and bsl_the3
(F41K, W42E, P119E, Q121K, V149K, Q150E) were generated and validated through in silico
and in vitro approaches for improved activity and thermostability. ZnO nanoparticles were
synthesized by precipitation method and functionalized using polyethylenimine, APTES and
glutaraldehyde for lipase immobilization. The immobilization was confirmed through various
analytical techniques. Analysis revealed bsl_wt showed optimum activity at 35 °C and pH 8
which was increased to 60 °C and pH 10 in case of ZnO-bsl_the3. The ZnO-bsl_the3 showed 80
%
of their initial activity after 60 days of storage stability and retained 78 % of activity after 20
cycles of reuse. Lipases were applied for oil and grease stain removal from fabric. ZnO-bsl_the3
removed 90 % and 82 % of oil and grease stains, respectively. Conclusively, it revealed a
promising perspective of low-cost nanobiocatalysts in detergent formulation.
Keywords
Protein engineering; Thermostability; Immobilization
2

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1
. Introduction
Enzymes are catalytic proteins having absolute selectivity, specificity and can act as green
alternative replacing conventional chemical catalysts [1]. Since last two decades, enzymes have
found increased application in various industrial sectors [2]. Amongst different industries,
enzymes provide remarkable benefits in detergent formulations by reducing energy consumption
through shorter washing times, less water consumption, and reduced chemical load. To enhance
the cleaning ability of detergents, enzymes such as proteases, lipases, amylases, etc. are
excessively used in detergent formulations to remove tough stains and make them environment
friendly. Lipases are one such hallmark enzymes that can easily remove greasy and oily stains
from the fabrics and act as a potential additive that exhibits better stability towards commercial
detergents [3]. However, thermostability and alkaline tolerance of lipases is the major concern
for their commercialization as detergent additive [4]. This necessitates the development of
thermo-alkaline tolerant lipase to sustain activity even at harsh detergent conditions. Till date,
various techniques are employed to attain thermostability of enzymes such as protein
engineering, enzyme immobilization, etc. [5]. Protein engineering is the most promising aspect
and provides improved properties to the proteins. Majorly, it is classified into two categories;
random mutagenesis/directed evolution and rational protein design/computer-aided protein
engineering [6]. Directed evolution provides genetic diversification, library screening and
selection. The major drawback of this method is that it requires robust screening and selection of
the mutants for desired characteristics making it time consuming and cost-intensive. However,
the present rationalized protein engineering approach integrates computer-aided, rapid and
potential method for predicting stability of the designed mutations [7]. Therefore, the novelty of
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the present research lies in moving from random mutagenesis to a pre-predicted rationalized
protein engineering platform by ranking the contributing protein attributes of thermostability.
To increase the thermostability of proteins through rationalized approach, the flexible sites need
to be predicted and rigidified. The flexible sites have less number of interactions with local
amino acids which triggers unfolding of proteins due to thermal fluctuations [5,8].
Thermostability thus, can be enhanced by substituting these amino acids with those that increases
non-covalent interactions (hydrogen bonds, ionic interactions, salt-bridges, hydrophobic
interactions) between contact residues. This necessitates the search for mutable hotspots in the
flexible region using in silico prediction to compute the effects of mutations in such regions on
stability changes in proteins [8,9]. In the process, the chosen mutable residues should not belong
to any stabilizing centre(s) and active site pocket(s). Thus, mutations should be done on the loop
regions and surface exposed regions for improving thermostability [10,11]. The mutable hotspots
identified are designed for in silico mutagenesis followed by experimental validation through
site-specific mutagenesis. However, these mutable residues play crucial role in attaining
thermostability, so it is imperative to identify the specific amino acids and their preference for
protein thermostability. The present approach is an attempt that involves amino acids
prioritization to select the favored mutations for thermostability.
Another additional stabilization approach that can function in combination to protein engineering
is enzyme immobilization. Enzymes are capable of working at room temperature. However,
immobilized enzymes have the advantages of exhibiting better activity, specificity,
thermostability, storage stability and reusability as compared to free enzymes [12]. The improved
performance of immobilized enzymes are dependent upon multipoint attachment with support,
structure of the support, properties of the spacer, microenvironment of the immobilization carrier
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and immobilization conditions [12,13]. Amongst various methods and carriers for
immobilization, nanoscale carriers offer a versatile technology that provides large surface areas
to load more enzyme and reduces mass transfer resistance for substrates. However, when
immobilized in non-porous nanomaterials, enzyme molecules are located in the external surface
of the support which poses problems such as exposure of the enzyme to hydrophobic interfaces
and interaction of enzyme molecules in one nanoparticle with the enzyme molecules in other
nanoparticle. To prevent such difficulties, the immobilized support is often coated with a
polymer [14]. Thus, it is essential to carefully design the optimal enzyme immobilization
strategy for various industrial processes. Use of glutaraldehyde as the crosslinker has the
broadest applicability as a versatile tool in enzyme immobilization [15–17]. Moreover, for
lipases, immobilization through glutaraldehyde cross-linking on NH -active supports in presence
2
of detergents can improve enzyme performance as well as increase their activity. This is because
detergents shift lipase conformation towards the open form in the aqueous media permitting
hydrophobic active site accessible to the hydrophobic substrate through interfacial activation of
the enzyme [18,19].
In an attempt to attain thermostability in a mesophilic enzyme through a strategic approach, the
amino acids rendering thermostability were ranked and knowledge-based site-specific mutations
were predicted. Thus, this study makes an effort to (a) identify the contributing amino acid
attributes, (b) rank them for their preferences to attain enzyme thermostability employing in
silico designing approaches followed by validation through in vitro mutagenesis, and (c)
immobilization of the improved engineered biocatalyst onto zinc oxide (ZnO) nanoparticles for
enzymatic detergent formulation. In a nutshell, the present study aims at converging rationalized
protein engineering and enzyme immobilization approaches to put forth an integrated nano-
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immobilized enzyme engineering system for enhancing thermostability thereby, increasing its
applicability in detergent industry.
2
2
. Materials and Methods
.1 Bacterial strains and chemicals
Escherichia coli DH5α and BL21(DE3) (Invitrogen, USA) were used for cloning and protein
overexpression, respectively and cultured in Luria-Bertani (LB) medium (Merck, USA). The
plasmid pET-28a(+) from Addgene (USA) was used for recombinant enzyme production. Quick
TM
Ligation kit, BamHI, NdeI, DNA and protein markers were purchased from New England
Biolabs (UK). StrataPrep Plasmid Miniprep Kit, StrataPrep DNA Gel Extraction Kit,
QuikChange Site-Directed Mutagenesis Kit and SYPRO orange dye were purchased from
Agilent Technologies (USA). Ni-NTA resin and polypropylene columns were obtained from
Qiagen (USA) for His-tagged enzyme purification. Reagents such as kanamycin sulphate, p-
nitrophenyl octanoate (p-NPO), polyethyleneimine (PEI), (3-aminopropyl) triethoxysilane
(APTES) and glutaraldehyde (25%, aqueous solution) were purchased from Sigma-Aldrich
(
Germany). Zinc sulphate (ZnSO ), sodium hydroxide (NaOH), surfactants (Tween 20; Tween
4
8
0; Triton X-100; NP-40 and sodium dodecyl sulfate, SDS), oxidizing agents (hydrogen
peroxide, H O ; sodium perborate, NaBO and sodium hypochlorite, NaOCl) were procured
2
2
3
from Hi-Media (India). Commercial detergents like Ariel, Surf Excel, Henko, Tide and Vanish
were used for fabric destaining. All other reagents, solvents used were of molecular
biology/analytical grade and obtained from Merck (USA) and Himedia (India).
2
.2 Data collection and generation of contributing amino acids for thermostability
Thermophilic protein sequences were collected from UniProtKB and filtered for non-redundancy
with the removal of partial and putative proteins. Homologous mesophilic proteins were
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searched for each thermophilic proteins using BLASTp program (Table S1). Amino acid
compositions were determined from collected thermophilic and mesophilic proteins using
PEPSTATS. Data pertaining to amino acid compositions were subjected to non-parametric two-
sample Kolmogorov-Smirnov (KS) test using MATLAB. It filtered statistically significant amino
acids by p-value ≤0.05. The final datasets consisted of only statistically significant amino acids.
It was used for computing relative abundance and generating ranking model for amino acids
contributing to protein thermostability.
2
.3 Computing relative abundance of amino acids in thermophiles and mesophiles
Relative abundance of significant amino acids were computed for understanding their preference
either to thermophilic proteins or to mesophilic proteins. The weighted average of significant
amino acids for each protein were calculated followed by calculating the difference in the
weighted averages of those amino acids from thermophilic proteins w.r.t. mesophilic proteins.
Each weighted average difference for a significant amino acid was normalized with the highest
valued weighted average difference that resulted into enumeration of relative abundances of
statistically significant amino acids. Mathematically, relative abundance was calculated using
derived equation described earlier [20].
2
.4 Ranking of amino acids for thermostability
Analytic hierarchical process (AHP) is a multi-criteria decision-making (MCDM) method that
was applied as a prediction method to rank the amino acids contributing to protein
thermostability. The method comprised of three hierarchies namely, goal, criteria and alternative.
Here, the goal was to predict thermostabilizing mutations based on ranking of significant amino
acids as criteria and to validate the generated model through thermophilic and mesophilic
proteins as alternatives. Fig. S1 of the Supplementary Information details out the steps involved
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in generation of AHP ranking model. To validate the performance of generated AHP ranking
model, two blind tests were performed independently. First test compared statistically significant
amino acids of any randomly chosen thermophilic proteins and mesophilic proteins whereas,
second test compared amino acids of any two randomly chosen mesophilic proteins from the
existing dataset. These protein pairs were considered as alternatives for validation of generated
AHP ranking model. The model assigned ranks to the comparing thermophilic and mesophilic
protein pairs and classified as thermophilic or mesophilic. Thus, performance of the model was
graded in terms of accuracy percentage enumerated using equation (1).
푇푃+푇푁
퐴푐푐푢푟푎푐푦 % =
× 100
(1)
푇푃+푇푁+퐹푃+퐹푁
ꢀ
ꢁ (true positive), number of thermophilic proteins correctly classified as thermophilic proteins
and ꢀꢂ (true negative), number of mesophilic proteins correctly classified as mesophilic proteins
whereas, ꢃꢁ (false positive), number of mesophilic proteins incorrectly classified as
thermophilic proteins and ꢃꢂ (false negative), number of thermophilic proteins incorrectly
classified as mesophilic proteins.
2
.5 In silico prediction of thermostabilizing mutations and their validation
According to the amino acid priorities in the relative abundance and ranking model, the
thermostabilizing mutations were predicted in mesostable model enzyme Bacillus subtilis lipase
with optimum stability at 35°C and pH 8 [21]. The structure of mutant lipases were modeled
through I-TASSER server and the stability of the selected point mutation was confirmed through
various prediction tools (such as I-Mutant 2.0, Cupsat, HotSpot Wizard, ERIS, etc.). The mutants
were further validated through Ramachandran plot analysis (PROCHECK), structural
superimposition analysis (PyMol V0.99), docking analysis using p-nitrophenol octanoate (p-
NPO) substrate (Autodock 4.2 version) and contact map analysis (CMView 1.1.1).
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2
.6 Cloning, mutagenesis, overexpression and purification of lipase
Bacillus subtilis 168 lipase was cloned in pET-28a (+) using BamHI and NdeI restrictions sites.
The site directed mutagenesis was carried out for attaining thermostability through designed
®
mutagenic primers (Table S2) using QuikChange Multi Site-Directed Mutagenesis Kit (Agilent
Genomics) as per manufacturer's protocol. The selected thermostabilizing mutations were
derived as per the in silico analysis. The cloning and mutations of lipases was confirmed through
restriction digestion and sequence analysis.
For overexpression and purification, the recombinant plasmids of wild-type and mutant lipases
were transformed into competent E. coli BL21 (DE3). The induced cultured cells (100 µM
IPTG) were harvested by centrifugation (7000 ×g, 4°C, 10 min) followed by lysis in 10 mL
-1
phosphate buffer (20 mM, pH 8) containing 0.5 mg mL of lysozyme. The suspended cells were
sonicated to recover the soluble enzymes by centrifugation (14000 × g, 4°C, 20 min). N-terminal
poly-histidine tag was used for purification of recombinant lipases using Ni-NTA affinity
chromatography. Purified lipases were eluted by same buffer containing 250 mM imidazole. The
purification of lipases was confirmed by SDS-PAGE (Bio-Rad laboratories) [22]. The free
imidazole in the purified lipases was removed by dialysis in the same buffer at 4°C, followed by
determination of concentrations of lipases by Bradford assay using BSA as standard (Fig. S2)
[
23].
.7 Lipase activity assay
2
Activity of wild-type and mutant lipases were determined spectrophotometrically (UV-2600,
Shimadzu) using p-NPO as substrate [24]. The amount of released p-nitrophenol (p-NP, ɛ = 15.1
-1
mM cm ) was measured at 410 nm. One enzyme unit (U) was defined as the lipase activity that
-1
-1
liberated 1 µmol equivalent of p-NP mL min under the standard assay conditions. The enzyme
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kinetic parameter such as activity, specific activity, K value, turnover number (k ) and
m
cat
catalytic efficiency (k /K ) was calculated for wild-type and mutant lipases.
cat
m
2
.8 Characterization of wild-type and mutant lipases
Optimum temperature and pH of wild-type and mutant lipases was determined by incubating
them with p-NPO for 10 min at varying range of temperatures (20 °C to 70 °C) and pH (pH 3 to
pH 12), respectively. The temperature and pH stability of lipases were performed at thermal
temperature of 55 °C and alkaline pH 9 at varying time intervals for 24 h. For melting
temperature (T ) determination of lipases, differential scanning fluorimetry (DSF) was
m
performed using real-time PCR (Agilent Technologies) as reported earlier [22]. The reaction
-1
mixtures containing 0.1 mg mL lipase and 5X SYPRO orange were placed in real-time PCR
-1
and set the temperature scan from 25 to 95 ºC, at 1 ºC min ramp rate. The fluorescence of
SYPRO orange dye (excitation maxima at 492 nm and emission maxima at 610 nm) was
measured to determine the T of the lipases.
m
2
.9 Determination of secondary structure changes of lipases by ATR-FTIR
Changes in secondary structures of lipases were investigated by FT-IR (Perkin-Elmer) in
-1
attenuated total reflection (ATR) mode in the range of amide I (1600-1700 cm ). Before
recording the spectra, the lipase preparations were desalted by dialysis and dried at 37 °C. Peak
positions in amide I range were resolved and fitted with a Gaussian shape using second
derivative (OriginPro 8.5 software).
2
.10 Determination of lipase compatibility with surfactants, oxidizing agents and
commercial detergents
The compatibility of wild-type and mutant lipases with various surfactants (10 % v/v or w/v),
oxidizing agents (1 % v/v or w/v) and commercial detergents (10 % v/v or w/v) was investigated
1
0

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-1
by incubating the lipase (50 U mL ) for 1ꢀh at 35 °C. For commercial detergents, the endogenous
lipases present were inactivated by heating the diluted detergents at 90 °C for 1ꢀh prior to the
addition of lipase preparation. Finally, the relative activity of wild-type and mutant lipases was
determined and compared with control (having wild-type lipase but without any surfactants,
oxidizing agents and commercial detergents). The detergent compatible lipases was chosen and
subjected for immobilization on ZnO nanoparticles.
2
.11 Covalent immobilization of lipase on synthesized ZnO nanoparticles
ZnO nanoparticles were synthesized by direct precipitation method using ZnSO and NaOH as
4
per Patel et al. (2016) [25]. The covalent immobilization procedure was developed using mild
glutaraldehyde (GLU) as cross-linking agent for immobilization of lipases on functionalized
ZnO nanoparticles (i.e. ZnO-PEI-APTES) via NH -groups. Glutaraldehyde is a powerful and
2
versatile cross-linker in enzyme immobilization due its polymeric conformations which are
formed depending on the reaction condition pH, temperature, concentration, etc. and can react
with various groups other than amines such as, imidazoles, thiols, phenols etc. [15,16]. It has
been found to be an effective way to attain improved activity and stability of lipases [26,27].
Briefly, 10 mg of functionalized ZnO-PEI/APTES was incubated in 10 mL phosphate buffer
solution (20 mM, pH 8.0) containing 0.25 % (w/v) GLU and allowed to react for 2 h under
−
1
stirring (100 rpm) at room temperature. Then, 1 mL of lipase solution (10 mg mL ) was added
into the mixture and stirred for 2 h. The stepwise protocol of synthesis, functionalization and
immobilization have been illustrated in Fig. 1.
2
.12 Characterization of as-synthesized and lipase immobilized ZnO nanoparticles
Structure and morphology of naïve and bioconjugated ZnO nanoparticles was characterized by
FESEM (Zeiss, Sigma, Germany) and TEM (JEM – 2100 HRTEM, JEOL, Japan). The particle
1
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size was determined by dynamic light scattering (DLS; Beckmen Coulter Inc.). The optical
absorption property of ZnO was studied by UV–vis spectroscopy (Shimadzu Corporation, Japan)
in the range of 200 to 700 nm. FT-IR spectrometer (Perkin-Elmer, USA) was used to record
-1
-1
-1
spectra from 400 cm to 4000 cm at 4 cm resolution with an average of 20 scans. The
crystallinity of ZnO nanoparticles was determined using X-ray diffractometer (XRD; Rigaku
TTRAX, Seifert XRD 3003 T/T) equipped with a Cu Kα radiation (K=1.54 Å) source,
maintaining applied voltage of 40 kV and current at 30 mA. XRD analysis of the ZnO
nanoparticles was performed in the 2θ range of 25° - 70° with speed of 5.0° as X-ray pattern of
ZnO samples can be determined by varying angle (??) of incident X-ray beam that transits from
constructive to destructive interference and referred as the function of X-ray angle i.e. 2??.
2
.13 Estimation of immobilization efficiency and yield
Lipase immobilization efficiency and yield was estimated by Bradford assay and was calculated
by equation (2) and (3), respectively [28].
ꢄꢅ−ꢄꢆ
퐸푓푓푖푐푖푒푛푐푦 표푓 푖푚푚표푏푖푙푖푧푎푡푖표푛 (%) =
× 100
(2)
(3)
ꢄꢅ
푌
ꢆ
퐼푚푚표푏푖푙푖푧푎푡푖표푛 푦푖푒푙푑 (%) = 푌ꢅ × 100
퐸 , amount of total protein introduced for immobilization; 퐸 , amount of protein present in the
ꢇ
ꢈ
filtrate solutions after immobilization; ꢉ , initial lipase activity for immobilization; and ꢉ ,
ꢇ
ꢈ
activity immobilized lipase on the support. Further, optimum temperature and pH was
determined for the immobilized enzymes.
2
.14 Reusability and storage stability of immobilized lipases
In order to investigate the operational reusability, the immobilized lipase was separated by
centrifugation, washed and eventually reused with fresh substrates for upto 20 cycles of
experiment. The storage stability of the free and immobilized wild-type and mutant lipases was
1
2

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investigated by calculating the residual activity till 60 days of experiment at regular intervals of 4
days.
2
.15 Compatibility and washing performance of immobilized lipases
The immobilized wild-type and mutant lipases were again tested for surfactants, oxidizing agents
and commercial detergents compatibility and further used for investigating washing performance
2
[
3]. Prior to soiling of cotton fabrics (4ꢀcm ) with olive oil and grease, defatting was performed
in boiling choloroform. For selection of better or optimized washing conditions, the washing
process was done at different washing temperatures (30 – 70 °C) and time intervals (10 – 70
-1
min) using different concentrations of detergents (upto 9 % w/v) and lipases (upto 70 U mL ).
Stained fabrics were incubated in different washing solutions in Erlenmeyer flasks. Then, the
washed soiled fabric was rinsed with 100ꢀmL distilled water for 2 min and air dried. The weight
of the fabric was measured for each washing formulation before and after the wash. The
percentage of oil and grease removal was calculated by using the following formula as shown in
equation (4).
푊 −푊
3
ꢆ
%
푂푖푙 표푟 퐺푟푒푎푠푒 푅푒푚표푣푎푙 =
× 100
(4)
푊 −푊
2
ꢆ
ꢊ , weight of cloth before applying oil/grease (mg); ꢊ , weight of cloth after applying
ꢈ
ꢋ
oil/grease (mg); and ꢊ , weight of cloth after total oil/grease removal (mg).
ꢌ
2
.16 Statistical Analyses
All the data were reported as mean ± S.D. and performed in triplicates. GraphPad Prism (version
) was used to analyze all the data for Analysis of Variance (ANOVA). The values of pꢀ<ꢀ0.05
were considered as statistically significant.
6
3
3
. Results and Discussion
.1 In silico analysis of amino acid attributes for prediction of thermostabilizing mutations
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Amino acids in a protein sequence plays pivotal role in attaining structural stability and increases
the content of various non-covalent interactions. This have been considered as the major
contributor for thermostability that makes the protein compact and rigid to sustain structural and
functional integrity under elevated temperatures [29–31]. To understand the molecular basis of
protein thermostability, a dataset was created containing 116 non-redundant and homologous
thermophilic-mesophilic pairs. The mesophilic counterparts were chosen through BLASTp
program with a threshold of ≥70% homology (Table S1). PEPSTATS server was used to
enumerate percentage compositions of 29 amino acid features which included 20 standard amino
acids (Ala, Arg, Asn, Asp, Cys, Gln, Glu, Gly, His, Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp,
Tyr and Val) and 9 amino acid classes (polar, Pol; non-polar, NPol; small, Sml; tiny, Tiny;
aromatic, Aro; aliphatic, Ali; charged, Chrg; basic, Bsc; and acidic, Acd). Two-sample KS-test
was further employed to filter the statistically significant amino acids in the comparing proteins.
KS-test does not depend on cumulative distribution function of the comparing samples and
adapted to give the correct p-values. It measured the statistically significant difference in the
distribution of bipartite dataset on the basis of D-statistics which states the difference between
empirical distribution functions of the two samples [32]. In the present study, KS-test showed
that among 29 features, 19 amino acid features (Ala, Arg, Asn, Asp, Cys, Glu, Gln, His, Ile, Lys,
Ser, The, Val, Acd, Bsc, Chrg, Pol, Sml and Tiny) were statistically significant. It showed that
they were preferred either in thermophilic or mesophilic proteins. Similar analysis was carried
out by Kumwenda et al. (2013) in homologous sequences of Thermus scotoductus and Thermus
thermophilus. The amino acids were tested using Student t-test and Wilcoxon t-test in such
sequences and showed increased substitution of charged amino acids (Arg and Glu) in T.
thermophilus HB27 [33].
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Next, to understand the importance of each amino acid contributing to protein thermostability,
the relative abundance and ranking analysis were employed. Relative abundance was derived by
calculating the weighted arithmetic mean difference of the same amino acid of thermophilic
proteins and its mesophilic counterpart.[20] Results showed that the relative abundance of an
amino acid can be either positive or negative. The positive relative abundance of an amino acid
showed its preference for thermophilic proteins and negative relative abundance of an amino
acid showed its preference for mesophilic proteins (Fig. 2A). The charged amino acid features
(Chrg > Glu > Acd > Bsc > Arg > Lys) had higher abundance whereas, small and tiny amino
acid features (Sml > Tiny > Gln > Ser > Thr > Ala > Asn > Pol) had lower abundance in
thermophilic proteins. These preferences showed inverse trend in mesophilic proteins. Haney et
al. (1999) depicted lower content of small/tiny uncharged polar residues (Ser, Asn, and Gln) and
higher percentage of charged amino acids (Glu, Arg and Lys) in proteins of thermophilic
Methanococcus species [34]. The positive contribution of charged amino acids are involved in
ionic interaction and salt bridge formation for higher capacity to bind surface water molecules
[
30]. Earlier in 1998, Scandurra et al. reported charged amino acids play a significant role in
protein thermostability and their higher contents make the protein robust [35]. Chan et al. (2011)
reported that the charged amino acids are accumulated in thermophilic proteins in order to
stabilize the surface exposed structures. Such amino acids form salt-bridges and ion-pairing for
improved thermostability [36]. Contrarily, the small and tiny amino acids tend to impair ionic
and hydrophobic interactions of secondary structures in thermophilic proteins [37]. It was also
found that substitution of serine with bulky and charged residues increased protein
thermostability [33]. Mizuguchi et al. (2007) corroborated these results and revealed substituting
small uncharged amino acids (such as Gln and Ser) with charged amino acids (Glu, Arg and Lys)
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enhanced protein thermostability [38]. Thus, amino acids substituted with charged amino acids
enhance the non-covalent interactions to stabilize the structures under higher temperature.
To comprehend the order of amino acid preference for thermostability, AHP was employed that
uses multi-criteria decision-making platform [39]. Here, AHP helps in prioritizing amino acid
attributes which are contributing to thermostability as it assumes mutations and their selection
parameters are empirical and balances the protein's biophysical pleiotropy under various
selection pressures [40]. Though the other most commonly used method for rational designing is
B-Factor-directed mutagenesis, it has been used to enhance thermostability of many enzymes
through substitution of rigidifying residues at the mutagenic hotspots with increased B-value in
their three-dimensional structures [41]. However, the advantage of AHP method is that it arrives
at an empirical rule to rationalize the approach for engineering mutations in vitro. This was
carried out by creation of a dataset of homologous thermophilic protein and mesophilic
counterparts, prioritizing the multiple amino acid attributes leading to protein thermostability and
development of a ranking model thereafter. The generated ranking model was trained by
enumerating the priorities (or ranks) of differentiating amino acid attributes, and used these
values to predict thermostabilizing mutations.
The priorities of amino acids for thermostability were predicted by calculating eigenvectors from
the generated reciprocal pairwise comparison matrix (Table S3). According to Saaty (2008), the
derived ranks can only be accepted as consistent if the consistency ratio (CR) of comparison
matrix is less than 0.1 [39]. In the present analysis, CR was found to be 0.0013, indicating it to
be consistent. This was followed by generation of ranking model that prioritized the amino acids
for thermostability. It showed a trend of Glu > Arg > (Lys, Chrg, Bsc, Acd) > (Ile, Val) > Pol >
(Ala, Asp, Tiny, Sml) > (His, Asn, Ser, Thr) > Cys > Gln (Fig. 2B). The results corroborated
1
6

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with the relative abundance analysis and is in accordance with earlier reports [34,42]. It depicted
that Glu had the highest priority followed by Arg and an equal contribution of Lys, charged,
basic and acidic amino acids whereas, Gln was the least preferred. De Farias and Bonato (2003)
found an interesting rule that the (Glu+Lys)/(Gln+His) ratio can differentiate between
hyperthermophilic proteins (>4.5), thermophilic proteins (3.2–4.6) and mesophilic proteins
(<2.5). This substantiated the present result where Glu and Lys (as in numerator) had higher
priorities for hyperthermophiles/thermophiles than Gln and His (as in denominator). At higher
temperatures, protein destabilization occurs due to the presence of thermolabile residues such
as Asn and Gln that are prone to deamidation and Cys that leads to oxidation and β-
elimination [43].
The performance of generated AHP ranking model was validated through two independent blind
tests for predicting thermostabilizing mutations. The comparing proteins in both the blind tests
were assigned ranks from which mean ranks were calculated. Results showed higher mean rank
value of thermophilic proteins as they had large contents of amino acids having higher priority
values. So, the rank difference of thermophilic and mesophilic proteins in the first blind test was
higher than that of second blind test that compared mesophilic-mesophilic parts (Table 1). Thus,
the performance accuracy of ranking model was calculated by first blind test and it was found to
be 96.0 %. Therefore, this ranking model could easily differentiate the thermophilic-mesophilic
protein pairs as well as mesophilic-mesophilic protein pairs. The generated model could also
empirically enumerate the weightage of individual point mutation for attaining protein
thermostability. Thus, any mutation in a mesophilic protein which leads to a rank higher than 0.5
compared to its wild-type counterpart can be predicted to be thermostabilizing.
3
.2 Prediction and validation of thermostabilizing mutations in a mesophilic enzyme
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B. subtilis 168 lipase was chosen as a model mesophilic enzyme for prediction followed by
validation of thermostabilizing mutations through AHP model. Its use for engineering has
various advantages over other proteins such as (a) smaller molecular weight, (b) well
characterized 3D structure (PDB ID: 1I6W), (c) typical α/β hydrolase fold, (d) lack of lid
domain, disulfide bonds and metal coordination in its structure, and (e) has been previously
studied for thermostabilization through different engineering approaches [44–46]. Various
combinations of mutations were tested in the lipase to substitute existing amino acids (mutational
hotspots) with those having higher order in the ranking model (Fig. 3A). They were designed at
loop and surface exposed regions not affecting any stabilizing center region(s) and active site(s)
of the enzyme. Three lipase mutants were chosen with substitution of Glu (E) and Lys (K) viz.
bsl_the1 (V149K, Q150E; AHP rank, 0.021), bsl_the2 (F41K, W42E, V149K, Q150E; AHP
rank, 0.043) and bsl_the3 (F41K, W42E, P119E, Q121K, V149K, Q150E; AHP rank, 0.062)
(Fig. 3B). Previously, Ozawa et al. (2001) reported the replacement of residues with Lys (K) and
Glu (E) that caused thermostabilization [47].
The mutant enzymes were modeled using I-TASSER server. The stability of modeled mutants
were checked by various web servers such as HotSpot Wizard, I-Mutant2, Cupsat, iPTREE-
STAB, WET-STAB and ERIS on the basis of ΔΔG value change in the mutant with respect to
wild-type.[30] The stabilizing mutations showed increased protein stability of mutant as their
ΔΔG > 0 whereas, destabilizing mutations showed ΔΔG < 0. Fig. 3C summarizes the stabilizing
mutations (represented by S) and destabilizing mutations (represented by D).
The structural superimpositions were analyzed in terms of root mean square deviation (RMSD)
[
48]. Higher the RMSD value, more is the deviation of the mutated structure from its template.
Structural superimpositions of generated lipase mutants was performed using PyMol V0.99
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showing negligible changes in RMSD (bsl_the1, 0.225 Å; bsl_the2, 0.234 Å; and bsl_the3, 0.242
Å) and has been represented in Fig. 3D. Further, docking of wild-type and mutant lipases was
performed using C8 (p-NPO) substrate in Autodock 4.2 version. The docking results indicated
enhanced stability and better substrate binding capacity of the mutant lipases. The binding
energy of substrate for each lipases was calculated which is the sum of intermolecular energy
and torsional energy [49]. The mutant lipases had slightly less binding energy (bsl_the1, -6.11
-1
-1
-1
kcal mol ; bsl_the2, -6.36 kcal mol ; and bsl_the3, -7.61 kcal mol ) than that of the native wild-
-1
type (bsl_wt, -5.99 kcal mol ). The lower binding energy of mutants depicted stable lipase
mutants and thus, the binding pocket of mutants were found to be intact [49]. Massive decrease
in inhibition constant (kI) was observed indicating higher potential of substrate for binding with
mutant lipases (Fig. 3E).
The rotations of the polypeptide backbone of wild-type and mutant lipases around their bonds
between N-C (φ) and C -C (ψ) was determined by Ramachandran plot analysis. To validate the
α
α
backbone structure of lipase variants, Ramachandran plots were generated using PROCHECK
server. Fig. 3F represents the generated Ramachandran plot for wild-type and mutant lipases and
summarizes the percentage of residues in the allowed (favoured) region. Mutation increased the
percentage of residues in the favoured region implying the stable conformation of the mutants.
Finally, contact map analysis revealed increase in the number of unique contacts in all mutant
lipases (Fig. 3G). The increased in new contacts (interactions) formed were more than the
number of contacts lost in the mutant lipases suggesting that the increased contacts may have
induced compactness in the enzyme. The increase contacts are the characteristics of thermostable
proteins since they induces compactness [50]. Thus, the generated lipase mutants have high
probability of being thermostable.
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3
.3 Recombinant production and kinetic studies of wild-type and mutant lipases
The wild-type lipase (bsl_wt) was successfully cloned into pET28a vector followed by designing
of chosen mutations in bsl_the1, bsl_the2 and bsl_the3. The mutations were carried out at
V149K, Q150E in bsl_the1; F41K, W42E, V149K, Q150E in bsl_the2; and F41K, W42E,
P119E, Q121K, V149K, Q150E in bsl_the3. The clones were confirmed through restriction
digestion analysis (Fig. S3A) and mutations by sequencing. The cloned lipase variants were
overexpressed, purified and resolved on SDS-PAGE which confirmed molecular weight at 19.4
-
1
-1
kDa (Fig. S3B) [21]. The concentrations of purified lipases were 3.4 g L for bsl_wt, 3.6 g L
-1
-1
for bsl_the1, 3.2 g L for bsl_the2 and 3.5 g L for bsl_the3. Further, kinetic parameters of
lipase variants were determined from Michaelis-Menten and Lineweaver-Burk plots (Fig. S4).
These studies revealed 1.5 fold increase in the activity of bsl_the3 followed by 1.2 fold in
bsl_the2 and 1.1 fold in bsl_the1 compared to bsl_wt. The lipase mutants also showed increased
turnover number as well as improved catalytic efficiency than of bsl_wt (Table 2).
3
.4 Effect of temperature and pH on activity and stability of lipases
The effect of temperature on wild-type and mutant lipases were determined in terms of thermal
activity and stability (Fig. 4A and 4B). The optimum enzyme activity of bsl_wt was obtained
maximum at 35 °C which was maximally increased to 55 °C in bsl_the3 followed by 50 °C for
bsl_the2 and 40 °C for bsl_the1. Previously, a thermostable mutant of Bacillus subtilis lipase
(PDB ID: 3D2C) was obtained with 9 mutations by directed evolution strategies that showed
optimum activity at 55 °C.[10] However, the optimum activity of 55 °C was achieved in mutant
bsl_the3 by only 6 mutations using present approach of pre-predicted protein engineering.
Further, all the three mutants and wild-type lipases were exploited for thermal stability analysis.
The mutant bsl_the3 showed thermal tolerance i.e. retained 70 % of its activity after an
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incubation period of 24 h, bsl_the2 retained around 50 % activity, bsl_the1 and bsl_wt retained <
3
0 % activity.
The change in pH affects the enzyme structure and thus, stability as well as activity [51]. The
effect of different pH (at 35 °C) on activity and stability of lipases were determined (Fig. 4C and
4
D). The optimum activity of bsl_wt was obtained at pH 8 that increased to pH 9 in bsl_the2 and
bsl_the3 and pH 8.5 in bsl_the1. The mutants slightly shifted their optimum activity towards
alkaline pH. Thus, the pH 9.0 was chosen for studying alkalistability of the lipases. The
maximum relative activity was retained around 65 % for bsl_the3 and 50 % for bsl_the2
incubating at pH 9.0 for 24 h. However, the bsl_the1 and bsl_wt retained activity to < 30 %.
Thus, the analysis of pH stability showed alkaline tolerance with retained activity for all the
mutant variants.
Thermal shift assay was carried out through differential scanning fluorimetry and employed to
monitor the temperature at which a protein unfolds i.e. melting temperature (T ) of the enzyme
m
[
52]. The thermal profiles of mutant lipases showed thermal shift as bsl_the3 mutant showed
highest increase in the T to 66.0 °C from 59.0 °C (bsl_wt) (Fig. 4E). The bsl_the1 (T = 60.5
m
m
°
C) and bsl_the2 (T = 63.0 °C) also showed increase in their T . The predicted mutations
m m
resulted in increased thermal tolerance leading to structural rigidity. Song et al. (2000) and Kuo
et al. (2017) independently showed that the thermostability and catalytic activity can be
improved through evolutionary molecular engineering by a significant increase in the T of
m
mutant enzyme [53,54].
3
.5 Determination of secondary structural changes in mutant lipases
In protein infrared spectrum, the amide vibrations (A, B, I to VII) were determined for the
-1
presence of peptide groups. Amide I band (1600-1700 cm ) is mainly associated with C=O
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1

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stretching in the peptide bond and modulated by the protein secondary structure. In the present
study, the amide I range in the FTIR spectrum of lipases were fitted with Gaussian curves and
the content of secondary structures such as α-helix, β-sheet, β-turn, random coil were determined
-1
-1
(
Table 3 and Fig. S5). Peak for α-helix was found around 1655 cm , β-sheet in 1620-1636 cm ,
-1
-1
β-turn in 1662-1688 cm and random coil at 1645 cm . However, the reported crystal structure
of B. subtilis lipase showed 46.93 % α-helix, 21.79 % β-sheets, 19.55 % turns and 11.73 %
random coil (PDB ID: 1I6W). These values differed in mutant lipases and showed increase in α-
helix and β-sheet by reducing the random coil that makes the lipase rigid and stable. The
bsl_the2 and bsl_the3 showed higher percentage of α-helix and β-sheet. It is attributed to the
increased hydrogen bonding between amine and carboxylic groups of the mutant lipases and
contribute to make their structures robust after mutagenesis [55].
3
.6 Screening of detergent compatible lipases
Lipase is an effective enzyme used to remove oily and greasy stains from the fabrics. Thus, they
should be tolerant to harsh detergent industry conditions [56]. The wild-type and mutant lipases
were incubated with various surfactants, oxidizing agents and commercial detergents and then
assayed under standard conditions (Fig. 5). Comparative analysis revealed bsl_the3 was the most
compatible lipase with various surfactants, oxidizing agents and commercial detergents. The
highest relative activity (%) of 100.9 ± 2.7, 113.0 ± 2.0 and 101.0 ± 1.6 was obtained with
Triton-X 100, NaClO and Ariel, respectively. Further, to improve the stability of lipases (the
bsl_the3 and bsl_wt as control), immobilization was carried out on synthesized ZnO
nanoparticles as they are cost-effective, biocompatible, non-toxic and environmental friendly.
Previously it was reported that ZnO nanoparticles have compatibility with textile fabrics as they
are UV-blocking, self-cleaning and have anti-bacterial properties [57]. This suggests that the use
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2

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of lipase immobilized ZnO nanoparticles in laundry detergent formulation for removal of stains
from fabrics can be a promising approach.
3
.7 Characterization of the as-synthesized and lipase immobilized ZnO nanoparticles
The most prevailing strategies of enzyme stabilization for any commercial application requires
coupling of site-directed mutagenesis and chemical modification mediated enzyme
immobilization [58]. The present study also aimed at immobilizing the engineered lipases on
chemically modified ZnO nanoparticles with GLU, PEI and APTES to further improve their
catalytic performance, storage stability and reusability. ZnO nanoparticles was synthesized using
ZnSO as precursor and immobilized with bsl_wt and bsl_the3. Their morphologies were
4
characterized through FESEM (at 100 nm) and TEM (at 20 nm) analysis (Fig. 6A-F). The
analyses showed flower or star-like morphology with multi-pod structures that consisted of 4-6
pod-like structures attached together in the center of ZnO crystal seed with the sharp part at the
end of the branches. TEM analysis showed highly dense ZnO nanoparticles due to the increase in
the concentration of precursor thereby increasing the growth and density of ZnO nanoparticles
[
59]. From the morphological data, it can be inferred that it may be due to Ostwald ripening
phenomenon that the small crystals dissolved and deposited on larger particles surface leading to
lower energy levels. The crystallization of the star-like structures may be due to (i) joining of the
nuclei and generation of the symmetrical form in the primary steps of the growth process; (ii)
agglomeration of the grown particles and formation of the ordered structure in the next steps of
the crystallization; and (iii) alignment of the particles creating the flake-like pieces through the
particle coalescence. Similar morphology of ZnO nanoparticles was previously reported in many
literatures and these patterns exists due to the more stability in symmetric configurations [60,61].
Further, laser diffraction determined the particle hydrodynamic diameter and the particle size
2
3

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distribution of as-synthesized and lipase immobilized ZnO nanoparticles in the suspension (Fig.
6
1
G-I). The average hydrodynamic diameter of the as-synthesized ZnO nanoparticles was
57.4±8.5 nm with polydispersity index (PdI) of 0.176. The analyzed size of nanoparticles was
based on their lengths [62]. It is in close agreement with earlier literature [63,64]. In TEM
micrographs, the presence of nanoparticle aggregates could be clearly observed, which further
supports the larger hydrodynamic diameter obtained from DLS [65,66].
The morphology and size distribution of bsl_wt and bsl_the3 immobilized ZnO nanoparticles
were also studied using FESEM, TEM and DLS. The functionalized ZnO nanoparticles carries
free NH -groups at their surfaces due to coating of PEI and APTES which was exploited for
2
covalent immobilization of lipases via their surface NH -groups (Lys and Arg) using
2
glutaraldehyde as cross-linking agent. This immobilization strategy is very versatile and rapid as
glutaraldehyde utilizes NH -groups of PEI and APTES present on functionalized ZnO
2
nanoparticles at low ionic strength which can cause effective covalent immobilization of lipases
on the NH -active support through equilibrium shift [67]. In fact, glutaraldehyde are the small
2
molecules which can cause effective intermolecular cross-linking of lipases with the NH -active
2
support if increased lipase concentration is provided. This can lead to increased immobilization
rate, activity and stability of lipases [27]. The possible mechanism behind the crosslinking of
lipase and NH -active support via glutaraldehyde under alkaline conditions is through the
2
formation of Schiff base linkages. This results into the formation of C–N bond between
glutaraldehyde and amino groups of lipases or NH -active support through Michael addition
2
[
17]. The analysis in the present work showed lipase immobilization on to the as-synthesized
ZnO nanoparticles that was clearly visible as the traces of protein deposited on their surfaces.
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4

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However, occasionally, the immobilized enzyme shows reduced stability, conformational
change, inactivation and aggregations due to changes in the surrounding microenvironment and
extent of multipoint attachment [68]. Thus, in such cases, for instance in lipases, the better
efficiency after immobilization can be achieved by low ionic strength environments that reduces
the lipase aggregation and cause multipoint immobilization through hydrophobic surfaces
surrounding their catalytic site, as their activity greatly increases at the lipid-water interface by a
phenomenon called interfacial activation. Recently, Arana-Peña et al. (2019) developed a novel
strategy to reuse the most stable lipase by co-immobilizing different lipases on glyoxyl-octyl
agarose via interfacial activation [69].
Upon covalent binding of the enzyme, the size and shape of the nanoparticles almost remained
unchanged. It was observed that the surface of the nanoparticles was porous in nature. The pores
and cracks may be attributed to the agglomeration of nanoparticles on the surface as reported
earlier [25,70]; DLS analysis showed average size of 208.0±6.1 nm (PdI = 0.221) in ZnO-bsl_wt
and 238.6±6.9 nm (PdI = 0.252) in ZnO-bsl_the3. The increase in the average size of as-
synthesized ZnO revealed lipases were successfully immobilized on the nanoparticles. Next,
from the diffraction rings of SAED pattern (inset of Fig. 6D-F), concentric rings indicated highly
crystalline nature as well as (101) and (103) planes of the ZnO nanoparticles were identified
which is in good agreement with the XRD results discussed in the proceeding section.
The optical property of the as-synthesized and lipase immobilized ZnO nanoparticles was
examined through UV-Vis spectroscopy (Fig. 7A). The as-synthesized ZnO nanoparticles
maximally absorbed the UV radiations at 372 nm and almost all the visible spectrum radiations
were transmitted. The optical band gap energy has been calculated by 퐸(푒푉) = 1.ꢍꢎ98/휆(휇푚)
and was found to be 3.33 푒푉. The ZnO-bsl_wt and ZnO-bsl_the3 exhibited an additional protein
2
5

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characteristics absorbance peak at ~280 nm (aromatic ring of amino acids) and ~220 nm (peptide
bonds) with a featured characteristic peak of ZnO at 375 nm and 377 nm. This confirmed the
immobilization of bsl_wt and bsl_the3 on to the ZnO nanoparticles [71]. The calculated band
gap energy was 3.30 푒푉 for ZnO-bsl_wt and 3.29 푒푉 for ZnO-bsl_the3. Thus, slight red shift
behaviour in the band gap was observed upon lipase immobilization. It leads to quenching the
electrons in the conduction band in case of lipase immobilized ZnO nanoparticles [72]. While
some authors reported blue shifting due to unfavorable interactions of dipole moments of polar
solvent molecules with fluorophores of enzyme under excited state [70], others have observed
red shifts due to the reduced nature of enzymes which resulted in the reduction of surface states
(pre-adsorbed oxygen) of the ZnO nanoparticles [73],
The synthesis and immobilization of ZnO nanoparticles was also investigated by FTIR analysis
–
1
(
Fig. 7B). The FTIR spectra indicated the typical ZnO peaks beyond 600 cm in both as-
synthesized and lipase immobilized ZnO nanoparticles suggesting the presence of ZnO
−
1
material.[28] The stretching peak of -OH groups was noticed around 3600 - 3200 cm indicating
their presence of water molecules on the surface of ZnO nanoparticles. The ZnO-PEI showed
–
1
–1
strong twin peaks at 1636 and 1044 cm and a broad peak in the 3200 - 3450 cm region. The
−
1
glutaraldehyde-activated ZnO-PEI-APTES showed peaks in 1035 - 1050 cm which could be
assigned to Si-O stretching vibration.[70] Most characteristic bands ranging from 1350 - 1750
–
1
cm were associated in ZnO immobilized lipases which were due to amide bonds (C-N, C=O
and N-H groups) in the protein. This range mainly comprised of amide I (stretching vibration of
C=O), amide II and amide III (both associated with coupled C–N stretching and N–H bending
vibrations of the peptide group) that are the main signal for the identification of protein
secondary structures [28]. The peaks of amide bonds reflect the structure of the main polypeptide
2
6

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chain irrespective of side groups. The amide bond linking the PEI generally comes in the near
–1
region of 1600 cm . In the present spectra, it was overlapped with the N-H bending mode of
−1
−1
ZnO immobilized lipases. The peaks from 1500 to 1700 cm and 2500 to 2900 cm represents
the functional groups corresponding to C-O stretching and C-H stretching mode, respectively.
These additional peaks were further specific to nanoparticle functionalization and lipase
immobilization.
According to the XRD spectrogram of the as-synthesized ZnO, the well-defined peaks of typical
ZnO was clearly noticed (Fig. 7C). The as-synthesized ZnO showed the diffraction peaks located
at 31.84°, 34.52°, 36.33°, 47.63°, 56.71°, 62.96°, 68.13°, and 69.18°. All the peaks of XRD were
matched with the wurtzite phase of ZnO by comparison with the data from reports on powder
diffraction standard ICDD card number 98-010-8249 [74]. The particle size was computed using
Debye-Scherrer formula (D = 0.9λ / β cos θ) and the FWHM (full width at half maximum) value
corresponding to the major plane (101), was ~18 nm. Interestingly, lipase immobilized ZnO
nanoparticles showed peaks at same locations but their intensity decreased significantly due to
loss of crystalline structure of ZnO nanoparticles after immobilization. Also, the XRD pattern for
as-synthesized ZnO nanoparticles showed much sharper peaks indicating more crystalline nature
of than ZnO-bsl_wt and ZnO-bsl_the3. Peak broadening of lipase immobilized ZnO
nanoparticles indicated a lattice distortion due to the enzyme introduction. Increase in the
intensity of (002) peak upon enzyme loading further suggested preferred unidirectional growth.
The compactness and clear resolution of the peaks towards lower angles suggested ordered and
compact enzyme layer on the surface of nanoparticles [70].
3
.10 Kinetic and physicochemical parameters of ZnO immobilized lipases
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The kinetics and physicochemical parameters of immobilized enzymes are influenced by the
carrier material [75]. Thus, factors like immobilization efficiency, immobilization yield,
optimum activity, etc. were determined for ZnO-bsl_wt and ZnO-bsl_the3 (Table 4). ZnO-bsl_wt
and ZnO-bsl_the3 showed immobilization efficiency of 82.5 % and 88.5 %, respectively. The
lipase activity of immobilized enzymes were estimated by calculating immobilization yield. It
revealed that 71.9 % activity was retained in ZnO-bsl_wt and 79.5 % in ZnO-bsl_the3. The
decrease in the activity after immobilization may be contributed to the covalent binding of the
lipases onto the ZnO nanoparticles [76]. Further, the optimum temperature and pH were
investigated for immobilized lipases at various temperatures (10 °C to 70 °C) and pH (pH 3 to
pH 12). Further, post immobilization, the ZnO-bsl_wt activity was enhanced to 40 °C, pH 8.5
and ZnO-bsl_the3 to 60 °C, pH 10. This covalent immobilization of lipases with ZnO
nanoparticles also reduces conformational flexibility leading to higher activation energy for the
enzymes to reorganize proper conformation of substrate binding site thereby enhancing activity
under high temperature and pH [77].
3
.11 Reuse and storage stability of ZnO immobilized lipases
The major limitations of enzymes in industrial process are their reusability in continuous
reactions. The ZnO immobilized lipases can easily be separated from the reaction mixture
through centrifugation and subsequently can be reused for hydrolysis of the substrate. The
reusability of ZnO-bsl_wt and ZnO-bsl_the3 were assayed over 20 reaction cycles under
optimized conditions. Relative activity of the first run was set to 100% and the retention activity
for further reuses were determined (Fig. 8A). ZnO-bsl_wt and ZnO-bsl_the3 retained 38 % and
7
8 % of its initial activity after 20 cycles. The decrease in the activity of immobilized lipases
after recycling was either due to weakening of binding strength between ZnO matrix and lipases
2
8

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leading to enzyme leaching or the recurrent encountering of substrate with the active site of
immobilized lipases causing distortion and loss of activity [78].
The storage stability of the free (bsl_wt and bsl_the3) and immobilized lipases (ZnO-bsl_wt and
ZnO-bsl_the3) were determined at room temperature and their catalytic activities were evaluated
till 60 days at an interval of 4 days. Both the immobilized lipases showed improved storage
stability (Fig. 8B). ZnO-bsl_wt and ZnO-bsl_the3 retained 63 % and 80 % of its initial activity
whereas the free lipases could retain less than 30% of the initial activity. Nevertheless, the
immobilization analysis of ZnO-bsl_wt and ZnO-bsl_the3 did not show a large difference on
storage stability but more in reusability. It may be due to early leaching of bsl_wt than bsl_the3
from the solid matrix during its reuse. This is because mutant lipase (bsl_the3) has three surface
exposed Lys substitutions at F41, P119 and V149 which may be responsible for increased
multipoint attachments of mutant with functionalized nanoparticles. However, immobilization
significantly prevented enzyme deactivation and subsequently improved the storage stability as
well as reusability of the mutant enzyme [79].
3
.12 Compatibility of immobilized lipases as detergent additive for destaining of fabric
The ZnO-bsl_wt and ZnO-bsl_the3 were examined for compatibility with various surfactants,
oxidizing agents and commercial detergents (Fig. 9A). The activity of ZnO-bsl_wt was
considered as control (without any detergent additives) with 100 % activity. The relative activity
of ZnO-bsl_the3 gave better performance with detergent additives and showed better
compatibility with Ariel. Therefore, Ariel was used for oil and grease destaining experiments and
its indigenous lipases was thermal deactivated prior to its use for destaining. Earlier reports
showed that the removal of oily and greasy stains was enhanced using commercial detergent
when used indigenously with lipase of Pseudozyma sp. NII 08165 and Pseudomonas aeruginosa
2
9