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\begin{document}

\title{An effective computational-screening strategy for simultaneously
improving both catalytic activity and thermostability of
\selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase\selectlanguage{english}}



\author[1]{Lijun Li}%
\author[1]{Wenjing Li}%
\author[1]{Jianye Gong}%
\author[2]{Yanyan Xu}%
\author[1]{Zheyu Wu}%
\author[1]{Zedong Jiang}%
\author[3]{Yi-sheng Cheng}%
\author[2]{Qingbiao Li}%
\author[1]{Hui Ni}%
\affil[1]{Jimei University}%
\affil[2]{Xiamen University}%
\affil[3]{National Taiwan University}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Catalytic efficiency and thermostability are the two most important
characteristics of enzymes. However, it is always tough to improve both
catalytic efficiency and thermostability of enzymes simultaneously. In
the present study, a computational strategy with double-screening steps
was proposed to simultaneously improve both catalysis efficiency and
thermostability of enzymes; and a fungal \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase was used to
validate the strategy. As the result, by molecular docking and sequence
alignment analysis within the binding pocket, seven mutant candidates
were predicted with better catalytic efficiency. By energy variety
analysis, three among the seven mutant candidates were predict with
better thermostability. The expression and characterization results
showed the mutant D525N had significant improvements in both enzyme
activity and thermostability. Molecular dynamics simulations indicated
that the mutations located within the 5 \selectlanguage{ngerman}Å range of the catalytic domain,
which could improve RMSD, electrostatic, Van der Waal interaction and
polar salvation values, and formed water bridge between the substrate
and the enzyme. The study indicated that the computational strategy
based on the binding energy, conservation degree and mutation energy
analyses was effective to develop enzymes with better catalysis and
thermostability, providing practical approach for developing industrial
enzymes.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{1. Introduction}

Natural enzymes usually only evolve relevant catalytic performance
according to their own needs. When enzymes are used in industry, methods
are needed to be explored to tailor their activity towards industrially
relevant substrates, and these enzymes should also be optimized towards
industrial reaction conditions.\textsuperscript{1} To improve the
production in industry which involves biocatalysts, increasing activity
of enzymes for specific substrates is the
key.\textsuperscript{2}Besides, given that high temperatures in
industrial processes including reaction, purification, packaging etc.
provide benefits such as increased substrate solubility, improved
diffusivity, decreased viscosity of the medium, and a lower risk of
microbial contamination, thermostability of enzymes is another important
property.\textsuperscript{3, 4}

Enzymes obtained from natural recruitment and protein engineering have
greatly contributed in various sets of applications. Over recent
decades, the newly developed methods in the protein engineering,
including directed evolution, semi-rational design, and \emph{de
novo}design, have enabled to obtain numerous better enzymes for the
industrial application.\textsuperscript{5-7} The catalytic activity and
thermostability of many enzymes has been improved by directed evolution.
By screening the triple mutant C168T/Q192H/Y7L with error-prone PCR and
site-saturation, the thermostability and enzyme activity of GH11
xylanase from \emph{Aspergillus fumigatus} RT-1 were
improved.\textsuperscript{8} Lin et al. identified a N255D mutant by
random mutagenesis with 14-fold higher activity than the wild type
Horseradish Peroxidase.\textsuperscript{9} Yin et al. also constructed a
mutation library with error-prone PCR. In this library, three
five-linked mutants Bgl1D2, Bgl1D6 and Bgl1D20 stands out to have
2.3-2.6 times higher hydrolytic activity, while only Bgl1D2 becomes more
stable. It has seven times higher thermostability, whereas Bgl1D6,
Bgl1D20 shows no significant change in thermostability compared with the
wild type.\textsuperscript{10}

Despite of the success of the above trials, directed evolution is
unfavorable when one considers the experimental resources it requires
for mutants in a large library. Semi-rational design of proteins deals
with this problem by introducing bioinformatics to rationally reduce the
size of the mutant's library. At present, the catalytic activity and
thermostability of enzyme are mainly improved by semi-rational design.
Generally, the non-conservative amino acids around the active site may
be related to the catalytic performance of the enzyme, and hence the
catalytic activity of enzymes can be improved by changing the
non-conservative amino acids.\textsuperscript{11} Moreover, based on the
correlation between thermostability of proteins and factors such as
hydrophobicity, packing density,\textsuperscript{12, 13} number of
disulfide bonds,\textsuperscript{14} strength of electrostatic
interactions,\textsuperscript{15, 16} length of surface
loops,\textsuperscript{13} conformational rigidity,\textsuperscript{17,
18} amino acid coupling patterns,\textsuperscript{19} and local
structural entrop,\textsuperscript{20} bioinformatics software is
generally developed to design proteins with good thermostability using
proline theory,\textsuperscript{21}
B-fitter,\textsuperscript{18}Rosetta,\textsuperscript{22} molecular
dynamics simulations\textsuperscript{23} and disulfide by design et
al\textsuperscript{24}.

Notably, among these semi-rational methods, mutants which have been
designed to show improved thermostability, all display lower enzyme
activity,\textsuperscript{25, 26} and vice versa.\textsuperscript{11} It
is reasonable when we noticed the contradiction in the adjustment of
protein structures between the two design strategies. Specifically, high
catalytic activity was often obtained by reducing their surface
hydrophobicity and hence increasing the flexibility of the
structure,\textsuperscript{27-29} while enzymes with good stability were
designed by making the structure more rigid, which involves enhancing
their surface hydrophobicity.\textsuperscript{30-31} Thus, it is
impossible to simply combine these strategies to design enzymes with
both properties improved. Given that the high catalytic activity and
good thermostability are both related to reduced costs, and vice versa,
when one considers to optimize the total costs, which is often the case
in the industry, new effective semi-rational design method is needed to
be developed to simultaneously improve the catalytic activity and
thermostability of enzymes.

To fill this need, here we proposed a double-screening strategy to
obtain mutants with both properties improved based on compactional
analysis and prediction of enzyme properties. Firstly, given that the
non-conservative amino acids around the active site is related to the
catalytic performance of the enzyme, mutation on the non-conserved
residues in the catalytic region could bring potential higher activity.
These mutants could be further screened virtually to select mutants with
favorable heat stability. In this way, a relatively small library of
mutants of potential simultaneous higher activity and thermostability
could be further constructed and tested experimentally. Thus, not only
does this strategy overcome the disadvantages of directed evolution,
i.e. too large a library to do experiments, but also consider the
shortcoming of the current semi-rational methods, i.e. limit the design
too much on the local structure without considering the global effect.
We tested this strategy on an extensively investigated enzyme in our
lab, \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase.

\selectlanguage{greek}α-\selectlanguage{english}L-Rhamnosidase is a glycoside hydrolase, which can effectively
hydrolyze the rhamnose group at the end of most glycosides. It is widely
used in the debittering of citrus juices,\textsuperscript{32,
33}improving the aroma components of beverages.\textsuperscript{34, 35}
To date, only 29 \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidases have been biochemically
characterized, and six of them, namely \emph{Bs} RhaB (PDB entry 2OKX),
\emph{Bt} 1001 (PDB entry 3CIH), \emph{Sa} Rha78A (PDB entry 3W5M),
\emph{Ko} Rha (PDB entry 4XHC), \emph{At} Rha (PDB entry 6GSZ), and
\emph{Dt} Rha (PDB entry 6I60) have been illustrated in crystal
structures in GH78 family.\textsuperscript{36-40} In previous studies,
we cloned and expressed \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase (Rha1) from \emph{Aspergillus
niger}JMU-TS528,\textsuperscript{41} which belongs to the GH78 family in
the CAZy database. Simultaneously, we applied semi-conservative
site-directed mutagenesis on the catalytic domain to increase the enzyme
activity of Rha1.\textsuperscript{42} We also used two different methods
to improve thermostability of Rha1, PoPMuSiC algorithm and
lysine-arginine mutation on the surface of rRha1.\textsuperscript{43,
44} In this study, we applied the strategy mentioned above to improve
the catalysis efficiency and thermostability simultaneously.
Specifically, we computationally predicted the mutation in the catalytic
region of Rha1 with the potential to improve the catalysis efficiency
and thermostability by the aid of molecular docking and conservation
degree and energy variation analysis. The predicted mutants were then
expressed and validated in the enzymatic activity and thermostability.
This is the first semi-rational design to improve catalysis efficiency
and thermostability simultaneously of enzymes, which could be helpful to
effective design \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidases and other important enzymes in the
industry.

\textbf{2. Results and discussion}

\textbf{2.1 Compactional screening the mutant candidates with better
catalysis efficiency and thermostability}

\selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidases, a glycoside hydrolase, exhibit a low sequence
identity at only 20\%--30\%, but share a similar (\selectlanguage{greek}α/α\selectlanguage{english})6-barrel catalysis
domain and several \selectlanguage{greek}β-\selectlanguage{english}sandwiches.\textsuperscript{36-40} Six crystal
structures (PDB: 2OKX, 3W5M, 3CIH, 4XHC, 6GSZ and 6I60) from the GH78
family have been determined so far. By homology modelling with the
crystal structures of \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidases 2OKX, 3W5M, and 4XHC as
templates, the structure of r-Rha1 was built and used for our research.
In order to find out how the non-active-site residues of
\selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase contribute to the catalytic efficiency, bioinformatics
analysis was performed. Specifically, docking between r-Rha1 and its
substrate \emph{p} NPR were performed and the interaction between them
could offer a guide for site-directed mutagenesis. For example, Lu et
al.\textsuperscript{45} have reported that some amino acids identified
from protein docking play key roles in catalysis. Bernardi has reported
that hydrogen bonds involved amino acids around the catalytic domain
contribute most to the contact between enzyme and substrate, which could
be mutated to adjust enzymatic activity.\textsuperscript{46} In the
binding mode of r-Rha1 and substrate \emph{p} NPR (Fig.1A),9 residues
(Trp\textsuperscript{253}, Tyr\textsuperscript{293},
Thr\textsuperscript{301}, Val\textsuperscript{302},
Ser\textsuperscript{303}, Ala\textsuperscript{355},
Ser\textsuperscript{356}, Asp\textsuperscript{525},
Trp\textsuperscript{640}) of r-Rha1 were located around \emph{p} NPR
(Fig, 1B). Notably, some of these residues could be significant for
enzyme activity and should not be mutated. According to structure of
bound L-rhamnose from Fujimoto\textsuperscript{37}, which was sandwiched
between two aromatic residues, Trp\textsuperscript{640} and
Trp\textsuperscript{747}, the corresponding residues in r-Rha1,
Trp\textsuperscript{253} and Trp\textsuperscript{640}, are such
essential residues and should not be modified in site-directed
mutagenesis. An amino acid sequence alignment of the available
\selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidases from different sources revealed that, r-Rha1 shared a
low similarity with other \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidases (Figure S1), but the
residues in the catalytic domains are well conserved (Table 1). The
model of r-Rha1 exhibited that the side chains of
Tyr\textsuperscript{293} and Trp\textsuperscript{253} were located near
the substrate binding loop which were important in the binding of the
substrate. In the predicted catalytic site, the aromatic rings of
Tyr\textsuperscript{293} and Trp\textsuperscript{253} were parallel to
the ring of rhamnose and presumably play roles in fixing the substrate
through the pi-pi stacking interaction, which is consistent with what Xu
et al \textsuperscript{47} have showed, i.e. hydrophobic residues were
located around the catalytic pocket. As a consequence,
Trp\textsuperscript{253}, Tyr\textsuperscript{293} and
Trp\textsuperscript{640} should be excluded for mutagenesis. Thus, the
rest residues identified from the molecule docking,
Thr\textsuperscript{301}, Val\textsuperscript{302},
Ser\textsuperscript{303}, Ala\textsuperscript{355},
Ser\textsuperscript{356}, Asp\textsuperscript{525}, were selected for
mutation to test the effect on its catalytic activity. Particularly, 14
mutations, T301S, T301Q, T301G, V302S, V302A, V302N, S303V, S303G,
A355N, A355G, S356I, S356Y, D525N, D525G were designed, according
results of sequence alignment (Table 1).

To filter out mutants with high thermostability, the thermostability of
14 mutants were evaluated by mutation energy (stable) module of
Discovery studio 2019 at 60, 65, 70, 75, 80 \selectlanguage{ngerman}ºC, and the calculation
results were shown in Fig 1C. As a result, seven mutants were found to
be stable at 60, 65, 70, 75, 80ºC. They are D525N, S356Y, D525G, S356I,
A355N, S303V, V302N, with the stability from high to low in sequence.
While the structure of mutant T301Q, T302S, T301S, V302A, S303G, A355G,
T301G were unstable at different temperatures, it means the seven
mutants have lower thermostability (see Table S2 for detailed data).
Therefore, we obtained seven mutants D525N, S356Y, D525G, S356I, A355N,
S303V, V302N through two rounds of screening, which may have both high
enzyme activity and good thermostability, and could be verified
experimentally.

\textbf{2.2 Expression and characterization of mutant candidates with
better catalysis efficiency and thermostability}

The seven mutated enzymes were obtained by site-directed mutagenesis and
further expressed in \emph{Pichia pastoris.} Similar to the wild type
(recombinant Rha1, named as r-Rha1), the purified mutant enzymes showed
a molecular mass of approximately 100 kDa (Fig. S2). Fig. 2A showed the
relative hydrolytic activities of the mutants compared with the wild
type r-Rha1. The mutants V302N, S303V, S356I and D525G show lower
hydrolytic activities compared to r-Rha1. On the contrary, the
hydrolytic activities of A355N, S356Y and D525N were noticeably enhanced
by 45\%, 80\% and 180\% than that of r-Rha1, respectively. To further
investigate the effects of A355N, S356Y and D525N on the catalytic
machinery of enzyme, the kinetic behaviors of A355N, S356Y and D525N
on\emph{p} NPR were evaluated. As shown in Table 2, both\emph{K}
\textsubscript{m} and \emph{k} \textsubscript{cat} values of A355N,
S356Y and D525N were lower than the values of r-Rha1, whereas\emph{k}
\textsubscript{cat}/\emph{K} \textsubscript{m} values of all three
mutants were increased. The mutant D525N showed the highest hydrolytic
activity and catalytic efficiency among the mutants and r-Rha1.
Specifically, D525N increased 2.85-fold of hydrolytic activity, and
1.54-fold of \emph{k} \textsubscript{cat}/\emph{K}
\textsubscript{m}value compared to the wild type r-Rha1.

To gain a deeper insight of the mutagenesis effects of A355N, S356Y and
D525N on the characteristics of enzyme, the optimum temperatures and
thermostabilities were examined. Similar to wild type r-Rha1, the
optimum temperatures of three mutants were 60ºC (Fig. 2A). While the
mutants A355N and S356Y showed markedly weaker thermostabilities than
r-Rha1 (Fig. 2C), mutant D525N exhibited slightly stronger
thermostability than r-Rha1. When the thermostabilities of r-Rha1 and
its mutants were tested at 60ºC, 65ºC and 70ºC, the half-lives of
D525Nare shown to be 42 min, 2 min and 1 min longer
(T\emph{\textsubscript{1/2}} ) than r-Rha1(Fig. 2B(b-d)), respectively.
Thus, D525N was identified to have both improved thermostability as well
as increased enzyme activity, which demonstrates our screening method is
helpful in identifying mutants both improved enzyme activity and
favorable thermostability.

\textbf{2.3 Secondary structural characteristics of mutants with better
catalysis efficiency and thermostability}

In order to further explore the reasons why mutants A355N, S356Y and
D525N with improved enzyme activity have two different characterizations
for thermostability, far-UV CD was used to investigate the effect of
site-directed mutations on protein structure. The result showed that the
CD spectra of both WT and mutants were nearly 100\% superimposed (Table
S2). As showed in Fig. S3, both r-Rha1 and three mutants presented a
negative peak at 205-245 nm a typical feature for \selectlanguage{greek}α-\selectlanguage{english}helices, where the
strong positive peak at 185-195 nm represented a typical signature of
\selectlanguage{greek}β-\selectlanguage{english}sheets. The secondary structure of r-Rha1 and the mutants A355N, S356Y
and D525N were calculated based on the CD spectra. Slight decreases of
\selectlanguage{greek}α-\selectlanguage{english}helix and \selectlanguage{greek}β-\selectlanguage{english}turn were observed in the three mutants, whereas \selectlanguage{greek}β-\selectlanguage{english}sheet
and random were increased. Specifically, the content of \selectlanguage{greek}α-\selectlanguage{english}helix of WT,
A355N, S356Y and D525N was 27.5\%, 25.3\%, 25.3\% and 25.7\% while the
content of \selectlanguage{greek}β-\selectlanguage{english}sheet was 26.6\%, 27.6\%, 29.7\% and 28.6\%, respectively.
Meanwhile, the \selectlanguage{greek}β-\selectlanguage{english}turn component decreased by 0.7\%, 0.4\% and 0.6\% in
mutants A355N, S356Y and D525N, respectively. In Fig. S4,
the\emph{T\textsubscript{m}} value of the mutant A355N was almost
unchanged compared to the wild type: Tm of S356Y was 0.73 times that of
wild type while D525N was 1.1 times. This is consistent with what Mohd
et al\textsuperscript{48} have showed, i.e. the substitutions of one or
a few amino acids, which lead to the improvements in thermostability and
optimum temperature of the mutants did not cause a substantial change in
the secondary structures of the enzymes. This means that the change of a
single amino acid does not cause major changes in the secondary
structure, and the difference of thermostability among these three
mutants could not be attributed to the change of the secondary
structure.

\textbf{2.4 Computational analyses of inner structure and molecular
interaction of mutants with better catalysis efficiency and
thermostability}

\textbf{2.5.1 MD and conformational stability analysis}

Based on the aforementioned experimental analyses of A355N, S356Y and
D525N, MD simulations were performed on these three mutants to analyze
their structural changes. The root mean squared deviation (RMSD) is the
essential parameters to validate the quality of MD simulation. In the
present study, the stability of wild type r-Rha1 and its mutants were
determined by the deviations produced during the course of MD
simulations. As illustrated in Fig. S5, r-Rha1 reached equilibrium after
4 ns of MD simulation, with a RMSD value of 0.5 \selectlanguage{ngerman}Å, while A355N reached
equilibrium after 11 ns, with a RMSD value of about 0.8 Å, and S356Y and
D525N reached equilibrium after 5 ns, with a RMSD value of about 0.7 Å.
The RMSD is used for measuring the differences between the backbones of
a protein from its initial structural conformation to its final
position.\textsuperscript{46} The higher RMSD values of three mutants
than that of r-Rha1 suggested that mutants A355, S356Y and D525N can
influence the backbone stability of the enzyme. A similar observation
was reported earlier for several systems, including glycoside hydrolases
and other enzymes. \textsuperscript{49, 50}

\textbf{2.5.2 Binding free energy analysis}

To gain more insights of the effects of point mutations on the
interactions between \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase and substrate, the binding free
energies and individual energy components were calculated by using
MM-GBSA method. The results showed that A355N, S356Y and D525N mutations
enhanced the binding affinity between \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase and substrate,
which were consistent with our experimental results. Comparing the
individual components contributing to the binding free energy (Table 3),
it can be concluded that the electrostatic and Van der Waal interaction
contributed the most in the changes of the binding strength of A355N and
S356Y, while in the case of D525N, besides electrostatic and Van der
Waal interaction, polar salvation also plays a significant role.
Overall, MM/GBSA binding free-energy analyses corroborated perfectly
with the outcome of molecular docking and dynamics analyses, and
revealed paramountly lower binding and energy for optimum activity
temperature which indicated stably mutate enzyme substrate
complex.\textsuperscript{51, 52}

\textbf{2.5.3 Interaction analysis}

Interaction analysis of the conformations in the simulations of wild
type and the mutant shows that when the alanine at position 355 was
mutated to asparagine (Fig.3A and 3B), the amino acid side chain of
asparagine was extended. Due to the effect of steric hindrance,
Ser\textsuperscript{356} was pushed forward. Moreover, water bridge was
formed between substrate \emph{p} NPR and Ser\textsuperscript{356},
which strengthens the hydrogen bond interaction between the enzyme and
the substrate, thereby increasing the activity of the enzyme on the
substrate. Thus, these results were in conformity with the results of
the docking MD simulation studies of previous reports, in which the
formation of hydrogen bonds increases the activity of the enzyme on the
substrate.\textsuperscript{46, 52} When the serine at position 356 was
mutated to tyrosine (Fig.3C and 3D), a \selectlanguage{greek}π-π \selectlanguage{english}stacking was formed between
the phenyl of tyrosine and the nitrophenyl ring of the substrate\emph{p}
NPR, which strengthens the interaction force between the enzyme and the
substrate, and bring out improved enzyme activity. The correlation
between the pi-pi stacking and the strength of the substrate-enzyme
complexes is also seen in a coplanar mu-S bridged
1,10-phenanthrolinepalladium(II) dinuclear complex. \textsuperscript{53}

As for mutant D525N, when aspartic acid at position 525 was mutated to
asparagine, as shown in Figure 3E and 3F, i.e. the acidic negatively
charged amino acid was mutated to a polar uncharged amino acid, the
negative charge in the vicinity of Asn\textsuperscript{525} suddenly
changed to positive charge. The strong change of local environmental
charge led to a large change in the enzymatic properties, and could
justify the increase of both enzyme activity and thermostability, as
results from Naito et al.\textsuperscript{54} that the change in the
charge of the catalytic domain led to the increase of the activity and
thermostability of the enzyme.

Thermostable enzymes with favorable catalytic efficiency are very
limited in nature and hard to obtain. Therefore, how to effectively
improve the thermal stability and catalytic efficiency of enzymes has
always been a hot issue in research. In most literature
reports,\textsuperscript{28-31, 55} it is shown that the increased
flexibility of the enzyme structure can improve the catalytic activity
of the enzyme, and the increased rigidity of the enzyme structure can
improve the thermostability of the enzyme. Based on the similar
designing ideas, Sniha et al\textsuperscript{28}, Humer et
al\textsuperscript{25} and Ashraf et al\textsuperscript{26} have
demonstrate that it is impossible to use the corresponding rational or
semi-rational design to achieve the goal of enzyme engineering, that is,
to improve the catalytic activity and thermostability of the enzyme at
the same time. Here, instead of adjusting the global rigidity, we
selected mutation sites in the catalytic domain near the substrate
binding, modifying only the local rigidity without changing the overall
structure of the enzyme, and used a double screening strategy,
successfully obtaining mutants with improved catalytic activity as well
as thermostability. By using the semi-rational design procedure, the
mutation at Asp\textsuperscript{525} , which is located within the range
of 5 \selectlanguage{ngerman}Å of the catalytic domain bound to the substrate (Fig. 1A and 1B),
were shown effective in simultaneous improvement of the catalysis
activity and thermal stability. Th results indicate the new
semi-rational strategy is very effective to improves both enzyme
activity and thermostability. In addition, our study suggests that the 5
Å range around the substrate binding site is the effective candidate to
conduct protein engineering mutation for improve enzyme activity and
thermal stability.

\textbf{Conclusion}

In this study, we designed a computational strategy with
double-screening step for the first time, with the attempt to develop
enzymes with increased catalysis activity and thermostability. The
fungal \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase was used to validate the strategy. First,
through molecular docking and sequence alignment, seven mutant
candidates, i.e., D525N, S356Y, D525G, S356I, A355N, S303V and V302N
were predicted with improved catalysis efficiency. Furthermore, three of
the seven mutant candidates were predicted with better thermostability
by mutation energy (stable) analysis. By enzyme expression and
characterization analysis, the mutant D525N among the three candidates
was confirmed with improved catalysis efficiency and thermostability.
Moreover, microstructure analysis in MD simulations revealed the
mutation D525N was located within the range of 5 \selectlanguage{ngerman}Å of the catalytic
domain, improving RMSD, electrostatic, Van der Waal interaction and
polar salvation values, and forming water bridge between the substrate
and the enzyme. These results not only provide an effective strategy for
developing excellent enzymes for industrial applications, not only add
the theoretical basis for enzyme engineering.

\textbf{3. Materials and Methods}

\textbf{3.1 Strains, plasmids, reagents and substrates}

\emph{Escherichia coli} strains DH5\selectlanguage{greek}α \selectlanguage{english}were used for propagation and
manipulation of plasmids, and \emph{P. pastoris} SMD1168 was used for
protein expression. The plasmids pPIC9K were used for gene cloning and
expression, respectively. The tool enzymes of \emph{Sal} I,
T\textsubscript{4} DNA ligase, \emph{Taq} DNA polymerase, DNA marker,
and \emph{d} NTP were obtained from Takara Biomedical Technology
(Beijing) Co., Ltd. And the antibiotic ampicillin was obtained from
Solarbio/Life Sciences (China) Co., Ltd. (Beijing, China). All primers
were synthesized by Invitrogen/Life Technologies (Carlsbad, USA). The
substrate 4-nitrophenyl-\selectlanguage{greek}α-\selectlanguage{english}L-rhamnopyranoside (\emph{p} NPR), was
purchased from Sigma Co. Ltd. (St. Louis, MO, USA). All other reagents
used in this study were of analytical grade.

\textbf{3.2 Compactional screening the mutant candidates with better
catalysis efficiency and thermostability}

The structures of \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase Rha1 (GenBank code: AGN92963.1) and
its mutants were obtained by homology modeling using Modeller 9.15.
Energy minimization was carried out using Discovery Studio2019. The
three-dimensional structure model of \emph{p} NPR was contructed by Chem
Bio Office 2017. The simulated protein structure was docked
against\emph{p} NPR using AutoDock4.2. In general, the docking
parameters for AutoDock4.2 were kept as default values. {[}(145.704,
166.348, 34.581), (60 \selectlanguage{ngerman}Å×60 Å×60 Å){]} grid map was used in docking
calculations. Finally, the best poses were analyzed and visualized using
Discovery studio 2019.

\selectlanguage{greek}\textbf{3.3 Construction of α-L-rhamnosidase r-Rha1 mutants}\selectlanguage{english}

Site-directed mutagenesis to replace the specific residue as identified
above was achieved by the KOD-Plus-Mutagenesis Kit (Toyobo, Japan) using
the specific primer pairs and the DH5\selectlanguage{greek}α/\selectlanguage{english}19-9k plasmid as a template for
polymerase chain reaction (PCR) {[}42{]}. Then the product of PCR was
transformed into freshly prepared \emph{E. coli}DH5\selectlanguage{greek}α \selectlanguage{english}by thermal shock at
42\selectlanguage{ngerman}ºC for 90 s. The cell suspension was cultivated at 37 °C in
luria-bertani medium containing 100 \selectlanguage{greek}μ\selectlanguage{english}g/mL ampicillin.

The genes were transformed into \emph{P. pastoris} SMD1168 cells for
protein expression. \emph{E. coli} transformants were pooled, and the
plasmid DNA was extracted using a TIANprep Mini Plasmid Kit (Tiangen,
Beijing, China). The resulting recombinant vectors were separately
linearized with \emph{Sal} I, and transformed into \emph{P. pastoris}
SMD1168 using a Gene Pulser Apparatus (Bio-Rad, Hercules, USA). The
transformants were scanned after growing the clones on minimal dextrose
medium plates at 30\selectlanguage{ngerman}ºC for 3 d. Transformed yeast cells were selected on
yeast extract peptone dextrose agar-plates containing 2.5 mg/mL G418
(Transgen Biotech, China). Colony PCR was carried out to confirm that
whether or not the \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase gene has integrated into the genome.
The General (5'AOX, 3'AOX) and Specified primers (Q9K-F, Q9K-R) as shown
Table S3 were used for the selection of clones.

\textbf{3.4 Measurement of catalytic activity}

Enzyme expression and purification of r-Rha1 mutants, the
\selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase activity follows the same protocol in the previous
study.\textsuperscript{56} For kinetic experiments, five different
concentrations (within 2-6 mM) of \emph{p} NPR were used to incubate the
purified \selectlanguage{greek}α-\selectlanguage{english}L-rhamnosidase at 60\selectlanguage{ngerman}ºC and pH 4.0 (20 mM of citrate acid
buffer) for 10 min. The \emph{K} \textsubscript{m} and\emph{V}
\textsubscript{max} were calculated from Lineweaver-Burk
plots.\textsuperscript{57} These values were further used to calculate
the \emph{k} \textsubscript{cat} and the catalytic efficiency.

\textbf{3.5 Measurement ofthermostability}

The optimal temperatures for wild type and mutants were determined by
measuring the enzyme activities at different temperatures (ranging from
30 to 80 °C) in 20mM citrate acid buffer (pH 4.0) with \emph{p} NPR as
the substrate. The thermostability was estimated by measuring the ratio
of the residual activity to the initial activity of the enzyme. Samples
were diluted in 20mM buffer (pH 4.0) to a protein concentration of 0.7
mg/mL followed by incubating at 60 °C, 65 °C, and 70 °C, respectively.
After heat treatment, the samples were cooled on ice immediately.
Relative activity was estimated with original activity taken as 100 \%.

\textbf{3.6 Circular dichroism spectroscopy analysis}

Circular dichroism (CD) measurements were recorded from 190 to 260 nm at
25°C with a Chirascan Circular Dichroism spectrometer (Applied
Photophysics, UK) at a scan rate of 100 nm/min, 0.25 s of interval and
1nm of bandwidth for identifying secondary structure. CD measurements
were recorded from 190 to 260 nm at 20-100°C to determination of heat
distortion temperature (T\textsubscript{m}).

\textbf{3.7 In-silico analyses}

\textbf{3.7.1 MD simulation}

The MD simulation was performed using GROMACS 5.1.4 on in-house super
computing platform as earlier implementing the GROMACS 53a6 force
field.\textsuperscript{58} The initial structure was solvated with a
simple point-charge model of SPC water in a box.
Na\textsuperscript{+}ions were added to neutralize the negative charges
in the system. The system was then subjected to a steepest descent
energy minimization to give the maximum force below 1000 KJ/mol/nm.
After energy minimization, the position restraint simulation of 2000
steps (2fs each steep) was implemented under NVT (the constant Number of
particles, Volume and Temperature) and NPT (the constant Number of
particles, Pressure and Temperature) condition. In the end, 50ns MD
simulation was conducted on each ensemble. The root means square
deviation (RMSD) and the root mean square fluctuation (RMSF), which is
an important index for evaluating the protein structure, were calculated
using GROMACS rms and rmsf tools. Time dependent secondary structure was
calculated using do\_dssp tool. The Particle Mesh Ewald method was used
calculated long-range electrostatic forces. The neighbor list was
determined with the Grid method. The time step of the simulations was 2
fs, and the coordinates were saved for analysis every 2 ps.
Postprocessing and analysis were performed using standard GROMACS tools
and Visual Molecular Dynamics (VMD).

\textbf{3.7.2 The Binding free energy calculation}

The binding free energies (\selectlanguage{greek}Δ\selectlanguage{english}G bind) between amino acid residues were
calculated using the molecular mechanics Poisson Boltzmann surface area
(MM/PBSA) method.\textsuperscript{59} The binding free energy (\selectlanguage{greek}Δ\selectlanguage{english}G
binding) between residues was calculated using the MM-PBSA method from
a10-20 ns MD simulation trajectory. The binding free energies computed
by this method could be represented using the following equations:

Protein + Subtract -Complex

Therefore, the binding free energy for each complex is calculated using
the following equation:

\begin{equation}
{G}_{\text{bind}}=G_{\text{complex}}-\left(G_{\text{protein}}+G_{\text{subtract}}\right)\nonumber \\
 \end{equation}

Where \({G}_{\text{bind}}\) is the total binding free
energy,\(\ G_{\text{complex}}\), \(G_{\text{protein}}\),\(G_{\text{subtract}}\) are the
energies for the complex, the protein (wild type and mutants), and the
subtract (\emph{p} NPR), respectively.

\textbf{Conflicts of Interest:} The authors have no conflicts of
interest to declare.

\textbf{Author contributions}

Lijun Li and Hui Ni designed research, Wenjing Li and Zheyu Wu performed
experiments; Lijun Li and Wenjing Li analyzed data, Jianye Gong
performed computational experiments and analyzed, Lijun Li, Wenjing Li
and Jianye Gong wrote the paper, Yanyan Xu, Zedong Jiang, Hui Ni and
Yi-Sheng Chen modified the paper. All authors read and approved the
final manuscript.

\textbf{Acknowledgment}

This work was supported by the Natural Science Foundation of China
(U1805235), the Science and Technology Project of Xiamen City
(3502Z20183029) and Fujian Province Natural Science Foundation
(2017J01634).

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