- Email: [email protected]
Cellulase enzyme: Homology modeling, binding site identification and molecular docking
Accepted Manuscript Cellulase enzyme: Homology modeling, binding site identification and molecular docking
K. Selvam, D. Senbagam, T. Selvankumar, C. Sudhakar, S. Kamala-Kannan, B. Senthilkumar, M. Govarthanan PII:
S0022-2860(17)31135-3
DOI:
10.1016/j.molstruc.2017.08.067
Reference:
MOLSTR 24205
To appear in:
Journal of Molecular Structure
Received Date:
07 July 2017
Revised Date:
17 August 2017
Accepted Date:
18 August 2017
Please cite this article as: K. Selvam, D. Senbagam, T. Selvankumar, C. Sudhakar, S. KamalaKannan, B. Senthilkumar, M. Govarthanan, Cellulase enzyme: Homology modeling, binding site identification and molecular docking, Journal of Molecular Structure (2017), doi: 10.1016/j.molstruc. 2017.08.067
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Cellulase enzyme: Homology modeling, binding site identification and molecular docking
2
K. Selvama,b, D. Senbagamc, T. Selvankumarb, C. Sudhakarb, S. Kamala-Kannand,
3
B. Senthilkumara,e*, M. Govarthananb,f*
4
aCentre
for Biotechnology, Muthayammal College of Arts and Science, Rasipuram, Namakkal 637 408, Tamil Nadu, India
5 6
bPG
and Research Department of Biotechnology, Mahendra Arts and Science College (Autonomous), Kalippatti, Namakkal 637 501, Tamil Nadu, India
7 8
cDepartment
of Marine Biotechnology, Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu, India
9 10
dDivision
of Biotechnology, College of Environmental and Bioresource Sciences, Chonbuk National University, Iksan 570-752, Korea
11 12
eDepartment
Haramaya University, P.O. Box 235, Harar, Ethiopia
13 14 15
of Medical Microbiology, Health and Medical Science College
fDepartment
of Energy and Environmental System Engineering, University of Seoul, Seoul, Republic of Korea
16 17 18 19 20
*Corresponding authors: B. Senthilkumar, E-mail: [email protected];
21
M. Govarthanan: [email protected] 1
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ABSTRACT
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Cellulase is an enzyme that degrades the linear polysaccharide like cellulose into glucose
24
by breaking the β-1,4- glycosidic bonds. These enzymes are the third largest enzymes with a
25
great potential towards the ethanol production and play a vital role in degrading the biomass. The
26
production of ethanol depends upon the ability of the cellulose to utilize the wide range of
27
substrates. In this study, the 3D structure of cellulase from Acinetobacter sp. was modeled by
28
using Modeler 9v9 and validated by Ramachandran plot. The accuracy of the predicted 3D
29
structure was checked using Ramachandran plot analysis showed that 81.1% in the favored
30
region, compatibility of an atomic model (3D) with amino acid sequence (1D) for the model was
31
observed as 78.21% and 49.395% for Verify 3D and ERRAT at SAVES server. As the binding
32
efficacy with the substrate might suggests the choice of the substrate as carbon and nitrogen
33
sources, the cellobiose, cellotetraose, cellotetriose and laminaribiose were employed in the
34
docking studies. The docking of cellobiose, cellotetraose, cellotetriose and laminaribiose with
35
cellulase exhibited the binding energy of -6.1523 kJ/mol, -7.8759 kJ/mol,-6.1590 kJ/mol and -
36
6.7185 kJ/mol, respectively. These docking studies revealed that cellulase has the greater
37
potential towards the cellotetraose as a substrate for the high yield of ethanol.
38 39 40 41
Keywords: Cellulase, homology modeling, docking, cellobiose, cellotetraose, cellotetriose,
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laminaribiose
2
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1. Introduction
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Cellulases are inducible enzymes, which can be synthesized through microorganisms
45
during their growth on cellulosic substances. They are studied drastically because of their
46
application in the hydrolysis of cellulose, the amplest biopolymer and the potential source of
47
utilizable sugars, which serves as a raw material in the manufacturing of chemicals and fuel [1].
48
Cellulose is an unbranched glucose polymer composed of D-glucose units connected through a
49
1,4-β-D glucosidic bond. It is degraded by enzymes produced with the aid of both bacteria and
50
fungi. As cellulose is a completely stable polymer, powerful hydrolysis of it requires the
51
synergistic action of several enzymes, which include endo-β-1,4-glucanases, exo-β-1,4-
52
glucanases (or cellobiohydrolase) and β-glucosidases [2-6]. The mechanism of cellulose
53
hydrolysis by means of cellulases has been studied enormously [7].
54
Cellulases are used in lots of biotechnological applications, such as fiber modification in
55
the paper and textile industries, but they also have great ability in the rising industry of ethanol
56
production from lignocellulose. To decrease the water utilization and reduce the expenses of
57
equipment and distillation, the hydrolysis of lignocellulose need to be conducted at an excessive
58
concentration of solids. This approach unavoidably outcomes in excessive concentrations of the
59
hydrolysis end-products cellobiose and glucose, and it has been proposed that the end-product
60
inhibition of cellulases is rate limiting for lignocelluloses hydrolysis in high-solid conditions [8,
61
9]. Thus, relieving the product inhibition becomes a major challenge within the approach, as well
62
as in enzyme engineering [10].
63
Molecular docking is a computational tool to study protein–ligand interactions and used
64
to predict the structure of the intermolecular complex formed between the molecules. Docking is
65
a crucial technique that places a small molecule (ligand) in the binding site of its macromolecular 3
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target (receptor) and estimates its binding affinity. The most interesting case is the protein ligand
67
interaction, because of its applications in industries. In molecular docking, based on the protein
68
structures between the ligand and evaluated using an energy scoring function; the pose with the
69
lowest energy score is predicted as the ‘‘best match’’, i.e., the binding mode [11-13].
70
Cellobiose a structural unit of cellulose is a highly unbranched polymer consisting of
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glucose residues firmly linked by means of β-1,4-glycosidic bonds, which tends to form an
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insoluble crystallites, that are not even soluble by water. In general, cellulose is known to be as
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the most promising abundant polysaccharide compound that can be an alternative fuel as a
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renewable biomass. Cellulases are a class of enzymes that can hydrolyze cellulose structure into
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cellobiose which is further degraded as glucose monomers. There are three major enzymes in
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cellulase systems that work in synergy to hydrolyze cellulose into glucose monomer which are
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endo-cellulase, exocellulase and β-glucosidase [14-16].
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The biodegradation of cellulose begins with endo-cellulase by restricting randomly at β-
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1,4- glucosidic linkages in the cellulose via generating numerous lengths of oligosaccharides
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with the brand new chain ends. This movement eventually breaks down the crystalline shape of
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the cellulose. Then, exocellulase cleaves the reducing and non-reducing ends of this new
82
oligosaccharides
83
(cellobiohydrolase) as the major products [8]. Subsequently, β-glucosidase completes this
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process through hydrolyzing the remaining cellobiose or cellotetraose into glucose. Cellobiose
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that’s an intermediate product is also a strong inhibitor for endoglucanase and exoglucanase and
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it becomes one of the key bottlenecks in enzymatic hydrolysis. Accordingly, to prevent this
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inhibition process, cellobiose unit must be immediately removed. Thus, it is important to
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understand the catalytic activity of β-glucosidase in order to improve the efficiency of this
chain
generating
either
glucose
4
(glucanohydrolases)
or
cellobiose
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enzyme. This will help in designing an enhanced β-glucosidases. However, little is known about
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the catalytic interactions between β-glucosidase and cellobiose. Hence, on this present study, an
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attempt has been made to understand the binding efficiency of cellulase enzyme of Acinetobacter
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sp. with four polysaccharides sub units, cellobiose, cellotetraose, cellotetriose and laminaribiose
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through molecular docking studies.
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2. Materials and methods
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2.1. Isolation and identification of cellulase producers
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A potential cellulase producing bacteria were isolated from the coffee pulp waste
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disposing site at Yercaud, Tamil Nadu, India. The partial 16S rRNA gene was amplified and
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sequenced the use of ABI PRISM. The sequences were as compared using BLAST (NCBI) for
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the identification of isolated TSK-MASC.
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2.2. Sequence and Template search for homology modeling
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The Acinetobacter sp. cellulase 3D structures are not available in Protein Data Bank
102
(PDB) database, the homologous sequences for building the 3D structure was searched against
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PDB using NCBI-BLAST (Basic Local Alignment Search Tool) [17, 18]. The homologous
104
sequences are ability template structure for homology modeling. The atomic coordinate report of
105
the template structure was obtained from the PDB [19].
106
2.3. Comparative modeling and Model confirmation
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The atomic coordinate file of the template along with target and template final sequence
108
alignment file was used to build the model using the automated homology modeling tool
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MODELER 9v9 [20]. A bundle of models from the random generation of the starting structure
110
was calculated and among the generated models, the best model with the least Root Mean Square
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Deviation (RMSD) value was selected by superimposing the model with its template [21]. This
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model was used for further analysis after subjecting it for energy minimization using GROMOS
113
of SwissPDBviewer [22]. The quality of the generated model was assessed by checking the
114
stereo chemical parameters using PROCHECK [23], Verfiy3D [24, 25] and ERRAT [26] at
115
SAVES server (http://nihserver.mbi.ucla.edu/SAVES) [27].
116
2.4. Prediction of binding site
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To determine the binding affinities between four polysaccharide sub units, cellobiose,
118
cellotetraose, cellotetriose and laminaribiose, the modeled structure of cellulase from
119
Acinetobacter sp. and the amino acids in the binding site was predicted by submitting the
120
structure to DoGSiteScorer: Active Site Prediction and Analysis Server [28].
121
2.5. Ligand generation
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The 2D structures of the four polysaccharide sub units, cellobiose, cellotetraose,
123
cellotetriose and laminaribiose compounds were drawn in ACD-Chemsketchversion 11 (2006)
124
[29] and their SMILES notation was obtained. The 3D structures were obtained and converted
125
into SDF files by using ‘Online SMILES convertor and Structure file generator’ server [30].
126
2.6. Flexible Docking
127
The 3D structures of cellobiose, cellotetraose, cellotetriose and laminaribiose were
128
docked with the binding sites of modeled cellulase from Acinetobacter sp. by using FlexX [31]
129
with following parameters like (a) default general docking information’s, base placement the
130
usage of triangle matching, scoring of complete rating contribution and threshold of 0,30 and no
131
rating contribution and threshold of 0,70. (b) Chemical parameters of clash handling values for
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protein ligand clashes with the maximum allowed overlap quantity of 2.9 A03 and intra-ligand
133
clashes with clash factor of 0.6 and considering the hydrogen in internal clash tests. (c) Default
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docking info values of 200 for both the maximum wide variety solutions per generation and most
135
number of solutions according to fragmentation.
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2.7. Prediction of ligand-receptor interactions
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The interactions between cellobiose, cellotetraose, cellotetriose and laminaribiose and
138
cellulase from Acinetobacter sp. as docked compound were analyzed by the pose-view of LeadIT
139
[32].
140
3. Results and discussions
141
3.1. Sequence analysis
142
The cellulase producing bacterial strain was identified as Acinetobacter sp. through 16s
143
rRNA gene sequence analysis (earlier reported by Selvam et al. (2014) [33]. Sequenced
144
amplicon has been submitted to NCBI database and accession number was obtained KC309425.
145
Based on above information the cellulase sequences were retrieved from PDB for homology
146
modeling. The BLASTP search for target sequences of cellulase from Acinetobacter sp. against
147
PDB database resulted that crystal structure of β-glycosidase from Bacteroides vulgatus (PDB
148
ID: 3gm8_A chain) as the most homologous with the sequence identity of 40%, at an E-value
149
cut-off of 1.73e-147.
150
3.2. Homology modeling
151
The 3D structure of cellulase from Acinetobacter sp was developed by considering the X-
152
ray structure coordinate files of β-glycosidase from Bacteroides vulgatus (PDB ID: 3gm8_A
153
chain) as a template. Modeller 9v9 was used to develop the 3D structure by providing the
154
alignment file, template file, and target file. The alignment file was adjusted by taking into the
155
account of overlap between the secondary structure elements of the template and the predicted
156
secondary structure profile of the sequence. Further, considering the parameter provided for a
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number of the model to be calculated as five, modeller provided five initial models of cellulose
158
by using random generation and by applying spatial resistance. These generated models were
159
superimposed with template structure to reveal the degree of modeled structure with the template
160
by calculating the Root Mean Square Deviation (RMSD). Among the five generated model,
161
model-3 exhibited the lowest RMSD of 1.45 A0. This model-3 was considered for further
162
refinement of the structure by energy minimization and loop refinements. The energy was
163
minimized process by using GROMOS 43B1 force fields of SwissPDBviewer. After energy
164
minimization and loop refinements, the RMSD of the model was found to be 1.095 in
165
comparison to the template which significantly implies the better quality of the model. However,
166
RMSD values just reflect the sequence similarity. Thus considering that RMSD alone could not
167
be a definite solution for evaluating the model, the quality of the models was assessed further by
168
using other tools. The modeled and energy minimized structure of cellulase from Acinetobacter
169
sp. was shown in cartoon representation with group color using rasmol visualization tool (Figure
170
1).
171
3.3. Model Assessment
172
The satisfactory of the generated model was assessed by using the general stereo
173
chemical parameters by PROCHECK, Verfiy3D, and ERRAT of SAVES server. Ramachandran
174
plot of energy minimized model of cellulase structures had been generated. The x axis of the
175
Ramachandran plot corresponds to the Phi angles and the y axis represents Psi angles. The plots
176
split into four quadrants which includes, low energy region, allowed region, generously allowed
177
region and disallowed region [23]. The cellulase showed 81.1% of the residues within the most
178
favorable region, 14.9% within the moreover allowed region, 2.5% in the generously allowed
179
region, and 1.5% in the disallowed region (Figure 2 and Table 1). The corresponding values for
8
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the template Bacteroides vulgatus (PDB ID: 3gm8_A chain) (Figure 46 (b)) were 87.5%, 11.8%,
181
and 0.8%. Further, the overall quality factor and compatibility of an atomic model (3D) with
182
amino acid sequence (1D) for the model was observed as 78.21% and 49.395 for Verify3D and
183
ERRAT respectively at SAVES server. Further, the PROSA Z-score and QMEANS value are
184
observed as -5.9 and -3.66 respectively. Thus the results of Ramachandran plot, ERRAT,
185
Verify-3D, PROSA, and QMEANS confirms that the generated model is reliable and of good
186
quality.
187
3.4. Binding site Prediction
188
The modeled cellulase from Acinetobacter sp. was submitted to DoGSiteScorer: Active
189
Site Prediction and Analysis Server and detects 13 potential pockets on the surface of the protein
190
structure, listed in the result Table 2. The first five pockets are estimated to be druggable. The
191
selection of the pocket with the highest druggability of 0.80 opens a detailed descriptor page.
192
Similarly, Volkmer et al. [34], reports the tyrosine protein kinase Abl1 (cALB) with its inhibitor
193
Gleevec (imatinib) is exemplarily shown (PDB code 1iep - chain B). Pocket with the highest
194
druggability of 0.81. Among the predicted binding sites, the catalytic site is considered based on
195
the ligands in the template structures were also observed in the same region and the same site
196
was used for the further docking studies.
197
3.5. Docking studies
198
Considering that the energy minimization reflects the stability of the molecule, the
199
flexibilities of both the ligand and receptor plays a significant role in docking since both change
200
their conformations to exhibit a perfect-fit complex with minimum energy. However, the very
201
high computational cost comes into play while the receptor is also flexible. Thus as a common
202
approach that can handle the accuracy and computational time, most of the docking software
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algorithms are treating the ligand as flexible while the receptor is kept rigid. In this study, we
204
have performed flexible ligand and rigid receptor docking by using four polysaccharides sub-
205
units and modeled protein. The docking complex and the interactions of cellobiose, cellotetraose,
206
cellotetriose and laminaribiose were docked with in the predicted site of modeled cellulase from
207
Acinetobacter sp. The docking interactions between the binding site residues and the compounds
208
with their respective binding score were given in Figure 3a-d and Table 3. The binding efficiency
209
of cellulase enzyme of Acinetobacter sp. with four polysacchadire sub units, cellobiose,
210
cellotetraose, cellotetriose, and laminaribiose was determined by the docking interactions.
211
3.5.1. Cellobiose docking interactions
212
The docking interactions of the cellobiose within the catalytic site of modeled cellulase
213
from Acinetobacter sp. with docking score of -6.1523 kJ/mol that the cellubiose is bounded by
214
the means of 4 H bonds favored by Trp223, His216, Ala157, and Leu156. This interaction is also
215
favored by the means of hydrophobic interactions (Non-bonded) by Leu156, Phe172, Trp223,
216
Ser155, Leu219, and Ala157residues (Figure 3a).
217
3.5.2. Cellotetraose docking interactions
218
The interactions of cellotetraose is favored by Leu156, Ala157, and Val213 by forming H
219
bonds and the amino acids Ala157, Ile217, His216, Leu219, and Pro220 contributed the
220
hydrophobic the interactions with the docking score of -7.8759 kJ/mol (Figure 3b).
221
3.5.3. Cellotetriose docking interactions
222
The cellotetriose is found to be docked with -6.1590 kJ/mol supported by Leu156,
223
Ala157, Ser 155, and His216 by H bonds and non bonded interactions by Leu219, Leu156,
224
Trp223, Ala157, and His216 (Figure 3c).
225
3.5.4. Laminaribiose docking interactions
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The interaction of laminaribiose is supported by Leu156, Ala157, Ser155, and Trp223
227
and the non bonded interactions by Leu156, Leu219, Ser155 and Trp223 with docking score of -
228
6.7185 kJ/mol (Figure 3d).
229
From these docking studies, it is observed that the amino acids Ala157 and Leu156 in the
230
catalytic site enhance the binding of all the four compounds by the formation of H bonds.
231
Interestingly, it is observed that Ala157 and Leu156 were found to contribute the H bond
232
formation with all the four compounds, which signifies that these residues might play an
233
essential role in the degradation pathway as enzymatic hydrolysis. Similarly, Khairudin and
234
Mazlan, [35] reported catalytic interactions of β-glucosidase with its substrates such as
235
cellobiose, cellotetraose and cellotetriose by molecular docking studies. From their studies, it is
236
observed that the binding affinities of cellobiose, cellotetraose, and cellotetriose exhibited the
237
docking score of -6.2kJ/mol, -5.68 kJ/mol and -5.63 kJ/mol, respectively which is almost
238
significantly similar to the docking score of substrates used in this study. Further, they revealed
239
that Glutamic acid, Asparagine, Glutamine, Alanine, Tryptophan, Tyrosine and Histidine as a
240
key residue that involves in establishing the hydrogen bonds (H-bond) and also hydrophobic
241
interactions with the substrates. In contrast our results that Alanine and Leucine were the crucial
242
amino acids that favor the interactions with the substrates. Thus these findings may also provide
243
valuable insigths in designing better hydrolyzing enzymes with higher efficiency. This study
244
suggests that one can enhance the efficiency of enzymatic hydrolysis activity of cellulases by
245
modifying the residues in the catalytic site for the better yield and a significant impact on the
246
cost of production.
247
4. Conclusions
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Cellulases are a class of enzyme that hydrolyze cellulose into glucose monomers and play
249
a significant role in many industrial applications. The substrate specificity of the cellulase was
250
determined by docking studies. The cellulase from Acinetobacter sp. was modeled and validated
251
by Ramachandran plot. The binding affinities of the substrates cellobiose, cellotetraose,
252
cellotetriose, and laminaribiose were determined by their respective docking scores. Cellulase
253
showed the highest activity against cellotetraose with the docking score of -7.8759 kJ/mol. It is
254
observed that the binding of the substrates were emphasized by Ser155, Leu156, Ala157,
255
Phe172, His216, Leu219, and Trp223 in the formation of H bond and non-H-bond interactions.
256
The results of this study may prompt out new ideas for strategic approaches by experimentalists
257
to design higher enzymes aiming more efficient enzymatic hydrolysis process with higher yields
258
while also maintain the cost of production as low as possible.
259
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[23] R. A. Laskowski, M. W. MacArthur, D. S. Moss, J. M.Thornton, PROCHECK a program
318
to check the stereo chemical quality of protein structure, J. Appl. Crystallogr. 26 (1993)
319
283–291.
320 321 322 323 324 325
[24] J. U. Bowie, R. Luthy, D. Eisenberg, A method to identify protein sequences that fold into a known three-dimensional structure. Science. 253 (1991) 164–170. [25] R. Luthy, J. U. Bowie, D. Eisenberg, Assessment of protein models with threedimensional profiles, Nature. 356 (6364) (1992) 83-85. [26] C. Colovos, T. O. Yeates, Verification of protein structures: patterns of nonbonded atomic interactions, Protein Science. 2 (1993) 1511-1519.
326
[27] SAVES (2011) http://nihserver.mbi.ucla.edu/SAVES/
327
[28] A. Volkamer, D. Kuhn, T. Grombacher, F. Rippmann, M. Rarey. Combining global and
328
local measures for strucure-based druggability predictions. J. Chem. Inf. Model. 52
329
(2012) 360-372.
330 331 332 333 334 335
[29] ACD/ChemSketch Freeware, version 12 Advanced Chemistry Development, Inc. Toronto, ON, Canada, 2009. [http://www.acdlabs.com/download/chemsk.html]. [30] D. Weininger, SMILES, a chemical language and information system. Introduction to methodology and encoding rules, J. Chem. Inf. Model. 28 (1988) 31–36. [31] M. Rarey, B. Kramer, T. Lengauer, G. Klebe, A fast flexible docking method using an incremental construction algorithm, J. Mol. Biol. 261 (1996) 470–89.
15
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[32] K. Stierand, P. Maab, M. Rarey, Molecular complexes at a glance: automated generation of two-dimensional complex diagrams, Bioinformatics. 22 (2006) 1710–1716.
337 338
[33] K. Selvam, M. Govarthanan, S. Kamala-Kannan, M. Govindharaju, B. Senthilkumar, T.
339
Selvankumar, A. Sengottaiyan, Process optimization of cellulase production from alkali-
340
treated coffee pulp and pineapple waste using Acinetobacter sp. TSK-MASC. RSC Adv.
341
4 (2014) 13045.
342
[34] A. Volkamer, D. Kuhn, F. Rippmann, M. Rarey, DoGSiteScorer: a web server for
343
automatic binding site prediction, analysis and druggability assessment. Bioinformatics.
344
28(15) (2012) 2074-2075.
345
[35] N. B. A. Khairudin, N. S. F. Mazlan, Molecular docking study of β-Glucosidase with cellobiose, cellotetraose and cellotetriose. Bioinformation. 9(16) (2013) 813-817.
346 347 348
Figure legends
349
Figure 1. Ribbon diagrams of the modeled cellulase showing the α- Helices, β- strands, and
350
loops
351
Figure 2. Ramachandran plot for modeled cellulase (a) template (b) obtained by PROCHECK
352
Figure 3. (a) Docking complex and interactions of cellobiose and amino acids within the
353
catalytic site of modeled cellulase
354
cellotetriose (d) interactions of laminaribiose
(b) interactions of cellotetraose (c) interactions of
355
16
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Fig.1.
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Fig.2. (a)
(b)
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Fig.3. (a)
(b)
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(c)
(d)
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Highlights
3D models of cellulase from Acinetobacter sp.
Prediction of active sites of the substrates.
Active site characterization based on docking studies of the substrates.
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Table 1. Evaluation plot statistics results of model and template by PROCHECK, VERIFY3D and ERRAT
Proteins
PROCHECK Most favored
Additional
Generally
regions
Allowed
allowed
regions
regions
Verify 3D
ERRAT
Disallowed 3D-ID
Quality
Score
Factor
regions
Model
81.1%
14.9%
2.5%
1.5%
78.21%
49.395
Template
87.5%
11.8%
0.8%
0%
97.40%
89.27
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Table 2. Pockets and descriptors have been calculated for XPRO
Name
Volume [ų]
Surface [Ų]
Lipo surface [Ų]
Depth [Å]
Dock Score
P0
1152.70
1756.19
1115.18
22.82
0.80
P1
634.24
1009.80
682.60
17.11
0.79
P2
494.59
724.51
535.36
13.69
0.70
P3
378.11
519.73
358.86
15.35
0.70
P4
360.13
717.69
475.25
18.62
0.76
P5
288.38
488.25
354.62
13.89
0.61
P6
243.20
509.73
371.23
15.18
0.61
P7
215.04
288.53
220.23
10.50
0.41
P8
183.68
248.53
173.29
12.30
0.46
P9
139.78
237.64
145.89
9.17
0.31
P10
117.44
192.64
151.72
6.52
0.21
P11
116.74
268.97
215.63
7.00
0.22
P12
113.02
85.49
58.93
11.63
0.38
P13
106.24
430.05
332.46
6.52
0.19
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Table 3. Docking complex and interactions of amino acids within the catalytic site of modeled Cellulase from Acinetobacter sp.
Cellubiose *Ser155 *Leu156 Leu156 *Ala157 Ala157 *Phe172 His216 *Leu219 *Trp223 Trp223 Docking score -6.1523 (kJ/mol) * indicate H bond
Cellotetraose Leu156 *Ala157 Ala157 Val213 *Leu219 *Ile217 Trp223 Docking score -7.8759 (kJ/mol)
Cellotetriose Ser 155 *Leu156 Leu156 *Ala157 Ala157 *His216 His216 *Leu219 *Trp223 Docking score -6.1590 (kJ/mol)
Laminaribiose *Ser155 Ser155 *Leu156 Leu156 Ala157 *Leu219 *Trp223 Trp223 Docking score -6.7185 (kJ/mol)
K. Selvam, D. Senbagam, T. Selvankumar, C. Sudhakar, S. Kamala-Kannan, B. Senthilkumar, M. Govarthanan PII:
S0022-2860(17)31135-3
DOI:
10.1016/j.molstruc.2017.08.067
Reference:
MOLSTR 24205
To appear in:
Journal of Molecular Structure
Received Date:
07 July 2017
Revised Date:
17 August 2017
Accepted Date:
18 August 2017
Please cite this article as: K. Selvam, D. Senbagam, T. Selvankumar, C. Sudhakar, S. KamalaKannan, B. Senthilkumar, M. Govarthanan, Cellulase enzyme: Homology modeling, binding site identification and molecular docking, Journal of Molecular Structure (2017), doi: 10.1016/j.molstruc. 2017.08.067
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1
Cellulase enzyme: Homology modeling, binding site identification and molecular docking
2
K. Selvama,b, D. Senbagamc, T. Selvankumarb, C. Sudhakarb, S. Kamala-Kannand,
3
B. Senthilkumara,e*, M. Govarthananb,f*
4
aCentre
for Biotechnology, Muthayammal College of Arts and Science, Rasipuram, Namakkal 637 408, Tamil Nadu, India
5 6
bPG
and Research Department of Biotechnology, Mahendra Arts and Science College (Autonomous), Kalippatti, Namakkal 637 501, Tamil Nadu, India
7 8
cDepartment
of Marine Biotechnology, Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu, India
9 10
dDivision
of Biotechnology, College of Environmental and Bioresource Sciences, Chonbuk National University, Iksan 570-752, Korea
11 12
eDepartment
Haramaya University, P.O. Box 235, Harar, Ethiopia
13 14 15
of Medical Microbiology, Health and Medical Science College
fDepartment
of Energy and Environmental System Engineering, University of Seoul, Seoul, Republic of Korea
16 17 18 19 20
*Corresponding authors: B. Senthilkumar, E-mail: [email protected];
21
M. Govarthanan: [email protected] 1
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22
ABSTRACT
23
Cellulase is an enzyme that degrades the linear polysaccharide like cellulose into glucose
24
by breaking the β-1,4- glycosidic bonds. These enzymes are the third largest enzymes with a
25
great potential towards the ethanol production and play a vital role in degrading the biomass. The
26
production of ethanol depends upon the ability of the cellulose to utilize the wide range of
27
substrates. In this study, the 3D structure of cellulase from Acinetobacter sp. was modeled by
28
using Modeler 9v9 and validated by Ramachandran plot. The accuracy of the predicted 3D
29
structure was checked using Ramachandran plot analysis showed that 81.1% in the favored
30
region, compatibility of an atomic model (3D) with amino acid sequence (1D) for the model was
31
observed as 78.21% and 49.395% for Verify 3D and ERRAT at SAVES server. As the binding
32
efficacy with the substrate might suggests the choice of the substrate as carbon and nitrogen
33
sources, the cellobiose, cellotetraose, cellotetriose and laminaribiose were employed in the
34
docking studies. The docking of cellobiose, cellotetraose, cellotetriose and laminaribiose with
35
cellulase exhibited the binding energy of -6.1523 kJ/mol, -7.8759 kJ/mol,-6.1590 kJ/mol and -
36
6.7185 kJ/mol, respectively. These docking studies revealed that cellulase has the greater
37
potential towards the cellotetraose as a substrate for the high yield of ethanol.
38 39 40 41
Keywords: Cellulase, homology modeling, docking, cellobiose, cellotetraose, cellotetriose,
42
laminaribiose
2
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43
1. Introduction
44
Cellulases are inducible enzymes, which can be synthesized through microorganisms
45
during their growth on cellulosic substances. They are studied drastically because of their
46
application in the hydrolysis of cellulose, the amplest biopolymer and the potential source of
47
utilizable sugars, which serves as a raw material in the manufacturing of chemicals and fuel [1].
48
Cellulose is an unbranched glucose polymer composed of D-glucose units connected through a
49
1,4-β-D glucosidic bond. It is degraded by enzymes produced with the aid of both bacteria and
50
fungi. As cellulose is a completely stable polymer, powerful hydrolysis of it requires the
51
synergistic action of several enzymes, which include endo-β-1,4-glucanases, exo-β-1,4-
52
glucanases (or cellobiohydrolase) and β-glucosidases [2-6]. The mechanism of cellulose
53
hydrolysis by means of cellulases has been studied enormously [7].
54
Cellulases are used in lots of biotechnological applications, such as fiber modification in
55
the paper and textile industries, but they also have great ability in the rising industry of ethanol
56
production from lignocellulose. To decrease the water utilization and reduce the expenses of
57
equipment and distillation, the hydrolysis of lignocellulose need to be conducted at an excessive
58
concentration of solids. This approach unavoidably outcomes in excessive concentrations of the
59
hydrolysis end-products cellobiose and glucose, and it has been proposed that the end-product
60
inhibition of cellulases is rate limiting for lignocelluloses hydrolysis in high-solid conditions [8,
61
9]. Thus, relieving the product inhibition becomes a major challenge within the approach, as well
62
as in enzyme engineering [10].
63
Molecular docking is a computational tool to study protein–ligand interactions and used
64
to predict the structure of the intermolecular complex formed between the molecules. Docking is
65
a crucial technique that places a small molecule (ligand) in the binding site of its macromolecular 3
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target (receptor) and estimates its binding affinity. The most interesting case is the protein ligand
67
interaction, because of its applications in industries. In molecular docking, based on the protein
68
structures between the ligand and evaluated using an energy scoring function; the pose with the
69
lowest energy score is predicted as the ‘‘best match’’, i.e., the binding mode [11-13].
70
Cellobiose a structural unit of cellulose is a highly unbranched polymer consisting of
71
glucose residues firmly linked by means of β-1,4-glycosidic bonds, which tends to form an
72
insoluble crystallites, that are not even soluble by water. In general, cellulose is known to be as
73
the most promising abundant polysaccharide compound that can be an alternative fuel as a
74
renewable biomass. Cellulases are a class of enzymes that can hydrolyze cellulose structure into
75
cellobiose which is further degraded as glucose monomers. There are three major enzymes in
76
cellulase systems that work in synergy to hydrolyze cellulose into glucose monomer which are
77
endo-cellulase, exocellulase and β-glucosidase [14-16].
78
The biodegradation of cellulose begins with endo-cellulase by restricting randomly at β-
79
1,4- glucosidic linkages in the cellulose via generating numerous lengths of oligosaccharides
80
with the brand new chain ends. This movement eventually breaks down the crystalline shape of
81
the cellulose. Then, exocellulase cleaves the reducing and non-reducing ends of this new
82
oligosaccharides
83
(cellobiohydrolase) as the major products [8]. Subsequently, β-glucosidase completes this
84
process through hydrolyzing the remaining cellobiose or cellotetraose into glucose. Cellobiose
85
that’s an intermediate product is also a strong inhibitor for endoglucanase and exoglucanase and
86
it becomes one of the key bottlenecks in enzymatic hydrolysis. Accordingly, to prevent this
87
inhibition process, cellobiose unit must be immediately removed. Thus, it is important to
88
understand the catalytic activity of β-glucosidase in order to improve the efficiency of this
chain
generating
either
glucose
4
(glucanohydrolases)
or
cellobiose
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89
enzyme. This will help in designing an enhanced β-glucosidases. However, little is known about
90
the catalytic interactions between β-glucosidase and cellobiose. Hence, on this present study, an
91
attempt has been made to understand the binding efficiency of cellulase enzyme of Acinetobacter
92
sp. with four polysaccharides sub units, cellobiose, cellotetraose, cellotetriose and laminaribiose
93
through molecular docking studies.
94
2. Materials and methods
95
2.1. Isolation and identification of cellulase producers
96
A potential cellulase producing bacteria were isolated from the coffee pulp waste
97
disposing site at Yercaud, Tamil Nadu, India. The partial 16S rRNA gene was amplified and
98
sequenced the use of ABI PRISM. The sequences were as compared using BLAST (NCBI) for
99
the identification of isolated TSK-MASC.
100
2.2. Sequence and Template search for homology modeling
101
The Acinetobacter sp. cellulase 3D structures are not available in Protein Data Bank
102
(PDB) database, the homologous sequences for building the 3D structure was searched against
103
PDB using NCBI-BLAST (Basic Local Alignment Search Tool) [17, 18]. The homologous
104
sequences are ability template structure for homology modeling. The atomic coordinate report of
105
the template structure was obtained from the PDB [19].
106
2.3. Comparative modeling and Model confirmation
107
The atomic coordinate file of the template along with target and template final sequence
108
alignment file was used to build the model using the automated homology modeling tool
109
MODELER 9v9 [20]. A bundle of models from the random generation of the starting structure
110
was calculated and among the generated models, the best model with the least Root Mean Square
111
Deviation (RMSD) value was selected by superimposing the model with its template [21]. This
5
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112
model was used for further analysis after subjecting it for energy minimization using GROMOS
113
of SwissPDBviewer [22]. The quality of the generated model was assessed by checking the
114
stereo chemical parameters using PROCHECK [23], Verfiy3D [24, 25] and ERRAT [26] at
115
SAVES server (http://nihserver.mbi.ucla.edu/SAVES) [27].
116
2.4. Prediction of binding site
117
To determine the binding affinities between four polysaccharide sub units, cellobiose,
118
cellotetraose, cellotetriose and laminaribiose, the modeled structure of cellulase from
119
Acinetobacter sp. and the amino acids in the binding site was predicted by submitting the
120
structure to DoGSiteScorer: Active Site Prediction and Analysis Server [28].
121
2.5. Ligand generation
122
The 2D structures of the four polysaccharide sub units, cellobiose, cellotetraose,
123
cellotetriose and laminaribiose compounds were drawn in ACD-Chemsketchversion 11 (2006)
124
[29] and their SMILES notation was obtained. The 3D structures were obtained and converted
125
into SDF files by using ‘Online SMILES convertor and Structure file generator’ server [30].
126
2.6. Flexible Docking
127
The 3D structures of cellobiose, cellotetraose, cellotetriose and laminaribiose were
128
docked with the binding sites of modeled cellulase from Acinetobacter sp. by using FlexX [31]
129
with following parameters like (a) default general docking information’s, base placement the
130
usage of triangle matching, scoring of complete rating contribution and threshold of 0,30 and no
131
rating contribution and threshold of 0,70. (b) Chemical parameters of clash handling values for
132
protein ligand clashes with the maximum allowed overlap quantity of 2.9 A03 and intra-ligand
133
clashes with clash factor of 0.6 and considering the hydrogen in internal clash tests. (c) Default
6
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134
docking info values of 200 for both the maximum wide variety solutions per generation and most
135
number of solutions according to fragmentation.
136
2.7. Prediction of ligand-receptor interactions
137
The interactions between cellobiose, cellotetraose, cellotetriose and laminaribiose and
138
cellulase from Acinetobacter sp. as docked compound were analyzed by the pose-view of LeadIT
139
[32].
140
3. Results and discussions
141
3.1. Sequence analysis
142
The cellulase producing bacterial strain was identified as Acinetobacter sp. through 16s
143
rRNA gene sequence analysis (earlier reported by Selvam et al. (2014) [33]. Sequenced
144
amplicon has been submitted to NCBI database and accession number was obtained KC309425.
145
Based on above information the cellulase sequences were retrieved from PDB for homology
146
modeling. The BLASTP search for target sequences of cellulase from Acinetobacter sp. against
147
PDB database resulted that crystal structure of β-glycosidase from Bacteroides vulgatus (PDB
148
ID: 3gm8_A chain) as the most homologous with the sequence identity of 40%, at an E-value
149
cut-off of 1.73e-147.
150
3.2. Homology modeling
151
The 3D structure of cellulase from Acinetobacter sp was developed by considering the X-
152
ray structure coordinate files of β-glycosidase from Bacteroides vulgatus (PDB ID: 3gm8_A
153
chain) as a template. Modeller 9v9 was used to develop the 3D structure by providing the
154
alignment file, template file, and target file. The alignment file was adjusted by taking into the
155
account of overlap between the secondary structure elements of the template and the predicted
156
secondary structure profile of the sequence. Further, considering the parameter provided for a
7
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number of the model to be calculated as five, modeller provided five initial models of cellulose
158
by using random generation and by applying spatial resistance. These generated models were
159
superimposed with template structure to reveal the degree of modeled structure with the template
160
by calculating the Root Mean Square Deviation (RMSD). Among the five generated model,
161
model-3 exhibited the lowest RMSD of 1.45 A0. This model-3 was considered for further
162
refinement of the structure by energy minimization and loop refinements. The energy was
163
minimized process by using GROMOS 43B1 force fields of SwissPDBviewer. After energy
164
minimization and loop refinements, the RMSD of the model was found to be 1.095 in
165
comparison to the template which significantly implies the better quality of the model. However,
166
RMSD values just reflect the sequence similarity. Thus considering that RMSD alone could not
167
be a definite solution for evaluating the model, the quality of the models was assessed further by
168
using other tools. The modeled and energy minimized structure of cellulase from Acinetobacter
169
sp. was shown in cartoon representation with group color using rasmol visualization tool (Figure
170
1).
171
3.3. Model Assessment
172
The satisfactory of the generated model was assessed by using the general stereo
173
chemical parameters by PROCHECK, Verfiy3D, and ERRAT of SAVES server. Ramachandran
174
plot of energy minimized model of cellulase structures had been generated. The x axis of the
175
Ramachandran plot corresponds to the Phi angles and the y axis represents Psi angles. The plots
176
split into four quadrants which includes, low energy region, allowed region, generously allowed
177
region and disallowed region [23]. The cellulase showed 81.1% of the residues within the most
178
favorable region, 14.9% within the moreover allowed region, 2.5% in the generously allowed
179
region, and 1.5% in the disallowed region (Figure 2 and Table 1). The corresponding values for
8
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180
the template Bacteroides vulgatus (PDB ID: 3gm8_A chain) (Figure 46 (b)) were 87.5%, 11.8%,
181
and 0.8%. Further, the overall quality factor and compatibility of an atomic model (3D) with
182
amino acid sequence (1D) for the model was observed as 78.21% and 49.395 for Verify3D and
183
ERRAT respectively at SAVES server. Further, the PROSA Z-score and QMEANS value are
184
observed as -5.9 and -3.66 respectively. Thus the results of Ramachandran plot, ERRAT,
185
Verify-3D, PROSA, and QMEANS confirms that the generated model is reliable and of good
186
quality.
187
3.4. Binding site Prediction
188
The modeled cellulase from Acinetobacter sp. was submitted to DoGSiteScorer: Active
189
Site Prediction and Analysis Server and detects 13 potential pockets on the surface of the protein
190
structure, listed in the result Table 2. The first five pockets are estimated to be druggable. The
191
selection of the pocket with the highest druggability of 0.80 opens a detailed descriptor page.
192
Similarly, Volkmer et al. [34], reports the tyrosine protein kinase Abl1 (cALB) with its inhibitor
193
Gleevec (imatinib) is exemplarily shown (PDB code 1iep - chain B). Pocket with the highest
194
druggability of 0.81. Among the predicted binding sites, the catalytic site is considered based on
195
the ligands in the template structures were also observed in the same region and the same site
196
was used for the further docking studies.
197
3.5. Docking studies
198
Considering that the energy minimization reflects the stability of the molecule, the
199
flexibilities of both the ligand and receptor plays a significant role in docking since both change
200
their conformations to exhibit a perfect-fit complex with minimum energy. However, the very
201
high computational cost comes into play while the receptor is also flexible. Thus as a common
202
approach that can handle the accuracy and computational time, most of the docking software
9
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algorithms are treating the ligand as flexible while the receptor is kept rigid. In this study, we
204
have performed flexible ligand and rigid receptor docking by using four polysaccharides sub-
205
units and modeled protein. The docking complex and the interactions of cellobiose, cellotetraose,
206
cellotetriose and laminaribiose were docked with in the predicted site of modeled cellulase from
207
Acinetobacter sp. The docking interactions between the binding site residues and the compounds
208
with their respective binding score were given in Figure 3a-d and Table 3. The binding efficiency
209
of cellulase enzyme of Acinetobacter sp. with four polysacchadire sub units, cellobiose,
210
cellotetraose, cellotetriose, and laminaribiose was determined by the docking interactions.
211
3.5.1. Cellobiose docking interactions
212
The docking interactions of the cellobiose within the catalytic site of modeled cellulase
213
from Acinetobacter sp. with docking score of -6.1523 kJ/mol that the cellubiose is bounded by
214
the means of 4 H bonds favored by Trp223, His216, Ala157, and Leu156. This interaction is also
215
favored by the means of hydrophobic interactions (Non-bonded) by Leu156, Phe172, Trp223,
216
Ser155, Leu219, and Ala157residues (Figure 3a).
217
3.5.2. Cellotetraose docking interactions
218
The interactions of cellotetraose is favored by Leu156, Ala157, and Val213 by forming H
219
bonds and the amino acids Ala157, Ile217, His216, Leu219, and Pro220 contributed the
220
hydrophobic the interactions with the docking score of -7.8759 kJ/mol (Figure 3b).
221
3.5.3. Cellotetriose docking interactions
222
The cellotetriose is found to be docked with -6.1590 kJ/mol supported by Leu156,
223
Ala157, Ser 155, and His216 by H bonds and non bonded interactions by Leu219, Leu156,
224
Trp223, Ala157, and His216 (Figure 3c).
225
3.5.4. Laminaribiose docking interactions
10
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226
The interaction of laminaribiose is supported by Leu156, Ala157, Ser155, and Trp223
227
and the non bonded interactions by Leu156, Leu219, Ser155 and Trp223 with docking score of -
228
6.7185 kJ/mol (Figure 3d).
229
From these docking studies, it is observed that the amino acids Ala157 and Leu156 in the
230
catalytic site enhance the binding of all the four compounds by the formation of H bonds.
231
Interestingly, it is observed that Ala157 and Leu156 were found to contribute the H bond
232
formation with all the four compounds, which signifies that these residues might play an
233
essential role in the degradation pathway as enzymatic hydrolysis. Similarly, Khairudin and
234
Mazlan, [35] reported catalytic interactions of β-glucosidase with its substrates such as
235
cellobiose, cellotetraose and cellotetriose by molecular docking studies. From their studies, it is
236
observed that the binding affinities of cellobiose, cellotetraose, and cellotetriose exhibited the
237
docking score of -6.2kJ/mol, -5.68 kJ/mol and -5.63 kJ/mol, respectively which is almost
238
significantly similar to the docking score of substrates used in this study. Further, they revealed
239
that Glutamic acid, Asparagine, Glutamine, Alanine, Tryptophan, Tyrosine and Histidine as a
240
key residue that involves in establishing the hydrogen bonds (H-bond) and also hydrophobic
241
interactions with the substrates. In contrast our results that Alanine and Leucine were the crucial
242
amino acids that favor the interactions with the substrates. Thus these findings may also provide
243
valuable insigths in designing better hydrolyzing enzymes with higher efficiency. This study
244
suggests that one can enhance the efficiency of enzymatic hydrolysis activity of cellulases by
245
modifying the residues in the catalytic site for the better yield and a significant impact on the
246
cost of production.
247
4. Conclusions
11
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248
Cellulases are a class of enzyme that hydrolyze cellulose into glucose monomers and play
249
a significant role in many industrial applications. The substrate specificity of the cellulase was
250
determined by docking studies. The cellulase from Acinetobacter sp. was modeled and validated
251
by Ramachandran plot. The binding affinities of the substrates cellobiose, cellotetraose,
252
cellotetriose, and laminaribiose were determined by their respective docking scores. Cellulase
253
showed the highest activity against cellotetraose with the docking score of -7.8759 kJ/mol. It is
254
observed that the binding of the substrates were emphasized by Ser155, Leu156, Ala157,
255
Phe172, His216, Leu219, and Trp223 in the formation of H bond and non-H-bond interactions.
256
The results of this study may prompt out new ideas for strategic approaches by experimentalists
257
to design higher enzymes aiming more efficient enzymatic hydrolysis process with higher yields
258
while also maintain the cost of production as low as possible.
259
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Figure legends
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Figure 1. Ribbon diagrams of the modeled cellulase showing the α- Helices, β- strands, and
350
loops
351
Figure 2. Ramachandran plot for modeled cellulase (a) template (b) obtained by PROCHECK
352
Figure 3. (a) Docking complex and interactions of cellobiose and amino acids within the
353
catalytic site of modeled cellulase
354
cellotetriose (d) interactions of laminaribiose
(b) interactions of cellotetraose (c) interactions of
355
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Fig.1.
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Fig.2. (a)
(b)
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Fig.3. (a)
(b)
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(c)
(d)
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Highlights
3D models of cellulase from Acinetobacter sp.
Prediction of active sites of the substrates.
Active site characterization based on docking studies of the substrates.
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Table 1. Evaluation plot statistics results of model and template by PROCHECK, VERIFY3D and ERRAT
Proteins
PROCHECK Most favored
Additional
Generally
regions
Allowed
allowed
regions
regions
Verify 3D
ERRAT
Disallowed 3D-ID
Quality
Score
Factor
regions
Model
81.1%
14.9%
2.5%
1.5%
78.21%
49.395
Template
87.5%
11.8%
0.8%
0%
97.40%
89.27
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Table 2. Pockets and descriptors have been calculated for XPRO
Name
Volume [ų]
Surface [Ų]
Lipo surface [Ų]
Depth [Å]
Dock Score
P0
1152.70
1756.19
1115.18
22.82
0.80
P1
634.24
1009.80
682.60
17.11
0.79
P2
494.59
724.51
535.36
13.69
0.70
P3
378.11
519.73
358.86
15.35
0.70
P4
360.13
717.69
475.25
18.62
0.76
P5
288.38
488.25
354.62
13.89
0.61
P6
243.20
509.73
371.23
15.18
0.61
P7
215.04
288.53
220.23
10.50
0.41
P8
183.68
248.53
173.29
12.30
0.46
P9
139.78
237.64
145.89
9.17
0.31
P10
117.44
192.64
151.72
6.52
0.21
P11
116.74
268.97
215.63
7.00
0.22
P12
113.02
85.49
58.93
11.63
0.38
P13
106.24
430.05
332.46
6.52
0.19
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Table 3. Docking complex and interactions of amino acids within the catalytic site of modeled Cellulase from Acinetobacter sp.
Cellubiose *Ser155 *Leu156 Leu156 *Ala157 Ala157 *Phe172 His216 *Leu219 *Trp223 Trp223 Docking score -6.1523 (kJ/mol) * indicate H bond
Cellotetraose Leu156 *Ala157 Ala157 Val213 *Leu219 *Ile217 Trp223 Docking score -7.8759 (kJ/mol)
Cellotetriose Ser 155 *Leu156 Leu156 *Ala157 Ala157 *His216 His216 *Leu219 *Trp223 Docking score -6.1590 (kJ/mol)
Laminaribiose *Ser155 Ser155 *Leu156 Leu156 Ala157 *Leu219 *Trp223 Trp223 Docking score -6.7185 (kJ/mol)