Efficacy of Insect Endogeneous Cellulases for Ligno-cellulosic Biofuels Deciphered Using Molecular Docking Studies

doi:10.21203/rs.3.rs-1892415/v1

Cellulose, the substance that makes up most of a plant’s cell wall, is pondered to be one of the most abundant natural organic polymers on earth made up of glucose units linked by β-1, 4 glycosidic bonds. Insects possess cellulolytic system capable of producing variegate enzymes with multifarious specificities to break down complex lignocellulosic products. Astonishingly, endoglucanases, exoglucanase, and β-glycosidases act sequentially in a synergistic system to facilitate the breakdown of cellulose to utilizable energy source glucose. These extremely versatile enzymes are a better source in terms of environmental performance and overall energy efficiency. Pertaining to four main glycosyl hydrolase families (GHF), insect cellulases are distributed in all the insect orders explored up until now. In silico docking studies of endo-β-1,4-glucanase from 19 different insects belonging to six different orders identified that it possesses high affinity for all the six substrates, including CMC, cellulose, cellotriose, cellotetraose, cellopentose, and cellohexaose. Additionally, β-glucosidase from nearly all the reported insect sources also showed considerable affinity towards cellobiose.

Van der Waals, conventional hydrogen bonds, and carbon-hydrogen bonds stabilize the interaction between the enzyme and different substrates. Molecular dynamics simulations also held up the stability of various complexes. With lignocelluloses-based biofuels becoming a major focus of industrial and academic communities worldwide, this study can perhaps complement the propensity of insect cellulases for prospected applications.

Cellulose

β-glycosidase

docking

endo-β-1

4-glucanase

biofuels

Being the pre-eminent component of most plant cell walls, cellulose is contemplated to be the most predominant organic polymer on Earth comprising of β-(1,4) linked D-glucopyranosyl monomers [12]. Noteworthily, cellulose occupies a crucial position among various raw materials bestowed by nature at our disposal for various industrial purposes [22]. Currently, second-generation biofuels, particularly bioethanol, are gaining attention because of their higher energy yields and sustainability and are considered an efficient energy substitute [5]. The potentiality of ligno-cellulose based biofuels as alternative renewable energy fuel have captivated global recognition to attenuate greenhouse emissions. The degradation of cellulose to fermentable sugars, a vital limiting step in bioethanol production, relies on the utilization of cellulases. These enzymes bring about the hydrolysis of cellulosic polymers to release monomeric fermentable sugars to produce biofuels. Bacteria and fungus form the major industrial source of cellulase enzyme. Nevertheless, under biorefinery conditions, these cellulases tend to have low activity and are easily inhibited [7]. Consequently, the pursuit for additional sources of cellulase is a pressing priority for the subsequent scale up and competitiveness of replenishable fuels [6].

Captivatingly, insects are ahead of the curve and are capable of producing variegate enzymes with multifarious specificities or amalgamates with other organisms (micro) that produce enzymes to break down complex lignocellulosic products [44]. Intriguingly, in the mid of those are xylophagous insects that inhabit symbiotic bacteria or protozoa in the gut that lend a hand in the breakdown of this composite polymer [38]. The accustomed view that animals cannot produce their own cellulase was repudiated by discovering the first endogenous cellulase in the termite Reticulitermes speratus. This elevation in the apprehension of a cellulolytic system specifically in insects was state-of-the-art of its function. In addition, endogenous cellulase was also reported in different orders, including Blattaria, Coleoptera and Orthoptera. Besides, the existence of putative cellulase genes in the orders Phthiraptera, Hemiptera, and Hymenoptera affirmed by insect genome sequencing data further illuminate the endogenous nature of the cellulolytic system in insects [40, 14, 23, 43].

Insect cellulases pertain to four main glycosyl hydrolase families (GHF), including GHF1, GHF5, GHF9 and GHF45 with class 9 glycosyl hydrolases (GH9) distributed in all the insect orders explored up until now [4, 38]. Consequently, it is plausible that a common ancestor might have possessed the GH9 gene and was further vertically transferred to extant insects [7]. Although GH5 and GH45 possess the same substrate specificity as GH9, the structures are perceptible and are found more apparently in the Order Coleoptera [15, 31]. Accordingly, whether the GH5 and GH45 were transferred to the coleopterans vertically from common ancestors or horizontally from more distantly related organisms is still obscure [7]. The theory regarding the evolutionary origins of the cellulase gene in insects is nevertheless ambiguous due to its unstable nature.

The propensity of insect cellulases to accomplish efficient cellulolysis is quite bewitching, especially for industrial applications. The activity of endo-β-1,4-glucanase is measured through its ability to hydrolyze and utilize a wide range of substrates such as cellotriose to cellohexose [17]. The crux of the matter in the bioconversion of lignocellulosic biomass into liquid biofuels is the search for glucose-tolerant beta-glucosidase that accomplishes the conversion of cellobiose to glucose [30]. This, in turn, determines the efficiency of cellulase enzymes for prospected industrial applications of biofuel production. Therefore, we attempted to study and compare the interaction of endo-β-1,4-glucanase and β-glucosidase from different insects belonging to six different orders reported so far with different substrates using homology modelling and molecular docking studies. Furthermore, molecular dynamics simulations can bring forth particulars on macromolecular structure-to-function relationships. Exploration of these interactions can provide insights on the status quo of insect cellulases for prospected applications and strategies to improve its efficiency and, in consequence, fully fetch the benefits of second-generation biofuels.

2.1 Target sequences retrieval

The endo-β-1,4-glucanase and β-glucosidase sequences from insects belonging to different orders reported so far were retrieved from National Centre for Biotechnology Information (NCBI).

2.2 Template selection and comparative modelling

The amino acid sequences were deduced from the nucleotide sequences of the retrieved target sequences using ExPASy translate tool. These amino acids were then subjected to BLASTP analysis and appropriate templates were chosen for each sequence based on environment likeliness, phylogenetic similarity and the quality of the experimental template structure. Modeller v9.24 was used for comparative modelling of three-dimensional protein structures by the satisfaction of spatial restraints. The modelled structures were then validated using Protein Structure Validation Suite (PSVS). An extensive set of quality scores and constraint analyses reported by the server were analyzed for structure validation. These include secondary structure elements, Ramachandran plot, Global quality scores for Verify3D, Prosall, Procheck and MolProbity.

2.3. Receptor preparation

The three-dimensional structures of endo-β-1,4-glucanase and β-glucosidase were modelled using Modeller v9.24 [24] and used as receptors for molecular docking analysis. In addition, optimization of the protein model geometry was performed prior to docking using Bio via Discovery studio 20.1.0.

2.4. Ligand preparation

Seven different ligands viz., carboxymethyl cellulose (CMC), microcrystalline cellulose, cellobiose, cellotriose, cellotetrose, cellopentose, cellohexose were downloaded from PubChem database (Table 1). Open Babel [20] in PyRx 0.8 was used for ligand preparation. The energy of the ligands was minimized, and the GUI (Graphical User Interface) was used to change energy minimization parameters. Finally, the ligand was converted into AutoDock ligand (PDBQT) format for docking.

2.5. Active site prediction

The active site of the receptors was predicted using CASTp (Computed Atlas of Surface Topography of Proteins) prior to docking analysis. This was based on recent theoretical and algorithmic results of Computational Geometry. Then, these predicted binding sites were retrieved for analysis of binding site residues and docking results comparison.

2.6. Molecular docking and binding site analysis

PyRx 0.8 that uses Vina and AutoDock 4.2 as docking software with Lamarckian Genetic Algorithm (LGA) as the scoring function was used for molecular docking analysis. Docking of endo-β-1,4-glucanase with different ligands such as CMC, microcrystalline cellulose, cellotriose, cellotetrose, cellopentose, cellohexose and docking of β-glucosidase with cellobiose was carried out using AutoDock vina [36], PyRx 0.8. A grid box that defines the docking site on the protein receptor was generated to select search space. The calculations were done using the Lamarckian Genetic Algorithm (LGA) method for protein-fixed ligand-flexible docking. The best conformation for each receptor with the lowest docked energy was chosen. The interactions, including hydrogen bonding, Van der waals were visualized, and a 2D diagram was generated using Biovia Discovery Studio 20.1.0.

2.7. Molecular dynamics simulations

WebGro macromolecular simulations were used to investigate the binding stability of final complexes (https://simlab.uams.edu/) which uses the GROMACS simulation package [1]. The ligand topology files were generated using PRODRG server by providing input in the form of PDB coordinates [27]. The input parameter Forcefield was set as GROMOS96 43a1. The whole system was solvated in water model, neutralized and 0.15M NaCl was added. Energy minimization was achieved using steepest descent approach in 5000 steps. The temperature and pressure were set at 300K and 1.0 bar, respectively. Simulation time of 100 ns and 1000 number of frames per simulation was also set. The equilibration type of NVT/NPT was used. By providing the above input parameters, RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), Rg (Radius of Gyration), SASA (Solvent-Accessible Surface Area), average number of H-bonds in each frame over time were computed as a function of time.

3.1. Template selection, Comparative modelling and Model Quality Assessment

Template that showed maximum identity to the target sequence was used for comparative modelling. An extensive set of quality scores and constraint analyses including elements of secondary structure, Ramachandran plot, Global quality scores for Verify3D, Prosall, Procheck and MolProbity were calculated using Protein Structure Validation Suite (PSVS) and shown in Tables 2, 3, 4, 5. Using the Ramachandran plot, the analysis of modelled structures identified more than 80% residues in the most favoured region. The model quality assessment using Protein Structure Validation Suite (PSVS) showed that all the generated models were of good quality and used for docking analysis.

Table 2

**Structure assessment of endo-β-1,4-glucanase from various insect sources**
Computed protein	Verify 3D		ProsaⅡ (-ve)		Procheck (Phi- Psi)³		Procheck (all)³		Molprobity clashscore
Computed protein	Raw score	Z score	Raw score	Z score	Raw score	Z score	Raw score	Z score	Raw score	Z score
Apis mellifera	0.21	-4.01	0.53	-0.50	0.09	0.67	0.03	0.18	4.12	0.82
Acyrthosiphon pisum	0.20	-4.17	0.43	-0.91	0.17	0.98	0.10	0.59	7.40	0.26
Blattella germanica	0.20	-4.17	0.57	-0.33	0.46	2.12	0.31	1.83	2.95	1.02
Apriona germari	0.03	-6.90	-0.24	-3.68	-0.20	-0.47	-0.12	-0.71	7.07	0.31
Phaedon cochleariae	0.08	-6.10	-0.10	-3.10	-0.19	-0.43	-0.22	-1.30	0.00	1.53
Cryptocercus clevelandi	0.19	-4.33	0.63	-0.08	0.43	2.01	0.32	1.89	1.49	1.27
Hodotermopsis japonica	0.19	-4.33	0.67	0.08	0.44	2.05	0.34	2.01	1.54	1.26
Mastotermes darwinensis	0.22	-3.85	0.46	-0.79	0.24	1.26	0.14	0.83	1.29	1.30
Neotermes koshunensis	0.22	-3.85	0.66	0.04	0.36	1.73	0.27	1.60	3.65	0.90
Nasonia vitripennis	-4.33	0.19	-0.99	0.41	0.75	0.11	0.30	0.05	1.29	1.39
Odontotermes formosanus	0.19	-4.33	0.62	-0.12	0.43	2.01	0.35	2.07	4.96	0.67
Teleogryllus emma	0.23	-3.69	0.38	-1.12	0.24	1.26	0.13	0.77	2.42	1.11
Panesthia angustipennis spadica	0.21	-4.01	0.59	-0.25	0.41	1.93	0.27	1.60	3.96	0.85
Periplaneta americana	0.20	-4.17	0.61	-0.17	0.46	2.12	0.35	2.07	1.55	1.26
Pediculus humanus	0.21	-4.01	0.51	-0.58	0.19	1.06	0.11	0.65	2.62	1.08
Polyphaga aegyptiaca	0.18	-4.49	0.61	-0.17	0.40	1.89	0.29	1.71	8.08	0.14
Psacothea hilaris	0.16	-4.82	0.28	-1.53	-0.29	-0.83	-0.20	-1.18	0.00	1.53
Reticulitermes flavipes	0.24	-3.53	0.52	-0.54	0.24	1.26	0.15	0.89	1.89	1.20
Tribolium castaneum	0.18	-4.49	0.44	-0.87	0.12	0.79	0.02	0.12	0.00	1.53

Table 3

**Ramachandran Plot-Procheck Server and Richardson lab results of structures of endo-β-1,4-glucanase from various insect sources**
Species Name	Ramachandran Plot Summary for selected residues³ from Procheck				Ramachandran Plot Summary for selected residues³ from Richardson Lab's Molprobity
Species Name	Most favoured regions	Additionally allowed regions	Generously allowed regions	Disallowed regions	Most favoured regions	Allowed regions	Disallowed regions
Apis mellifera	88.7%	9.2%	1.4%	0.7%	93.6%	3.9%	2.5%
Acyrthosiphon pisum	91.4%	7.4%	1.2%	0.0%	96.8%	2.3%	0.9%
Blattella germanica	93.1%	6.5%	0.5%	0.0%	97.9%	1.2%	0.8%
Apriona germari	81.3%	13.6%	3.5%	1.5%	89.4%	6.8%	3.8%
Phaedon cochleariae	83.2%	10.2%	4.1%	2.5%	85.8%	8.3%	5.8%
Cryptocercus clevelandi	94.0%	5.6%	0.5%	0.0%	97.9%	1.2%	0.8%
Hodotermopsis japonica	93.2%	6.8%	0.0%	0.0%	98.3%	1.2%	0.4%
Mastotermes darwinensis	91.2%	7.8%	0.8%	0.3%	96.2%	2.5%	1.3%
Neotermes koshunensis	92.7%	7.3%	0.0%	0.0%	97.7%	1.7%	0.6%
Nasonia vitripennis	90.6%	7.8%	1.4%	0.2%	95.7%	2.1%	2.3%
Odontotermes formosanus	93.2%	6.5%	0.4%	0.0%	98%	1.4%	0.7%
Teleogryllus emma	91.0%	8.3%	0.8%	0.0%	97.8%	0.9%	1.3%
Panesthia angustipennis spadica	93.2%	6.8%	0.0%	0.0%	97.6%	2%	0.3%
Periplaneta americana	94.9%	4.6%	0.5%	0.0%	97.5%	2.1%	0.4%
Pediculus humanus	91.7%	7.6%	0.5%	0.2%	97.8%	1.4%	0.8%
Polyphaga aegyptiaca	94.0%	5.0%	0.5%	0.5%	97.1%	2.1%	0.8%
Psacothea hilaris	84.1%	11.5%	1.7%	2.7%	90.7%	5.3%	4%
Reticulitermes flavipes	90.6%	8.1%	1.0%	0.3%	96.9%	2%	1.1%
Tribolium castaneum	88.8%	8.7%	2.2%	0.2%	95.1%	3.1%	1.8%

Table 4

Structure assessment of β-glucosidases from various insect sources
Computed protein	Verify 3D		ProsaⅡ (-ve)		Procheck (Phi- Psi)³		Procheck (all)³		Molprobity clashscore
Computed protein	Raw score	Z-score	Raw score	Z-score	Raw score	Z-score	Raw score	Z-score	Raw score	Z-score
Cryptocerus punctulatus	0.22	-3.85	0.29	-1.49	0.18	1.02	0.09	0.53	2.55	1.09
Panesthia angustipennis	0.24	-3.53	0.39	-1.08	0.09	0.67	0.08	0.47	1.39	1.29
Periplaneta americana	0.20	-4.17	0.27	-1.57	0.02	0.39	0.01	0.06	0.00	1.53
Salganea esakii	0.14	-5.14	0.08	-2.36	-0.22	-0.55	-0.15	-0.89	0.00	1.53
Anoplophora glabripennis	-0.04	-8.03	-0.58	-5.09	-0.01	0.28	-0.16	-0.95	0.00	1.53
Diabrotica virgifera	-0.09	-8.83	-0.63	-5.29	-0.08	0.00	-0.07	-0.41	0.00	1.53
Rhynchophorus ferrugineus	0.23	-3.69	0.21	-1.82	0.07	0.59	0.05	0.30	0.00	1.53
Tenebrio molitor	0.27	-3.05	0.45	-0.83	0.07	0.59	0.02	0.12	2.28	1.13
Bactrocera tryoni	-0.03	-7.87	-0.54	-4.92	-0.28	-0.79	-0.23	-1.36	0.00	1.53
Drosophila takahashii	0.07	-6.26	-0.19	-3.47	-0.31	-0.90	-0.24	-1.42	26.40	-3.00
Musca domestica	0.13	-5.30	0.00	-2.69	-0.17	-0.35	-0.20	-1.18	0.00	1.53
Rhagoletis zephyria	0.14	-5.14	0.02	-2.61	-0.48	-1.57	-0.32	-1.89	11.83	-0.50
Teleopsis dalmanni	0.04	-6.74	-0.43	-4.47	-0.32	-0.94	-0.24	-1.42	0.00	1.53
Lygus lineolaris	0.26	-3.21	0.38	-1.12	0.05	0.51	-0.07	-0.41	14.26	-0.92
Nilaparvata lugens	0.14	-5.14	0.18	-1.94	-0.13	-0.20	-0.09	-0.53	0.00	1.53
Trachymyrmex cornetzi	-0.12	-9.31	-0.76	-5.83	-0.28	-0.79	-0.25	-1.48	0.00	1.53
Anoplotermes schwarzi	0.24	-3.53	0.25	-1.65	0.04	0.47	-0.08	-0.47	6.76	0.37
Coptotermes formosanus	0.23	-3.69	0.31	-1.41	0.07	0.59	0.03	0.18	1.59	1.25
Macrotermes barneyi	0.21	-4.01	0.35	-1.24	0.13	0.83	0.10	0.59	8.67	0.04
Macrotermes carbonarius	0.25	-3.37	0.34	-1.28	0.00	0.31	-0.10	-0.59	0.00	1.53
Microtermes sp.	0.22	-3.85	0.32	-1.36	0.12	0.79	0.12	0.71	3.89	0.86
Nasutitermes takasagoensis	0.25	-3.37	0.33	-1.32	0.02	0.39	-0.08	-0.47	2.04	1.18
Neotermes koshunensis	0.23	-3.69	0.36	-1.20	0.14	0.87	0.13	0.77	1.49	1.27
Odontotermes formosanus	0.27	-3.05	0.55	-0.41	0.14	0.87	0.07	0.41	0.00	1.53
Reticulitermes flavipes	0.24	-3.53	0.38	-1.12	0.12	0.79	0.09	0.53	2.85	1.04
Reticulitermes speratus	0.10	-5.78	0.24	-1.70	-0.13	-0.20	-0.10	-0.59	22.39	-2.32
Reticulitermes perilucifugus	0.25	-3.37	0.55	-0.41	0.10	0.71	0.04	0.24	0.00	1.53
Rhynchotermes bulbinasus	0.23	-3.69	0.31	-1.41	0.04	0.47	-0.05	-0.30	16.61	-1.32
Sphaerotermes sphaerothorax	0.25	-3.37	0.26	-1.61	-0.01	0.28	-0.08	-0.47	1.95	1.19
Zootermopsis nevadensis	0.14	-5.14	0.18	-1.94	-0.12	-0.16	-0.13	-0.77	0.00	1.53
Bombyx mori	0.24	-3.53	0.27	-1.57	-0.03	0.20	-0.09	-0.53	5.70	0.55
Manduca quinquemaculata	0.10	-5.78	0.02	-2.61	0.19	1.06	-0.02	-0.12	0.00	1.53
Spodoptera fulgens	0.14	-5.14	0.06	-2.44	-0.21	-0.51	-0.23	-1.36	0.00	1.53
Zerene cesonia	0.08	-6.10	-0.29	-3.89	-0.20	-0.47	-0.19	-1.12	0.00	1.53
Zygaena filipendulae	0.19	-4.33	0.22	-1.78	0.04	0.47	0.02	0.12	1.77	1.22
Pediculus humanus	0.17	-4.65	-0.42	-4.43	-0.62	-2.12	-0.58	-3.43	0.00	1.53
Frankliniella occidentalis	0.17	-4.65	-0.06	-2.94	-0.21	-0.51	-0.23	-1.36	3.90	0.86

Table 5

Ramachandran Plot-Procheck Server and Richardson lab results of structures of β-glucosidases from various insect sources.
Species Name	Ramachandran Plot Summary for selected residues³ from Procheck				Ramachandran Plot Summary for selected residues³ from Richardson Lab's Molprobity
Species Name	Most favoured regions	Additionally allowed regions	Generously allowed regions	Disallowed regions	Most favoured regions	Allowed regions	Disallowed regions
Cryptocerus punctulatus	91.9	7.6	0.5	0.0	98.4	1.6	0
Panesthia angustipennis	90.5	7.9	0.7	0.9	96.4	2.4	1.2
Periplaneta americana	89.4	8.1	0.9	1.6	95	3.4	1.6
Salganea esakii	78.6	17.2	2.8	1.4	91.8	4.7	3.5
Anoplophora glabripennis	90.0	8.3	0.0	1.7	95.4	3.1	1.5
Diabrotica virgifera	86.7	11.7	1.7	0.0	98.4	1.6	0
Rhynchophorus ferrugineus	88.1	8.7	1.0	2.3	94.7	2.8	2.5
Tenebrio molitor	87.8	9.7	1.1	1.4	94.9	3.1	1.9
Bactrocera tryoni	84.4	13.0	2.6	0.0	90.2	8.5	1.2
Drosophila takahashii	84.3	12.0	2.6	1.0	92	5.7	2.3
Musca domestica	84.1	13.8	0.8	1.2	91.6	6.6	1.8
Rhagoletis zephyria	75.2	18.0	4.3	2.5	87.5	7.3	5.2
Teleopsis dalmanni	81.0	13.7	3.0	2.4	87.9	9.5	2.6
Lygus lineolaris	90.4	7.2	1.7	0.7	95.5	2.7	1.8
Nilaparvata lugens	84.4	12.3	0.6	2.8	90.6	7.9	1.5
Trachymyrmex cornetzi	83.1	12.8	2.7	1.4	91	5.1	3.8
Anoplotermes schwarzi	90.4	8.2	1.2	0.2	95.7	3.1	1.2
Coptotermes formosanus	90.1	8.5	0.7	0.7	96.4	2.2	1.4
Macrotermes barneyi	93.3	5.8	0.5	0.5	96.5	2.6	0.8
Macrotermes carbonarius	89.1	8.3	0.9	1.7	93.6	4.1	2.3
Microtermes sp.	92.3	6.8	0.7	0.2	97.4	1.4	1.2
Nasutitermes takasagoensis	89.9	7.7	1.4	0.9	94.9	2.9	2.3
Neotermes koshunensis	91.5	7.8	0.2	0.5	98.2	1.2	0.6
Odontotermes formosanus	90.8	7.7	0.5	1.0	96.6	2.1	1.3
Reticulitermes flavipes	92.2	7.1	0.5	0.2	97.4	2.2	0.4
Reticulitermes speratus	85.4	11.7	2.2	0.7	95.3	2	2.7
Reticulitermes perilucifugus	89.7	8.0	1.1	1.1	95.5	2.6	1.8
Rhynchotermes bulbinasus	90.3	7.8	1.2	0.7	95.7	2.7	1.6
Sphaerotermes sphaerothorax	87.5	9.6	1.2	1.7	93.9	4.3	1.8
Zootermopsis nevadensis	82.9	14.4	1.4	1.4	94.6	3	2.4
Bombyx mori	86.9	9.8	1.2	2.1	93.5	3.5	3.1
Manduca quinquemaculata	95.7	4.3	0.0	0.0	94.8	2.6	2.6
Spodoptera fulgens	83.1	12.0	2.9	2.0	89.5	6.5	3.9
Zerene cesonia	81.1	13.9	2.5	2.5	89.9	3.6	6.5
Zygaena filipendulae	89.8	8.5	1.2	0.4	95.2	3.5	1.3
Pediculus humanus	76.1	15.1	5.5	3.3	83.1	9.6	7.3
Frankliniella occidentalis	83.3	13.8	1.6	1.2	90.1	7.7	2.2

3.2. Molecular docking

Docking was carried out using Autodock Vina in PyRx interface. Prior to docking, the energy of all the ligands was minimized using OpenBabel in PyRx. The force field was set to UFF, and the energy minimization parameters were changed using GUI in PyRx. The best conformations of substrates to fit into the binding pocket of the enzymes were calculated using the Lamarckian algorithm as a scoring function. In silico docking studies of endo-β-1,4-glucanase from 19 different insects belonging to diverse orders identified that it possesses a high affinity for all the six substrates, including CMC, cellulose, cellotriose, cellotetraose, cellopentose and cellohexaose. Additionally, β-glucosidase from nearly all the reported insect sources also showed considerable affinity towards cellobiose. The binding energy calculations of protein-ligand interactions are given in Tables 6 and 7. All the lowest energy complexes predicted computationally were stabilized by Van der Waals, conventional hydrogen bonds, and carbon-hydrogen bonds sustaining the interaction between the enzyme and different substrates. The binding site was analyzed, indicating that many amino acids were involved in hydrogen bonding and other stacking interactions. All the amino acids involved in the best three interactions of endo-β-1,4-glucanase with the six substrates (CMC, cellulose, cellotriose, cellotetraose, cellopentose and cellohexaose) and β-glucosidase with cellobiose are shown in Tables 8 & 9. 2D diagrams of the best three interactions of

endo-β-1,4-glucanase with the six substrates (CMC, cellulose, cellotriose, cellotetraose, cellopentose and cellohexaose) and β-glucosidase with cellobiose are shown in Fig. 1–7. 2D diagrams showing various interactions with the substrates of the remaining interactions are shown in Supplementary Figure S1-S19.

Table 6

**Binding affinity (kcal/mol) of endo-β-1,4-glucanase from various insect sources with different substrates**
S. No.	Insect source	Binding affinity (kcal/mol)
S. No.	Insect source	CMC	Cellulose	Cellotriose	Cellotetrose	Cellopentose	Cellohexose
1	Apis mellifera	-4.9	-5.7	-6.6	-7.0	-7.0	-7.3
2	Acyrthosiphon pisum	-5.1	-6.5	-7.3	-7.1	-6.8	-8.3
3	Blatella germanica	-6.0	-6.1	-6.2	-6.7	-6.9	-7.2
4	Apriona germari	-5.1	-6.5	-7.3	-7.1	-6.8	-8.3
5	Phaedon cochleariae	-5.0	-6.0	-6.2	-7.0	-5.4	-7.5
6	Cryptocerus clevelandi	-6.2	-7.0	-6.5	-7.1	-6.2	-7.3
7	Hodotermopsis japonica	-6.1	-6.9	-6.7	-7.4	-6.3	-7.1
8	Mastotermes darwinensis	-5.0	-7.0	-7.7	-8.7	-8.1	-9.1
9	Neotermes koshunensis	-6.5	-7.3	-8.7	-7.4	5.6	-7.9
10	Nasonia vitripennis	-5.1	-6.5	-6.8	-7.3	-7.3	-6.9
11	Odontotermes formosanus	-6.1	-6.4	-6.9	-6.9	-6.3	-7.3
12	Teleogryllus emma	-5.0	-7.2	-7.9	-8.2	-8.0	-6.6
13	Panesthia angustipennis spadica	-5.7	-6.8	-7.6	-7.0	-6.3	-7.1
14	Periplaneta americana	-6.2	-6.9	-7.0	-7.4	-6.4	-8.0
15	Pediculus humanus	-5.0	-6.1	-6.6	-8.1	-7.1	-8.4
16	Polyphaga aegyptiaca	-6.5	-6.1	-7.3	-7.3	-6.5	-8.9
17	Psacothea hilaris	-5.2	-5.8	-7.5	-6.9	-6.1	-7.1
18	Reticulitermes flavipes	-5.1	-7.5	-8.0	-7.9	-7.7	-8.7
19	Tribolium castaneum	-5.0	7.4	-7.5	-8.3	-6.6	-8.6

Table 7

Binding affinity (kcal/mol) of β-glucosidases from various insect sources with different substrates
S. No.	Insect source	Binding affinity (kcal/mol) Cellobiose
1.	Anoplophora glabripennis	-5.2
2.	Anoplotermes schwarzi	-5.7
3.	Bactrocera tryoni	-5.3
4.	Bombyx mori	-7.4
5.	Coptotermes formosanus	-7.2
6.	Cryptocerus punctulatus	-6.4
7.	Diabrotica virgifera	-5.6
8.	Drosophila takahashii	-7.2
9.	Frankinelia occidentalis	-7.6
10.	Lygus lineolaris	-7.5
11.	Macrotermes barneyi	-7.9
12.	Macrotermes carbonarius	-6.3
13.	Manduca quinquemaculata	-6.1
14.	Microtermes sp	-7.4
15.	Musca domestica	-6.8
16.	Nasutitermes takasagoensis	-5.7
17.	Neotermes koshunensis	-7.6
18.	Nilaparvatha lugens	-6.2
19.	Odontotermes formosanus	-7.4
20.	Panesthia angustipennis spadica	-6.5
21.	Pediculus humanus	-6.8
22.	Reticulitermes speratus	-7.0
23.	Rhagoletis zephyria	-6.2
24.	Rhyncophorus ferrugenesis	-7.6
25.	Rhynocophorus bulbinasus	-5.8
26.	Salganea esakii	-7.6
27.	Sphaerotermes sphaerothorax	-7.6
28.	Spodoptera fugens	-4.6
29.	Teleopsis dalmanni	-6.9
30.	Tenebrio moliter	-6.3
31.	Trachymyrmx cornetzi	-6.3
32.	Zerene cesonia	-6.0
33.	Zootermopsis nevadensis	-6.3
34.	Zygaena filipendulae	-5.7

Table 8

Amino acids involved in the best interactions of endo 1,4, glucanase from various insect sources with different substrates
Sl. No.	Substrates	Source of Endo 1,4, glucanase	Interacting residues
1.	CMC	Cryptocerus clevelandi	SER144, GLY145, ASP86, ASP146, ASP190, TRP189, GLU90
		Hodotermopsis japonica	MET1, TYR3, ALA2, GLY145, ASP86, TRP189, GLU150
		Neotermes koshunensis	ASP86, TRP189, LYS192, ASP190, MET1, TYR3, ALA2
		Odontotermes formosanus	GLU150, TRP189, ASP190, ASP146, ASP86, TYR145, SER144
		Periplanata americana	ASP86, GLU150, ALA89, ALA2, TRP153, THR6, GLU90
		Polyphaga aegyptica	MET1, ALA2, LEU87, TRP189, GLU150, ASP86, TYR3
2.	Cellulose	Teleogryllus emma	ASP77, GLU432, TYR226, ARG325, PHE225
		Reticulitermes flavipes	TYR432, ARG320, TRP268, PHE220, TYR221, ASP69
		Tribolium castaneum	ASP71, ASP68, TYR220, TYR267, TYR219
3.	Cellotriose	Neotermes koshunensis	GLY51, TYR131, ALA143, SER144, GLY145, ASP149, GLU150, TRP189, ASP86, LEU87, MET1
		Teleogryllus emma	GLN379, TYR437, PHE225, TYR226, ASP77
		Reticulitermes flavipes	ASP69, GLU427, TYR221, ASP431, ARG320, ASP269, ASN267, TRP268
4.	Cellotetraose	Mastotermes darwinensis	HIS373, GLU426, ALA70, TYR221, ARG319, ASP269, THR267, TRP315, PHE220, HIS139
		Teleogryllus emma	GLN326, GLU432, TRP273, ARG325, SER272
		Tribolium castaneum	GLU221, ASP431, SER315, TYR220, ASP68, THR429, TYR267
5.	Cellopentose	Mastotermes darwinensis	HIS139, ASP72, SER270, ASP269, ASN263, SER224, ASP313, MET320, TRP268, ARG319, TYR431, ARG375, HIS373, GLU426
		Teleogryllus emma	ARG381, HIS144, ASP274, TRP321, ARG325, ASP436, TRP273, TYR437, TYR226, ASP74, GLN379
		Reticulitermes flavipes	GLY71, GLU427, ASP72, HIS139, TYR221, PHE220, TYR432, ASP269, TRP268
6.	Cellohexose	Polyphaga aegyptica	TYR142, TYR3, LYS192, SER145, ASP190, ARG143, ASP86, MET1, TRP189, GLY238, ARG241
		Reticulitermes flavipes	ASP212, ASP269, TRP268, ASN267, ARG320, ASP72, HIS139, TYR221, ASP431, GLY224, SER223
		Tribolium castaneum	GLN302, GLU264, ILE312, ASN268, TYR267, HIS374, SER70, TYR220, TYR432, SER315, ASP313, ASN263

Table 9

Amino acids involved in the best interactions of β-glucosidases from various insect sources with cellobiose
S. No	Insect source	Cellobiose
1.	Frankinelia occidentalis	GLN59, ARG355, GLU472, SER474, ARG479, CYS211
2.	Lygus lineolaris	GLU398, ASP449, TRP447, TYR333
3.	Macrotermes barneyi	TYR332, GLU397, GLU190, ASN250, GLN252, TRP369, TRP439, GLN42, GLU446
4.	Neotermes koshunensis	TRP444, TYR337, GLU402, ASN255
5.	Salganea esakii	ASP131, ARG22, TRP98, PHE129, ASP172, ARG140
6.	Sphaerotermes sphaerothorax	GLN355, TRP366, ALA331, GLU187, HIS248, TYR265

3.3 Molecular Dynamics simulations

To investigate the kinetics of enzyme and substrate interaction of some enzyme-substrate complexes, molecular dynamics simulation was used. The stability of the enzymes in complex with carboxymethyl cellulose was explored for a period of 100 ns simulation duration. The RMSD plots of interaction of several insect cellulases with CMC are shown in the Fig. 8. The changes were inconsistent and vary between various complexes. In the case of Nasonia vitripennis, for initial few seconds from 0.4 to 0.8 nm there were some fluctuations. Nevertheless, the system remained stable thereafter till 100 ns whereas, in the case of

Psacothea hilaris, the changes from the initial few seconds till 100 ns were rather obscure. All the other complexes were showing changes that vary over time. It should be noted here that the complex of enzyme with substrate did not show much fluctuations except initially for a few seconds presumably indicating its stability. The RMSF plot which is another important mobility measure of enzyme-substrate interaction is also in shown in Fig. 9.

The quest for prospective strategies to pitch in and help find novel enzymes and improve their efficiencies is ceaseless. Without exception, cellulases are in the middle of them and possess unprecedented applications. Unearthing the dual cellulolytic system in insects, where both the endogenous and exogenous cellulases coexist, was a quantum leap in the contemporaneous knowledge of the insect cellulolytic system. This revelation brought forth a glimmer of hope for efficacious cellulolysis in many industrial applications that rely on bacterial and fungal cellulases in this day and age. Cellulose digestion in insects is pondered to be a matter of considerable deliberation and has been extensively studied over the last few years. Most insects are impuissant to endure lignocellulosic biomass as their only food, whereas a proportion of them can subsist on the same. The type of biomass these insects can utilize ranges from crops to forest woody substrates. Cellulose digestion has been manifested in 20 different families belonging to 10 different insect orders [32]. These microscale biomass conversion systems have consistently fascinated researchers around the clock.

Xylophagous insects parallel small-scale processing factories that take in lignocellulose to produce sugars and facilitate them for their growth and survival. An efficiency of about 99% for cellulose and 87% for hemicellulose is achieved in each compartment of the insect gut [21]. These wood-feeding insects are well-resourced with masticating organs, gut structures, digestive enzymes and symbiotic systems that favour them to prosper on cellulosic substances [38]. The termite, Reticulitermes speratus was able to retain its ability to feed on wood even when the gut fauna was removed, and this led to the advancement of the endogenous nature of cellulose digestion in insects [40]. Exhilaratingly, this overturned the assumption that insects relied entirely on gut microorganisms for cellulose digestion. With this headway, close to 30 insects confer with the existence of endogenous cellulases belonging to a spectrum of orders including Blattaria, Coleoptera, Hymenoptera, Phthiraptera, and Orthoptera was demonstrated and putative cellulase genes were also identified in many insect species [34, 18, 19, 25, 31, 16, 33, 42, 13, 38, 3, 41].

The propensity to hydrolyze CMC and small β-1,4-glucan oligomers determine

endo-β-1,4-glucanase activity [17]. A wood-feeding cockroach, Panesthia cribrate possesses both endo-β-l,4-glucanase and β-glucosidase that can demonstrate action against crystalline cellulose unassisted by symbionts. In them, endoglucanases act on CMC and microcrystalline cellulose, whereas β-glucosidase acts on cellodextrins [28]. In the same manner, endoglucanases of R. speratus exhibited action against CMC. Nevertheless, it could also hydrolyze other small β-1,4-glucan oligomers, including cellotetrose, cellopentaose and crystalline cellulose [39]. Deficient in a crystalline structure, CMC hydrolysis is only suggestive of the potentiality of the enzyme to break β-1,4-glycosidic bonds; nonetheless, hydrolysis of crystalline cellulose was also contemplated to be the accurate indicator of cellulase activity [17]. β-glucosidases, the ubiquitous endogenous enzymes existing in insects, though not wholly involved in cellulose digestion, possess the propensity to catalyze the digestion of other linkages [29, 38].

Molecular modelling simulates the molecular behaviour and enables acquiring particulars on its physicochemical properties [9]. In silico docking studies of endo-β-1,4-glucanase from 19 different insects belonging to different orders identified that it possesses high affinity for all the six substrates, including CMC, cellulose, cellotriose, cellotetraose, cellopentose and cellohexaose. Additionally, β-glucosidase from nearly all the reported insect sources also showed considerable affinity towards cellobiose.

Van der Waals, conventional hydrogen bonds, and carbon-hydrogen bonds stabilize the interaction between the enzyme and different substrates. These hydrogen bonds are considered significant for interaction specificity [10]. Panesthia angustipennis spadica, the wood feeding cockroach possess midgut cellulolytic system belonging to GH1 family exhibiting substrate specificity to cellobiose and relies solely on β-glucosidase for energy production. Further, in the common cockroach, Periplaneta americana

endo-β-l,4-glucanase is strongly involved in the degradation of the moss starch, lichenin and found to be active on crystalline cellulose [8]. One of the paradigmatic examples of maintaining conventional cellulose degradation can be traced among termites. In Reticulitermes speratus, endoglucanases from salivary glands act against CMC and one among those was able to act on cellotetraose [39]. Coptotermes formosanus endoglucanase is known to hydrolyse filter paper cellulose and crystalline cellulose [46, 19]. Additionally, in Odontotermes formosanus, endoglucanase from the whole-body extract demonstrated highest activity against CMC [45]. A midgut endoglucanase exhibiting highest activity on CMC and cellopentose, and a β-glucosidase (GH1) with astounding thermo-tolerance in midgut with substrate specificity to cellobiose is found in Nasutitermes takasagoensis [35, 37]. Similarly, in Nasutitermes walkeri endoglucanase acting on cellulose and cellotetraose and β-glucosidase acting on cellobiose and on cellotriose was found [26]. Phasmatodea cellulase enzymes are ubiquitous and have a greater significance in obligate leaf eater’s anterior midgut being the primary location [29]. Gut fluids from 68 phytophagous insects belonging to 8 orders act more on CMC than crystalline cellulose. Moreover, the exploration of plant proteins that can inhibit cellulase enzymes in phytophagous insects is also gaining traction in the area of insect pest management, which relies on knocking down cellulases[11]. In a nutshell, endoglucanases and β-glucosidase from insects are magnificent sources predominantly expressed in salivary glands, foregut and midgut which are highly active on copious substrates including amorphous cellulose, crystalline cellulose, and several other oligomers. Further, the molecular dynamics simulations revealed the complexity and stability of interaction of enzyme with the substrate based on the mobility measures RMSD and RMSF

Insect cellulases are extremely versatile enzymes to be reckoned with and find their applications predominantly in biofuel production. One of the onerous challenges for the commercialization of a process for bioconversion of lignocellulose to ethanol is the coherent saccharification of cellulose at low cellulase concentrations [2]. Both endo-β-l,4-glucanase and β-glucosidase from insects have a higher affinity for all the substrates and, consequently, a better source in terms of environmental performance and overall energy efficiency. Using CMC as the substrate, the cellulase activity in the wood-feeding species of Coptotermes formosanus is estimated to be 10³–10⁴ µmol of reducing sugar min^− 1 ml^− 1 [33]. Bridging the gap between xylophagous insects and industrial platform for the production of cellulosic ethanol is an absolute necessity. The current in silico study is an attempt to comprehend the competence and propensity of insect cellulases for industrial applications. Nevertheless, critical investigation is indeed necessary to improve the efficiency of insect cellulases.

Funding

The first author gratefully acknowledges Dr. Kalaignar M. Karunanidhi Endownment Scholarship (2018-19), University of Madras (No. F.11-Endow/Ph.D Scholarship/2018-19/712 dt 21 May 2019).

Conflicts of interest

The authors declare that they have no conflict of interest

Authors' contributions

All authors have contributed equally.

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Table 1 is available in the Supplementary Files section

Efficacy of Insect Endogeneous Cellulases for Ligno-cellulosic Biofuels Deciphered Using Molecular Docking Studies

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Target sequences retrieval

2.2 Template selection and comparative modelling

2.3. Receptor preparation

2.4. Ligand preparation

2.5. Active site prediction

2.6. Molecular docking and binding site analysis

2.7. Molecular dynamics simulations

3. Results

3.1. Template selection, Comparative modelling and Model Quality Assessment

3.2. Molecular docking

3.3 Molecular Dynamics simulations

4. Discussion

Declarations

References

Table

Supplementary Files

Status:

Version 1