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In silico exploration of lignin peroxidase for unraveling the degradation mechanism employing lignin model compounds

Lignin peroxidase is a heme-containing biocatalyst, well-known for its diverse applications in the fields from environmental chemistry to biotechnology. LiP-mediated oxidative catalysis is H2O2-dependent, and can oxidize phenolic, and non-phenolic substrates by oxidative cleavage of the C–C and C–O bonds of lignin. In contrast to fungi-derived LiP, the binding affinity of bacterial-derived LiP to lignin at the molecular level is poorly known to date. Tremendous wet-lab studies have been unveiled that provide degradation and biotransformation information on kraft lignin, whilst studies on the completely transformed compounds and the degradation of each transformed compounds simultaneously during degradation are scarce. To gain an understanding of the degradation process using docking, and MDS based studies, we assessed the binding affinity of selected lignin model compounds with bacterial origin LiP and validated such docked complexes exploiting 30 ns molecular dynamics simulations. We selected and picked a total of 12 lignin model compounds for molecular modeling analysis, namely two chlorinated lignin model compounds (monomer) (2-chlorosyringaldehyde and 5-chlorovanillin), eight standard lignin model compounds (veratryl alcohol, syringyl alcohol, sinapyl alcohol, methyl hydroquinone, guaiacol, coniferyl alcohol, catechol, and 4-methoxy phenol), while, two 4-O-5, and β-O-4 linkage-based multimeric model compounds (dimer: 2-methoxy-6-(2-methoxy-4-methylphenoxy)-4-methylphenol; trimer: syringyl β-O-4 syringyl β-O-4 sinapyl alcohol). Far more specific binding residues were observed from XP-Glide docking, as TYR, HIP (protonated histidine), PHE, VAL, ASP, THR, LYS and GLN. The binding affinity was confirmed by the Gibbs free energy or binding energy (ΔG) score; furthermore, it is found that the maximum binding energy seems to be observed for 4-methoxyphenol with a Glide score of −3.438 with Pi–Pi stacking and H-bond type bonding interactions, whilst the lowest XP Gscore as −8.136 with Pi–Pi stacking and H-bond (side chain) type bonding interactions were found for the trimer model compound. The docked complexes were further evaluated for deep rigorous structural and functional fluctuation analyses through high-performance molecular dynamics simulations-DESMOND, after a post simulation run of 30 ns. The RMSD trajectory analyses of the protein-ligands were found to be in the equilibrium state at the end of simulation run for multimeric lignin model compounds. In addition, ionic ligand–protein interaction occurs among chlorinated compounds, while hydrophobic and H-bond contacts have frequently been observed in all lignin-model compounds. The findings herein demonstrate that bacterial LiP can effectively catalyze multiple lignin model compounds, and it might further be used as an effective tool for sustainable mitigation of diverse environmental contaminants.


Introduction
Lignin is found as a primary organic chemical in plant cells, along with cellulose and hemicellulose. 1 Three sorts of phenylpropanoids act as a polymer forming unit in the complex structure of lignin polymers, i.e. p-hydroxyphenyl (H), guaiacyl (G) and syringyl (S) (from p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol, respectively). 2,3 As a natural organic polymer, lignin exists near about 20% in the lignocellulosic component. 4 Several cross-linked phenolic linkages provide strength and complex structural framework as key linkage contributors to the lignin polymer. The key component of so and hardwood lignin contains a variety of common linkages, i.e. b-5/a-O-4, b-O-4, a-carbonyl, etc. [5][6][7] The pulp & paper industries utilize a massive amount of plants as raw materials (lignocellulosic) for manufacturing of papers in different quality. During the paper processing steps, the lignin undergoes with several chemical reactions, which leads to depolymerization of lignin polymer and cleavage, consequently many lignin derivative as low molecular weight compounds generates, and expelled in wastewater (i.e.chlorinated, phenolics). The pulping, bleaching, and deinking processes are the key chemical process stages where specic chemical agents, i.e. Na 2 S, NaOH and ClO 2 , are utilized, which are oen accountable for producing different ligninderived pollutants. 8 This contributes to the processing and drainage of chlorinated, sulfonated phenol, and several free phenolic monomer units through processing plants, and such may triggers signicant environmental hazards in the form of different toxicological endpoints. 9,10 Therefore, sufficient degradation of lignin-based compounds is needed to keep the environment clean and safe from diverse ligninrelated contaminants. The high complexity of the lignin structure in a polymer raises the main challenge in the study of molecular routes for the biodegradation of lignin. 11,12 The presence of phenolic compounds, including chlorine, sodium, and sulte, makes it highly biologically recalcitrant toward degradation. Since the structure and chemical composition are dependent on lignin, whether it is sowood or hardwood, the full structural, functional attributes of lignin is still less known to date. 13 Thus, the uncertainty in the structural chemistry ranges from plant to plant is the second biggest challenge. Biotransformation and biodegradation of kra lignin have been evaluated tremendously with ligninolytic enzymes; however, clear information on completely bio-transformed compounds is very scarce. In silico studies could provide the screening of each transformed compound to understand the degradation mechanism at the molecular level. [14][15][16] For this, various lignin model compounds have been selected to replace lignin for the degradation process. KL as a model lignin has been widely studied in conventional in situ bioremediation. Depolymerized lignin yields various lignin model compounds, which are poorly studied at the molecular level. In this context, numerous lignin model compounds have been used in a computational study, including veratryl alcohol, syringyl alcohol, sinapyl alcohol, methyl hydroquinone, guaiacol, coniferyl alcohol, catechol, and 4-methoxyphenol as standard lignin model compounds. Besides the standard monomeric lignin model compounds, numerous polymeric lignin model compounds (b-O-4, and 4-O-5 linkage-based), i.e. a dimer and trimer (dimer: 2-methoxy-6-(2-methoxy-4-methylphenoxy)-4-methylphenol; trimer: syringyl b-O-4 syringyl b-O-4 sinapyl alcohol) have also been exploited in computational studies to understand the lignin degradation mechanism using ligninolytic enzymes. [17][18][19][20][21] To carry out such a computational study, numerous members of the ligninolytic enzyme family (Oen oxidoreductases in catalytic nature) have been exploited in molecular dynamics simulations and molecular docking assays to determine their binding affinity and behavior in the catalysis of lignin model compounds. [22][23][24][25] Lignin peroxidase (EC 1.11.1.14) is a member of the ligninmodifying enzymes (LME), oen heme-containing H 2 O 2dependent oxidoreductases, which seems to act on a wide range of non-phenolic, phenolic, and lignin-derived compounds. [26][27][28] In addition to the remediation of several phenolic and non-phenolic compounds, LiP has been used as a promising biocatalyst in industrial processing for diverse biotechnological applications. [29][30][31] Numerous bacteria and fungi are known for the extracellular production of LiP. 12,32 The LiP-mediated catalytic reaction is hydrogen peroxide (H 2 O 2 )-dependent and completes it catalytic cycle in four specic catalytic reaction cycles. 33,34 Radical cations are responsible for the basic oxidative properties of LiP by oneelectron oxidation, oxidation of an electron, leading to the cleavage of the side chain component, resulting in demethylation, intramolecular addition, and subsequently rearrangement. 29,35 Specic catalytic oxidative processes related to LiP include hydroxylation of benzyl methylene groups and benzyl alcohol oxidation into aldehydes or ketones, along with the oxidation of phenol. 36,37 A range of protein structures (LiP) at different resolutions have been reported since the 1990s, which are freely available in the Protein Databank (https://www.rcsb.org) and can be accessed worldwide for scientic/academic purposes. [38][39][40][41] Structures of fungalderived LiP at varying resolution are available in the PDB, and are quite a bit more prevalent than structures of bacterial-derived LiPs or dye-decolorizing peroxidases (DyP). From the structural perspective, the protein structure of LiP from Phanerochaete chrysosporium comprises 351 amino acid residues, and two chains (A and B) in its complex (PDB: 1B85). 42,43 The structure and functional attributes of LiP are similar or identical to those of DyP. 44 LiP has been shown to have similar ligninolytic activity to DyP-type peroxidase from Enterobacter lignolyticus. DyP-type peroxidase comprises 318 amino acid residues, and four chains (A, B, C and D) in its protein complex (PDB ID: 5VJ0). 43,45 Similar to DypA, a DypBbased study has been reported for the oxidizing catalysis of polymeric lignin, and lignin model compounds by cleavage of C(a)-C(b) bonds, which concluded the signicant potential for lignin oxidation. 46,47 As stated above, fungi-derived LiPs have been studied tremendously in the last few decades for catalytic activity (oxidative cleavage) toward lignin, and other similar compounds. 48,49 However, no bacterial-origin LiP structure is available for in silico study, whilst several bacterial-derived LiPs have already been reported for conventional lignin degradation and dye decolorisation studies. 9,50-53 The active site with key binding residues of bacterial LiP is unknown to date. Furthermore, there is no clear understanding of the molecular basis for lignin biodegradation in association LiP with lignin. The scarce knowledge about the degraded compounds and their toxicity assessment is the key aw of conventional bioremediation. To address this aw, we conducted an in silico study to evaluate the degradation potential of LiP utilizing several lignin model compounds. 14,15,54 To achieve this, comparative modeling of bacterial-derived LiP, and the use of build protein model for docking studies, and simulations of 30 ns, were performed with selected lignin model compounds, which could provide the binding and structural interactions. Furthermore, such molecular modeling ndings might be used for protein-ligand binding/catalyzing interaction (LiPlignin model compound) at the molecular and atomic levels. Therefore, in the present study, we performed the protein structural and functional analyses using ve stage computational methodologies (Fig. 1). Furthermore, we evaluated the degradation efficiency of LiP through Glide-docking by contrasting with a known control (azure-b). A total of 12 standard lignin model compounds in three avor were used and comparatively analyzed for binding energy, key interacting AA residues, and certain bondings type for all chlorinated, phenolic, and polymeric compounds. The comparative modeling, comparative binding energy, ligand interactions with possible bond interactions and three-dimensional binding pocket residue analysis, followed by rigorous structural and functional analysis through MDS of 30 ns, are the focus of this study. For further validation, the docked complexes were inspected through Desmond (High-performance molecular dynamics simulations) simulation of 30 ns. Post simulation analysis indicates that multimeric complexes were considered to be in an equilibrium state with hydrophobic and H-bond type contacts. In addition to the binding affinity with several lignin model compounds, binding and interactional attributes have indeed been highlighted in the structural and functional dynamics of the protein (LiP).

Physicochemical properties analysis
The PPs help to determine the essential characteristics of a protein in different biological forms. The PPs of the selected protein sequence (LiP) were predicted using the ExPASy Prot-Param tool (https://web.expasy.org/protparam/). 55,56 The described server was utilized to predict, e.g. molecular weight (mol. wt), molecular calculations, the composition of amino acid residues, the molecular formula, the composition of atoms, the theoretical isoelectric point (pI), the total number of positive (Arg + Lys) and negative (Asp + Glu) residues (+R/ÀR), the extinction coefficient (EC), the instability index (AI), and the grand average of hydropathicity (GRAVY).
Secondary structure (SS) and sequence annotated by structure (SAS) analysis Secondary Structure Prediction (SS) is an in silico technique, which is entirely based on hydrogen bonding patterns and certain geometric constraints; it assigns all residues from possible states. 57 The annotated secondary structure elements (SSEs) could be predicted using the available PSSP tools. 58 Three basic SSEs are; alpha-helix, beta-sheet, and turn, which could be predicted from the available SSP tool with query sequence of intrest. The secondary structure elements for the a-helix, b-sheet, and turn of the amino acid sequences of the modeled LiP protein were predicted using two SSP tools, namely; PSI-blast-based secondary structure prediction (PSIPRED) (http://bioinf.cs.ucl.ac.uk/psipred/) and the selfoptimized prediction method with alignment (SOPMA) (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page¼/ NPSA/npsa_sopma.html). [59][60][61] Secondary structure prediction of the query protein gives insight into the various elementary components of the protein. However, an overview of regions or sites of interest in a protein sequence including; posttranslation modications, binding sites, active sites present in the enzyme, etc., could be predicted and marked with SAS. We performed the "SAS -Sequence Annotated by Structure" prediction for the target modeled protein (LiP) to understand and predict annotation for various functional attributes throughout the protein sequence. The "SAS -Sequence Annotated by Structure" server from EMBL-EBI was used by accessing the following URL address: https://www.ebi.ac.uk/ thornton-srv/databases/sas/. 62,63 The listed web resource was exploited for SAS prediction of the query protein sequence, corresponding to each amino acid of bacterial LiP.

Protein 3D model prediction exploiting comparative modeling
We picked the translated protein sequence from the nucleotide sequence of the bacterial LiP gene from NCBI with GenBank: CP002272.1. 64 The retrieved nucleotide sequence from the genome of Enterobacter lignolyticus SCF1 was further exploited for translation to obtain the protein sequence using the standard tool available on NCBI. 65, 66 The nucleotide sequence length was 900 bp in size, while the translated FASTA sequence comprised of 299 amino acid residues count ( Table 1). The protein FASTA sequence was used to building a protein model and such model further proceeded into protein exibility modeling and docking studies follwed by MDS. The obtained protein sequence (LiP) was used as a query sequence for constructing a protein model through comparative modeling. We used the SWISS-MODEL (http://swissmodel.expasy.org) server for 3D structure prediction from the query sequence in automated mode. 67,68 Comparative modeling was carried out using the best matching template in automated mode.

Model evaluation/assessment
Built protein models oen include numerous errors and quality issues, as a consequence of which such a model would not be useful in protein studies. 69 In order to assess the reliability and accuracy of the stereochemical quality of the model, it is important to conrm and evaluate the quality of the model. A different methods have been used to evaluate the internal consistency and reliability of the modeled lignin peroxidase (LiP) structure. PROCHECK, MolProbity and a few other programs were used to assess the stereochemical quality of the model by quantifying residues in the permitted Ramachandran plot zones. [70][71][72] Model exibility prediction The CABS-ex 2.0 (http://biocomp.chem.uw.edu.pl/CABSex2) is a protein exibility prediction server, with an efficient simulation engine that could be used for large-scale protein conformational transitions of protein systems. 73 The CABS-ex model is a computationally efficient alternative to all-atomic molecular dynamicsa conventional protein simulation methodology. We used the query protein's (modeled-LiP) FASTA sequence for the prediction of protein exibility, to access the protein uctuations; the prediction methodologies of the server rely on molecular dynamics simulation-based data and methodology.

Protein model preparation
Protein preparation, protein structure renement, and energy minimization. Before being used for molecular docking, the selected protein model, LiP, was applied for protein structure renement and energy minimization using the protein preparation wizard of the Schrödinger suite. The modeled PDB structure le is not appropriately suitable for molecular docking with Glide. 86,87 A standard PDB le oen consists of heavy atoms and may contain a co-crystal ligand itself; other components, including water molecules, metal ions, and other cofactors, might be present. The selected protein (LiP) was prepared by exploiting the protein preparation wizard (pre-processed, optimized, and minimized), through the addition of hydrogen atoms, hydrogen bond optimization, and verifying the protonation states of HIS, GLN, and ASN amino acid residues (Schrödinger release 2020-3: Glide, Schrödinger, LLC, New York, NY, 2020). 82,88 Energy minimization was achieved with the help of a default constraint of 0.3 A RMSD and applying the OPLS 2005 force eld. 85 Extra precision Glide docking Grid preparation. The LiP receptor grid was generated by specifying the (active) site residue, which was identied by the SiteMap tool. Once the receptor grid was generated around the binding site of LiP, the ligands were docked with the protein (LiP) using the Glide version 5.8 (Grid-based ligand docking with energetics) docking protocol. 89 XP docking using Glide. Computational docking is a method oen used to measure the binding behavior of one molecule (protein or receptor) to the other molecule (ligand) that bonds to form a stable complex. A docking study was carried out with all compounds (A total of 12 compounds) using the Glide, Schrödinger suite (Schrodinger v2019-1, Maestro v11.9). All prepared ligands (optimized and minimized energy) were docked with the modeled protein (LiP) using a Monte Carlo based simulation algorithm (MCSA). In Glide docking, there are two types of scoring function -Glide score 2.5 SP (Standard Precision Glide) and Glide score 2.5 XP (Extra Precision Glide). SP is a default-docking procedure; it will screen out a large number of ligands of unknown quality. Nevertheless, the XP 12 docking and scoring function is a more inuential and discerning process, where the running time is longer than SP docking. In XP docking, each ligand generates 10 000 poses. The generated structure is highly accurate and nally suggests the excellent docked complexes based on the energy term Emodel. The best orientation structures were further ranked based on XP Gscore. 90 A lower XP Gscore for a structure indicates better interaction towards binding site residues. Glide evaluated the docked conformers (G) score. The G value was calculated accordingly as shown below:     complexes. 93 By extending the specications, a startup process with the system builder as SPC (simple point charge) was built, and a water model as a solvent within the periodic boundary condition of the cubic box set at 10 A Â 10 A Â 10 A for the complex of concern was also built. In addition, the energy of the system was minimized, and salt of a specic concentration was also added, namely Na + with a concentration of 85.130 mM (total charge + 52) and Cl À with a concentration of 50.751 mM (total charge À 31). For a simulation run of 30 ns, the said system was built, and also initiated under the NPT ensemble, where a 300 K temperature was maintained by the Berendsen thermostat algorithm and 1 atm pressure by the Berendsen barostat algorithm throughout the simulation process. 90 Coulombic interactions were analyzed using the smooth particle mesh Ewald simulation method with a cut-off of 9.0 A distance by implementing the SHAKE algorithm. 94 Output parameters as the default were set for output report generation with respect to C a , and the ligand. A 30 ns trajectory of the protein (C a ) and ligand interaction was captured to evaluate protein and ligand behavior throughout the simulation run of about 30 ns.

Physicochemical properties analysis
ProtParam is an online tool for determining the specic physical and chemical parameters of a query protein stored in the Protein Data Bank, SWISS-Prot or TrEMBL or even from a userinput based protein sequence. Computational physicochemical analysis of the modeled protein (modeled LiP) was obtained from ProtParam online tools. The computed parameters of the PP tool include the molecular weight, theoretical pI, amino acid    Table 3, whilst the amino-acid residues in percentage with the atomic distribution has shown in Fig. 2(A and B) respectively.
Secondary structure (SS) and sequence annotated by structure (SAS) analysis Secondary structure elements (SSEs) of a protein, like alphahelix and beta-strands, are the key constituent parts of a protein structure in its three-dimensional architecture. The SS was analyzed, and we found the result of the query protein sequence using the PSIPRED and SOPMA SSP tools. Furthermore, the sequence annotated by structure was also obtained from the EMBL-EBI server. The secondary structure prediction results of the modeled LiP as the query protein predicted using SOPMA were found to be 35.45% as alpha-helix, 18.06% as extended strand, 42.81% as random coil. Results from the PSIPRED server were also obtained in graphical cartoon form, with helices, strands, and coils in the color-coded legends ( Fig. 3(A and B)). The annotation of various extended vital parts of SSEs, including sheets, beta hairpins, beta bulges, strands, helices, helix-helix interactions, beta turns, gamma turns, domains, ligand-binding sites and loops, was obtained from SAS -(Sequence Annotated by Structure) as shown in Fig. 4. The detailed results of SSP obtained from SOPMA has been explained in Table 4.

Protein 3D model prediction exploiting comparative modeling
We obtained a protein model of the query protein sequence (bacterial-LiP) using the SWISS-MODEL server in automated mode. For the query sequence, we found a total of 33 matching templates. We picked the 5vj0.1. A template with maximum identity (100%) for model building and further model assessment reports. We built a model by picking a maximum identity template chain as a 100% sequence identity, GMQE-0.97, QSQE-0.77, resolution-1.9 A, and the oligo state as a homodimer with 2X heme ligands in a protein complex (Fig. 5). The built model based on the stated template was analyzed for quality parameters that were found to be QMEAN-0.32, cb-0.31, solvation-0.87 with torsion-0.10. The built model was found to belong to the DyP type peroxidase family, which is a ligninolytic enzyme. A further modeled LiP model was subjected to structural assessment to evaluate the structure for various parameters, i.e. Ramachandran favored MolProbity, Rotamer Outliers, and so on. We found MolProbity score ¼ 0.65, 98.82% for

Model evaluation/assessment
The structural validation of the built protein model (Modeled LiP) was carried out using PROCHECK, RAMPAGE, ERRAT, and Verify 3D online server. The model evaluation analysis was revealed and have described in subsequent sections. PRO-CHECK was used for prediction of the analysis of residues by residential geometry and the overall structural geometry governs the stereochemical quality of a protein structure. Verify 3D determines a structural class based on its location and environment (alpha, beta, loop, polar, non-polar, etc.) and compares results with suitable structures, to determine the compatibility of an atomic model (3D) with its amino acid sequence (1D). ERRAT is a crystallography-determined verication program for protein structures. The Ramachandran plot provides a convenient view of the torsion angle distribution in a protein structure. PROCHECK was used to measure the stereochemical quality residue-by-residue of protein structures with the Ramachandran plot (RP) analyses. The RP analysis found that 92.0% of the model and template amino acids were in the most favorable region and 7.2% were in the additional allowed region, 0.6% of amino acids generously are in allowed regions, and 0.1% are in disallowed regions. The above validation indicates that the backbone conformation of the constructed LiP models and their non-bonded interactions are both reasonable within a typical standard range. PROCHECK, RAMPAGE, ERRAT, and Verify 3D have checked and veried the modeled LiP protein model. A reasonable sequence to structure agreement was shown by the Verify 3D assessment, since no amino acid has a negative score, and 97.32% of the residues have averaged 3D-1D score S 0.2, it should be noted that the above zero compatibility values match the acceptable side-chain

Model exibility prediction
The CABS-ex is an effective modeling method for fast protein structure exibility simulations. The exibility of the modeled protein (LiP) was predicted using the CABS ex server, utilizing the built LiP model. It is focused on the CABS models, an established tool for the modeling of coarse-grained proteins. A Protein Structure in PDB (or Protein PDB Code) is the only data needed as an input. A signicant feature of CABS protein models is that their spatial resolution (in C-alpha chain) allows the reconstruction of a whole-atomic depiction of physically realistic models. As a starting point for CABS simulation, the input structure has been used for predicting the exibility of the LiP model. We found results in different parameters, including project information, model detail (a total of ten models were built), and contact maps with trajectories along with uctuations plot. The exibility prediction of the LiP model has summarized in Fig. 8A-C.

Extra precision Glide docking
Docking results in different parameters were obtained from the docking program, in a single output le consisting of the energy score and several interactional parameters. Protein + ligand complex les were also obtained for each ligand, and each were further explored for three-dimensional ligand interactions, exploring active site amino acid residues with possible bond interactions. The Glide docking score for chlorinated ligand complexes was found as À4.763 kcal mol À1 (XP Gscore) for 2chlorosyringaldehyde with XP H-bond score À0.517 kcal mol; this docked complex interacts with HIP 226 and PHE 261 amino acids with Pi-Pi stacking and H-bond (side-chain) interacting bonds. The Glide docking score was À4.723 kcal mol À1 (XP Gscore) for 5-chlorovanillin with a À0.607 XP H-bond score found; TYR 187, HIP 226, ARG 244, and PHE 261 residues recognized as active site residues were further found to have making contact with Pi-Pi stacking, H-bond (side-chain), salt bridge, and hydration site (displacement) type interactions. Moreover, the binding free energy for the docked chlorinated phenolic docked complex was predicted using prime/MM-GBSA, which gave a signicant score of À55.388 kcal mol À1 for 2-chlorosyringaldehyde and À47.558 kcal mol À1 for 5chlorovanillin.  Three amino acid residues, HIP 226, PHE 261, and THR 271, are involved in the interaction with LiP by forming PI-Pi stacking, H-bond, and hydration site (displaced) type interactions in veratryl alcohol with a Glide docking score value of À4.829 kcal mol À1 (XP Gscore). Two amino acid residues (HIP 226 and THR 271) were found to be involved in forming an interaction with syringyl alcohol with a Glide docking score value of À5.047 kcal mol À1 (XP Gscore). Sinapyl alcohol was    (XP Gscore), and À4.007 kcal mol À1 (XP Gscore), respectively. Coniferyl alcohol reveals interactions with VAL 152, ASP 153, THR 209 and LYS 210 amino acid residues with H-bond (side chain) and H-bond (backbone) type interactions with a Glide docking score value of À5.312 kcal mol À1 (XP Gscore). Catechol was found to form interactions with VAL 152, THR 155 and ARG 244 residues by Pi-Pi stacking and H-bond type interactions with a Glide docking score value of À5.544 kcal mol À1 (XP Gscore). 4-Methoxy phenol was found to interact with three amino acid residues (GLN 185, HIP 226 and PHE 261) by forming Pi-Pi stacking and H-bond (side chain) type interactions with a Glide docking score value of À3.438 kcal mol À1 (XP Gscore). The polymeric lignin model compounds (dimer and trimer) were also analyzed for binding or interacting key residues with possible bond interactions. For the dimer compound, the Glide docking score value was obtained as À7.117 kcal mol À1 (XP Gscore), with two interacting amino acid residues (HIP 226 and PHE 261) by forming Pi-Pi stacking type bonding. For trimer compound docking, the Glide docking score value was obtained as À8.136 kcal mol À1 (XP Gscore), with three key interacting amino acid residues (HIP 226, VAL 227 and ARG 244), involved in Pi-cation and H-bond type bonding. We compared all docked compounds with a known control (azureb) which seems to have a docking score value as À4.707 kcal mol À1 (XP Gscore). The trimer compound was found to have the lowest binding affinity, in contrast to the control, which indicates that the dimer and trimer compounds have signicantly lower binding energy scores. The comparative results of the docked compounds have revealed in Table 5. The comparative statistical XP Gscore graph has shown in Fig. 9.
However, the binding interactions of the stated ligands in 3-D view have shown in Fig. 10-12.

Molecular dynamics simulation event analyses
The LiP and ligand (a set of a total of 12 lignin model compounds) were employed in molecular dynamics simulation to investigate the protein-ligand behaviors and interaction over the duration run of 30 ns. We analyzed all of the docked complexes rigorously for protein-ligand interactions, ligand conformation, protein ligand contacts, and also for the system potential energy score at the end of simulation run. The protein-ligand RMSD trajectory was analyzed for each complex, using simulation quality analysis (SQA), simulation event analysis (SEA) and the simulation interaction diagram (SID); such features are an in-built part of the Desmond module for calculating the energies, root-mean-square deviation (RMSD), and root-mean-square uctuation (RMSF). SQA is a useful parameter to qualitatively validate the system stability throughout the simulated length of time for the given temperature, pressure, and volume of the total simulation. The Root Mean Square Deviation (RMSD) is used to measure the average change in displacement of a selection of atoms for a particular frame concerning a reference frame. The docked conformations of the lignin model compounds at the active sites of LiP enzymes exhibited similar molecular contacts, including hydrogen bonding (Hb) contact, hydrophobic (Ph) contact, aromatic-aromatic (Ar) contact, hydrophilic-hydrophobic (HH) contact, and acceptor-acceptor (AA) contact, which were consistently present in each docked complex. MD simulations were further assessed and evaluated for the robustness and stability of the predicted 3D structures of the LiP-lignin complexes. At the end of the 30 ns simulation, it was observed that such LiP-lignin complexes (all 12 model compounds) were stable. The RMSD for the LiP (C a ) was also found to be stable, corresponding to lignin as the ligands (Fig. 13A-L). The protein backbone became equilibrated aer 30 ns with a mean RMSD value of 1.5 A. The complexes C, E, G, I, J, K and L clearly showed the stability of the protein backbone and ligand compounds by attaining the system in an equilibrium state at the end of the simulation run. However, the lignin model compounds (A, B, D, F, and H) become stable initially, but relatively less stable at the end of the simulation run. Hydrophobic and H-bond both seemed essential points for binding of the lignin model compounds to LiP. All 12 lignin model compounds were found to have hydrophobic and H-bond type contacts during simulation, the detailed interactions of the ligand and LiP residues are depicted in Fig. 14. The chlorinated lignin model compounds ( Fig. 14A and B) were found to form an ionic contact with residue HIS 226. (Fig. 16 depicts the protein-ligand contact histogram, and displays the type of protein-ligand interactions; a plot provides a timeline representation of the same contacts, which can help with visualizing the possible interactions found during the simulation run). In chlorinated complexes (2-chlorosyringaldehyde, 5-chlorovanillin), HIS 226 was found to have an ionic-type protein-ligand contact. Eventually, the MD simulation results demonstrated that complexes during a simulation run were very stable. Proteinligand binding is only feasible when the change in the system's Gibbs free energy (DG) is negative when the system achieves equilibrium at constant pressure and temperature. Since the extent of the ligand-protein interaction is dened by the magnitude of the negative DG, it could be considered to determine the stability or, alternatively, the ligand affinity to a provided acceptor. The average potential energy of all systems were also analyzed (Fig. 15). However, the maximum P_E was found for 2-chlorosyringaldehyde, which was observed as À132 506.645 kcal mol À1 . Meanwhile, the minimum was observed for sinapyl alcohol as À115 583.316 kcal mol À1 . The PE plots with detailed descriptions in tabular form has described in Fig. 15.

Discussion
The environmentally sustainable nature of biodegradation technology has made it even more advantageous for environmental remediation. Lignin and lignin-derivatives, phenolics, are among notable contaminants, oen produced during the industrial manufacturing process, and thus as byproducts expelles from several industries. LiP-mediated biodegradation of lignin and its derivates is a most promising example. In our previous study on Bio-transformation of kra lignin, we achieved a 65% bacterial-mediated degradation of kra lignin with Serratia liquefaciens. 9 Wet-lab studies are scarce in complete information on the interactions among AA and ligands, and even the clear-cut role of enzymes at the atomic level is still unclear. To address such aws, to increase the catalytic/ degradation efficiency of LiP, and determine the binding mechanism, virtual screening, predictive approaches, and structural analysis must be exploited in context to enhance the catalytic potential of the enzyme. Computational studies, including molecular docking and molecular dynamics simulation, are among the vital computational techniques that have been increasingly adopted in the eld of predictive bioremediation. 15,75 A similar ligninolytic enzyme (Laccase)-based study, exploiting several lignin model compounds, has been conducted in the last couple of years. 23,77 However, bacterial-derived LiP-mediated computational studies are poorly known at present. Therefore, we conducted this study to ll the research gap with observing the binding affinity of lignin model compounds (2 chlorinated, 8 monomer, and 2 multimeric compounds) with bacterial derived LiP. To assess the protein structural and functional attributes of bacterial derived LiP, we predicted the secondary structure of bacterial LiP; protein exibility and computational physicochemical properties were also analyzed from a catalytic point of view, we accessed the protein (LiP) interactions through docking to evaluate and understand the binding affinity with the corresponding key amino acid residues. We found the key binding interacting amino acid residues were PHE, VAL, ASP, THR, LYS, ARG and GLN as contributing residues of the active side with Pi-Pi stacking, H-bond (side-chain), salt bridge, and hydration site (displacement) type interactions. Molecular docking with Glide, the protein preparation oen carried out using protein preparing wizard, that perform a notable change in specic amino acids, i.e. HIS, CYS by protonated them that lead to slightly labeling conversion in 2D results diagram, HIP for HIS, and CYX for CYS seemed to have rotamer property of amino acids. In our current study, the key ndings included the protein modeling, namely comparative modeling, model exibility predictions, comparative molecular docking, and MDS-30 ns for exploring the binding/catalysis behavior of bacterial LiP with a set of 12 lignin model compounds of 3 types (i.e., monomer, chlorinated, and multimeric). From the docking results, the binding energy has been evaluated comparatively to elucidate the binding behavior of bacterial LiP with different lignin compounds, which might be further interpreted for catalytic efficiency of the enzyme. However, to validate the docked complexes, MDS was performed, which further indicated that the complexes were in the equilibrium state at the end of the 30 ns simulation run. The ionic interactions (HIS-226) have been observed as protein-ligand contacts in the chlorinated compounds during the simulation. However, hydrophobic and H-bond type interactions were frequently observed in the remaining 10 lignin model compounds. The potential energy was also measured aer the end of the simulation run. We observed maximum P_E in 2-chlorosyringaldehyde, which was observed as À132 506.645 kcal mol À1 . Meanwhile, the minimum P_E was observed for sinapyl alcohol as À115 583.316 kcal mol À1 .
In recognition and support of our present research, we picked a set of 12 lignin model compounds in 3 avor; 2 chlorinated compounds (2-chlorosyringaldehyde, 5-chlorovanillin). A set of 8 standard monomeric lignin model compounds (veratryl alcohol, syringyl alcohol, sinapyl alcohol, methyl hydroquinone, guaiacol, coniferyl alcohol, catechol, 4-methoxyphenol), and 2 multimeric lignin model compounds in b-O-4, and 4-O-5 linkage form (dimer and trimer) were used to carry out the docking study, followed by validation of the docked complexes through DESMOND-assisted high performance molecular dynamics simulation (MDS). Awasthi et al., (2014) conducted a similar study, but laccase instead of LiP was used for the docking study, followed by MDS with GROMACS 4.5.5 package for evaluation of the degradation behavior of lignin with laccase. 76 Awasthi et al. (2014) reported laccase and several lignin model compounds (four lignin model compounds; monomer, dimer, trimer, tetramer) for catalysis behavious, in a subsequent study, a total of 11 amino acid residues of fungal laccase were found to interact with the lignin model compounds (LEU185, ASP227, ASN229, PHE260, SER285, PHE286, GLY413, ALA414, PRO415, ILE476, and HIS479). Subsequently, laccases with higher redox potential phenylalanine were found at the active site in the laccase as the key binding residue to bind with lignin model compounds, which is related to the ndings currently being made. From a contrasting perspective, our study revealed a total of 9 amino acid residues (VAL-152, ASP-153, GLN-185, TYR-187, LYS-210, HIS-226, ARG-244, PHE-261, and THR-271) interacting with a total of 12 lignin model compounds.
Another similar molecular modeling based study (molecular docking, and MDS) for understanding the binding/catalysis mechanism at the atomic level has been reported exploiting laccase and a set of 5 lignin model compounds (2,6-dimethoxyphenol, ferulic acid, guaiacol, sinapic acid, and vanillyl alcohol). 78 In that subsequent study, Chen et al. (2015) performed laccasemediated predictive biodegradation (ligninolytic enzyme) exploiting 5 lignin model compounds, which were docked, and simulations of 10 ns or 10 000 ps were carried out to understand the catalysis potential of laccase. Subsequently, 7 hydrophobic amino acid residues (PHE, PRO, ALA, GLY, ASP, and PHE) were found to interact during the docking with lignin model compound 2,6dimethoxyphenol. 77 LEU, SER, PRO, PRO, PHE, and ALA were reported as hydrophobic interactions with feluric acid. ASP, ILE, SER, ARG, GLN, and THR for guaiacol. PRO, GLY, ASP, ASN, ILE, PRO, PHE, ALA, and ASN for sinapic acid. However, such research lacked the ligand conformation, rigorous protein-ligand contacts analysis and potential energy of the system.

Conclusion
This present study explored the molecular basis of bacterial derived LiP through structural and molecular modeling analysis. A total of 12 different lignin model compounds have been used to evaluate the binding/catalytic affinity with LiP. We assessed the structure and PPs of bacterial-derived LiP exploiting secondary structure prediction for constituent elementary component analysis. Comparative modeling has shown that the query sequence coincides with the highest identity of DyP-type peroxidase, suggesting that the model enzyme involved is identical to the DyP-type peroxidase. The binding energy score was found to be the lowest in the trimer compound in comparison to the control compound. Furthermore, MDS indicated the equilibrium states of the docked complexes. Such binding affinity can be interpreted to understand the biodegradation mechanism of lignin model compounds at the atomic level, which further indicates that LiP has signicant potential to catalyze several types of lignin and phenolic model compounds. In order to gain a deep understanding of the catalytic efficiency of bacterialderived LiP, both molecular docking and subsequent wet-lab studies must be carried out. High potential LiP, with effective strategies, could play a crucial role in developing engineered microorganisms (GEMs) for mitigation of lignin, and ligninderived pollutants from the environment. Such approaches would help signicantly in the eld of bioremediation and other miscellaneous applications at the industrial level.

Author contributions
All listed authors have made signicantl contribution to this work, however, AKS contributed to designing, performing and analyzing the LiP mediated in silico study, the design and preparation of selected lignin model compounds, LiP optimization, model building, comparative modeling, structural & functional prediction, table preparation, and drew all of the gures used in this manuscript. Moreover, AKS developed this manuscript by using, and compiling the data derived from computational experimentations. The author KSK performed XP-Glide-Docking, DESMOND-assisted simulation study on their under licensed workstation (Schrodinger-Maestro Suite) and conrmed and validated the nal docking, and simulations results. AU evaluated and analyzed the results for binding interactions, simulations interactions and nally approved the results to be used in this manuscript. AR edited the manuscript, proofread, and approved the contents before submission in RSC-Advances.

Conflicts of interest
All listed authors conrm that there is no conict of interest for this work.