Virtual screening of gut microbiome bacteriocins as potential inhibitors of stearoyl-CoA desaturase 1 to regulate adipocyte differentiation and thermogenesis to combat obesity

Abstract The gut bacterial strains and their metabolites have been shown to play a significant role in obesity, but the molecular mechanisms underlying this association are largely unresolved. Obesity is a multifactorial problem and is controlled by various mechanisms and pathways to produce and store fat cells. Bacteriocins are secondary metabolites produced by gut bacteria to defend themselves against their competitors. Recently, they have gained great attention due to their role in metabolic disorders, including obesity. Stearoyl-CoA desaturase 1 (SCD1) is a key enzyme involved in the differentiation of adipocytes. The aim of this study is to show the regulation of SCD1 by bacteriocins and thus their importance in obesity control. We screened the human gut bacteriome for the presence of bacteriocins, predicted their structures, and showed their inhibitory role by molecular docking with SCD1. Further, to confirm the docking results, MDS of six top scoring SCD1-bacteriocin complexes were carried out for 100 ns. These six bacteriocins namely, Plantaricin S-beta, Carnolysin, Lactococcin B, Bacteriocin Iic, Plantaricin N, and Thermophilin A, with strong binding affinities, are primarily produced by bacterial strains from the Lactobacillaeacea family. These findings can be the basis of further experiments for enhanced understanding of the underlying mechanisms for obesity control, specifically bacteriocins driven regulation of the SCD1 enzyme. In addition, a consortium of bacterial strains producing these bacteriocins can be developed and used as probiotics for the amelioration of obesity and other metabolic complications. Communicated by Ramaswamy H. Sarma


Introduction
Obesity has transformed into "globesity" over the past decade as it continues to rise among populations and has become an epidemic, accounting for more than one billion individuals (WHO report on World Obesity Day, 2022).There has been a shift in the demography of obesity due to its transition from rich to poor populations, leading to its high prevalence in lowincome countries (Templin et al., 2019).Obesity is determined by a person's genetic make-up, environment, socioeconomic status, lifestyle, etc. and has an association with pathological conditions, such as type-2 diabetes, cardiovascular diseases, nonalcoholic fatty liver disease (NAFLD), cancer, etc. (Dharmalingam & Yamasandhi, 2018;Singhvi et al., 2020;Tabish, 2017;Tseng & Wu, 2019).Physiological factors, such as an increase in the amount and size of adipocytes, due to excessive energy consumption compared to expenditure, are responsible for obesity (Altınova, 2022).The fat stored in the adipose tissue is used for maintaining the metabolic homeostasis of the body and immune regulation (Ghaben & Scherer, 2019).Adipocytes arise from adipose tissue-derived mesenchymal stem cells (ADSCs) and act as fuel reservoirs to conserve heat.Isoforms of adipocytes are characterised as white and beige adipocytes based on their molecular composition and unique cell origin.The storage of energy in the form of triglycerides is associated with white adipose tissue (WAT) derived from Myf5-negative progenitors, whereas brown adipose tissue (BAT) is derived from the dermomyotome, which aids in weight regulation and obesity by thermogenesis (Liu et al., 2020;Tanaka et al., 2022).The orchestrated balance between lipogenesis, lipolysis and oxidative events regulates adipocyte development, and their disorderliness can lead to diseased conditions (Saponaro et al., 2015).For instance, triacylglycerol accumulation in adipocytes is associated with increased expression and activity of lipogenic enzymes like stearoyl-CoA desaturase 1 (SCD1).SCD1 is one of the major rate-limiting enzymes in lipogenesis and catalyses the conversion of saturated fatty acids (SFAs), palmitic acid and stearic acid into mono-unsaturated fatty acids (MUFAs), palmitoleic acid and oleic acid.SCD1 is also involved in fatty acid oxidation, insulin signaling, thermogenesis and inflammation (Sampath & Ntambi, 2011).SCD1's implication in tumorigenesis is well-documented, and it has been identified as a key therapeutic target (Huang et al., 2021;Koundouros & Poulogiannis, 2020;Vriens et al., 2019).A high level of SCD1 expression is significantly correlated with obesity and other metabolic diseases like insulin resistance, NAFLD, etc. (Kotronen et al., 2009).In obesity, hypertrophy of the WAT is observed due to excessive lipid accumulation (Tanaka et al., 2022).Furthermore, studies on mouse models have revealed that SCD1 down-regulation promotes the formation of beige adipocytes from WAT by directing de novo beige adipogenesis.In addition, absence of the SCD1 has been associated with the accumulation of succinate, which enhances the process of transformation of white adipocytes into beige adipocytes via mitochondrial complex II (Liu et al., 2020).Therefore, SCD1 can be an important theurapatic target for various lifestyle disorders, including obesity.
The human gastrointestinal tract is the largest niche of the human microbial ecosystem, containing trillions of microbes.Their contribution to overall host health, including immunity, metabolic homeostasis and pathogen protection, is well documented (Kumari et al., 2022;Singhvi et al., 2020;Visconti et al., 2019;Xiao & Kang, 2020).The major phyla constituting the gut microbiota are: Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and Verrucomicrobia (Huttenhower et al., 2012).Any disturbance in their composition or dysbiosis encourages pathogen invasion and slows down host metabolism, leading to disorders like obesity.The association between the gut microbiome and obesity is well-established (Lee et al., 2020;Sun et al., 2022;Tseng & Wu, 2019).A lower abundance of Bacteroidetes in obese individuals implies a beneficial relationship between body weight and Bacteroidetes (Crovesy et al., 2020).Furthermore, Lactobacillus rhamnosus and L. gasseri have been recently used as probiotics to treat obese mouse models, where they were found to accelerate metabolism and reduce BMI (Cornejo-Pareja et al., 2019).Enterococcus faecalis can also significantly reduce obesity by increasing BAT activity and thermogenesis (Quan et al., 2020).
The gut microbiota executes these functions by regulating various signaling pathways in the host via a vast repertoire of metabolites like SCFA, D-amino acids, vitamins and bacteriocins (Krautkramer et al., 2021).These metabolites produced by gut microbes are part of a humongous network with molecular crosstalk among different pathways.Therefore, single metabolite-centric studies are vital to determine their relevance to host physiology.One of the major metabolites recently gaining significant importance is bacteriocins, since they have been reported to be beneficial against various lifestyle disorders, such as diabetes, cancer, obesity, etc. (Bai et al., 2020;Huang et al., 2021;Sharma & Yadav, 2022).Bacteriocins, ribosomally produced antimicrobial peptides, safeguard gut bacteria from their close competitors.Their mild antibacterial and antiviral properties make them a preferred choice for food preservation, for example, Nisin has been licensed as a biopreservative (Chikindas et al., 2018;Sharma & Yadav, 2022;Soltani et al., 2021).
Recently, an in vivo study on 3T3-L1 cells demonstrated the involvement of bacteriocin, gassericin A, in the regulation of fat-induced obesity by decreasing SCD1 levels, which resulted in diverting adipocyte differentiation and enhanced thermogenesis (Taghizad et al., 2021).Many reports have shown a significant role of bacteriocins in modifying fat-induced obesity, especially those produced by the Lactobacillaeacea family; however, this association is still very bleak (Cornejo-Pareja et al., 2019;Hossain et al., 2020).
Based on these observations, online tools were used in this study to virtually screen gut bacteria for the presence of bacteriocins.Structure based virtual screening methods which include in silico tools like protein-protein docking and molecular dynamic simulation (MDS), have been immensely helpful for elucidating the interactions among biomolecules.This is highly useful for reducing the number of ligands, that need to be assessed for their potential biological role in various physiological conditions (Hassan Baig et al., 2016;Lee et al., 2021;Vidal-Limon et al., 2022).We identified a total of 99 bacteriocins and predicted their structures, followed by bacteriocins docking with the SCD1 model.Further, MDS were executed for 100 ns to confirm the inhibiting efficiencies of these bacteriocins and, thus, their potential role in combating obesity via regulation of adipocyte differentiation.

Identification of bacteriocins
An extensive literature search was carried out to identify important gut bacterial species that are reported to be involved in the amelioration of obesity in humans.More than 100 genomes of such bacterial species/strains were retrieved from the Human Microbiome Project's reference genome database (https://www.hmpdacc.org/hmp/catalog/grid.php?dataset= genomic) and used for the screening of bacteriocins (Turnbaugh et al., 2007).The genomes of the selected species were scanned with the BAGEL4 online tool (http://bagel4.molgenrug.nl/index.php) using gassercin A sequence as a reference/probe to fish out potential bacteriocins (van Heel et al., 2018).

Molecular dynamics simulations (MDS)
MDS were carried out on the complexes of the six best docked human SCD1 model and the bacteriocins, using the Desmond MDS package of Schrodinger (Desmond Molecular Dynamics System, DE Shaw Research, 2018).The OPLS_2005 force field was employed for the SCD1-bacteriocin docked complexes.
Using the system builder tool of Desmond, the complexes were solvated in a cubical water box (TIP3P water model) keeping 12 Å buffer space in x, y and z dimensions.Each system was neutralised by adding appropriate counterions, and an ionic concentration of 0.15 M was maintained by adding Na þ and Cl À ions.The systems were minimised with 10,000 steepest descent steps followed by gradual heating from 0 to 300 K, under NVT ensemble.The systems were thermally relaxed before the production run using the Nose-Hoover Chain Thermostat method for 5 ns and 5 ns of pressure relaxation with the Martyna-Tobias-Klein barostat method to maintain 1.01 bar pressure (Kaczor et al., 2015).Finally, a 100 ns production run under NPT ensemble was carried out for each system using a cut-off distance of 12 Å for non-bonded interactions.
Coordinates were saved at each 100 ps to generate trajectories of 1000 frames each.Simulation interaction diagrams were used for trajectory analyses.

Screening of bacteriocins
The involvement of the gut microbiome in combating obesity has been well-established through different studies (Lee et al., 2019;2020;Lim et al., 2020;Molina-Tijeras et al., 2021;Quan et al., 2020).Recently, bacteriocins have been shown to modulate obesity by regulating fat synthesis, adipocyte differentiation, and increased thermogenesis in mouse models (Bai et al., 2020;Taghizad et al., 2021).In the present study, >100 gut bacterial strains were screened for the presence of potential bacteriocins in order to determine their role in the divergence of adipocyte differentiation favouring the formation of BAT through SCD1 regulation.
A total of 99 putative bacteriocins were identified, majorly belonging to the bacterial species/strains of the genus Lactobacillus, Lacticaseibacillus, Lactiplantibacillus, Lactococcus, Latilactobacillus, Ligilactobacillus, Enterococcus, Bacteroides, Bifidobacterium and Blautia.The predicted bacteriocins, their amino acid sequences, IDs, Ramachandran values, binding energies, and RMSDs are listed in Supplementary Table 1.The bacteriocins identified in this study are largely from bacteria belonging to the Lactobacillaceae family, known to produce the highest number of bacteriocins (Bai et al., 2020;Heeney et al., 2019;Hossain et al., 2020;Silva et al., 2018).However, not all bacterial species analysed were found to produce bacteriocins.Additionally, some of the bacteriocins identified from different species/sub-strains were found to be identical (Supplementary Table 1).

Prediction and validation of SCD1 and bacteriocins
To ascertain receptor-ligand binding affinities, elucidation of the 3D structures of SCD1 (receptor) and bacteriocins (ligands) from their respective amino acid sequences was a prerequisite and crucial step.The choice of method for protein modeling from amino acid sequences depends on its similarity with the pre-existing templates in the database.If the similarity is more than 30%, the homology modeling method is preferred.It is highly reliable and precise in closing the gaps between the template and unknown protein structure with ease of interpretation of the results generated computationally (Waterhouse et al., 2018).SCD1 structure was predicted through homology modeling using SwissModel (Template ID: 4ymk).After models were created, the Qualitative Model Energy ANalysis (QMEAN) was used to evaluate the quality of these predicted structures by estimation at the global and local scale (Benkert et al., 2011).QMEANDisCo estimates interatomic distances by using information from homologous protein structures experimentally confirmed, which is used as a template and to validate the modeled structure (Waterhouse et al., 2018).QMEANDisco and Global scores in the range of 0-1 are acceptable to validate a modeled structure, and for SCD1, they are found to be 0.89 and 0.84, respectively.The other universal criterion for verification of these predicted structures is the Ramachandran plot, which provides an estimate of the stability of the protein structures.For SCD1, > 91% residues of the predicted structure fell in the favoured region, further confirming its stability (Figure 1).Bacteriocins were modeled using Phyre2 and Pep-Fold3.5,which allow modeling of the protein of interest based on homology and de novo, respectively.The obtained structures were then refined and optimised based on backbone topology, position of the side chains, and hydrogen bonds.The final predicted structures of the top six bacteriocins are depicted in Supplementary Figure 1.It was also observed that for bacteriocin models, over 90% of the amino acid residues lie in the favourable region of the Ramachandran plot, as required for further analysis.The quality of these structures was assessed using the ProQ server to determine the Levitt-Gerstein (LG) score.
LGscore indicates similarity with a known structure by superimposing two of them and was used to evaluate the quality of these structures.The protein/peptide models with LGscore > 4 are accepted well (Levitt & Gerstein, 1998).Our top six bacteriocin models have a range for LGscore between 8.7 and11.4.The scores of ProQ (LGscores) and Ramachandran values for these bacteriocin structures are provided in Table 1.

Docking of bacteriocins against SCD1
CADD methods are important to screen the large number of potential ligands available for any protein target.A flexible docking and scoring algorithm takes care of the conformational space available to the protein in the cell (Hassan Baig et al., 2016).Computer assisted SCD1-bacteriocin interactions were studied to understand their binding dynamics with each other using the molecular docking method.The binding pose, or molecular docking, relies on many 3D interactions (like hydrogen bonding, hydrophobic, and other non-covalent interactions) within the active sites of SCD1 with the bacteriocins.This method enables the virtual screening of inhibitory molecules for a receptor protein, and good docking tools can rank them based on a scoring method for their respective wholesome interactions (Morris & Lim-Wilby, 2008).Autodock Vina, is one such in silico tool that does binding and scoring based on side chain conformation flexibly.In addition, it evaluates the spatial configuration adopted by the biomolecules in their bound state.
The results of the docking of SCD1 with all 99 bacteriocins, along with their binding energies, are listed in Supplementary Table 1.Among these 99 bacteriocin docking complexes, we selected the best scoring six bacteriocins for further analysis based on their binding energies and RMSD values (Table 1).The six bacteriocins which showed strong binding with SCD1 include, Plantaricin S-beta, Carnolysin, Lactococcin B, Bacteriocin Iic, Plantaricin N and Thermophilin A are produced by Latilactobacillus sakei strain FLEC01, Enterococcus faecalis strain EnGen0336 strain T5, Lactococcus lactis strain J1101437_171009_F11, Lactobacillus johnsonii strain GHZ10a, Lactiplantibacillus plantarum strain LZ95 and Lacticaseibacillus paracasei subsps., respectively.As reported earlier, in this study also the majority of bacterial species harbouring these crucial bacteriocins belong to the genus Lactobacillus (Hossain et al., 2020).The docking site analysis showed high binding affinity with SCD1, attributed to the low binding energies upon interaction with these six bacteriocins (Table 1).The bacteriocin docking to different binding sites present on SCD1 was studied in detail.The binding energy scores of the top six complexes during the docking interaction ranged from À 12.3 to À 11.The docking poses of the six bacteriocins against the chosen target are depicted in Figure 2A.The binding energy of Plantaricin S-beta with SCD1 was found to be the lowest, that is, À 12.3, confirming its strongest interaction among the top six bacteriocins.Further, Carnolysins and Lactococcin B docked with a binding energy of À 11.4.The binding energies increased from À 11.2, À 11.1 and À 11 for Bacteriocin Iic, Plantaricin N and Thermophilin A, respectively, also confirming their strong interactions.The hydrogen bond formation as well as hydrophobic interactions and the participating residues are depicted (Figure 2B).These complexes were further validated using Ramachandran plots, and the values are listed in Supplementary Table 2 (Figure 2C).All the above-mentioned interactions and validation results confirmed the binding ability of these six bacteriocins.

Analyses of structural properties of SCD1-bacteriocin complexes
MDS technique is opted to monitor the stabilities of the proteinligand or protein-protein interactions under dynamic conditions to mimic the cell environment.By using the latest sampling techniques on MDS, we can now elucidate various intermolecular interactions between biomolecules in just few nanoseconds to microseconds (Vidal-Limon et al., 2022).In order to explore the effects on the structural dynamics of SCD1 and the stability of docked bacteriocins in the complex, MDS studies were performed.Variations in the structural properties, such as RMSD and root mean square fluctuations (RMSF) of SCD1, were analysed in order to compare its dynamic behaviour when bound to these bacteriocins.The RMSDs of all the heavy atoms of SCD1 in the six complexes during 100 ns MDS are shown (Figure 3A).
The RMSD plot indicates that all the complexes underwent conformational changes to some extent, with RMSD up to 3 Å in the initial 40 ns.The complexes with Lactococcin B and Thermophilin A show stable RMSD profiles below 3 Å, while the complexes with Plantaricin S-beta and Plantaricin N equilibrate at about 3.5 Å.The RMSD of the complex with Carnolysin rises sharply after 40 th ns and it equilibrates at around 4.5 Å along with the complex with Bacteriocin Iic, indicating that these complexes undergo higher conformational changes during the simulations.The RMSF of residue (all the heavy atoms of each residue were considered) of SCD1 in the six complexes during the simulations was determined (Figure 3B).RMSF quantifies the displacement of the centre of mass of the residues from a mean position during the simulation.The RMSF plot shows that except for the terminal residues, the other SCD1 residues maintain RMSF between 0 and 3 Å.Very few residues, for example Gly104 in Carnolysin bound complex or Arg175 in Plantaricin S-beta bound complex show higher RMSF, however, these residues do not participate in protein binding.

Comparative analysis of patterns and strengths of interactions between the selected bacteriocins and SCD1
To ensure stable binding, bacteriocins engage in a variety of non-covalent interactions with SCD1 throughout simulations.H-Bonds and Van der Waals contacts are the most crucial types of interactions among others.In addition, a number of other significant complexes were also formed.In some complexes, Pi-interactions were formed between aromatic side chains.In the supplementary information, brief video clips depicting the formation of different types of interactions between selected bacteriocins complexed with SCD1 over the course of the entire simulation time are provided (legend attached).The number of H-bonds formed between SCD1 and the bacteriocins during 100 ns MDS are also depicted in Figure 3C and Table 2.
The average number of H-bonds between SCD1 and Plantaricin S-beta, Lactococcin B, Carnolysin, Plantaricin N, Bacteriocin Iic and Thermophilin A during the simulations were calculated to be 2.48, 0.82, 4.10, 1.02, 1.02 and 3, respectively.For Lactococcin B, the number of H-bonds decreases towards   the end of simulation, that is, just before 100 ns while an opposite trend was observed for the H-bond formation of Carnolysin.Trp101 of SCD1 forms the most stable H-bond with Asp35 of Plantaricin S-beta.For Lactococcin B, most of the Hbonds are lost by the end of the simulations.The H-bond between Tyr236 of SCD1 and Glu29 of Lactococcin B was found to be relatively stable (13.47% occupancy).Plantaricin N did not form many stable H-bonds with SCD1; however, three stable H-bonds were observed with Arg175, Phe177 and Tyr71.Bacteriocin Iic forms stable H-bond interactions with H316 and I317 residues of SCD1.Thermophilin A forms the most stable interaction with Arg74 (152% occupancy), followed by Hbonds with Asp149 and Asn75.Carnolysin too formed several stable H-bonds with SCD1 after 40 ns.These observations confirm the conformational changes observed from the RMSD plot, enabling the formation of several favourable interactions between Carnolysin and SCD1.The H-bond between Arg74 of SCD1 and Asp16 of Carnolysin was observed to be the most stable one with 137% occupancy, followed by the interaction between Lys219 of SCD1 and Glu15 of Carnolysin with 35% occupancy.Interestingly, the binding patterns and interactions of Thermophilin A and Carnolysin with the binding site residue Arg74 of SCD1 were found to be the most stable.
To quantify the binding strengths of the selected bacteriocins, average free energies of binding (MM-GBSA dG Bind) were calculated using the MM-GBSA method by the Prime module of the Schrodinger Suite (Jacobson et al., 2004).MM-GBSA binding energy comprises individual contributions of various types.The important contributors to the binding energy in case of noncovalent interactions are the Coulombic or electrostatic interaction energy contribution (Coulomb), hydrogen-bonding energy component (Hbond), lipophilic energy component (Lipo) and Van der Waals energy component (vdW).The MM-GBSA binding energy and its components (Coulomb, Hbond, Lipo and vdW) for the bacteriocins and SCD1 complexes are depicted (Figure 4).The free energies of binding of the selected bacteriocins with SCD1 range between À 62 and À 92 kcal/mol.Carnolysin and Thermophilin A show the most favourable binding energies of À 91.46 and À 85.8 kcal/mol, respectively.A detailed analysis of the various components of the binding free energies shows that although the bacteriocins Lactococcin B, Plantaricin N and Bacteriocin Iic formed a negligible number of H-bonds during the simulations, their complexes with SCD1 were stabilised by other non-covalent interaction components, such as Coulombic interactions, lipophilic contacts and Van der Waals interactions.

Physicochemical analysis of the selected bacteriocins
The size of the top six best docked bacteriocins was found in the range of 47-84 aa.According to different studies, the size of bacteriocins varies based on their types and origin (Umu et al., 2017;van Heel et al., 2018;Yang et al., 2014).The overall size range of these 99 bacteriocins predicted with BAGEL was found to be in the range of 29-350 aa.The present study aims to decipher the role of these bacteriocins in modulating the activity of SCD1, hence, it was important to elucidate the stability of these peptides in their bound form.A physico-chemical analysis of the best docked bacteriocins was conducted to check their stability index, aliphatic index, and Grand Average of Hydropathy (GRAVY) score.The stability index for the top six bacteriocins ranged between 7 and 20, indicating their stability during the complex formation.This method is based on the comparison of various dipeptides (�400) present in stable and unstable proteins by assigning a specific weightage to each dipeptide, and values below 40.0 have been reported to be stable (Guruprasad et al., 1990).The bacteriocins were also found to have a high aliphatic index in the range of 68-113, which is the total volume occupied by aliphatic side chains, and a higher value is seen as a favourable factor for greater thermal stability.
The GRAVY index of the five out of six best docking bacteriocins calculated was found to be in the range of À 0.018 to À 0.682, which indicates the hydrophilic nature of these bacteriocins, except Thermophilin A, which has a positive value of 0.644.The GRAVY score is calculated by adding the hydropathy values of all amino acids present in a protein and dividing the sum by the total number of residues.This score for proteins falls between À 2 and þ2, with a negative score indicating hydrophilicity and a positive score indicating hydrophobicity (Gasteiger et al., 2005;Kyte & Doolittle, 1982).Thus, these bacteriocins are hydrophilic globular peptides and can be easily transported through the blood from the gut to adipose tissues, where they may participate in adipogenesis by regulating SCD1.The amino acid sequence and physicochemical properties of the best six bacteriocins bound with SCD1 are given in Table 3.

Conclusion and future prospects
Bacteriocins have recently gained great attention as they have been shown to play important roles in immunity, cancer development, obesity, etc.In this study, gut bacteria were screened for the presence of bacteriocins, and their structures were predicted using various in silico tools.The binding of these bacteriocins with the SCD1 enzyme was analysed, and six bacteriocins showed a strong affinity, as evident from their binding energies.The blocking of SCD1 by bacteriocins could be a potential mechanism employed by these gut bacteria to divert adipocyte differentiation and enhance thermogenesis.
The simulation results from this study have confirmed the strong binding interactions of bacteriocins and SCD1.Therefore, these gut microbiome bacteriocins have a strong potential for blocking SCD1.The binding energies, RMSD, RMSF and H-bond occupancy for two bacteriocins, namely, Carnolysin and Thermophilin A are very encouraging.However, in vivo validation of these findings is necessary to confirm the role of these bacteriocins in the inhibition of SCD1 as there is an amalgam of other factors/mechanisms involved in the molecular regulation of adipocyte differentiation.Inhibition of SCD1 can lead to diversion of adipocyte differentiation to form beige adipocytes and browning of adipose tissue.Currently, improving the gut bacteriome through probiotics as a part of any disease treatment regime is highly encouraged and recommended.This study highlights the promising prospects of these bacteriocin-producing strains in the development of probiotic formulations as potential therapeutics to ameliorate obesity and other metabolic disorders.

Figure 1 .
Figure 1.3D structure of the SCD1 and its validation by Ramachandran plot.(A) Ribbon representation of 3D structure of SCD1 protein.(B) Validation of 3D structure predicted using the Ramachandran Plot.For SCD1, 91% residues (blue dots) were found in the favoured regions.(C) The secondary structure where curls represent the alpha helix (n ¼ 22) and b represents beta pleated sheet (n ¼ 18).

Figure 2 .
Figure 2. Molecular docking analysis of bacteriocins with SCD1.(A) Panel showing the docked bacteriocins with SCD1 for overall interacting residues.(B) Detailed analysis of the docked bacteriocins and SCD1 interactions w.r.t.hydrogen and hydrophobic bonds.(C) Confirmation of stability for the docked complexes through the Ramachandran plot.(I) Plantaricin S-beta (II) Carnolysins (III) Lactococcin B (IV) Bacteriocin Iic (V) Plantaricin N (VI) Thermophilin A.

Figure 3 .
Figure 3. Molecular dynamic simulations analyses of the six top scoring SCD1 and bacteriocin complexes.(A) RMSD of the complexes of selected bacteriocins with SCD1.All the bacteriocins-SCD1 complexes stabilised up to 4.5 Å. (B) Number of H-bonds formed between the bacteriocins and SCD1.H-bonds were formed in all complexes and were found in the range of 2-9.(C) RMSF of residues (all the heavy atoms of each residue were considered) of SCD1 in the complexes during 100 ns.

Figure 4 .
Figure 4. MM-GBSA binding energy and its components for the complexes of bacteriocins with SCD1.Free energies of binding were found in the range of À 62 to À 92 kcal/mol.The best scoring bacteriocin is Carnolysin with �G ¼ À 91.46 kcal/mol.

Table 1 .
Structure validation parameters and binding energies of the six bacteriocin with SCD1.

Table 2 .
List of stable H-bonds formed between SCD1 and the selected bacteriocins during MD simulations.Occupancies of the H-bond interactions were calculated using VDM.It shows the percentage of time when H-bond existed between the two residues i.e., donor (D) and acceptor (A).If two H-bonds are formed between the same residues, the occupancies are added up.Details of H-bonds with >10% occupancies are mentioned here. �