{"paper_id":"0f8002a2-d59e-492b-bf54-0ad16d834b8a","body_text":"Computational analysis revealed Triamcinolone acetonide produced by Bacillus velezensis YEBBR6 as having antagonistic activity against Fusarium oxysporum f. sp. cubense | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Computational analysis revealed Triamcinolone acetonide produced by Bacillus velezensis YEBBR6 as having antagonistic activity against Fusarium oxysporum f. sp. cubense Krishna Nayana R U, Nakkeeran S, Saranya N, Saravanan R, Mahendra K, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2133897/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fusarium oxysporum f. sp. cubense is one of the most serious and threatening pathogens of banana causing Panama wilt worldwide. Bacterial endophytes were reported to have antifungal action through various mechanisms, which include the production of secondary metabolites during their interaction with pathogen. One such endophyte, Bacillus velezensis YEBBR6 antagonistic to Fusarium oxysporum f. sp. cubense produced antimicrobial biomolecules against the pathogen during confrontation assay. Those molecules were screened for their antifungal property by an in-silico approach. Modelling of the fungal targets and docking them with those biomolecules was done to refine the potential antifungal compounds among the various biomolecules they generated during their di-trophic interaction with the pathogen. Protein targets were selected based on literature mining and those targets were modelled and validated for docking with the biomolecules through the AutoDock Vina module of the PyRx 0.8 server. Among the compounds screened, Triamcinolone acetonide was possessing the maximum binding affinity with chosen pathogen targets. It had the maximum binding affinity of 11.2 kcal/mol with XRN2 (5´ → 3´ Exoribonuclease 2) an enzyme involved in degrading m-RNA -. Kinetics of the protein-ligand complex formation for the further validation of docking results was done through Molecular Dynamic Simulation studies. Besides, the antifungal nature of the biomolecule was also confirmed against Foc by screening in wet lab through poisoned plate technique. Foc Endophytes biomolecules molecular modelling docking Molecular Dynamic Simulation Triamcinolone acetonide Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Banana ( Musa sp.) is one of the most important fruit crops in the world which is produced, consumed and traded globally in large quantities. The estimated export volume of bananas was 21.5 million tonnes in 2020 [ 1 ] and 20 million tonnes in 2021 [ 2 ]. Next to mango, it is the second most important fruit crop in India and ranked first in production and third in area. It is being cultivated in an area of 876 ha and with annual production of 31799 tonnes [ 3 ]. But the productivity is hampered worldwide by “Panama wilt” caused by a soil-borne fungal pathogen Fusarium oxysporum f sp. cubense ( Foc ). The disease first dragged the attention of the growers as a serious issue during late 19th century when it devastated the banana cultivars in Central America and the Caribbean region [ 4 ]. The pathogen, Foc -Race1 infected the banana cultivar Gros Michel, and resulted in shifting the cultivation of this cultivar with the resistant, Cavendish cultivar [ 5 ]. Apart from this, it also infected ‘I.C.2’ AAAA, ‘Silk,’ ‘Pome’ AAB, ‘Pisang Awak’ ABB and ‘Maqueño’ AAB. Race 2 infect cooking bananas, Bluggoe subgroup ABB [ 6 ]. Currently, the Cavendish cultivar is also affected by Foc Tropical Race 4 (TR 4) coming under Race 4, which was reported in the early 1990s in Southeast Asia. Further it is still restricted to Asia and northern Australia, but a major concern for the farmers is the disease is posing a great menace in the tropics where it is raised as commercial plantations with continuous monocropping [ 7 ]. The pathogen is a filamentous- hemibiotrophic saprophytic fungi [ 4 ] with more than 20 Vegetative Compatible Groups (VCGs) known [ 8 ]. It colonized plant through roots and clogs the vascular tissue resulting in wilt and death of the plant and death [ 9 ]. As pathogen reproduce through vegetative means and remain in soil as saprophytes, they are bestowed to survive for more than 20 years as chlamydospores [ 10 , 11 ] and thus management of the pathogen remains as a challenging task. To date, there are only a few effective options available for management of the pathogen. Although physical and chemical methods are available, it does not provide a long-term effect. Besides, chemical methods are posing threat to the environment and human beings [ 12 ]. Moreover, cultural control of the pathogen also remains as a challenging task, due to the non-availability of resistant cultivars. Even though the resistant cultivars are available, they are not cultivated as they don’t meet the consumer preference [ 13 ]. Due to these conditions, biological control with the endophytes in the resistant cultivar will pave the way for the management of Foc [ 11 ]. Antagonistic bacterial endophytes will complement their host with multiple endowments including nutrient uptake, promotion of plant growth and resistance to diseases [ 14 ]. They deploy different mechanisms to resist pathogens [ 15 ] either through mycoparasitism or competition for nutrients or root niches, antibiosis through the production of secondary metabolites, quorum sensing and signalling or by inducing immune response in the host [ 16 ]. Bacterial endophytes do exist in bananas also. A quest for those antagonistic bacterial endophytes against Foc from resistant banana genotype YKM5, resulted in the identification of Brachybacterium paraconglomeratum YEBPT2, Brucella melitensis YEBPS3, Bacillus velezensis YEBBR6, and the one associated with nectar Bacillus albus YEBN2 which inhibited Foc [ 17 ]. Confrontational assay of Foc with the endophyte Bacillus velezensis YEBBR6 resulted in the identification of several biomolecules through GCMS analysis. Though the biomolecules were identified, the research on the role of identified biomolecules against Foc was not ascertained. Hence, to fish out the role of biomolecules for their antifungal action, molecular docking was done with biomolecules, to explore and harness the potential biomolecules which can be used for the management of pathogen by identifying the behaviour and interaction of these biomolecules with the selected fungal protein targets necessary for their survival, proliferation, virulence and parasitism. Thus, it will ultimately lead to the unveiling of the mode of action of biomolecules through in-silico approach. Molecular docking involves the interaction study of the protein with a ligand by taking only its rigid structure, which needs further substantiation as the recognition and binding with the ligand itself involves conformational changes in the protein [ 18 ]. Moreover, dynamics seem to be a key player in studying their properties. Apart from this, binding is also affected by hydrogen bonds along with van der Waals forces, whose local rearrangements result in the final stable protein-ligand binding [ 19 ]. Correspondingly other factors include solvent surrounding the protein, and the interaction and energy exchanged between them as a thermodynamic system [ 20 ]. Molecular Dynamic simulation (MD Simulation) is a computational technique that models molecular systems at the atomic scale level and studies the static, structural, thermodynamic, and dynamic properties over a time scale. In order to find the exact mechanism of binding of ligands to the target proteins, the time extend to which it binds, and the stability of the complex in the particular presumed environmental conditions (Temperature, pressure, volume), molecular dynamic simulation is usually employed [ 21 ]. Hence, the ligand Triamcinolone acetonide and the protein XRN2 with the highest binding affinity was subjected for MD simulation for further validation of its effect on protein targets of the pathogen. Further, antifungal nature of the biomolecules was also confirmed in a wet lab through the poisoned food technique. Methods Identification of fungal targets Identification of potential target proteins of the pathogen is the foremost important step for assessing the interaction of protein with the ligand molecules through docking. Based on the literature survey and analysis, antifungal protein targets in Foc were identified as G protein ß subunit (FGB1) [ 22 ], Six Gene Expression 1 (SGE1) [ 23 ], RHO type GTPase (RHO1) [ 24 ], 5´ → 3´ Exoribonuclease 2 (XRN2) [ 25 ], C-24 sterol methyltransferase (ERG6), C-4 sterol methyl oxidase (ERG25) [ 26 ], Fusarium transcription factor 1 (FTF1), velvet [ 27 ], MADS-box transcription factor Rlm1 (Resistance to lethality of MKK1P386) [ 28 ], MAP kinase kinase 2 (Mkk2) [ 29 ], Secreted in Xylem 1 (SIX1), Secreted in xylem 6 (SIX6), Secreted in xylem 8 ( SIX8), Secreted in xylem 10 (SIX10) and Secreted in xylem (SIX13) [ 30 ]. Details on these protein targets of Fusarium oxysporum f. sp. cubense associated with virulence and pathogenicity are given in the Table S1. Modelling of the targets The tertiary structure of proteins formed from the sequence of the amino acids determines the function embodied by them [ 31 ] and modelling involves the construction of these structures in-silico by bioinformatic tools. Protein Data Bank is a primary database that serves as a global repository for structural data on proteins and other biomolecules obtained through experimental results. Selected targets were searched for their structure in the PDB database and none of them was found. Hence, the modelling of the protein targets was done by using the template-based modelling and through comparative modelling method based on the NCBI’s BLASTp [ 32 ] search results. For that, primary protein sequences of the targets were retrieved in FASTA format from the UniProt database [ 33 ] and BLAStp analysis was done using the PDB database. Different modelling servers were selected as per the query coverage and the percentage identity of the BLASTp results. Template-based modelling servers such as SWISS-MODEL, PHYRE 2 software’s and ROBETTA (Metaserver), the comparative modelling-based server was used for modelling the targets. SWISS-MODEL is a homology-modelling server that functions based on sequence comparison [ 34 ] and PHYRE 2 pertains to threading method of structure prediction based on fold recognition [ 35 ]. ROBETTA server functions based on ab initio method of modelling that uses comparative modelling if the matching sequences are found from BLAST analysis or de novo Rosetta fragment insertion method otherwise [ 36 ]. Structure predictions using SWISS-MODEL software were done for the targets including XRN2 and FGB1. The percent identity, coverage {maximum} and similarity {30–50 percent} between target and template sequence, and Global Mean Quality Estimation (GMQE) {close to 1} were used as the parameters to ensure the quality of modelled structures through homology modelling using SWISS-MODEL. The protein sequences, which did not have any matching or similar sequences or with lower query coverage (less than 50%) and with very low percent identity in BLAST query, the 3-D models were developed using ROBETTA server. Majority of selected targets including Rlm1, FTF1, ERG6, ERG25, RHO1, SIX1, SIX6, SIX8, SIX10, and SIX13 were modelled using ROBETTA server The target proteins SGE1, Velvet, and Mkk2 of Foc , was having the query coverage of 50–80% with 30% identity using BLASTp analysis. As it could not be used for SWISS MODELLING, the model was built using PHYRE 2 server for the protein targets SGE1, Velvet, and Mkk2. Modelling of the above target proteins with PYRE2 server, displays several models. Among the several models, the top model will be selected based on the template protein PDB ID, confidence score, and coverage percentage. The ideal structures which have the maximum confidence score of > 90% will be used for further analysis. Model Validation The quality of the modelled protein structure was validated using “model building” with known sequences. Reliability of the modelled protein structures was confirmed through Structural Analysis and Verification Server, SAVES v6.0 ( https://saves.mbi.ucla.edu/ ). using PROCHECK v.3.5 (Programs to check the Stereochemical Quality of Protein Structures) [ 37 ]. It was used to evaluate the overall stereochemistry of protein and checked the quality of modelled structure by comparing with the well-refined structure of same resolution. Subsequently, PDB format file of the structure was uploaded to run the program, so as to get the Ramachandran plot to find out the residues in the allowed and disallowed regions. Further, visualization, energy minimization and loop building of the structure was done for the residues in the disallowed region using SWISS PDB VIEWER. After loop refinement, residues were checked for their feasible conformations. Prediction of target protein site with ligands Prediction of binding site of the target protein with the ligands or biomolecules for the interaction was detected using Computed Atlas of Surface Topography of proteins (CASTp) ( http://sts.bioe.uic.edu/castp/index.html ). These sites were used for setting the grid box in docking studies [ 38 ]. Preparation of ligands 2-D structures of biomolecules Triamcinolone acetonide, 3.4-Dihydrocoumarin, 3-trifluoroacetoxypentadecane, 5-fluorouracil, allobarbital, citramalic acid, quinazoline, succinic acid, tebuconazole and trifloxystrobin were obtained from PubChem database in SDF format. Tebuconazole and trifloxystrobin were used as the positive check. Using the Open Babel software the SDF file of the biomolecule was converted into their respective PDB format and was further used for docking studies. Molecular docking Molecular docking was performed through Autodock vina in PyRx 0.8 [ 39 ] software which implements Python language. Ligands were energy minimized using a conjugate gradient and converted to “pdbqt” format. The protein target of Foc structure was imported into Autodock vina module and then converted into macromolecule using the “Convert to Macromolecule” in the Autodock option. Further, the ligands and proteins were selected using Vina wizard option seen in Autodock vina module. Aminoacid residues at the binding site based on CASTp results was chosen for grid setting in docking protocol. Screening of small molecules for the antifungal nature against Foc ChemMine ( http://chemmine.ucr.edu .) web-based tool was used for assessing the antifungal nature of the screened ligand molecules [ 40 ], using chemical similarity approach. The commercially available fungicides viz., tebuconazole and trifloxystrobin were used as positive checks. Quantification of the structural similarity between the selected effective ligands and the positive check was computed using the Tanimoto coefficient (Atom pair Tanimoto and MCS Tanimoto) and Maximum common substructure (MCS) parameters [ 41 ]. Tanimoto coefficient refers to the comparison or ratio of the structural features which were commonly shared between both the compounds and those which are distinct to the selected ligands. Tanimotto coefficient, T = c/(a + b-c) where a and b denoted bits set in their Molecular ACCess System (MACCS) fragment bit-strings whereas c denoted bits being set in the fingerprints of both compounds. It ranged from 0 to 1, larger the coefficient, greater was the similarity. Whereas the MCS denoted the largest substructure common for both the compounds, which in turn signified the similar physiochemical properties. Compound pairs with the largest difference in MCS maximum and MCS minimum (> 20) and MCS size (> 9) will have the highest similarity [ 42 ]. Molecular dynamic simulation The results obtained through molecular docking were further affirmed through molecular dynamic (MD) simulation. XRN2-Triamcinolone acetonide complex was subjected to MD simulation using GROMACS - GROningen MAchine for Chemical Simulations, version 5.1.2. [ 43 ] The force field used for MD simulation was GROMOS96 43a1. Followed by it, the command pdb2gmx was executed to generate topology, position restraint file (for keeping atoms at a position during equilibration) and post-processed structure file. Ligand topological parameters were obtained from GlycoBioChem PRODRG2 Server to execute MD simulation. Subsequently, the periodic simulation box was set up using the editconf module and it was solvated using spc216.gro solvent model. The net charge of the system was found using the grompp program and the file was passed to the genion module for adding ions, Na +, and Cl − for neutralizing the system. During the MD simulation process, steric hindrances was avoided and errors in the geometry, energy minimization was carried out using the steepest descent algorithm. After energy minimization, position restraining was done using the lincs algorithm for equilibrating the solvent around the solute. After equilibration, MD simulations in the conventional NVT ensemble have been used to maintain the temperature at 300K. Subsequently equations of motion were integrated using the velocity-verlet integrator, followed by MD simulation in the NPT ensemble, P = 1 atm. Finally, short-range and long-range coulomb interactions were studied using the smooth particle mesh Ewald (PME) method. Thus, the frames capturing the system's dynamic motions were exported during a 50-ns period. All of the trajectory files were assessed using the trajectory analysis module of GROMACS built-in tools. Graphs were generated using the Grace visualization tool. Validation of the antifungal property through wet-lab technique The poisoned food technique was used to assess the antifungal efficacy of the selected biomolecule in vitro in wetlab. For the same, 10,000 ppm stock solution of the compound was prepared and into 100 ml of PDA media, 1 ml and 2 ml of the above stock solution were added separately to get a final concentration of 250 ppm and 500 ppm respectively. Untreated control was also maintained separately. Fifteen ml of the media with different concentrations were poured into petri-plates and allowed to solidify. Later, from the 7 days old active culture of Fusarium oxysporum f. sp. cubense S16, 9mm mycelial disc was placed at the center of the plate. The plates were incubated at 28 ± 2ºC for 5 days and colonies were monitored for growth at periodical intervals. The mycelial growth of FOC was measured in different treatments and compared with the untreated control to assess the percent reduction of mycelial growth over untreated control. Percentage inhibition of the mycelia in treated over the control = ((Growth of mycelia in the control plate-Growth of mycelia in the treated plate) *100/ (Growth of mycelia in the control plate)) Scanning electron microscopic image Scanning electron microscopic images of the mycelia of Foc exposed to triamcinolone acetonide at 250 ppm and the mycelium in the untreated control plate were captured. Samples were mounted on a pedestal with graphite conductive paint and applying gold using an evaporation process and sputtering (Quorum gold sputter coating machine- Q150RS). A FESEM-SIGMA − 5 field emission scanning electron microscope was used for the test. It was examined at various high tension voltage ranges between 0 and 30 KV (HT- anode voltage) and apertures until the loaded sample could be viewed with the necessary resolution in high vacuum (HV) mode. Ultramicroscopic changes induced by the biomolecule triamcinolone acetonide was analyzed by comparing with the ultramicroscopic structures in the hyphae of untreated control. Samples were enclosed in gold palladium by the process of sputtering and the images were gathered. Results And Discussion From its first description in the 1870s in Australia, till now the Fusarium wilt of banana is emerging as more and more threatening. It started its voyage as Foc race1 by infecting Gros Michael cultivars in various parts of the world and continuing now as Tropical race 4 affecting Cavendish cultivars in the tropics (8). Monocropping in the field [ 44 ], pathogen dissemination through the water with the chlamydospores even enduring running water [ 45 ] along with the transfer of infected rhizomes free of symptoms to the unaffected areas [ 8 ], are the major reasons behind their survival and spread. Due to their long-term survival in the soil and colonization in non-host plants [ 46 ] and lack of feasible management practices, the disease is globally causing serious harm to the farming community [ 10 ]. Endophytic bacteria infiltrate inner host tissues in large numbers without causing harm to the host or provoking strong defense responses [ 47 ]. These endophytes have been employed in agriculture for biocontrol, plant growth stimulation through the generation of plant hormones, nutrient absorption augmentation, nitrogen fixation, mobilization of immobilized nutrients, hormone level regulation, etc [ 48 , 49 ]. Various strategies are employed by endophytes to provide biocontrol such as by interacting with pathogens directly through mycoparasitism, antibiosis, or competition for nutrients or root niches, or indirectly by establishing resistance mechanisms in the host [ 50 ]. These endophytes have been exploited in the biocontrol of diseases such as Botrytis cinerea employing the endophytic Burkholderia cepacia Cs5 for vine plantlet protection and Verticilium wilt of cotton [ 51 ], Fusarium wilt of cotton by Burkholderia cenocepacia , citrus canker by different Bacillus spp., [ 52 ] and many more. Like-wise a study conducted in Panama wilt resistant and susceptible cultivars lead to the unveiling of fungal antagonistic endophytes and the compounds produced during their interaction [ 17 ]. Protein modelling Two protein targets (FGB1 and XRN2 were modelled by homology modelling method, SWISS-MODEL ( Table S2 ). One among them was the G protein ß subunit (FGB1), which was modelled by a template protein from PDB with ID 7CX2 having a percentage identity of 52, coverage of 94 percent and 0.83 GMQE score. Third one was the XRN2, with template protein (PDB ID-3FQD) of 51.57 percent identity, 84 percent coverage and 0.62 GMQE score. The other method, PHYRE 2 was used for modelling SGE1, Velvet and Mkk2 with 100 percent confidence score ( Table S3 ). Template protein for SGE1 was (PDB ID 4m8b) having 52 percent coverage. Velvet protein of length 123 was retrieved from UniProt (ID:W0G578) and modelled with template protein (PDB ID 4n6r) of 98 percent coverage. Protein with PDB ID 1s9r and 52 percent coverage was the template for Mkk2. Other targets were modelled by using ROBETTA server ( Table S4 ). The sequences of proteins, Rlm1 (652 residues), Ftf1 (1079 residues), ERG6 (382 residues), RHO1 (1154 residues), ERG25 (305 residues) were retrieved from UniProt and submitted in ROBETTA server resulted in structures with a confidence score of 0.19, 0.36, 0.79, 0.09 and 0.77 respectively. Apart from these, SIX target proteins, SIX1 (confidence score − 0.60), SIX6 (confidence score-0.54), SIX8 (confidence score-0.43), SIX10 (confidence score − 0.46) and SIX13 (confidence score − 0.59) were also modelled with the same server (Fig. 1 ). Model validation Ramachandran plot obtained through PROCHECK programme in SAVES server was used to check the validity of the modelled structures. The target protein FGB1 had 87.3 percent residues in the most energetically favoured region, 12.6 percent in the additional allowed region, 0.3 percent in the generously allowed region as per the Ramachandran Plot. Percentage of residues in the most favoured region, additional allowed region and generously allowed region for XRN2 were 84.8, 13.7, and 1.5 respectively. SGE1 target protein had 88.2 percent of its residues in the most favoured region, 11.1 percent in the additional allowed region and 0.7 percent in the generously allowed region. Whereas the RHO1 had 93.3 percent, 5.9 percent, 0.7 percent residues in core region, additional allowed region and generously allowed region respectively. Ramachandran Plot obtained for Rlm1 target had 81.9 percent of its residues in the most favoured region, 27.3 percent in additional allowed regions and 0.8 percent in generously allowed regions. ERG6 was characterized with 94.7 percent residues in most favoured regions, with 4.8 percent in additional allowed regions and 0.9 percent and 5.3 percent residues in the generously allowed region whereas ERG25 with 92.4 percent residues in most favoured regions, 7.3 percent in additional allowed regions and 5.3 percent residues in the generously allowed region. Mkk2 protein expressed 67.1 percent residues in the core region and 27.6 percent residues in the additional allowed region with the rest of its residues in the generously allowed region. The percent residues of FTF1 target protein coming under most favoured region, additional allowed region and generously allowed region was 83.3, 15.2 and 1.5 percent respectively. Out of 123 residues of velvet protein 86 residues (86.0 percent) were coming in the most favoured region, 13 (13 percent) in the additional allowed region and 1 (1 percent) in the generously allowed region. SIX protein targets SIX1, SIX6, SIX8, SIX10 and SIX13 showed 88.3, 81.3, 72.4, 72.1 and 86.2 percent of its residues respectively in core region and correspondingly 10.4,17.6, 25.2, 23.8 and 12.3 percent residues in the additional allowed region. Rest of its residues were distributed in generously allowed regions (Fig. S1). Virtual screening and molecular docking Molecular docking is the process by which the interaction between a ligand and a protein target can be studied at the atomic level which provides an insight into the behaviour of the molecule at the binding site as well as the strength of their interaction from the binding energy (50). Virtual screening methods like molecular modelling and docking are frequently utilized in drug discovery and development, as well as in the research of protein-ligand interactions. Typically, the process starts with the modelling of target sites for which the structures are not available. The overall results of the docking analysis is shown in Fig. 2 . Triamcinolone acetonide Behaviour and strength of interaction of biomolecules with target proteins were documented through docking studies. Binding affinity of Triamcinolone acetonide with XRN2 was the highest of all the targets, -11.2 kcal/mol and had H-bonds with ASN A:350, ARG A:121 and GLN A:114 residues. FGB1 target had binding affinity, -7.8 kcal/mol (H bonds-CYS C:165, ARG C:167, SER C:206, ILE C 20, MET C:205) whereas with RHO1 binding affinity was − 7.5 kcal/mol (H-bonds; GLU A:110). Transcription factors SGE1 (H bonds-ASP 69, LYS 163), velvet (H Bonds-ALA 107), FTF1 (H Bonds-ALA A:647) and Rlm1(PRO A:188, HIS A:190) showed binding affinity of -7.8 kcal/mol, -7.8 kcal/mol, -10.4 kcal/mol and − 7.7 kcal/mol correspondingly with the compound. The binding affinity of ERG6 (ALA A:382) and ERG25 with the compound was − 5.9 kcal/mol and − 3.1 kcal/mol respectively. Among SIX target proteins, the compound had a stronger binding affinity in terms of binding energy with SIX1 (-9.3 kcal/mol) with two H bonds (SER A:277, SER A:50). Whereas SIX6 had a binding affinity of -6.9 kcal/mol (H Bonds- ARG A:114, SER A: 192, ASN A:193), -5.9 kcal/mol with SIX8 (H Bonds-ALA A:68, GLN A:67, GLY A:71), -6.9 kcal/mol with SIX10 (SER A:148) and − 8.8 kcal/mol with SIX13 (SER A:253, GLU A:254). A binding affinity of only − 3.7 kcal/mol was observed with kinase, Mkk2 (H Bond- CYS 365). Among the various ligand molecules screened, triamcinolone acetonide had the highest binding affinity towards a higher number of targets (14 targets) than trifloxystrobin and tebuconazole (Table 1 ). An overview of the docking results of triamcinolone acetonide with protein targets of F. oxysporum f. sp. cubense is shown in Fig. 3 and Fig. 4 . Table 1 Binding energy, H-bonds and other interactions of triamcinolone acetonide with protein targets of F. oxysporum f. sp. cubense. TARGETS Binding Energy Hydrogen bonds Aminoacids Other interactions XRN2 -11.2 3 ASNAA 350 ARGA 121 GLNA 114 Van der Waals FGB1 -7.8 5 CYSC 165 ARGC 167 SERC 206 ILEC 250 METC 205 Van der Waals RHO1 -7.5 - - Van der Waals SIX1 -9.3 2 SERA 277 SERA 50 Van der Waals SIX6 -6.9 3 ARGA 114 SERA 192 ASNA 193 Van der Waals SIX8 -5.9 3 ALAA 68 GLNA 67 GLYA 71 Van der Waals SIX10 -6.9 1 SERA 148 Van der Waals Halogen (Fluorine) SIX13 -8.8 2 SERA 253 GLUA 254 Van der Waals SGE1 -7.8 2 ASP 69 LYS 163 Van der Waals Velvet -7.8 2 ALA 107 VAL 56 Van der Waals FTF1 -10.4 1 ALAA 647 Van der Waals Halogen (Fluorine) Mkk2 -3.7 1 CYS 365 Van der Waals Rlm1 -7.7 2 PROA 188 HISA 190 Van der Waals ERG6 -5.9 1 ALAA 382 Van der Waals ERG 25 -10.3 - - Van der Waals Tebuconazole Tebuconazole is a commercially used systemic fungicide coming under triazole family commonly used for the control Fusarium wilt. Therefore, this molecule was taken as a reference molecule to find the effectiveness of the biomolecules under the present investigation. The binding affinity of the compound with the different fungal targets selected were − 8.7 kcal/mol for XRN2 (ASP A:351, ALA A:110, TYR A:616), -6.8 kcal/mol for FGB1 (H-bonds; SER C:206, VAL C:294), -6.4 kcal/mol for RHO1 (H-bonds; SER A:137), -6.9 kcal/mol for velvet (H-bonds; GLY 47), -6.3 kcal/mol for SGE1(H-bonds; LYS 163), -7.8 kcal/mol for SIX13, -4 kcal/mol for Rlm1, -6.8 kcal/mol for SIX1 (H-Bonds; CYS A:253;ASP A:110), -5.7 kcal/mol for FTF1 (H-bonds; GLY A:573), -4.7 kcal/mol for Mkk2 (H-bonds;.ASP A:367), -6.6 kcal/mol for ERG6 (ASN A:161), -5.2 kcal/mol for ERG25 (H-bonds; LYS A:169), -5.8 kcal/mol for SIX6 (H-bonds; GLN A:112), -5.3 kcal/mol for SIX8 (H-bonds; TYR A:73) and − 4 kcal/mol for SIX10 (H-bonds;. ALA A:124, ASN A:127). Trifloxystrobin Trifloxystrobin is a part of the combination fungicide along with tebuconazole used in controlling the soil borne pathogen Fusarium oxysporum f. sp. cubense .It has been found to be having higher binding affinity with XRN2 (H-bonds; TRP A:564, ARG A:413), -8.7 kcal/mol, FGB1(H-bonds; SER C:334, ARG C:167, TRP C:350, ARG C:332), -8.5 kcal/mol, ERG6 (H bonds; TYR A:86, ASN A:162), -8.6 kcal/mol, and SIX1 (H-bonds; CYS A:253, ASP A:110), -8.4 kcal/mol. Other targets with which it exhibited good binding affinity includes RHO1( H-bonds; MET A:292, TYR A:153) -7.3 kcal/mol, SGE1(H-bonds; TRP 73,ASP 69, LYS 163) -6.8 kcal/mol, SIX10 (H-bonds; ARG A:30, TYR A:79 )-6.8 kcal/mol, FTF1(H-bonds; TRP A;583), -6.3 kcal/mol, Velvet (H-bonds; PHE 49, TYR 48), -7.3 kcal/mol, SIX13(H-bonds; THR A 140,VAL A 86), -8.2 kcal/mol, ERG25 (H-bonds; GLY A:16 ), -5.3 kcal/mol, SIX6 (H-bonds; ALA A:68, TYR A:69 ), -6.7 kcal/mol and SIX8 (H-bonds; TYR A:79, ARG A:30),-6.4 kcal/mol). Whereas it showed lower affinity towards Rlm1, -4.3 kcal/mol and Mkk2, -4.8 kcal/mol with no H bonding. The binding conformation and binding free energy of small molecules to the target are then predicted using docking. From the docking analysis, triamcinolone acetonide had a higher affinity towards XRN2 which will affect the normal turn-over of m-RNA in the pathogen. Inside the nucleus, XRN2 participates in the processing of noncoding RNA such as rRNA precursors, the production of snoRNAs, and the destruction of hypomodified tRNAs. The telomeric repeat-containing RNA (TERRA) is synthesized from telomere sequences at the ends of chromosomes. When TERRA builds up in Saccharomyces cerevisiae , it stops telomeres from getting longer, possibly by stopping telomerase from working. Rat1-mediated degradation of TERRA maintains telomere lengthening and, thus, chromosomal stability [ 25 , 54 , 55 ]. All these suggest the importance of XRN2 in maintaining the proper functioning of sRNAs in the fungus which could affect the normal functioning and survival of the pathogen. FGB1 ( Fusarium guanine binding protein), is a membrane protein involved in the signal transduction process regulating biological functions. G protein subunits are involved in transmembrane receptor activation via effector molecules. Gene expression, cellular function, and metabolism are all controlled by this so that it can affect cell differentiation, growth, virulence, heat resistance, and percentage of germination. Chosen biomolecule had a strong affinity for the target and hence hindering all the mentioned functions and responses in the Foc [ 22 ]. Inhibitory activity of the molecule on all SIX proteins that serve as effectors of Fusarium and favouring colonization by the pathogen will prevent the disease progression [ 30 ]. RHO type GTPase being a part of the signal transduction pathway will result in defects in morphogenesis, cell wall biosynthesis, and reduced virulence. As the test ligand molecule triamcinolone acetonide had the maximum binding energy with RHO type GTPase, it could block the normal functioning and thus suppress morphogenesis and virulence of Foc [ 24 ]. Velvet and Fusarium transcription factor 1 (ftf1) are two proteins that have been implicated in fungal proliferation, maturation and disease development with the earlier one having the role of regulating sexual and asexual development of the fungus [ 30 ]. SGE1 which is mainly regulating the expression of effector genes results in reduced pathogenicity of the organism [ 23 ]. Other transcription factor affected includes Rlm1 which is a MADS box transcription factor whose functional disruption can result in reduced aerial hyphal growth, virulence, increased susceptibility to oxidative stress, and reduced production of mycotoxins fusaric acid and beauvericin [ 28 ]. Similarly, in the present investigation, triamcinolone had binding affinity with effector proteins including velvet, FTF1, SGE1 and Rlm1 based transcription factors. As a consequence, hyphal growth regulation, susceptibility to oxidative stress, mycotoxin production, virulence and morphogenesis might be impaired leading to lysis and death. Two proteins involved in ergosterol biosynthesis include ERG25 and ERG6 and disabling their function results in the reduction or inhibition of conidial germination [ 26 ]. Compared to the reference compounds, triamcinolone acetonide showed higher binding affinity towards the targets except for SIX1 with maximum binding energy to trifloxystrobin. Thus, it could have also contributed to the suppression of ergosterol biosynthesis and thus affecting the survival of Foc . Hitherto, the multiple modes of action of triamcinolone acetonide, could be harnessed as an effective antifungal molecule for the management of fungal pathogen. In a study, in vitro analysis reported the antifungal activity of VOCs produced by Sarocladium brachiariae [ 56 ]. This present inquiry on the antifungal compounds led to the unraveling of new biomolecules which can be turned into an eco-friendly weapon to fight against Panama wilt of banana at field level. Small molecule analysis Similarity analysis of the biomolecule triamcinolone acetonide was done with the positive check (Table 2 , Table 3 ), tebuconazole and trifloxystrobin. Tanimoto coefficients for both the pairs were very less (.09-.1) which indicated the difference in its properties between the control. MCS parameter analysis between the compound and control resulted in values of ≤ 9 which was consistent with the conclusion derived from the Tanimoto coefficient. MCS represented the largest common substructure between a pair of compounds, which in turn indicated the similar physiochemical properties between them. So, the results suggest that the compound may be having multiple modes of action different from the fungicide control. Table 2 Coefficients obtained from similarity analysis of Triamcinolone acetonide with Tebuconazole Coefficients Values AP Tanimoto 0.104746 MCS Tanimoto 0.2093 MCS Size 9 MCS Min 0.4286 MCS Max 0.2903 Table 3 Coefficients obtained from similarity analysis of Triamcinolone acetonide with Trifloxystrobin Coefficients Values AP Tanimoto 0.0997475 MCS Tanimoto 0.0909 MCS Size 5 MCS Min 0.1724 MCS Max 0.1613 Screening of antifungal efficacy of the compounds in vitro and SEM image analysis Triamcinolone acetonide was having inhibitory activity in the wet lab. It exhibited 100% inhibition of the growth of the Foc at 1000 ppm, 72.96 percent at 500 ppm, and 37% at 250 ppm. (Fig. 5 ). Scanning electron microscopic images of treated culture showed shrinkage and distortion of mycelia (Fig. 6 ). Molecular Dynamic Simulation MD Simulation emerged as a tool in bioinformatics in the 1980s to decode the behavior or kinetics of the protein at different levels like protein folding, during catalysis, interaction with the ligands etc. [ 53 ]. Molecular docking along with simulation studies has reported iturin A and fengycin as potential; antifungal compounds by their interaction studies with β-tubulin target proteins [ 57 ]. Since during, ligand-protein interactions, both of them undergo conformational changes and other perturbations in the structure to attain a stable complex, the dynamics of the system were studied with MD simulation. Hence to further validate the ability of the biomolecule as hostile to Foc , MD simulation was carried out with the target protein XRN2. For the same, Triamcinolone acetonide-XRN2 (the protein that exhibited the highest binding affinity with the compound) were subjected to MD simulation. Trajectories for Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), interaction energy, the radius of gyration (Rg), solvent accessible surface area (SASA) and hydrogen bond of the complex were obtained at the end of the simulation. Root Mean Square Deviation (RMSD) RMSD is a measure of the conformational stability of the protein when it gets complexed with the ligand. Less fluctuation and the lower value of RMSD for the complex indicated the stability of the complex. A higher fluctuation indicated the instability or rather alteration of the conformation arising to attain the final stable complex. RMSD values for the XRN2- Triamcinolone acetonide complex were calculated at different times and were depicted ( Fig. 7 ). The average value of RMSD for XRN2- Triamcinolone acetonide complex was 0.72 nm which indicated the stability of the complex. Initially, the complex seemed to be unstable, but within 0.03 ns it gained stability with an RMSD value of 0.45 nm. Some major fluctuations were also found in the trajectory at 18 ns, 23 ns, 42 ns and 43 ns. Thus, the protein-ligand complex exhibited stability with a few conformational changes during the simulation. Root Mean Square Fluctuation (RMSF) RMSF is a measure of flexibility of the residues in a protein, which in turn can favour the bonding with ligand in higher stability. Usually, the residues in a particular part of protein showing larger fluctuation may be the regions representing coils or loops which are flexible enough. Other regions which didn’t exhibit fluctuations may be the rigid part like helix or sheets as well as those involved in the ligand binding. A graph was created for the RMSF values for the residues in the protein (Overall RMSF for the residues in the protein was 0.385 nm. A large fluctuation in the RMSF value for atoms from 474 to 502 (Aminoacids: ASN51, LUE52 and TYR53) indicated that they are not involved in interacting with the ligand strongly and interestingly it was not a part of the binding site of the protein with the ligand as per the results obtained from CASTp results as well as docking results. Lower fluctuations were seen in the ligand binding sites as is evident from the graph ( Fig. 8 ). Other than this, fluctuations were also seen in other regions that expressed their flexibility to achieve final stable conformation as a complex. Potential energy energy The Potential energy of a system is a measure of the stability of the complex. The average potential energy of the system was − 1016.9 kJ/mol. which expressed the conformational stability of the complex. ( Fig. 9 ). Radius of gyration (Rg) The compactness of the system was tested by evaluating the radius of gyration (Rg) over a time period of 50 ns. The overall radius of gyration was around 3.16 nm and there was no much fluctuation during the time period. It indicated that the protein was stable as a complex with the selected compound ( Fig. 10 ). Hydrogen bond A number of bonds or forces are involved in the formation of successful interaction of the protein with the ligand viz. Hydrogen bond, Van der Waals forces, hydrophobic interactions, and electrostatic interactions. But one of the key players is the hydrogen atoms which are numerous in number involved in hydrogen bonding. The number of hydrogen bonds formed between Triamcinolone acetonide and XRN2 was evaluated and represented in graphical form (fig). A minimum of 0 and a maximum of 10 H bonds were formed during the time period, with two hydrogen bonds that were almost constant during the simulation ( Fig. 11 ). Solvent Accessible Surface Area (SASA) Solvent Accessible Surface Area is the surface of a protein that is in contact with the solvent or the surface which is characterized by the solvent, which is hypothetical. This is a key factor in the stability analysis of the protein-ligand complexes. Here the average value of SASA for the complex over the simulated period was 369.48 nm and it showed a decreasing trend which shows that the protein has changed its conformation, such that it can form a stable complex and the ligand is completely buried inside the protein ( Fig. 12 ). Trajectories for RMSF, RMSD, Rg, H- bond, energy and SASA at different time periods were inconsistent with that obtained from docking. This provides computational evidence for their possibility to be antifungal. Thus, all these results might have been responsible for the synergistic interaction and suppression of the banana wilt pathogen Foc . Conclusion In - silico evaluation of the organic compounds produced by the bacteria Bacillus velezensis lead to an insight into the antifungal activity of the compound, Triamcinolone acetonide which was found to be having a binding affinity towards the target proteins which are necessary for the survival as well as the virulence of the pathogen. It was found to be obstructing the function of the targets including all SIX (Secreted in Xylem) proteins, all the transcription factors (XRN2, FGB1, RHO1 (RHO type GTPase) and ERG6). The ability of this biomolecule to hamper the function of the protein target as obtained from docking assessment was further justified with the MD simulation of the protein XRN2 with the biomolecule. Further, wet-lab screening of the biomolecule for their antifungal activity was also carried out by means of the poisoned plate technique. Thus, triamcinolone acetonide compound can be used as a novel antifungal molecule for the management of Fusarium wilt of banana. Declarations Acknowledgements This work was supported by DBT–BTIS facility available at Department of Plant Molecular Biology and Bioinformatics, Centre for Plant Molecular biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India. The authors acknowledge the Department of Plant Biotechnology, Centre for plant molecular biology and biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, and Department of nanotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, for providing facilities. Ethical Approval Not applicable Competing interests The authors state unequivocally that they do not have any known competing financial interests or personal relationships with third parties that would have given the appearence of influencing the work presented in this study. Authors’ Contributions NS and SN conceptualized the research; KN performed in silico analysis and wet-lab studies ; SA, SR, and MK have supported in the analysis of docking interactions and in the preparation of manuscript. Funding Not Applicable References FAO, Banana market review – Preliminary results 2020. Rome. https://www.fao.org/3/cb5150en/cb5150en.pdf (accessed 3rd May 2022) FAO, Banana market review – Preliminary results 2021. Rome https://www.fao.org/3/cb9411en/cb9411en.pdf (accessed 3rd May 2022) DAC & FW, GOI, Agricultural statistics at a glance. https://eands.dacnet.nic.in/PDF/Agricultural%20Statistics%20at%20a%20Glance%20-%202020%20(English%20version).pdf . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-2133897\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":145350125,\"identity\":\"317fe724-69c8-4f14-bc46-ebd6e8b3207b\",\"order_by\":0,\"name\":\"Krishna Nayana R U\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tamil Nadu Agricultural University\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Krishna\",\"middleName\":\"Nayana 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N\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tamil Nadu Agricultural University\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Saranya\",\"middleName\":\"\",\"lastName\":\"N\",\"suffix\":\"\"},{\"id\":145350129,\"identity\":\"646f16bc-8b42-4bdb-a0ea-423e59338fc3\",\"order_by\":3,\"name\":\"Saravanan R\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tamil Nadu Agricultural University Coimbatore\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Saravanan\",\"middleName\":\"\",\"lastName\":\"R\",\"suffix\":\"\"},{\"id\":145350131,\"identity\":\"f2dc0364-4266-42aa-9146-7815a179e773\",\"order_by\":4,\"name\":\"Mahendra K\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tamil Nadu Agricultural University Coimbatore\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mahendra\",\"middleName\":\"\",\"lastName\":\"K\",\"suffix\":\"\"},{\"id\":145350133,\"identity\":\"90836125-6849-4afb-9e03-736e484ff549\",\"order_by\":5,\"name\":\"Suhail Ashraf\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tamil Nadu Agricultural University\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Suhail\",\"middleName\":\"\",\"lastName\":\"Ashraf\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2022-10-05 05:44:25\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-2133897/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-2133897/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":28107581,\"identity\":\"55420934-8ede-40d3-8ecf-8050825675d8\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:28:03\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":542568,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThree-dimensional structures of protein targets associated with virulence and \\u003cbr\\u003e\\n pathogenicity of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/3bd5c21db4554be45839d617.jpg\"},{\"id\":28106897,\"identity\":\"37a625c7-16d4-42f1-8ff6-6996207c5977\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:18:03\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":75657,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eHeat map on the molecular docking analysis of biomolecules against protein targets of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHigher the binding energy lower is the binding affinity of compounds with the protein targets\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/ee78c37dcb3256d96f13b4ce.jpg\"},{\"id\":28107260,\"identity\":\"d9113c13-8df5-41db-bdcc-f9fcf9c465d4\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:23:03\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":151190,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAn overview of the docking results of triamcinolone acetonide with protein targets of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/7e8d8868271f3298401f8f6d.jpg\"},{\"id\":28191369,\"identity\":\"fb0976c3-0463-4afd-8778-9d43c7d55a55\",\"added_by\":\"auto\",\"created_at\":\"2022-10-24 17:47:55\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":151097,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAn overview of the docking results of triamcinolone acetonide with protein targets of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/1132c92d79f8e31a37e17f17.jpg\"},{\"id\":28191366,\"identity\":\"920d1c86-67f3-4904-8508-eceee240e309\",\"added_by\":\"auto\",\"created_at\":\"2022-10-24 17:47:34\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":75562,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePoisoned plate for the analysis of the antifungal activity of triamcinolone acetonide against \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/1725a0ed4d79b7e6fd0e2099.jpg\"},{\"id\":28106886,\"identity\":\"a380c2a9-e512-43d2-8d53-96a2d22afd89\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:18:03\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":58394,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSEM images of the \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003ecubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e treated with triamcinolone acetonide and untreated control\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/04fe7589dbd74171a7a76258.jpg\"},{\"id\":28191367,\"identity\":\"35e5b7dd-9142-416d-86d0-64bee98be710\",\"added_by\":\"auto\",\"created_at\":\"2022-10-24 17:47:44\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":38429,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRoot mean square deviation of the simulated complex of Triamcinolone acetonide with XRN2 of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/d1b2d50d5533e2c81fe5cc52.jpg\"},{\"id\":28107994,\"identity\":\"826b7899-8c25-4087-afb5-50852c404ec9\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:33:03\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":40465,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRoot mean square fluctuation of the simulated complex of Triamcinolone acetonide with XRN2 of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/44e2385f65de2ecad4ce986d.jpg\"},{\"id\":28107254,\"identity\":\"8e1a7b96-b15d-4102-9465-1eb933d591d1\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:23:03\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":46375,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eH bond trajectory of the simulated complex of Triamcinolone acetonide with XRN2 of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig9.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/aee442b88b52f2819d069c33.jpg\"},{\"id\":28106890,\"identity\":\"56f60065-799b-49b1-9264-3ccec630cadb\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:18:03\",\"extension\":\"jpg\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":53251,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRadius of gyration trajectory of the simulated complex of Triamcinolone acetonide with XRN2 of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig10.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/5e766ae3e41c6be046f8a60a.jpg\"},{\"id\":28108081,\"identity\":\"f84e4497-931d-44ba-b953-97b37b877252\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:38:03\",\"extension\":\"jpg\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":56500,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePlot on potential energy of the simulated complex of Triamcinolone acetonide with XRN2 of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig11.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/4390203281a9da611f76f6bf.jpg\"},{\"id\":28107263,\"identity\":\"cec23adc-a597-489b-bcc4-8a9b626dbd8f\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:23:03\",\"extension\":\"jpg\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":34036,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSASA plot of the simulated complex of Triamcinolone acetonide with XRN2 of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eF. oxysporum \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003ef. sp.\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e cubense\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig12.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/bb56cae6e34f9dd4b694cce6.jpg\"},{\"id\":28981419,\"identity\":\"4c6aabf6-7472-4093-ad85-a70be4b3edaf\",\"added_by\":\"auto\",\"created_at\":\"2022-11-12 10:29:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1561293,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/982e23f3-bed6-41c8-90b9-85fee275162a.pdf\"},{\"id\":28107587,\"identity\":\"555e316f-3d1b-4c5e-aab4-8e74280d7bb6\",\"added_by\":\"auto\",\"created_at\":\"2022-10-21 19:28:03\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":986545,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryarticle.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2133897/v1/30ec47329553891018ed118e.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Computational analysis revealed Triamcinolone acetonide produced by Bacillus velezensis YEBBR6 as having antagonistic activity against Fusarium oxysporum f. sp. cubense\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eBanana (\\u003cem\\u003eMusa\\u003c/em\\u003e sp.) is one of the most important fruit crops in the world which is produced, consumed and traded globally in large quantities. The estimated export volume of bananas was 21.5\\u0026nbsp;million tonnes in 2020 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e] and 20\\u0026nbsp;million tonnes in 2021 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Next to mango, it is the second most important fruit crop in India and ranked first in production and third in area. It is being cultivated in an area of 876 ha and with annual production of 31799 tonnes [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. But the productivity is hampered worldwide by \\u0026ldquo;Panama wilt\\u0026rdquo; caused by a soil-borne fungal pathogen \\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e f sp. \\u003cem\\u003ecubense\\u003c/em\\u003e (\\u003cem\\u003eFoc\\u003c/em\\u003e). The disease first dragged the attention of the growers as a serious issue during late 19th century when it devastated the banana cultivars in Central America and the Caribbean region [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The pathogen, \\u003cem\\u003eFoc\\u003c/em\\u003e-Race1 infected the banana cultivar Gros Michel, and resulted in shifting the cultivation of this cultivar with the resistant, Cavendish cultivar [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Apart from this, it also infected \\u0026lsquo;I.C.2\\u0026rsquo; AAAA, \\u0026lsquo;Silk,\\u0026rsquo; \\u0026lsquo;Pome\\u0026rsquo; AAB, \\u0026lsquo;Pisang Awak\\u0026rsquo; ABB and \\u0026lsquo;Maque\\u0026ntilde;o\\u0026rsquo; AAB. Race 2 infect cooking bananas, Bluggoe subgroup ABB [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Currently, the Cavendish cultivar is also affected by \\u003cem\\u003eFoc\\u003c/em\\u003e Tropical Race 4 (TR 4) coming under Race 4, which was reported in the early 1990s in Southeast Asia. Further it is still restricted to Asia and northern Australia, but a major concern for the farmers is the disease is posing a great menace in the tropics where it is raised as commercial plantations with continuous monocropping [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe pathogen is a filamentous- hemibiotrophic saprophytic fungi [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e] with more than 20 Vegetative Compatible Groups (VCGs) known [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. It colonized plant through roots and clogs the vascular tissue resulting in wilt and death of the plant and death [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. As pathogen reproduce through vegetative means and remain in soil as saprophytes, they are bestowed to survive for more than 20 years as chlamydospores [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e] and thus management of the pathogen remains as a challenging task. To date, there are only a few effective options available for management of the pathogen. Although physical and chemical methods are available, it does not provide a long-term effect. Besides, chemical methods are posing threat to the environment and human beings [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Moreover, cultural control of the pathogen also remains as a challenging task, due to the non-availability of resistant cultivars. Even though the resistant cultivars are available, they are not cultivated as they don\\u0026rsquo;t meet the consumer preference [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDue to these conditions, biological control with the endophytes in the resistant cultivar will pave the way for the management of \\u003cem\\u003eFoc\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Antagonistic bacterial endophytes will complement their host with multiple endowments including nutrient uptake, promotion of plant growth and resistance to diseases [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. They deploy different mechanisms to resist pathogens [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] either through mycoparasitism or competition for nutrients or root niches, antibiosis through the production of secondary metabolites, quorum sensing and signalling or by inducing immune response in the host [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Bacterial endophytes do exist in bananas also. A quest for those antagonistic bacterial endophytes against \\u003cem\\u003eFoc\\u003c/em\\u003e from resistant banana genotype YKM5, resulted in the identification of \\u003cem\\u003eBrachybacterium paraconglomeratum\\u003c/em\\u003e YEBPT2, \\u003cem\\u003eBrucella melitensis\\u003c/em\\u003e YEBPS3, \\u003cem\\u003eBacillus velezensis\\u003c/em\\u003e YEBBR6, and the one associated with nectar \\u003cem\\u003eBacillus albus\\u003c/em\\u003e YEBN2 which inhibited \\u003cem\\u003eFoc\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Confrontational assay of \\u003cem\\u003eFoc\\u003c/em\\u003e with the endophyte \\u003cem\\u003eBacillus velezensis\\u003c/em\\u003e YEBBR6 resulted in the identification of several biomolecules through GCMS analysis. Though the biomolecules were identified, the research on the role of identified biomolecules against \\u003cem\\u003eFoc\\u003c/em\\u003e was not ascertained. Hence, to fish out the role of biomolecules for their antifungal action, molecular docking was done with biomolecules, to explore and harness the potential biomolecules which can be used for the management of pathogen by identifying the behaviour and interaction of these biomolecules with the selected fungal protein targets necessary for their survival, proliferation, virulence and parasitism. Thus, it will ultimately lead to the unveiling of the mode of action of biomolecules through \\u003cem\\u003ein-silico\\u003c/em\\u003e approach.\\u003c/p\\u003e \\u003cp\\u003eMolecular docking involves the interaction study of the protein with a ligand by taking only its rigid structure, which needs further substantiation as the recognition and binding with the ligand itself involves conformational changes in the protein [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Moreover, dynamics seem to be a key player in studying their properties. Apart from this, binding is also affected by hydrogen bonds along with van der Waals forces, whose local rearrangements result in the final stable protein-ligand binding [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Correspondingly other factors include solvent surrounding the protein, and the interaction and energy exchanged between them as a thermodynamic system [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Molecular Dynamic simulation (MD Simulation) is a computational technique that models molecular systems at the atomic scale level and studies the static, structural, thermodynamic, and dynamic properties over a time scale. In order to find the exact mechanism of binding of ligands to the target proteins, the time extend to which it binds, and the stability of the complex in the particular presumed environmental conditions (Temperature, pressure, volume), molecular dynamic simulation is usually employed [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Hence, the ligand Triamcinolone acetonide and the protein XRN2 with the highest binding affinity was subjected for MD simulation for further validation of its effect on protein targets of the pathogen. Further, antifungal nature of the biomolecules was also confirmed in a wet lab through the poisoned food technique.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eIdentification of fungal targets\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eIdentification of potential target proteins of the pathogen is the foremost important step for assessing the interaction of protein with the ligand molecules through docking. Based on the literature survey and analysis, antifungal protein targets in \\u003cem\\u003eFoc\\u003c/em\\u003e were identified as G protein \\u0026szlig; subunit (FGB1) [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e], Six Gene Expression 1 (SGE1) [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], RHO type GTPase (RHO1) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], 5\\u0026acute; \\u0026rarr; 3\\u0026acute; Exoribonuclease 2 (XRN2) [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], C-24 sterol methyltransferase (ERG6), C-4 sterol methyl oxidase (ERG25) [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e], Fusarium transcription factor 1 (FTF1), velvet [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], MADS-box transcription factor Rlm1 (Resistance to lethality of MKK1P386) [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], MAP kinase kinase 2 (Mkk2) [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], Secreted in Xylem 1 (SIX1), Secreted in xylem 6 (SIX6), Secreted in xylem 8 ( SIX8), Secreted in xylem 10 (SIX10) and Secreted in xylem (SIX13) [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Details on these protein targets of \\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense\\u003c/em\\u003e associated with virulence and pathogenicity are given in the Table S1.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eModelling of the targets\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe tertiary structure of proteins formed from the sequence of the amino acids determines the function embodied by them [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] and modelling involves the construction of these structures \\u003cem\\u003ein-silico\\u003c/em\\u003e by bioinformatic tools. Protein Data Bank is a primary database that serves as a global repository for structural data on proteins and other biomolecules obtained through experimental results. Selected targets were searched for their structure in the PDB database and none of them was found. Hence, the modelling of the protein targets was done by using the template-based modelling and through comparative modelling method based on the NCBI\\u0026rsquo;s BLASTp [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e] search results. For that, primary protein sequences of the targets were retrieved in FASTA format from the UniProt database [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e] and BLAStp analysis was done using the PDB database. Different modelling servers were selected as per the query coverage and the percentage identity of the BLASTp results. Template-based modelling servers such as SWISS-MODEL, PHYRE 2 software\\u0026rsquo;s and ROBETTA (Metaserver), the comparative modelling-based server was used for modelling the targets. SWISS-MODEL is a homology-modelling server that functions based on sequence comparison [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e] and PHYRE 2 pertains to threading method of structure prediction based on fold recognition [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. ROBETTA server functions based on \\u003cem\\u003eab initio\\u003c/em\\u003e method of modelling that uses comparative modelling if the matching sequences are found from BLAST analysis or \\u003cem\\u003ede novo\\u003c/em\\u003e Rosetta fragment insertion method otherwise [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eStructure predictions using SWISS-MODEL software were done for the targets including XRN2 and FGB1. The percent identity, coverage {maximum} and similarity {30\\u0026ndash;50 percent} between target and template sequence, and Global Mean Quality Estimation (GMQE) {close to 1} were used as the parameters to ensure the quality of modelled structures through homology modelling using SWISS-MODEL. The protein sequences, which did not have any matching or similar sequences or with lower query coverage (less than 50%) and with very low percent identity in BLAST query, the 3-D models were developed using ROBETTA server. Majority of selected targets including Rlm1, FTF1, ERG6, ERG25, RHO1, SIX1, SIX6, SIX8, SIX10, and SIX13 were modelled using ROBETTA server The target proteins SGE1, Velvet, and Mkk2 of \\u003cem\\u003eFoc\\u003c/em\\u003e, was having the query coverage of 50\\u0026ndash;80% with 30% identity using BLASTp analysis. As it could not be used for SWISS MODELLING, the model was built using PHYRE 2 server for the protein targets SGE1, Velvet, and Mkk2. Modelling of the above target proteins with PYRE2 server, displays several models. Among the several models, the top model will be selected based on the template protein PDB ID, confidence score, and coverage percentage. The ideal structures which have the maximum confidence score of \\u0026gt;\\u0026thinsp;90% will be used for further analysis.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eModel Validation\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe quality of the modelled protein structure was validated using \\u0026ldquo;model building\\u0026rdquo; with known sequences. Reliability of the modelled protein structures was confirmed through Structural Analysis and Verification Server, SAVES v6.0 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://saves.mbi.ucla.edu/\\u003c/span\\u003e\\u003cspan address=\\\"https://saves.mbi.ucla.edu/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). using PROCHECK v.3.5 (Programs to check the Stereochemical Quality of Protein Structures) [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. It was used to evaluate the overall stereochemistry of protein and checked the quality of modelled structure by comparing with the well-refined structure of same resolution. Subsequently, PDB format file of the structure was uploaded to run the program, so as to get the Ramachandran plot to find out the residues in the allowed and disallowed regions. Further, visualization, energy minimization and loop building of the structure was done for the residues in the disallowed region using SWISS PDB VIEWER. After loop refinement, residues were checked for their feasible conformations.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePrediction of target protein site with ligands\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003ePrediction of binding site of the target protein with the ligands or biomolecules for the interaction was detected using Computed Atlas of Surface Topography of proteins (CASTp) (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://sts.bioe.uic.edu/castp/index.html\\u003c/span\\u003e\\u003cspan address=\\\"http://sts.bioe.uic.edu/castp/index.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). These sites were used for setting the grid box in docking studies [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePreparation of ligands\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003e2-D structures of biomolecules Triamcinolone acetonide, 3.4-Dihydrocoumarin, 3-trifluoroacetoxypentadecane, 5-fluorouracil, allobarbital, citramalic acid, quinazoline, succinic acid, tebuconazole and trifloxystrobin were obtained from PubChem database in SDF format. Tebuconazole and trifloxystrobin were used as the positive check. Using the Open Babel software the SDF file of the biomolecule was converted into their respective PDB format and was further used for docking studies.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMolecular docking\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eMolecular docking was performed through Autodock vina in PyRx 0.8 [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e] software which implements Python language. Ligands were energy minimized using a conjugate gradient and converted to \\u0026ldquo;pdbqt\\u0026rdquo; format. The protein target of \\u003cem\\u003eFoc\\u003c/em\\u003e structure was imported into Autodock vina module and then converted into macromolecule using the \\u0026ldquo;Convert to Macromolecule\\u0026rdquo; in the Autodock option. Further, the ligands and proteins were selected using Vina wizard option seen in Autodock vina module. Aminoacid residues at the binding site based on CASTp results was chosen for grid setting in docking protocol.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eScreening of small molecules for the antifungal nature against\\u003c/b\\u003e \\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eFoc\\u003c/span\\u003e\\u003c/p\\u003e \\u003cp\\u003eChemMine (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://chemmine.ucr.edu\\u003c/span\\u003e\\u003cspan address=\\\"http://chemmine.ucr.edu\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.) web-based tool was used for assessing the antifungal nature of the screened ligand molecules [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e], using chemical similarity approach. The commercially available fungicides viz., tebuconazole and trifloxystrobin were used as positive checks. Quantification of the structural similarity between the selected effective ligands and the positive check was computed using the Tanimoto coefficient (Atom pair Tanimoto and MCS Tanimoto) and Maximum common substructure (MCS) parameters [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Tanimoto coefficient refers to the comparison or ratio of the structural features which were commonly shared between both the compounds and those which are distinct to the selected ligands.\\u003c/p\\u003e \\u003cp\\u003eTanimotto coefficient, T\\u0026thinsp;=\\u0026thinsp;c/(a\\u0026thinsp;+\\u0026thinsp;b-c)\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cem\\u003ea\\u003c/em\\u003e and \\u003cem\\u003eb\\u003c/em\\u003e denoted bits set in their Molecular ACCess System (MACCS) fragment bit-strings whereas c denoted bits being set in the fingerprints of both compounds. It ranged from 0 to 1, larger the coefficient, greater was the similarity. Whereas the MCS denoted the largest substructure common for both the compounds, which in turn signified the similar physiochemical properties. Compound pairs with the largest difference in MCS maximum and MCS minimum (\\u0026gt;\\u0026thinsp;20) and MCS size (\\u0026gt;\\u0026thinsp;9) will have the highest similarity [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMolecular dynamic simulation\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe results obtained through molecular docking were further affirmed through molecular dynamic (MD) simulation. XRN2-Triamcinolone acetonide complex was subjected to MD simulation using GROMACS - GROningen MAchine for Chemical Simulations, version 5.1.2. [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e] The force field used for MD simulation was GROMOS96 43a1. Followed by it, the command pdb2gmx was executed to generate topology, position restraint file (for keeping atoms at a position during equilibration) and post-processed structure file. Ligand topological parameters were obtained from GlycoBioChem PRODRG2 Server to execute MD simulation. Subsequently, the periodic simulation box was set up using the editconf module and it was solvated using spc216.gro solvent model. The net charge of the system was found using the grompp program and the file was passed to the genion module for adding ions, Na\\u003csup\\u003e+,\\u003c/sup\\u003e and Cl\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e for neutralizing the system. During the MD simulation process, steric hindrances was avoided and errors in the geometry, energy minimization was carried out using the steepest descent algorithm. After energy minimization, position restraining was done using the lincs algorithm for equilibrating the solvent around the solute. After equilibration, MD simulations in the conventional NVT ensemble have been used to maintain the temperature at 300K. Subsequently equations of motion were integrated using the velocity-verlet integrator, followed by MD simulation in the NPT ensemble, P\\u0026thinsp;=\\u0026thinsp;1 atm. Finally, short-range and long-range coulomb interactions were studied using the smooth particle mesh Ewald (PME) method. Thus, the frames capturing the system's dynamic motions were exported during a 50-ns period. All of the trajectory files were assessed using the trajectory analysis module of GROMACS built-in tools. Graphs were generated using the Grace visualization tool.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eValidation of the antifungal property through wet-lab technique\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe poisoned food technique was used to assess the antifungal efficacy of the selected biomolecule \\u003cem\\u003ein vitro\\u003c/em\\u003e in wetlab. For the same, 10,000 ppm stock solution of the compound was prepared and into 100 ml of PDA media, 1 ml and 2 ml of the above stock solution were added separately to get a final concentration of 250 ppm and 500 ppm respectively. Untreated control was also maintained separately. Fifteen ml of the media with different concentrations were poured into petri-plates and allowed to solidify. Later, from the 7 days old active culture of \\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense\\u003c/em\\u003e S16, 9mm mycelial disc was placed at the center of the plate. The plates were incubated at 28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2\\u0026ordm;C for 5 days and colonies were monitored for growth at periodical intervals. The mycelial growth of \\u003cem\\u003eFOC\\u003c/em\\u003e was measured in different treatments and compared with the untreated control to assess the percent reduction of mycelial growth over untreated control.\\u003c/p\\u003e \\u003cp\\u003ePercentage inhibition of the mycelia in treated over the control = ((Growth of mycelia in the control plate-Growth of mycelia in the treated plate) *100/ (Growth of mycelia in the control plate))\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eScanning electron microscopic image\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eScanning electron microscopic images of the mycelia of \\u003cem\\u003eFoc\\u003c/em\\u003e exposed to triamcinolone acetonide at 250 ppm and the mycelium in the untreated control plate were captured. Samples were mounted on a pedestal with graphite conductive paint and applying gold using an evaporation process and sputtering (Quorum gold sputter coating machine- Q150RS). A FESEM-SIGMA \\u0026minus;\\u0026thinsp;5 field emission scanning electron microscope was used for the test. It was examined at various high tension voltage ranges between 0 and 30 KV (HT- anode voltage) and apertures until the loaded sample could be viewed with the necessary resolution in high vacuum (HV) mode. Ultramicroscopic changes induced by the biomolecule triamcinolone acetonide was analyzed by comparing with the ultramicroscopic structures in the hyphae of untreated control. Samples were enclosed in gold palladium by the process of sputtering and the images were gathered.\\u003c/p\\u003e\"},{\"header\":\"Results And Discussion\",\"content\":\"\\u003cp\\u003eFrom its first description in the 1870s in Australia, till now the \\u003cem\\u003eFusarium\\u003c/em\\u003e wilt of banana is emerging as more and more threatening. It started its voyage as \\u003cem\\u003eFoc\\u003c/em\\u003e race1 by infecting Gros Michael cultivars in various parts of the world and continuing now as Tropical race 4 affecting Cavendish cultivars in the tropics (8). Monocropping in the field [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e], pathogen dissemination through the water with the chlamydospores even enduring running water [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e] along with the transfer of infected rhizomes free of symptoms to the unaffected areas [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], are the major reasons behind their survival and spread. Due to their long-term survival in the soil and colonization in non-host plants [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e] and lack of feasible management practices, the disease is globally causing serious harm to the farming community [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Endophytic bacteria infiltrate inner host tissues in large numbers without causing harm to the host or provoking strong defense responses [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. These endophytes have been employed in agriculture for biocontrol, plant growth stimulation through the generation of plant hormones, nutrient absorption augmentation, nitrogen fixation, mobilization of immobilized nutrients, hormone level regulation, etc [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. Various strategies are employed by endophytes to provide biocontrol such as by interacting with pathogens directly through mycoparasitism, antibiosis, or competition for nutrients or root niches, or indirectly by establishing resistance mechanisms in the host [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. These endophytes have been exploited in the biocontrol of diseases such as \\u003cem\\u003eBotrytis cinerea\\u003c/em\\u003e employing the endophytic \\u003cem\\u003eBurkholderia cepacia\\u003c/em\\u003e Cs5 for vine plantlet protection and \\u003cem\\u003eVerticilium\\u003c/em\\u003e wilt of cotton [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e], \\u003cem\\u003eFusarium\\u003c/em\\u003e wilt of cotton by \\u003cem\\u003eBurkholderia cenocepacia\\u003c/em\\u003e, citrus canker by different \\u003cem\\u003eBacillus\\u003c/em\\u003e spp., [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e] and many more. Like-wise a study conducted in Panama wilt resistant and susceptible cultivars lead to the unveiling of fungal antagonistic endophytes and the compounds produced during their interaction [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eProtein modelling\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTwo protein targets (FGB1 and XRN2 were modelled by homology modelling method, SWISS-MODEL (\\u003cb\\u003eTable S2\\u003c/b\\u003e). One among them was the G protein \\u0026szlig; subunit (FGB1), which was modelled by a template protein from PDB with ID 7CX2 having a percentage identity of 52, coverage of 94 percent and 0.83 GMQE score. Third one was the XRN2, with template protein (PDB ID-3FQD) of 51.57 percent identity, 84 percent coverage and 0.62 GMQE score. The other method, PHYRE 2 was used for modelling SGE1, Velvet and Mkk2 with 100 percent confidence score (\\u003cb\\u003eTable S3\\u003c/b\\u003e). Template protein for SGE1 was (PDB ID 4m8b) having 52 percent coverage. Velvet protein of length 123 was retrieved from UniProt (ID:W0G578) and modelled with template protein (PDB ID 4n6r) of 98 percent coverage. Protein with PDB ID 1s9r and 52 percent coverage was the template for Mkk2. Other targets were modelled by using ROBETTA server (\\u003cb\\u003eTable S4\\u003c/b\\u003e). The sequences of proteins, Rlm1 (652 residues), Ftf1 (1079 residues), ERG6 (382 residues), RHO1 (1154 residues), ERG25 (305 residues) were retrieved from UniProt and submitted in ROBETTA server resulted in structures with a confidence score of 0.19, 0.36, 0.79, 0.09 and 0.77 respectively. Apart from these, SIX target proteins, SIX1 (confidence score \\u0026minus;\\u0026thinsp;0.60), SIX6 (confidence score-0.54), SIX8 (confidence score-0.43), SIX10 (confidence score \\u0026minus;\\u0026thinsp;0.46) and SIX13 (confidence score \\u0026minus;\\u0026thinsp;0.59) were also modelled with the same server (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e \\u003cb\\u003eModel validation\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eRamachandran plot obtained through PROCHECK programme in SAVES server was used to check the validity of the modelled structures. The target protein FGB1 had 87.3 percent residues in the most energetically favoured region, 12.6 percent in the additional allowed region, 0.3 percent in the generously allowed region as per the Ramachandran Plot. Percentage of residues in the most favoured region, additional allowed region and generously allowed region for XRN2 were 84.8, 13.7, and 1.5 respectively. SGE1 target protein had 88.2 percent of its residues in the most favoured region, 11.1 percent in the additional allowed region and 0.7 percent in the generously allowed region. Whereas the RHO1 had 93.3 percent, 5.9 percent, 0.7 percent residues in core region, additional allowed region and generously allowed region respectively. Ramachandran Plot obtained for Rlm1 target had 81.9 percent of its residues in the most favoured region, 27.3 percent in additional allowed regions and 0.8 percent in generously allowed regions. ERG6 was characterized with 94.7 percent residues in most favoured regions, with 4.8 percent in additional allowed regions and 0.9 percent and 5.3 percent residues in the generously allowed region whereas ERG25 with 92.4 percent residues in most favoured regions, 7.3 percent in additional allowed regions and 5.3 percent residues in the generously allowed region. Mkk2 protein expressed 67.1 percent residues in the core region and 27.6 percent residues in the additional allowed region with the rest of its residues in the generously allowed region. The percent residues of FTF1 target protein coming under most favoured region, additional allowed region and generously allowed region was 83.3, 15.2 and 1.5 percent respectively. Out of 123 residues of velvet protein 86 residues (86.0 percent) were coming in the most favoured region, 13 (13 percent) in the additional allowed region and 1 (1 percent) in the generously allowed region. SIX protein targets SIX1, SIX6, SIX8, SIX10 and SIX13 showed 88.3, 81.3, 72.4, 72.1 and 86.2 percent of its residues respectively in core region and correspondingly 10.4,17.6, 25.2, 23.8 and 12.3 percent residues in the additional allowed region. Rest of its residues were distributed in generously allowed regions (Fig. S1).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eVirtual screening and molecular docking\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eMolecular docking is the process by which the interaction between a ligand and a protein target can be studied at the atomic level which provides an insight into the behaviour of the molecule at the binding site as well as the strength of their interaction from the binding energy (50). Virtual screening methods like molecular modelling and docking are frequently utilized in drug discovery and development, as well as in the research of protein-ligand interactions. Typically, the process starts with the modelling of target sites for which the structures are not available. The overall results of the docking analysis is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eTriamcinolone acetonide\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eBehaviour and strength of interaction of biomolecules with target proteins were documented through docking studies. Binding affinity of Triamcinolone acetonide with XRN2 was the highest of all the targets, -11.2 kcal/mol and had H-bonds with ASN A:350, ARG A:121 and GLN A:114 residues. FGB1 target had binding affinity, -7.8 kcal/mol (H bonds-CYS C:165, ARG C:167, SER C:206, ILE C 20, MET C:205) whereas with RHO1 binding affinity was \\u0026minus;\\u0026thinsp;7.5 kcal/mol (H-bonds; GLU A:110). Transcription factors SGE1 (H bonds-ASP 69, LYS 163), velvet (H Bonds-ALA 107), FTF1 (H Bonds-ALA A:647) and Rlm1(PRO A:188, HIS A:190) showed binding affinity of -7.8 kcal/mol, -7.8 kcal/mol, -10.4 kcal/mol and \\u0026minus;\\u0026thinsp;7.7 kcal/mol correspondingly with the compound. The binding affinity of ERG6 (ALA A:382) and ERG25 with the compound was \\u0026minus;\\u0026thinsp;5.9 kcal/mol and \\u0026minus;\\u0026thinsp;3.1 kcal/mol respectively. Among SIX target proteins, the compound had a stronger binding affinity in terms of binding energy with SIX1 (-9.3 kcal/mol) with two H bonds (SER A:277, SER A:50). Whereas SIX6 had a binding affinity of -6.9 kcal/mol (H Bonds- ARG A:114, SER A: 192, ASN A:193), -5.9 kcal/mol with SIX8 (H Bonds-ALA A:68, GLN A:67, GLY A:71), -6.9 kcal/mol with SIX10 (SER A:148) and \\u0026minus;\\u0026thinsp;8.8 kcal/mol with SIX13 (SER A:253, GLU A:254). A binding affinity of only \\u0026minus;\\u0026thinsp;3.7 kcal/mol was observed with kinase, Mkk2 (H Bond- CYS 365). Among the various ligand molecules screened, triamcinolone acetonide had the highest binding affinity towards a higher number of targets (14 targets) than trifloxystrobin and tebuconazole (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). An overview of the docking results of triamcinolone acetonide with protein targets of \\u003cem\\u003eF. oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense\\u003c/em\\u003e is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBinding energy, H-bonds and other interactions of triamcinolone acetonide with protein targets of \\u003cem\\u003eF. oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense.\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTARGETS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBinding Energy\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHydrogen bonds\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAminoacids\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eOther interactions\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eXRN2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-11.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eASNAA 350\\u003c/p\\u003e \\u003cp\\u003eARGA 121\\u003c/p\\u003e \\u003cp\\u003eGLNA 114\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFGB1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-7.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCYSC 165\\u003c/p\\u003e \\u003cp\\u003eARGC 167\\u003c/p\\u003e \\u003cp\\u003eSERC 206\\u003c/p\\u003e \\u003cp\\u003eILEC 250\\u003c/p\\u003e \\u003cp\\u003eMETC 205\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRHO1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-7.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSIX1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-9.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSERA 277\\u003c/p\\u003e \\u003cp\\u003eSERA 50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSIX6\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-6.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eARGA 114\\u003c/p\\u003e \\u003cp\\u003eSERA 192\\u003c/p\\u003e \\u003cp\\u003eASNA 193\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSIX8\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-5.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eALAA 68\\u003c/p\\u003e \\u003cp\\u003eGLNA 67\\u003c/p\\u003e \\u003cp\\u003eGLYA 71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSIX10\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-6.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSERA 148\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003cp\\u003eHalogen (Fluorine)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSIX13\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-8.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSERA 253\\u003c/p\\u003e \\u003cp\\u003eGLUA 254\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSGE1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-7.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eASP 69\\u003c/p\\u003e \\u003cp\\u003eLYS 163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eVelvet\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-7.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eALA 107\\u003c/p\\u003e \\u003cp\\u003eVAL 56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFTF1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-10.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eALAA 647\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003cp\\u003eHalogen (Fluorine)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMkk2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-3.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCYS 365\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRlm1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-7.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePROA 188\\u003c/p\\u003e \\u003cp\\u003eHISA 190\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eERG6\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-5.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eALAA 382\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eERG 25\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-10.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eVan der Waals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eTebuconazole\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTebuconazole is a commercially used systemic fungicide coming under triazole family commonly used for the control \\u003cem\\u003eFusarium\\u003c/em\\u003e wilt. Therefore, this molecule was taken as a reference molecule to find the effectiveness of the biomolecules under the present investigation. The binding affinity of the compound with the different fungal targets selected were \\u0026minus;\\u0026thinsp;8.7 kcal/mol for XRN2 (ASP A:351, ALA A:110, TYR A:616), -6.8 kcal/mol for FGB1 (H-bonds; SER C:206, VAL C:294), -6.4 kcal/mol for RHO1 (H-bonds; SER A:137), -6.9 kcal/mol for velvet (H-bonds; GLY 47), -6.3 kcal/mol for SGE1(H-bonds; LYS 163), -7.8 kcal/mol for SIX13, -4 kcal/mol for Rlm1, -6.8 kcal/mol for SIX1 (H-Bonds; CYS A:253;ASP A:110), -5.7 kcal/mol for FTF1 (H-bonds; GLY A:573), -4.7 kcal/mol for Mkk2 (H-bonds;.ASP A:367), -6.6 kcal/mol for ERG6 (ASN A:161), -5.2 kcal/mol for ERG25 (H-bonds; LYS A:169), -5.8 kcal/mol for SIX6 (H-bonds; GLN A:112), -5.3 kcal/mol for SIX8 (H-bonds; TYR A:73) and \\u0026minus;\\u0026thinsp;4 kcal/mol for SIX10 (H-bonds;. ALA A:124, ASN A:127).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eTrifloxystrobin\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTrifloxystrobin is a part of the combination fungicide along with tebuconazole used in controlling the soil borne pathogen \\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense\\u003c/em\\u003e .It has been found to be having higher binding affinity with XRN2 (H-bonds; TRP A:564, ARG A:413), -8.7 kcal/mol, FGB1(H-bonds; SER C:334, ARG C:167, TRP C:350, ARG C:332), -8.5 kcal/mol, ERG6 (H bonds; TYR A:86, ASN A:162), -8.6 kcal/mol, and SIX1 (H-bonds; CYS A:253, ASP A:110), -8.4 kcal/mol. Other targets with which it exhibited good binding affinity includes RHO1( H-bonds; MET A:292, TYR A:153) -7.3 kcal/mol, SGE1(H-bonds; TRP 73,ASP 69, LYS 163) -6.8 kcal/mol, SIX10 (H-bonds; ARG A:30, TYR A:79 )-6.8 kcal/mol, FTF1(H-bonds; TRP A;583), -6.3 kcal/mol, Velvet (H-bonds; PHE 49, TYR 48), -7.3 kcal/mol, SIX13(H-bonds; THR A 140,VAL A 86), -8.2 kcal/mol, ERG25 (H-bonds; GLY A:16 ), -5.3 kcal/mol, SIX6 (H-bonds; ALA A:68, TYR A:69 ), -6.7 kcal/mol and SIX8 (H-bonds; TYR A:79, ARG A:30),-6.4 kcal/mol). Whereas it showed lower affinity towards Rlm1, -4.3 kcal/mol and Mkk2, -4.8 kcal/mol with no H bonding.\\u003c/p\\u003e \\u003cp\\u003eThe binding conformation and binding free energy of small molecules to the target are then predicted using docking. From the docking analysis, triamcinolone acetonide had a higher affinity towards XRN2 which will affect the normal turn-over of m-RNA in the pathogen. Inside the nucleus, XRN2 participates in the processing of noncoding RNA such as rRNA precursors, the production of snoRNAs, and the destruction of hypomodified tRNAs. The telomeric repeat-containing RNA (TERRA) is synthesized from telomere sequences at the ends of chromosomes. When TERRA builds up in \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e, it stops telomeres from getting longer, possibly by stopping telomerase from working. Rat1-mediated degradation of TERRA maintains telomere lengthening and, thus, chromosomal stability [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]. All these suggest the importance of XRN2 in maintaining the proper functioning of sRNAs in the fungus which could affect the normal functioning and survival of the pathogen.\\u003c/p\\u003e \\u003cp\\u003eFGB1 (\\u003cem\\u003eFusarium\\u003c/em\\u003e guanine binding protein), is a membrane protein involved in the signal transduction process regulating biological functions. G protein subunits are involved in transmembrane receptor activation via effector molecules. Gene expression, cellular function, and metabolism are all controlled by this so that it can affect cell differentiation, growth, virulence, heat resistance, and percentage of germination. Chosen biomolecule had a strong affinity for the target and hence hindering all the mentioned functions and responses in the \\u003cem\\u003eFoc\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Inhibitory activity of the molecule on all SIX proteins that serve as effectors of \\u003cem\\u003eFusarium\\u003c/em\\u003e and favouring colonization by the pathogen will prevent the disease progression [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. RHO type GTPase being a part of the signal transduction pathway will result in defects in morphogenesis, cell wall biosynthesis, and reduced virulence. As the test ligand molecule triamcinolone acetonide had the maximum binding energy with RHO type GTPase, it could block the normal functioning and thus suppress morphogenesis and virulence of \\u003cem\\u003eFoc\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Velvet and Fusarium transcription factor 1 (ftf1) are two proteins that have been implicated in fungal proliferation, maturation and disease development with the earlier one having the role of regulating sexual and asexual development of the fungus [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. SGE1 which is mainly regulating the expression of effector genes results in reduced pathogenicity of the organism [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Other transcription factor affected includes Rlm1 which is a MADS box transcription factor whose functional disruption can result in reduced aerial hyphal growth, virulence, increased susceptibility to oxidative stress, and reduced production of mycotoxins fusaric acid and beauvericin [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Similarly, in the present investigation, triamcinolone had binding affinity with effector proteins including velvet, FTF1, SGE1 and Rlm1 based transcription factors. As a consequence, hyphal growth regulation, susceptibility to oxidative stress, mycotoxin production, virulence and morphogenesis might be impaired leading to lysis and death.\\u003c/p\\u003e \\u003cp\\u003eTwo proteins involved in ergosterol biosynthesis include ERG25 and ERG6 and disabling their function results in the reduction or inhibition of conidial germination [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Compared to the reference compounds, triamcinolone acetonide showed higher binding affinity towards the targets except for SIX1 with maximum binding energy to trifloxystrobin. Thus, it could have also contributed to the suppression of ergosterol biosynthesis and thus affecting the survival of \\u003cem\\u003eFoc\\u003c/em\\u003e. Hitherto, the multiple modes of action of triamcinolone acetonide, could be harnessed as an effective antifungal molecule for the management of fungal pathogen. In a study, \\u003cem\\u003ein vitro\\u003c/em\\u003e analysis reported the antifungal activity of VOCs produced by \\u003cem\\u003eSarocladium brachiariae\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]. This present inquiry on the antifungal compounds led to the unraveling of new biomolecules which can be turned into an eco-friendly weapon to fight against Panama wilt of banana at field level.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eSmall molecule analysis\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eSimilarity analysis of the biomolecule triamcinolone acetonide was done with the positive check (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), tebuconazole and trifloxystrobin. Tanimoto coefficients for both the pairs were very less (.09-.1) which indicated the difference in its properties between the control. MCS parameter analysis between the compound and control resulted in values of \\u0026le;\\u0026thinsp;9 which was consistent with the conclusion derived from the Tanimoto coefficient. MCS represented the largest common substructure between a pair of compounds, which in turn indicated the similar physiochemical properties between them. So, the results suggest that the compound may be having multiple modes of action different from the fungicide control.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCoefficients obtained from similarity analysis of Triamcinolone acetonide with Tebuconazole\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCoefficients\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eValues\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAP Tanimoto\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.104746\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Tanimoto\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.2093\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Size\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Min\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.4286\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Max\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.2903\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCoefficients obtained from similarity analysis of Triamcinolone acetonide with Trifloxystrobin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCoefficients\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eValues\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAP Tanimoto\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0997475\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Tanimoto\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0909\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Size\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Min\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1724\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMCS Max\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1613\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eScreening of antifungal efficacy of the compounds\\u003c/b\\u003e \\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003ein vitro\\u003c/span\\u003e \\u003cb\\u003eand SEM image analysis\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eTriamcinolone acetonide was having inhibitory activity in the wet lab. It exhibited 100% inhibition of the growth of the \\u003cem\\u003eFoc\\u003c/em\\u003e at 1000 ppm, 72.96 percent at 500 ppm, and 37% at 250 ppm. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Scanning electron microscopic images of treated culture showed shrinkage and distortion of mycelia (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e \\u003cb\\u003eMolecular Dynamic Simulation\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eMD Simulation emerged as a tool in bioinformatics in the 1980s to decode the behavior or kinetics of the protein at different levels like protein folding, during catalysis, interaction with the ligands etc. [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Molecular docking along with simulation studies has reported iturin A and fengycin as potential; antifungal compounds by their interaction studies with β-tubulin target proteins [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. Since during, ligand-protein interactions, both of them undergo conformational changes and other perturbations in the structure to attain a stable complex, the dynamics of the system were studied with MD simulation. Hence to further validate the ability of the biomolecule as hostile to \\u003cem\\u003eFoc\\u003c/em\\u003e, MD simulation was carried out with the target protein XRN2. For the same, Triamcinolone acetonide-XRN2 (the protein that exhibited the highest binding affinity with the compound) were subjected to MD simulation. Trajectories for Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), interaction energy, the radius of gyration (Rg), solvent accessible surface area (SASA) and hydrogen bond of the complex were obtained at the end of the simulation.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRoot Mean Square Deviation (RMSD)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eRMSD is a measure of the conformational stability of the protein when it gets complexed with the ligand. Less fluctuation and the lower value of RMSD for the complex indicated the stability of the complex. A higher fluctuation indicated the instability or rather alteration of the conformation arising to attain the final stable complex. RMSD values for the XRN2- Triamcinolone acetonide complex were calculated at different times and were depicted \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e\\u003cb\\u003e).\\u003c/b\\u003e The average value of RMSD for XRN2- Triamcinolone acetonide complex was 0.72 nm which indicated the stability of the complex. Initially, the complex seemed to be unstable, but within 0.03 ns it gained stability with an RMSD value of 0.45 nm. Some major fluctuations were also found in the trajectory at 18 ns, 23 ns, 42 ns and 43 ns. Thus, the protein-ligand complex exhibited stability with a few conformational changes during the simulation.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRoot Mean Square Fluctuation (RMSF)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eRMSF is a measure of flexibility of the residues in a protein, which in turn can favour the bonding with ligand in higher stability. Usually, the residues in a particular part of protein showing larger fluctuation may be the regions representing coils or loops which are flexible enough. Other regions which didn\\u0026rsquo;t exhibit fluctuations may be the rigid part like helix or sheets as well as those involved in the ligand binding. A graph was created for the RMSF values for the residues in the protein (Overall RMSF for the residues in the protein was 0.385 nm. A large fluctuation in the RMSF value for atoms from 474 to 502 (Aminoacids: ASN51, LUE52 and TYR53) indicated that they are not involved in interacting with the ligand strongly and interestingly it was not a part of the binding site of the protein with the ligand as per the results obtained from CASTp results as well as docking results. Lower fluctuations were seen in the ligand binding sites as is evident from the graph \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). Other than this, fluctuations were also seen in other regions that expressed their flexibility to achieve final stable conformation as a complex.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePotential energy energy\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe Potential energy of a system is a measure of the stability of the complex. The average potential energy of the system was \\u0026minus;\\u0026thinsp;1016.9 kJ/mol. which expressed the conformational stability of the complex. \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRadius of gyration (Rg)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe compactness of the system was tested by evaluating the radius of gyration (Rg) over a time period of 50 ns. The overall radius of gyration was around 3.16 nm and there was no much fluctuation during the time period. It indicated that the protein was stable as a complex with the selected compound \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e \\u003cb\\u003eHydrogen bond\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eA number of bonds or forces are involved in the formation of successful interaction of the protein with the ligand viz. Hydrogen bond, Van der Waals forces, hydrophobic interactions, and electrostatic interactions. But one of the key players is the hydrogen atoms which are numerous in number involved in hydrogen bonding. The number of hydrogen bonds formed between Triamcinolone acetonide and XRN2 was evaluated and represented in graphical form (fig). A minimum of 0 and a maximum of 10 H bonds were formed during the time period, with two hydrogen bonds that were almost constant during the simulation \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cb\\u003eSolvent Accessible Surface Area (SASA)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eSolvent Accessible Surface Area is the surface of a protein that is in contact with the solvent or the surface which is characterized by the solvent, which is hypothetical. This is a key factor in the stability analysis of the protein-ligand complexes. Here the average value of SASA for the complex over the simulated period was 369.48 nm and it showed a decreasing trend which shows that the protein has changed its conformation, such that it can form a stable complex and the ligand is completely buried inside the protein \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig12\\\" class=\\\"InternalRef\\\"\\u003e12\\u003c/span\\u003e\\u003cb\\u003e).\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eTrajectories for RMSF, RMSD, Rg, H- bond, energy and SASA at different time periods were inconsistent with that obtained from docking. This provides computational evidence for their possibility to be antifungal. Thus, all these results might have been responsible for the synergistic interaction and suppression of the banana wilt pathogen \\u003cem\\u003eFoc\\u003c/em\\u003e.\\u003c/p\\u003e \"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003e \\u003cem\\u003eIn\\u003c/em\\u003e-\\u003cem\\u003esilico\\u003c/em\\u003e evaluation of the organic compounds produced by the bacteria \\u003cem\\u003eBacillus velezensis\\u003c/em\\u003e lead to an insight into the antifungal activity of the compound, Triamcinolone acetonide which was found to be having a binding affinity towards the target proteins which are necessary for the survival as well as the virulence of the pathogen. It was found to be obstructing the function of the targets including all SIX (Secreted in Xylem) proteins, all the transcription factors (XRN2, FGB1, RHO1 (RHO type GTPase) and ERG6). The ability of this biomolecule to hamper the function of the protein target as obtained from docking assessment was further justified with the MD simulation of the protein XRN2 with the biomolecule. Further, wet-lab screening of the biomolecule for their antifungal activity was also carried out by means of the poisoned plate technique. Thus, triamcinolone acetonide compound can be used as a novel antifungal molecule for the management of \\u003cem\\u003eFusarium\\u003c/em\\u003e wilt of banana.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by DBT\\u0026ndash;BTIS facility available at Department of Plant Molecular Biology and Bioinformatics, Centre for Plant Molecular biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India. The authors acknowledge the Department of Plant Biotechnology, Centre for plant molecular biology and biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, and Department of nanotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, for providing facilities.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors state unequivocally that they do not have any known competing financial interests or personal relationships with third parties that would have given the appearence of influencing the work presented in this study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNS and SN conceptualized the research; KN performed \\u003cem\\u003ein\\u003c/em\\u003e \\u003cem\\u003esilico\\u0026nbsp;\\u003c/em\\u003eanalysis and wet-lab studies ; SA, SR, and MK have supported in the analysis of docking interactions and in the preparation of manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot Applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eFAO, Banana market review \\u0026ndash; Preliminary results 2020. 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PloS one.16. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0260747\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0260747\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCob-Calan NN, Chi-Uluac LA, Ortiz-Chi F, Cerqueda-Garc\\u0026iacute;a D, Navarrete-V\\u0026aacute;zquez G, Ruiz-S\\u0026aacute;nchez E, Hern\\u0026aacute;ndez-N\\u0026uacute;\\u0026ntilde;ez E (2019) Molecular Docking and Dynamics Simulation of Protein β-Tubulin and Antifungal Cyclic Lipopeptides, Molecules. 24:1\\u0026ndash;10. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/molecules24183387\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/molecules24183387\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Foc, Endophytes, biomolecules, molecular modelling, docking, Molecular Dynamic Simulation, Triamcinolone acetonide\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2133897/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2133897/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e \\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense\\u003c/em\\u003e is one of the most serious and threatening pathogens of banana causing Panama wilt worldwide. Bacterial endophytes were reported to have antifungal action through various mechanisms, which include the production of secondary metabolites during their interaction with pathogen. One such endophyte, \\u003cem\\u003eBacillus velezensis\\u003c/em\\u003e YEBBR6 antagonistic to \\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e f. sp. \\u003cem\\u003ecubense\\u003c/em\\u003e produced antimicrobial biomolecules against the pathogen during confrontation assay. Those molecules were screened for their antifungal property by an \\u003cem\\u003ein-silico\\u003c/em\\u003e approach. Modelling of the fungal targets and docking them with those biomolecules was done to refine the potential antifungal compounds among the various biomolecules they generated during their di-trophic interaction with the pathogen. Protein targets were selected based on literature mining and those targets were modelled and validated for docking with the biomolecules through the AutoDock Vina module of the PyRx 0.8 server. Among the compounds screened, Triamcinolone acetonide was possessing the maximum binding affinity with chosen pathogen targets. It had the maximum binding affinity of 11.2 kcal/mol with XRN2 (5\\u0026acute; \\u0026rarr; 3\\u0026acute; Exoribonuclease 2) an enzyme involved in degrading m-RNA -. Kinetics of the protein-ligand complex formation for the further validation of docking results was done through Molecular Dynamic Simulation studies. Besides, the antifungal nature of the biomolecule was also confirmed against \\u003cem\\u003eFoc\\u003c/em\\u003e by screening in wet lab through poisoned plate technique.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Computational analysis revealed Triamcinolone acetonide produced by Bacillus velezensis YEBBR6 as having antagonistic activity against Fusarium oxysporum f. sp. cubense\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-10-21 19:18:00\",\"doi\":\"10.21203/rs.3.rs-2133897/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"bafffbf2-310f-4a7a-9a52-759972e48fd5\",\"owner\":[],\"postedDate\":\"October 21st, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2022-11-17T11:14:36+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2022-10-21 19:18:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-2133897\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-2133897\",\"identity\":\"rs-2133897\",\"version\":[\"v1\"]},\"buildId\":\"7rjqhiLT3MXkJMwkYKINL\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}