Nimbolide as a natural fungicide against Black Mold disease of Allium cepa: A molecular docking and simulation-based study 

Nimbolide as a natural fungicide against Black Mold disease of Allium cepa: A molecular docking and simulation-based study | 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 Nimbolide as a natural fungicide against Black Mold disease of Allium cepa: A molecular docking and simulation-based study Pranshu Dangwal, Saransh Juyal, Arun Bhatt, Rajesh Kumar Pathak, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4521542/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 Black mold disease provoked by Aspergillus niger is one of the major postharvest diseases in Allium cepa . In the present study, efforts have been made to model the polygalacturonase protein of Aspergillus niger that is involved in disease progression as a promising molecular target for the identification of novel fungicides through computational approach. We used I-TASSER to determine the 3D structure of the target protein and docked it with naturally occurring phytoalexins which included nimbolide, nimbolin, Azadiradione, Quercetin and Azadirone. The result of present study revealed that nimbolide has the greatest affinity towards polygalacturonase as compared to other phytoalexins which binds the protein at amino acid residues Gln205, Gln261, Tyr262 with four hydrogen bonds and − 8.0 kcal/mol binding energy. Further, molecular dynamics simulation of protein and docked nimbolide-polyglacturonase complex was carried out to validate the stability of the system at the atomic level. Based on the study, this may lead to inhibition of pathogenic protein. Thus, it is of interest to consider the molecule for further validation at lab and field conditions for ensuring food and nutritional security. Allium cepa Phytoalexins Nimbolide Polygalacturonase Molecular modeling Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Allium cepa commonly known as onion, is a major vegetable crop grown all over the world. Onion is called “Pyaz” or “Kanda” in Hindi belongs to the family Alliaceae and is used for cooking or used in the form of salad. Onion has a number of medicinal benefits [Zhao X-X et.al.2021, Oyawoye et.al.2022]. Despite years of study and progress on enhanced methods for pests and disease control, they keep on to impact the yield and quality of crops worldwide. It is reported, that around 0.20–0.30 of harvest yield is damaged yearly in the meadow [O. Trott et.al.2010]. Black mold rooted by Aspergillus niger van Tieghem (An) onion acts as a restraining aspect in onion -yield globally [D.W. Buchan et.al.2013]. The presence of Aspergillus Niger as a soil saprophyte has been reported, whenever they find wounded tissues, it attacks/infects onion bulbs in the field or storage by secreting a different enzyme or toxin [S. Wu et.al.2007] have reported the relationship of Aspergillus niger with seeds of onion-shaped in warm (wasteland) climates and how the onion seedling get infected through the soil and already infected seeds, which can affect 30 to 80% damage of bulbs of onions. The handling of seeds with different biocides like leaf extract of plants in place of different fungicides has been reported to be safe. In several crops including onion, it has been reported that biocides help in increasing the germination of seeds and by reducing vigor index, the initial- and later-coming-out death [C.A. Lipinskiet.al.2001, Y. Shinbo et.al.2006]. Plants produce several secondary metabolites as a defence mechanism against pests and pathogens. These low molecular mass metabolites which show antimicrobial properties are collectively known as phytoalexins [Tiku et.al.2020]. Phytoalexins are considered a molecular marker of disease resistance that shows natural action towards 173 varieties of pathogens [A.W. Schuttelkopf et.al.2004, E.A. Schmelz et.al.2011] they are a miscellaneous compound [C. Geourjon et.al.1995]. The conception of phytoalexin was introduced many years ago based on a report that potato (Solanum tuberosum) tuber tissue contaminated with an irreconcilable species of Phytophthora ( Phytophthora infestans) develops induced resistance to a well-suited race of P.infestans . To understand the accurate instrument through which phytoalexin exerts its toxicity is at rest unidentified, however it has been shown the powerfully inhibit conidial germination, and germ tube elongation and also damage the cubicle crust of plant pathogens [Bizuneh et.al.2021]. Phytoalexins are well thought-out as necessary compounds for plant- resistance against pathogens however they are yet to be characterized in most of the plant species [Bizuneh et.al.2021]. Molecular modelling and docking are novel approaches that may help to understand the function of phytoalexins in a resistance device in opposition to plant pathogens. The study of molecular modelling and docking may be applied to understand significant phytoalexins that may inhibit the above protein all through pathogenesis. In the present study, we attempted to find out efficient molecule as a new class of fungicide for the protection of allium cepa against Aspergillus spp. using in silico approaches. Materials and methods series repossession and analysis of Physio-chemical properties The protein sequence of the pathogen, polygalacturonase (529bp), was obtained from the “National Center for Biotechnology Information ( https://www.ncbi.nlm.nih. gov ) database. A comprehensive analysis of its physio-chemical properties was conducted, which included determining various parameters such as molecular weight (MW), amino acid composition, theoretical isoelectric point (pI), aliphatic index (AI), extinction coefficient, grand average of hydropathicity (GRAVY), estimated half-life, and instability index. For the analysis of the entire key chain of the target protein, we utilized ProtParam (http://web.expasy.org/protparam/), a tool for understanding and analysing proteins, available on the ExPASy server. Protein secondary and tertiary structure estimation Secondary structural analysis of protein sequence involves assigning various regions that are to be expected to associate with secondary structures such as alpha helices, beta strands, or turns. Protein structure prediction server PSIPRED [P..Payal et.al.2016, E.C.Oerke et.al.2006] anda Self-optimized prediction method with alignment (SOPMA) [K. Guruprasad et.al.1990] was applied to predict the less important structures of the target proteins. Iterative threading assembly refinement (I-TASSER) [A. Huffaker et.al.2011, N. J. Hayden et.al.1992, Jani et.al.2021] was used to predict the 3D structures of the target proteins. The tertiary structural examination is performed to predict an arrangement of the secondary structure, along with its side shackles into a 3-D. The tertiary structure of the protein mostly decides its biological function [Y. Zhang et.al.2008]. I-TASSER mechanically develops tall quality 3-D formation of the protein molecule from amino acid series and ultimately uses this structure and amino acid sequences to predict the biological function of that protein molecule. It executes numerous threading algorithms and iterative formation-assembly simulations to discover the best possible sub-fragments inside the folder of structures or inside the client-specific composition [J. Yang et.al.2015, Y. Zhang et.al.2008]. Cello, prediction tools were applied to determine the sub-cellular localization of the queries of the protein. Structure evaluation Various structures anticipated by I-TASSER were authenticated by PROCHECK [I. Ahuja et.al.2012], as single-minded by Ramachandran plan information. I-TASSER generated the top four protein models; the model with the highest C- value was selected for further studies. Ligand preparation The structure of different phytoalexins viz. Nimbolide, nimbolin, azadiradione, quercetin, azadirone, oleuropein was retrieved from PubChem database of “National Centre for Biotechnology Information” ( http://pubchem.ncbi.nlm.nih.gov). The three-dimensional coordinates of ligand molecules were generated by Marvin Sketch (http://www.chemaxon .com /products/marvin/marvinsketch/) software and saved in pdb file format. The pdb file was then converted into pdbqt format using Autodock- tools, which can be used for docking. Docking The studies of molecular docking were done by AutoDock vina by means of the prepared 3D construction of different phytoalexin with polygalacturonase as molecular target. For every ligand, we chose all conformers based on their optimal interaction, considering their docking energy and the count of hydrogen bonds. The examination and illustration of the protein-ligand interaction were accomplished using Ligplot. Molecular Dynamics Simulation The MDS study was executed using GROMACS 4.6.5 [A.R. Oany et.al.2014, C.K. Jacob et.al.1988]. A two- system were formed and engaged for 10 ns time period reproduction studies, first system is to predict the stability of 3-D model of protein and another for protein-ligand composite. Both systems were immersed in a cubic container using a basic point charge concept. The ligand topology was created via the ProDRG programme [Lucas et.al.2020]. The protein topology was built via the GROMOS 9653a6 force field. [C. Oostenbrink et.al.2004]. A total of 16 sodium ions were introduced to the systems in order to achieve neutralisation. The systems were subjected to a consistently intense energy minimization process in order to achieve the highest power output below 1000 kJ/mol/nm and eliminate any conflicting interactions. The atom Mesh Ewald method was utilised to quantify electrostatic communications. Hydrogen bond lengths were restricted using the LINCS technique.[M.W. Walter et.al.2002]. The reproduction was programmed to occur at a pace of 2 femtoseconds. A short-range non-bonded interaction was anticipated, with a predicted cut-off distance of 10 Å. Extended-range electrostatics were computed in the PME system with a 1.6 Å Fourier grid spacing. The Shake algorithm was used to predefine all the bonds, including hydrogen bonds. [Deckers et .al .2022]. Simulations for NVT and NPT were executed for a duration of 1 ns. Subsequently, both systems underwent Molecular Dynamics Simulations (MDS) studies for 10 ns. Root Mean Square Fluctuation (RMSF) and Deviation (RMSD) were analysed. Image molecular dynamics and Chimaera 1.10.2 were used for the trajectory analysis [Badar et.al.2022]. The resulting plots were generated and visualized using the Origin tool. Results and discussion Analysis of Polygalacturonase sequence The protein was analyzed by means of ProtParam server. A polygalacturonase protein has 58.47 kDa as its theoretical pI value and 529 amino acids as its molecular weight. The residue composition of this enzyme was found as Ala (6%), Arg (3.4%), Asn (8.7%), Asp (7.6%), Cys (1.9%), Gln (3.8%), Glu (3.6%), Gly (9.1%), His (2.5%), 33 (6.2%), Leu (6.2%), Lys (3.2%), Met (2.3%), Phe (3.4%), Pro (4.5%), Ser (6.6%), Thr (6.4%), Trp (2.6%), Tyr (4.2%), and Val (7.8%). The total count of negatively charged residues (comprising Aspartic Acid and Glutamic Acid) and positively charged residues (consisting of Arginine and Lysine) was determined 59 and 35, correspondingly. A total number of atoms present in the target protein were 8038 with the chemical formula C2585H3920N706O805S22. Indicating the light absorption capacity, the extinction coefficient is computed at 280 nm and expressed in M-1 cm-1 [J. Yang et.al.2013]. Estimated half-life when M (Met) is considered as the N-terminal of the sequence. The projected half-life of the polygalacturonase protein was predicted 30 hours for mammalian reticulocytes in vitro model. Instability index of the polygalacturonase protein was found 37.97 that indicate a stable form of protein. Aliphatic index (AI) of a protein has a direct relation with the volume engaged by the surface chains of amino acids (alanine, leucine, valine, and isoleucine). Thermal stability of the globular proteins increases with an increase in AI value [A. Roy et.al.2010]. Aimed at protein, the aliphatic- index was computed to be (73.18). The GRAVY index for this protein was -0.331, which indicates its better transportation through the water (medium) solvent. Secondary and tertiary structural analysis SOPMA, a software tool was used for Secondary structure analysis of protein. Polygalacturonase protein is consisting of 18.90 % alpha-helices, 39.51% haphazard coils, 34.40%, extended beta strands and 7.18% beta turns (Fig. 1). The sub cellular localization by Cello indicates that this is an extracellular protein. The 3-D structure of Polygalacturonase was modeled using I-TASSER which is based on threading approach (Fig.2) [Nakamura et.al.2019]. Quality of tertiary model was assessed with the help of PROCHECK tool and related Ramachandran plot result indicates that 38.5% residues belongs to favored regions while 42.2% residues resides in allowed regions. Liberally allowed region contains 12.3% residues whereas 7% residues prohibited or outlier regions (Fig.3). Protein preparation, binding site analysis and construction of grid box The structure of polygalacturonase was constructed by I-TASSER. This structure was analyzed by AutoDock tool and converted into. pdbqt file format after the addition of polar hydrogen. I- TASSER also determined the binding site residues of the protein, Gln205, Gln261, Tyr262, Asn270, Ile271, Val273, and Asn320 were found to be present at active site. The grid map centred at the active site pocket of protein (www.scfbio.iitd.res.in ) lies in the centre x 66.361, centre y 66.444, centre z 68.583 with size 46, 44, 42. Recovery and groundwork of Ligand molecules Structures of phytoalexins were downloaded from The PubChem database. 3-dimensional coordinates of phytoalexins were organized by using Marvin sketch in .pdb format and these files were used to prepare. pdbqt files by autodock [R.K. Pathak et.al.2017] for molecular docking studies. Molecular docking, Visualization and analysis of protein ligand complex The studies of molecular docking were made by AutoDock vina by means of the prepared 3D structure of different phytoalexin viz. nimbolide, nimbolin, Azadiradione, Quercetin and Azadirone with polygalacturonase as molecular target whereas the visualization and analysis of protein ligand complex was done using ligplot. nimbolide, nimbolin, Azadiradione, Quercetin and Azadirone docked with polygalacturonase with docking energy -8.0, -8.0,-7.8,-7.6,-7.4 kcal/mol respectively. Hydrogen bonds between phytoalexis and amino acid residues of pathogenic protein are showed in Fig. 4. Table 1. Binding- free energy of all phytoalexins with target protein obtained through molecular docking studies. Sno. Name of compound Free Binding energy (Kcal/mol) No. of Hydrogen bond Interacting amino acid residue through Hbond 1 nimbolide -8.0 4 Gln205, Gln261, Tyr262 2 nimbolin -8.0 3 Lys235, Ser258 3 Azadiradione -7.8 3 Asn176, Asn179, Tyr262 4 Quercetin -7.6 7 Gln205, Asp229, Asp230, Ser258, Tyr290, lys292 5 Azadirone -7.4 3 Ser100, Gln205, Lys235 6 Oleuropein -7.4 13 Gln205, Asn206, Asp229, lys292, Asp230,lys235 ,Gln261, Tyr262 7 Nimbin -7.3 7 Trp173, Asn206, Ser258, Gln261, Tyr262 8 Salannin -7.3 2 Arg237, Gln261 9 Nimbinene -7.1 3 Asn206, Lys235, Ser258 10 Desacetylnimb in -7.0 3 Trp155, Asn176, Tyr262 11 Beta sitasterol -6.9 2 Gln205, Asp229 12 caryophyllene -6.1 - No significant interaction 13 Caffeic acid -6.0 7 Gln64, lys86, Thr88, Asn134, Asp169, Gln471 14 Carvacrol -5.9 7 Ile45, Met46, Phe49, Ala48, Phe49, Glu50, Glu51, Cys52, Gly53 15 Protocatechoic acid -5.8 5 Val204, Gln205, Gly228, Asn252, Tyr281 16 4-hydroxy benzoic acid -5.6 4 Gly228, Asn252, Tyr281, Asp284 17 Syringic acid -5.6 6 Trp173, Asp208, Ser258, lys292 18 Vanillic acid -5.5 3 Trp203, val204, Tyr281 19 Tsibulin2 -5.4 2 Asn252, Tyr281 20 linalool -5.1 2 Trp203, val204 21 Pyrocatechol -4.8 5 Asn134, Lys168, Asp169, Gln471 22 Allicin -4.2 1 Asn252 Molecular Dynamics Simulation of protein and protein-ligand complex The stability of the projected protein model was assessed during 10 ns MD simulation and the binding mode of the protein-ligand complex was also analyzed using 10 ns MD simulation. MDS applying solvent, pressure and set temperature predicted the mechanism of precise binding of the complex. We computed the root mean square volatility and deviation from the trajectory. Root Mean Square Deviation (RMSD) RMSD serves as an indicator of system stability. The RMSD of both the protein and the protein-ligand complex was observed to increase between 1 and 4 ns, indicating that both structures remained stable as they dissolved in the cubic box solution, and any internal repulsion was eliminated over time. After 5 ns, both systems reached equilibrium and maintained a steady trajectory for analysis. The protein and protein-ligand complex exhibited average RMSDs of 0.43 nm and 0.38 nm, respectively (as shown in Fig. 5). The RMSD values suggest that the protein-ligand complex was more stable compared to the protein alone(Sapundzhi et.al.2022). Root mean square fluctuation (RMSF) We computed RMSF values to compare the flexibility of every amino acid residue in the complex and the protein. RMSF gives light on the structural differences of every residue. Lower RMSF values indicate well-structured regions, while higher values suggest more flexible or disordered areas, such as loops or lethal domains. In the study, it was calculated the value of RMSF for the 10 ns. The peak of RMSF protein- ligand complex was to some extent higher than protein; however the average RMSF value was 0.15 nm and 0.14 nm for protein and protein-ligand complex (Fig. 6). The complex projected less variation as comparison to protein. Radius of gyration (Rg) Utilising Rg, the conformational variations and hardness of the protein-ligand complexes and the apo-protein were determined. The Rg value was determined for all the complexes with the apo-protein using the 10 ns trajectories. For the apo-protein and protein-ligand complex, the average Rg values were determined to be 2.38 and 2.33 nm, respectively. (Fig. 7). Protein-ligand complex showed lesser Rg value in comparison to apo-protein. The result suggested that protein ligand complex is more stable than the apo-protein. Hydrogen bonds Protein-ligand complexes are stabilised by many interactions, including hydrophobic, electrostatic, and hydrogen bonds. Highly definite and transitory interactions, hydrogen bonds are a crucial component of protein-ligand stabilisation. The many hydrogen bonds vs. time are explained in Figure 8. For the protein-ligand complex, the hydrogen bonds were counted normally between 0 and 1. It shows how the ligand keeps creating hydrogen bonds right up until the simulation is over. Prospect of phytoalexins as antifungal molecule in plant protection Phytoalexins acts as a significant role in plant fighting in opposition to plant pathogens, it not only in dicot species but in monocots as well [I. Ahuja et.al.2012],. It has been lately depicted that assault of maize stem by Rhizopus microspores and Collect otrichumgraminicola induces the gathering of 6 ent- kauranne pertaining to diterpenoids, communally termed kauralexins which hamper the expansion of the pathogens [E.A. Schmelz et.al.2011]. The outcomes of the current study noticeably shows, phytoalexin nimbolide can perform a direct molecule for the protection against fungal- diseases. Nimbolide is tiny hydrophobic molecule which can do cross cell membranes because of its perfect logP value and lower molecular weight that can maintain diffusion of the hydrophobic molecule in the course of the membrane. It has been ascertained that nimbolide have showed highest affinity towards pathogenic proteins of aspergilus niger. Therefore, nimbolide could be positive for safeguarding the Allium cepa in opposition to fungal diseases together with black mold (Fig 9). Conclusion The present in silico study provides insights about interaction of phytoalexins with pathogenic protein of Aspergillus niger to make clear the inhibition of the fungal action. The results obtained by docking of phytoalexin with a pathogenic protein of Aspergillus niger , suggests, the phytoalexin nimbolide is the pilot molecule that can act in opposition to Aspergillus spp. On the basis of the data obtained, the arrangement of nimbolide was appropriately modified to plan a prospective significant molecules agriculturally for the prevention of Allium cepa . Subsequent findings on the protein-ligand communication will cover the way to use phytoalexins, as alternative to currently used synthetic fungicides which are harmful for environment and soil fertility. The effectiveness and strength of such phytoalexin derivatives consisting the antifungal prospective to reduce incidences of black mold disease in Allium cepa , can be further confirmed through wet lab experiments. Declarations Conflict of interest The authors declare that they have no conflict of interest. Author Contribution "Pranshu Dangwal, Saransh Juyal, Rajesh Kumar Pathak, Ravindra Ojha, Arun Bhatt and Mamta Baunthiyal wrote the main manuscript text and prepared all the figures. Computational work done by Pranshu Dangwal, Saransh Juyal, Rajesh Kumar Pathak, and Ravindra Ojha. Arun Bhatt, Mamta Baunthiyal reviewed the manuscript Acknowledgement: The authors extend their thanks to Uttarakhand Technical University for their support in this work. References Zhao X-X, Lin F-J, Li H, Li H-B, Wu D-T, Geng F, Ma W, Wang Y, Miao B-H and Gan R-Y (2021) Recent Advances in Bioactive Compounds, Health Functions, and Safety Concerns of Onion (Allium cepa L.). Front. Nutr. 8:669805. doi: 10.3389/fnut. 2021.669805 Oyawoye, O.M., Olotu, T.M., Nzekwe, S.C. et al (2022). Antioxidant potential and antibacterial activities of Allium cepa (onion) and Allium sativum (garlic) against the multidrug resistance bacteria. Bull Natl Res Cent 46, 214. https://doi.org/10.1186/s42269-022-00908-8 . Tiku, A.R. (2020). Antimicrobial Compounds (Phytoanticipins and Phytoalexins) and Their Role in Plant Defense. In: Mérillon, JM., Ramawat, K. (eds) Co-Evolution of Secondary Metabolites. Reference Series in Phytochemistry. Springer, Cham. https://doi.org/10.1007/978-3-319-96397-6_63 Bizuneh, G.K (2021) The chemical diversity and biological activities of phytoalexins. ADV TRADIT MED (ADTM) 21, 31–43 .https://doi.org/ 10.1007/s13596-020-00442-w Lucas, James E.; Kortemme, Tanja; Dokholyan, Nikolay V. (2020). New computational protein design methods for de novo small molecule binding sites. PLOS Computational Biology, 16(10), e1008178–. doi:10.1371/journal.pcbi.1008178 Badar, M.S., Shamsi, S., Ahmed, J., Alam, M.A. (2022). Molecular Dynamics Simulations: Concept, Methods, and Applications. In: Rezaei, N. (eds) Transdisciplinarity. Integrated Science, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-94651-7_7 Nakamura, M., Iwai, H. (2019) Functions and mechanisms: polygalacturonases from plant pathogenic fungi as pathogenicity and virulence factors. J Gen Plant Pathol 85, 243–250. https://doi.org/10.1007/s10327-019-00856-8 Sapundzhi, F., Popstoilov, M., Lazarova, M. (2023). RMSD Calculations for Comparing Protein Three-Dimensional Structures. In: Georgiev, I., Datcheva, M., Georgiev, K., Nikolov, G. (eds) Numerical Methods and Applications. NMA 2022. Lecture Notes in Computer Science, vol 13858. Springer, Cham. https://doi.org/10.1007/978-3-031-32412-3_25 Jani, J., Pappachan, A. (2021). Protein Analysis: From Sequence to Structure. In: Singh, V., Kumar, A. (eds) Advances in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-33-6191-1_4 Bizuneh, G.K. (2021) The chemical diversity and biological activities of phytoalexins. ADV TRADIT MED (ADTM) 21, 31–43. https://doi.org/10.1007/s13596-020-00442-w A.R. Oany, T.P. Jyoti, S.A. Ahmad (2014) An in-silico approach for characterization of an aminoglycoside antibiotic-resistant methyl -transferase protein from Pyrococcus furiosus (DSM 3638), Bioinfo. and Biol. Insi. 8.65. A.W. Schuttelkopf, D.M. Aalten (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes, Acta Crystallogr. D. Biol. Crystallogr. 60.1355-1363. Ahuja, R. Kissen, A.M. Bones (2012) Phytoalexins in defense against pathogens, Tren. in Plant Sci. 17 73–90. C.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev.3-26. C.K. Jacob, K. Sivaprakasam, R. Jeyarajan (1988) Evaluation of some plant extracts antagonists for the control of pre-emergence damping off brinjal (Solanum melongena), J. of Biol. Control 2.117-119. D.W. Buchan, F. Minneci, T.C. Nugent, K. Bryson, D.T. Jones (2013) Scalable web services for the PSIPRED protein analysis workbench, Nucleic Acids Res. 41 .349–357. E.A. Schmelz, F. Kaplan, A. Huffaker, N.J. Dafoe, M.M. Vaughan, X. Ni, J.R. Rocca, H.T. Alborn, P.E. Teal (2011) Identity, regulation, and activity of inducible diterpenoid phytoalexins in maize, Proc. Natl. Acad. Sci. U. S. A. 108 .5455–5460. E.C. Oerke (2006) Crop losses to pests, J. of Agri. Sci. Cambridge 144.31-43. Geourjon, G. Deleage, (1995) SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments, Comp. App. in the Biosci. 11.681–684. Huffaker, F. Kaplan, M.M. Vaughan, N.J. Dafoe, J.R. Rocca, H.T. Alborn, P.E. Teal, E.A. Schmelz, (2011) Novel Acidic Sesquiterpenoids Constitute a Dominant Class of Pathogen-Induced Phytoalexins in Maize, Plant Physiol. 156.2082-2097. J. Yang, A. Roy, Y. Zhang (2013) Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment, Bioinfo. 29.2588–2595. J. Yang, Y. Zhang, I-TASSER server: (2015) .New development for protein structure and function predictions, Nucl. Aci. Res. 43.174–181. K. Guruprasad, B.B. Reddy, M.W. Pandit (1990) Correlation between stability of a protein and its dipeptide composition: A novel approach for predicting in vivo stability of a protein from its primary sequence, Prot. Eng. 4.155–161. M.W. Walter (2002) Structure-based design of agrochemicals, Nat. Prod. Rep. 19.278-291. N. J. Hayden, R.B. Maude (1992) The role of seed-borne Aspergillus niger in the transmission of the black mould of onion, Plant Pathol. 41.573-581. O. Trott, A.J. Olson (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. of comp. chem. 31.455-461. Oostenbrink, A. Villa, A.E. Mark, W.F. Gunsteren, (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6, J. Comput. Chem. 251.656-676. P. Payal, H. Panchal (2016) Primary, secondary and tertiary structural analysis of disease resistance protein RGA4 of ZEA maize using bioinformatics tools, Int. J. of Ad. Res. in Comp. and Comm. Eng. 5.7–12. R.K. Pathak, M. Baunthiyal, R. Shukla, D. Pandey, G. Taj, A. Kumar (2017) In Silico identification of mimicking molecules as defense inducers triggering jasmonic acid mediated immunity against Alternaria blight disease in Brassica species, Fron. in pla. sci. 8.1-17. Roy, A. Kucukural, Y. Zhang (2010) I-TASSER: A unified platform for automated protein structure and function prediction, Nat. Prot. 5.725–738. S. Wu, Y. Zhang (2007) LOMETS: A local meta-threading-server for protein structure prediction, Nuc. Aci. Res. 35.3375–3382. S.C. Gill, P.H. Hippel (1989) Calculation of protein extinction coefficients from amino acid sequence data, Anal. Biochem. 182.319–326. Y. Shinbo, Y. Nakamura, M. Altaf-Ul-Amin, K. Asahi, M. Kuokawa, et. al. (2006) KNApSAcK: A comprehensive species–metabolite relationship database, Biotech Agri. Fore. 57.165-181. Y. Zhang (2008) I-TASSER server for protein 3D structure prediction, BMC Bioinfo. 9.1-8. Deckers, F., Rasim, K. & Schröder, C. (2022). Molecular dynamics simulation of polypropylene: diffusion and sorption of H2O, H2O2, H2, O2 and determination of the glass transition temperature. J Polym Res 29, 463. https://doi.org/10.1007/s10965-022-0330 M.W. Walter (2002) Structure-based design of agrochemicals, Nat. Prod. Rep. 19.278-291. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4521542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313108047,"identity":"2e87a84e-0911-4f22-b701-fe2e1e950327","order_by":0,"name":"Pranshu Dangwal","email":"data:image/png;base64,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","orcid":"","institution":"GOVIND BALLABH PANT INSTITUTE OF ENGINEERING \u0026 TECHNOLOGY, PAURI GARHWAL","correspondingAuthor":true,"prefix":"","firstName":"Pranshu","middleName":"","lastName":"Dangwal","suffix":""},{"id":313108048,"identity":"0ee5583a-c6a1-4f25-b063-ec3838e37995","order_by":1,"name":"Saransh Juyal","email":"","orcid":"","institution":"GOVIND BALLABH PANT INSTITUTE OF ENGINEERING \u0026 TECHNOLOGY, PAURI GARHWAL","correspondingAuthor":false,"prefix":"","firstName":"Saransh","middleName":"","lastName":"Juyal","suffix":""},{"id":313108049,"identity":"e2fb9bc6-8f6b-42e0-94eb-243296181283","order_by":2,"name":"Arun Bhatt","email":"","orcid":"","institution":"GOVIND BALLABH PANT INSTITUTE OF ENGINEERING \u0026 TECHNOLOGY, PAURI GARHWAL","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Bhatt","suffix":""},{"id":313108050,"identity":"922df783-67c3-42ff-bed9-a8d4442d2ab1","order_by":3,"name":"Rajesh Kumar Pathak","email":"","orcid":"","institution":"Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"Kumar","lastName":"Pathak","suffix":""},{"id":313108051,"identity":"f89fc437-f2ba-42f2-a05a-a5d7fd096ce7","order_by":4,"name":"Mamta Baunthiyal","email":"","orcid":"","institution":"GOVIND BALLABH PANT INSTITUTE OF ENGINEERING \u0026 TECHNOLOGY, PAURI GARHWAL","correspondingAuthor":false,"prefix":"","firstName":"Mamta","middleName":"","lastName":"Baunthiyal","suffix":""},{"id":313108052,"identity":"e9f30b2b-9530-4b4d-8344-5014849c182f","order_by":5,"name":"Ravindra Ojha","email":"","orcid":"","institution":"GOVIND BALLABH PANT INSTITUTE OF ENGINEERING \u0026 TECHNOLOGY, PAURI GARHWAL","correspondingAuthor":false,"prefix":"","firstName":"Ravindra","middleName":"","lastName":"Ojha","suffix":""}],"badges":[],"createdAt":"2024-06-03 11:36:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4521542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4521542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58492242,"identity":"7dfe2882-3d95-412a-a12b-e2c3837e39ae","added_by":"auto","created_at":"2024-06-17 10:59:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366420,"visible":true,"origin":"","legend":"\u003cp\u003eSecondary structure composition analysis for polygalacturonase. Alpha helix(blue), extended strand (red), beta turn (green) and random coil (pink) vertical bars.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/adea5e79f992d9c6d044591b.png"},{"id":58493124,"identity":"467d3426-b6ea-403f-b76c-0cc5d8f9f006","added_by":"auto","created_at":"2024-06-17 11:07:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":286846,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted 3D structure of protein polygalacturonase\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/865abbfbb5c6f3f58ee1f94c.png"},{"id":58493131,"identity":"a42aa0d8-9fc7-49e7-9f05-d04130c3cdb4","added_by":"auto","created_at":"2024-06-17 11:07:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411498,"visible":true,"origin":"","legend":"\u003cp\u003eThe Ramachandran plot for the modeled protein polygalacturonase, which indicates the psi and phi (torsion) angle distribution for different residues.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/062eeeefea3a3c769e783bd5.png"},{"id":58493130,"identity":"abb553ae-5570-490b-9749-18f50097e99d","added_by":"auto","created_at":"2024-06-17 11:07:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":275803,"visible":true,"origin":"","legend":"\u003cp\u003e2D presentation of 3D docked structure of polygalacturonase\u003c/p\u003e\n\u003cp\u003e(nimbolide in active site of protein)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/4d01091ab51e10b832916fe3.png"},{"id":58492241,"identity":"234ba156-adca-4427-bcd3-a321f01d03a7","added_by":"auto","created_at":"2024-06-17 10:59:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91408,"visible":true,"origin":"","legend":"\u003cp\u003eA time-dependent root means square deviation (RMSD) of c-α backbone of polygalacturonase and its complex. The line represents in black and red colors, ploygalacturonase and polygalacturonase- nimbolide respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/9121af72276c7090015b81a2.png"},{"id":58492239,"identity":"525343d1-c4fe-4876-b54e-b114587f360b","added_by":"auto","created_at":"2024-06-17 10:59:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":165592,"visible":true,"origin":"","legend":"\u003cp\u003eThe fluctuation square root mean (RMSF) for c-α atoms of polygalacturonase, and its complex. The black and red line represents ploygalacturonase and polygalacturonase-nimbolide respectively.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/67e0d90b910690657e522880.png"},{"id":58493461,"identity":"bb791a3c-169b-4293-baf3-884a5cbc5f17","added_by":"auto","created_at":"2024-06-17 11:15:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":127159,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of the radius of gyration vs time for apo-polygalacturonase ,polygalacturonase- nimbolide.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/47656aa5e4bbb164ea9b7d34.png"},{"id":58492244,"identity":"0b907833-5973-430b-b344-14d8fc27b38e","added_by":"auto","created_at":"2024-06-17 10:59:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":201224,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluating Protein-Ligand Interactions over time in Polygalacturonase-Nimbolide through hydrogen bond analysis\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/0730dff89a546c2466d2bdf2.png"},{"id":58493129,"identity":"d5d4cd5e-7373-4597-a1e0-589ca4b0361f","added_by":"auto","created_at":"2024-06-17 11:07:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":31449,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of top identified molecule, nimbolide having antifungal potential\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/f8f4c67ff64774d559c08073.png"},{"id":63358651,"identity":"3ecb23e1-3062-4131-95df-7296fd361f0f","added_by":"auto","created_at":"2024-08-27 09:46:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2275583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4521542/v1/70b9037e-2d98-4348-be30-7a081341d54c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nimbolide as a natural fungicide against Black Mold disease of Allium cepa: A molecular docking and simulation-based study ","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eAllium cepa\u0026nbsp;\u003c/em\u003ecommonly known as onion, is a major vegetable crop grown all over the world. Onion is called \u0026ldquo;Pyaz\u0026rdquo; or \u0026ldquo;Kanda\u0026rdquo; in Hindi belongs to the family \u003cem\u003eAlliaceae\u003c/em\u003e and is used for cooking or used in the form of salad. Onion has a number of medicinal benefits [Zhao X-X et.al.2021, Oyawoye et.al.2022]. Despite years of study and progress on enhanced methods for pests and disease control, they keep on to impact the yield and quality of crops worldwide. It is reported, that around 0.20\u0026ndash;0.30 of harvest yield is damaged yearly in the meadow [O. Trott et.al.2010]. Black mold rooted by \u003cem\u003eAspergillus niger\u0026nbsp;\u003c/em\u003evan Tieghem (An) onion acts as a restraining aspect in onion -yield globally [D.W. Buchan et.al.2013]. The presence of \u003cem\u003eAspergillus Niger\u0026nbsp;\u003c/em\u003eas a soil saprophyte has been reported, whenever they find wounded tissues, it attacks/infects onion bulbs in the field or storage by secreting a different enzyme or toxin [S. Wu et.al.2007]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehave reported the relationship of \u003cem\u003eAspergillus niger\u0026nbsp;\u003c/em\u003ewith seeds of onion-shaped in warm (wasteland) climates and how the onion seedling get infected through the soil and already infected seeds, which can affect 30 to 80% damage of bulbs of onions. The handling of seeds with different biocides like leaf extract of plants in place of different fungicides has been reported to be safe. In several crops including onion, it has been reported that biocides help in increasing the germination of seeds and by reducing vigor index, the initial- and later-coming-out death [C.A. Lipinskiet.al.2001, Y. Shinbo et.al.2006].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlants produce several secondary metabolites as a defence mechanism against pests and pathogens. These low molecular mass metabolites which show antimicrobial properties are collectively known as phytoalexins [Tiku et.al.2020]. Phytoalexins are considered a molecular marker of disease resistance that shows natural action towards 173 varieties of pathogens [A.W. Schuttelkopf et.al.2004, E.A. Schmelz et.al.2011] they are a miscellaneous compound [C. Geourjon et.al.1995]. The conception of phytoalexin was introduced many years ago based on a report that potato (Solanum tuberosum) tuber tissue contaminated with an irreconcilable species of \u0026nbsp;\u003cem\u003ePhytophthora \u0026nbsp;\u003c/em\u003e(\u003cem\u003ePhytophthora infestans)\u0026nbsp;\u003c/em\u003edevelops induced resistance to a well-suited race of \u003cem\u003eP.infestans\u003c/em\u003e. To understand the accurate instrument through which phytoalexin exerts its toxicity is at rest unidentified, however it has been shown the powerfully inhibit conidial germination, and germ tube elongation and also damage the cubicle crust of plant pathogens [Bizuneh et.al.2021]. Phytoalexins are well thought-out as necessary compounds for plant- resistance against pathogens however they are yet to be characterized in most of the plant species [Bizuneh et.al.2021].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMolecular modelling and docking are novel approaches that may help to understand the function of phytoalexins in a resistance device in opposition to plant pathogens. \u0026nbsp;The study of molecular modelling and docking may be applied to understand significant phytoalexins that may inhibit the above protein all through pathogenesis. In the present study, we attempted to find out efficient molecule as a new class of fungicide for the protection of \u003cem\u003eallium cepa\u003c/em\u003e against \u003cem\u003eAspergillus\u0026nbsp;\u003c/em\u003espp. using \u003cem\u003ein silico\u003c/em\u003e approaches.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eseries repossession and analysis of Physio-chemical properties\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein sequence of the pathogen, polygalacturonase (529bp), was obtained from the \u0026ldquo;National Center for Biotechnology Information (\u003cu\u003ehttps://www.ncbi.nlm.nih. gov\u003c/u\u003e) database. A comprehensive analysis of its physio-chemical properties was conducted, which included determining various parameters such as molecular weight (MW), amino acid composition, theoretical isoelectric point (pI), aliphatic index (AI), extinction coefficient, grand average of hydropathicity (GRAVY), estimated half-life, and instability index. For the analysis of the entire key chain of the target protein, we utilized ProtParam (http://web.expasy.org/protparam/), a tool for understanding and analysing proteins, available on the ExPASy server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein secondary and tertiary structure estimation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSecondary structural analysis of protein sequence involves assigning various regions that are to be expected to associate with secondary structures such as alpha helices, beta strands, or turns. Protein structure prediction server PSIPRED [P..Payal et.al.2016, E.C.Oerke et.al.2006] anda Self-optimized prediction method with alignment (SOPMA) [K. Guruprasad et.al.1990] was applied to predict the less important structures of the target proteins. Iterative threading assembly refinement (I-TASSER) [A. Huffaker et.al.2011, N. J. Hayden et.al.1992, Jani et.al.2021] was used to predict the 3D structures of the target proteins. The tertiary structural examination is performed to predict an arrangement of the secondary structure, along with its side shackles into a 3-D. The tertiary structure of the protein mostly decides its biological function [Y. Zhang et.al.2008]. I-TASSER mechanically develops tall quality 3-D formation of the protein molecule from amino acid series and ultimately uses this structure and amino acid sequences to predict the biological function of that protein molecule. It executes numerous threading algorithms and iterative formation-assembly simulations to discover the best possible sub-fragments inside the folder of structures or inside the client-specific composition [J. Yang et.al.2015, Y. Zhang et.al.2008]. Cello, prediction tools were applied to determine the sub-cellular localization of the queries of the protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructure evaluation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVarious structures anticipated by I-TASSER were authenticated by PROCHECK [I. Ahuja et.al.2012], as single-minded by Ramachandran plan information. I-TASSER generated the top four protein models; the model with the highest C- value was selected for further studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLigand preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structure of different phytoalexins viz. Nimbolide, nimbolin, azadiradione, quercetin, azadirone, oleuropein was retrieved from PubChem database of \u0026ldquo;National Centre for Biotechnology Information\u0026rdquo; \u003cu\u003e(\u003c/u\u003e\u003cu\u003ehttp://pubchem.ncbi.nlm.nih.gov).\u003c/u\u003eThe three-dimensional coordinates of ligand molecules were generated by Marvin Sketch (http://www.chemaxon .com /products/marvin/marvinsketch/) software and saved in pdb file format. The pdb file was then converted into pdbqt format using Autodock- tools, which can be used for docking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Docking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies of molecular docking were done by AutoDock vina by means of the prepared 3D construction of different phytoalexin with polygalacturonase as molecular target. For every ligand, we chose all conformers based on their optimal interaction, considering their docking energy and the count of hydrogen bonds. The examination and illustration of the protein-ligand interaction were accomplished using Ligplot.\u003c/p\u003e\n\u003ch2\u003eMolecular Dynamics Simulation\u003c/h2\u003e\n\u003cp\u003eThe MDS study was executed using GROMACS 4.6.5 [A.R. Oany et.al.2014, C.K. Jacob et.al.1988]. A two- system were formed and engaged for 10 ns time period reproduction studies, first system is to predict the stability of 3-D model of protein and another for protein-ligand composite. Both systems were immersed in a cubic container using a basic point charge concept. The ligand topology was created via the ProDRG programme [Lucas et.al.2020]. The protein topology was built via the GROMOS 9653a6 force field. [C. Oostenbrink et.al.2004]. A total of 16 sodium ions were introduced to the systems in order to achieve neutralisation. The systems were subjected to a consistently intense energy minimization process in order to achieve the highest power output below 1000 kJ/mol/nm and eliminate any conflicting interactions. The atom Mesh Ewald method was utilised to quantify electrostatic communications. Hydrogen bond lengths were restricted using the LINCS technique.[M.W. Walter et.al.2002]. The reproduction was programmed to occur at a pace of 2 femtoseconds. A short-range non-bonded interaction was anticipated, with a predicted cut-off distance of 10 \u0026Aring;. Extended-range electrostatics were computed in the PME system with a 1.6 \u0026Aring; Fourier grid spacing. The Shake algorithm was used to predefine all the bonds, including hydrogen bonds. [Deckers et .al .2022]. Simulations for NVT and NPT were executed for a duration of 1 ns. Subsequently, both systems underwent Molecular Dynamics Simulations (MDS) studies for 10 ns. Root Mean Square Fluctuation (RMSF) and Deviation (RMSD) were analysed. Image molecular dynamics and Chimaera 1.10.2 were used for the trajectory analysis [Badar et.al.2022]. The resulting plots were generated and visualized using the Origin tool.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cstrong\u003eAnalysis of Polygalacturonase sequence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein was analyzed by means of ProtParam server. A polygalacturonase protein has 58.47 kDa as its theoretical pI value and 529 amino acids as its molecular weight. The residue composition of this enzyme was found as \u0026nbsp; Ala (6%), Arg (3.4%), Asn \u0026nbsp;(8.7%), Asp (7.6%), Cys (1.9%), Gln (3.8%), Glu (3.6%), Gly (9.1%), His (2.5%), 33 (6.2%), Leu (6.2%), Lys (3.2%), Met (2.3%), Phe \u0026nbsp;(3.4%), Pro (4.5%), Ser (6.6%), Thr (6.4%), Trp (2.6%), Tyr (4.2%), and Val (7.8%). The total count of negatively charged residues (comprising Aspartic Acid and Glutamic Acid) and positively charged residues (consisting of Arginine and Lysine) was determined 59 and 35, correspondingly. A total number of atoms present in the target protein were 8038 with the chemical formula C2585H3920N706O805S22. \u0026nbsp;Indicating the light absorption capacity, the extinction coefficient is computed at 280 nm and expressed in M-1 cm-1 [J. Yang et.al.2013]. \u0026nbsp;Estimated half-life when M (Met) is considered as the N-terminal of the sequence. The projected half-life of the polygalacturonase protein was predicted 30 hours for mammalian reticulocytes in vitro model. Instability index of the polygalacturonase protein was found 37.97 that indicate a stable form of protein. Aliphatic index (AI) of a protein has a direct relation with the volume engaged by the surface chains of amino acids (alanine, leucine, valine, and isoleucine). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThermal stability of the globular proteins increases with an increase in AI value [A. Roy et.al.2010]. Aimed at protein, the aliphatic- index was computed to be (73.18). The GRAVY index for this protein was -0.331, which indicates its better transportation through the water (medium) solvent. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary and tertiary structural analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSOPMA, a software tool was used for Secondary structure analysis of protein. Polygalacturonase protein is consisting of 18.90 % alpha-helices, 39.51% haphazard coils, 34.40%, extended beta strands and 7.18% beta turns (Fig. 1). The sub cellular localization by Cello indicates that this is an extracellular protein. The 3-D structure of Polygalacturonase was modeled using I-TASSER which is based on threading approach (Fig.2) [Nakamura et.al.2019]. \u0026nbsp;Quality of tertiary model \u0026nbsp;was assessed \u0026nbsp; with \u0026nbsp;the \u0026nbsp;help \u0026nbsp; of \u0026nbsp;PROCHECK \u0026nbsp;tool and related \u0026nbsp;Ramachandran plot result indicates that 38.5% residues belongs to favored regions while 42.2% residues resides in allowed regions. Liberally allowed region contains 12.3% residues whereas 7% residues prohibited or outlier regions (Fig.3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein preparation, binding site analysis and construction of grid box\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structure of polygalacturonase was constructed by I-TASSER. This structure was analyzed by AutoDock tool and converted into. pdbqt file format after the addition of polar hydrogen. I- TASSER also determined the binding site residues of the protein, Gln205, Gln261, Tyr262, Asn270, Ile271, Val273, and Asn320 were found to be present at active site. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe grid map centred at the active site pocket of protein (www.scfbio.iitd.res.in\u003ca href=\"http://www.scfbio.iitd.res.in/\"\u003e) lies in\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://www.scfbio.iitd.res.in/\"\u003ethe\u003c/a\u003e\u003ca href=\"http://www.scfbio.iitd.res.in/\"\u003e\u0026nbsp;\u003c/a\u003ecentre x 66.361, centre y 66.444, centre z 68.583 with size 46, 44, 42. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRecovery and groundwork of Ligand molecules\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eStructures of phytoalexins were downloaded from\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PubChem database. 3-dimensional coordinates of phytoalexins were organized by using Marvin sketch in .pdb format and these files were used to prepare. pdbqt files by autodock [R.K. Pathak et.al.2017] for molecular docking studies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMolecular docking, Visualization and analysis of protein ligand complex\u003c/h2\u003e\n\u003cp\u003eThe studies of molecular docking were made by AutoDock vina by means of the prepared 3D structure of different phytoalexin viz. \u0026nbsp;nimbolide, \u0026nbsp;nimbolin, \u0026nbsp; Azadiradione, \u0026nbsp;Quercetin and \u0026nbsp;Azadirone \u0026nbsp; with polygalacturonase \u0026nbsp;as \u0026nbsp;molecular target \u0026nbsp;whereas \u0026nbsp; the \u0026nbsp;visualization \u0026nbsp;and \u0026nbsp; analysis \u0026nbsp;of \u0026nbsp;protein \u0026nbsp; ligand complex \u0026nbsp;was done using ligplot. nimbolide, nimbolin, Azadiradione, Quercetin and Azadirone docked with polygalacturonase with docking energy -8.0, -8.0,-7.8,-7.6,-7.4 kcal/mol respectively. Hydrogen bonds between phytoalexis and amino acid residues of pathogenic protein are showed in Fig. 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1. \u0026nbsp;Binding- free energy of all phytoalexins with target protein obtained through molecular docking studies.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"729\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSno.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of compound\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFree Binding energy (Kcal/mol)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen \u0026nbsp;bond\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteracting amino acid residue through Hbond\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003enimbolide\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-8.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eGln205, \u0026nbsp;Gln261, \u0026nbsp;Tyr262\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003enimbolin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-8.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eLys235, \u0026nbsp;Ser258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eAzadiradione\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eAsn176, Asn179, Tyr262\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eQuercetin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eGln205, Asp229, Asp230, Ser258, Tyr290, lys292\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eAzadirone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eSer100, Gln205, Lys235\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eOleuropein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eGln205, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Asn206, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Asp229, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;lys292,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAsp230,lys235 ,Gln261, Tyr262\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eNimbin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eTrp173, Asn206, Ser258, Gln261, Tyr262\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eSalannin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eArg237, Gln261\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eNimbinene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eAsn206, Lys235, Ser258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eDesacetylnimb\u003c/p\u003e\n \u003cp\u003ein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-7.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eTrp155, Asn176, Tyr262\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eBeta sitasterol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-6.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eGln205, Asp229\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003ecaryophyllene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-6.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eNo significant interaction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eCaffeic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-6.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eGln64, lys86, Thr88, Asn134, Asp169, Gln471\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eCarvacrol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eIle45, Met46, Phe49, Ala48, Phe49, Glu50, Glu51, Cys52, Gly53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eProtocatechoic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eVal204, Gln205, Gly228, Asn252, Tyr281\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003e4-hydroxy benzoic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eGly228, Asn252, Tyr281, Asp284\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eSyringic acid \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eTrp173, Asp208, Ser258, lys292\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eVanillic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eTrp203, val204, Tyr281\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eTsibulin2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eAsn252, Tyr281\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003elinalool\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-5.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eTrp203, val204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003ePyrocatechol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-4.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eAsn134, Lys168, Asp169, Gln471\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.456043956043956%\" valign=\"top\"\u003e\n \u003cp\u003e22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35164835164835%\" valign=\"top\"\u003e\n \u003cp\u003eAllicin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-4.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.324175824175825%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.32967032967033%\" valign=\"top\"\u003e\n \u003cp\u003eAsn252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eMolecular Dynamics Simulation of protein and protein-ligand complex\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe stability of the projected protein model was assessed during 10 ns MD simulation and the binding mode of the protein-ligand complex was also analyzed using 10 ns MD simulation. MDS applying solvent, pressure and set temperature predicted the mechanism of precise binding of the complex. \u0026nbsp;We computed the root mean square volatility and deviation from the trajectory. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRoot Mean Square Deviation (RMSD) \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eRMSD serves as an indicator of system stability. The RMSD of both the protein and the protein-ligand complex was observed to increase between 1 and 4 ns, indicating that both structures remained stable as they dissolved in the cubic box solution, and any internal repulsion was eliminated over time. After 5 ns, both systems reached equilibrium and maintained a steady trajectory for analysis. The protein and protein-ligand complex exhibited average RMSDs of 0.43 nm and 0.38 nm, respectively (as shown in Fig. 5). The RMSD values suggest that the protein-ligand complex was more stable compared to the protein alone(Sapundzhi et.al.2022).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRoot mean square fluctuation (RMSF)\u003c/h2\u003e\n\u003cp\u003eWe computed RMSF values to compare the flexibility of every amino acid residue in the complex and the protein. RMSF gives light on the structural differences of every residue. Lower RMSF values indicate well-structured regions, while higher values suggest more flexible or disordered areas, such as loops or lethal domains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the study, it was calculated the value of RMSF for the 10 ns. The peak of RMSF protein- ligand complex was to some extent higher than protein; however the average RMSF value was 0.15 nm and 0.14 nm for protein and protein-ligand complex (Fig. 6). The complex projected less variation as comparison to protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRadius of gyration (Rg)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilising Rg, the conformational variations and hardness of the protein-ligand complexes and the apo-protein were determined. The Rg value was determined for all the complexes with the apo-protein using the 10 ns trajectories. \u0026nbsp;For the apo-protein and protein-ligand complex, the average Rg values were determined to be 2.38 and 2.33 nm, respectively. (Fig. 7). Protein-ligand complex showed lesser Rg value in comparison to apo-protein. The result suggested that protein ligand complex is more stable than the apo-protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHydrogen bonds\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein-ligand complexes are stabilised by many interactions, including hydrophobic, electrostatic, and hydrogen bonds. Highly definite and transitory interactions, hydrogen bonds are a crucial component of protein-ligand stabilisation. The many hydrogen bonds vs. time are explained in Figure 8. For the protein-ligand complex, the hydrogen bonds were counted normally between 0 and 1. It shows how the ligand keeps creating hydrogen bonds right up until the simulation is over.\u003c/p\u003e\n\u003ch2\u003eProspect of phytoalexins as antifungal molecule in plant protection\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePhytoalexins acts as a significant role in plant fighting in opposition to plant pathogens, it not only in dicot species but \u0026nbsp; in monocots as well [I. Ahuja et.al.2012],. \u0026nbsp; It \u0026nbsp;has been \u0026nbsp;lately \u0026nbsp; depicted that \u0026nbsp;assault \u0026nbsp;of \u0026nbsp; maize stem \u0026nbsp;by Rhizopus \u0026nbsp;microspores \u0026nbsp; and \u0026nbsp;Collect otrichumgraminicola \u0026nbsp;induces \u0026nbsp;the \u0026nbsp; gathering \u0026nbsp;of \u0026nbsp;6 \u0026nbsp;ent- kauranne pertaining to diterpenoids, communally termed kauralexins which hamper the expansion of the pathogens [E.A. Schmelz et.al.2011].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe outcomes of the current study noticeably shows, phytoalexin nimbolide can perform a direct molecule for the protection against fungal- diseases. Nimbolide is tiny hydrophobic molecule which can do cross cell membranes because of its perfect logP value and lower molecular weight that can maintain diffusion of the hydrophobic molecule in the course of the membrane. It has been ascertained that nimbolide have showed highest affinity towards pathogenic proteins of aspergilus niger. Therefore, nimbolide could be positive for safeguarding the Allium cepa in opposition to fungal diseases together with black mold (Fig 9).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present \u003cem\u003ein silico\u003c/em\u003e study provides insights about interaction of phytoalexins with pathogenic protein of \u003cem\u003eAspergillus niger\u003c/em\u003e to make clear the inhibition of the fungal action. The results obtained by docking of phytoalexin with a pathogenic protein of \u003cem\u003eAspergillus niger\u003c/em\u003e, suggests, the phytoalexin nimbolide is the pilot molecule that can act in opposition to \u003cem\u003eAspergillus\u003c/em\u003e spp. On the basis of the data obtained, the arrangement of nimbolide was appropriately modified to plan a prospective significant molecules agriculturally for the prevention of \u003cem\u003eAllium cepa\u003c/em\u003e. Subsequent findings on the protein-ligand communication will cover the way to use phytoalexins, as alternative to currently used synthetic fungicides which are harmful for environment and soil fertility. The effectiveness and strength of such phytoalexin derivatives consisting the antifungal prospective to reduce incidences of black mold disease in \u003cem\u003eAllium cepa\u003c/em\u003e, can be further confirmed through wet lab experiments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"Pranshu Dangwal, Saransh Juyal, Rajesh Kumar Pathak, Ravindra Ojha, Arun Bhatt and Mamta Baunthiyal wrote the main manuscript text and prepared all the figures. Computational work done by Pranshu Dangwal, Saransh Juyal, Rajesh Kumar Pathak, and Ravindra Ojha. Arun Bhatt, Mamta Baunthiyal reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eThe authors extend their thanks to Uttarakhand Technical University for their support in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhao X-X, Lin F-J, Li H, Li H-B, Wu D-T, Geng F, Ma W, Wang Y, Miao B-H and Gan R-Y (2021) Recent Advances in Bioactive Compounds, Health Functions, and Safety Concerns of Onion (Allium cepa L.). Front. Nutr. 8:669805.\u003cu\u003e\u0026nbsp;doi: 10.3389/fnut. 2021.669805\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eOyawoye, O.M., Olotu, T.M., Nzekwe, S.C. et al (2022). Antioxidant potential and antibacterial activities of Allium cepa (onion) and Allium sativum (garlic) against the multidrug resistance bacteria. Bull Natl Res Cent 46, 214. https://doi.org/10.1186/s42269-022-00908-8\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eTiku, A.R. (2020). Antimicrobial Compounds (Phytoanticipins and Phytoalexins) and Their Role in Plant Defense. In: M\u0026eacute;rillon, JM., Ramawat, K. (eds) Co-Evolution of Secondary Metabolites. Reference Series in Phytochemistry. Springer, Cham. https://doi.org/10.1007/978-3-319-96397-6_63\u003c/li\u003e\n \u003cli\u003eBizuneh, G.K (2021) The chemical diversity and biological activities of phytoalexins. ADV TRADIT MED (ADTM) 21, 31\u0026ndash;43 .https://doi.org/ 10.1007/s13596-020-00442-w\u003c/li\u003e\n \u003cli\u003eLucas, James E.; Kortemme, Tanja; Dokholyan, Nikolay V. (2020). New computational protein design methods for de novo small molecule binding sites. PLOS Computational Biology, 16(10), e1008178\u0026ndash;. \u003cu\u003edoi:10.1371/journal.pcbi.1008178\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eBadar, M.S., Shamsi, S., Ahmed, J., Alam, M.A. (2022). Molecular Dynamics Simulations: Concept, Methods, and Applications. In: Rezaei, N. (eds) Transdisciplinarity. Integrated Science, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-94651-7_7\u003cu\u003e\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eNakamura, M., Iwai, H. (2019) Functions and mechanisms: polygalacturonases from plant pathogenic fungi as pathogenicity and virulence factors. J Gen Plant Pathol 85, 243\u0026ndash;250. \u003cu\u003ehttps://doi.org/10.1007/s10327-019-00856-8\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eSapundzhi, F., Popstoilov, M., Lazarova, M. (2023). RMSD Calculations for Comparing Protein Three-Dimensional Structures. In: Georgiev, I., Datcheva, M., Georgiev, K., Nikolov, G. (eds) Numerical Methods and Applications. NMA 2022. Lecture Notes in Computer Science, vol 13858. Springer, Cham. https://doi.org/10.1007/978-3-031-32412-3_25\u003c/li\u003e\n \u003cli\u003eJani, J., Pappachan, A. (2021). Protein Analysis: From Sequence to Structure. In: Singh, V., Kumar, A. (eds) Advances in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-33-6191-1_4\u003c/li\u003e\n \u003cli\u003eBizuneh, G.K. (2021) The chemical diversity and biological activities of phytoalexins. ADV TRADIT MED (ADTM) 21, 31\u0026ndash;43. https://doi.org/10.1007/s13596-020-00442-w\u003c/li\u003e\n \u003cli\u003eA.R. Oany, T.P. Jyoti, S.A. Ahmad (2014) An in-silico approach for characterization of an aminoglycoside antibiotic-resistant methyl -transferase protein from Pyrococcus furiosus (DSM 3638), Bioinfo. and Biol. Insi. 8.65.\u003c/li\u003e\n \u003cli\u003eA.W. Schuttelkopf, D.M. Aalten (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes, Acta Crystallogr. D. Biol. Crystallogr. 60.1355-1363.\u003c/li\u003e\n \u003cli\u003eAhuja, R. Kissen, A.M. Bones (2012) Phytoalexins in defense against pathogens, Tren. in Plant Sci. 17 73\u0026ndash;90.\u003c/li\u003e\n \u003cli\u003eC.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev.3-26.\u003c/li\u003e\n \u003cli\u003eC.K. Jacob, K. Sivaprakasam, R. Jeyarajan (1988) Evaluation of some plant extracts antagonists for the control of pre-emergence damping off brinjal (Solanum melongena), J. of Biol. Control 2.117-119.\u003c/li\u003e\n \u003cli\u003eD.W. Buchan, F. Minneci, T.C. Nugent, K. Bryson, D.T. Jones (2013) Scalable web services for the PSIPRED protein analysis workbench, Nucleic Acids Res. 41 .349\u0026ndash;357.\u003c/li\u003e\n \u003cli\u003eE.A. Schmelz, F. Kaplan, A. Huffaker, N.J. Dafoe, M.M. Vaughan, X. Ni, J.R. Rocca, H.T. Alborn, P.E. Teal (2011) Identity, regulation, and activity of inducible diterpenoid phytoalexins in maize, Proc. Natl. Acad. Sci. U. S. A. 108 .5455\u0026ndash;5460.\u003c/li\u003e\n \u003cli\u003eE.C. Oerke (2006) Crop losses to pests, J. of Agri. Sci. Cambridge 144.31-43.\u003c/li\u003e\n \u003cli\u003eGeourjon, G. Deleage, (1995) SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments, Comp. App. in the Biosci. 11.681\u0026ndash;684.\u003c/li\u003e\n \u003cli\u003eHuffaker, F. Kaplan, M.M. Vaughan, N.J. Dafoe, J.R. Rocca, H.T. Alborn, P.E. Teal, E.A. Schmelz, (2011) Novel Acidic Sesquiterpenoids Constitute a Dominant Class of Pathogen-Induced Phytoalexins in Maize, Plant Physiol. 156.2082-2097.\u003c/li\u003e\n \u003cli\u003eJ. Yang, A. Roy, Y. Zhang (2013) Protein\u0026ndash;ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment, Bioinfo. 29.2588\u0026ndash;2595.\u003c/li\u003e\n \u003cli\u003eJ. Yang, Y. Zhang, I-TASSER server: (2015) .New development for protein structure and function predictions, Nucl. Aci. Res. 43.174\u0026ndash;181.\u003c/li\u003e\n \u003cli\u003eK. Guruprasad, B.B. Reddy, M.W. Pandit (1990) Correlation between stability of a protein and its dipeptide composition: A novel approach for predicting in vivo stability of a protein from its primary sequence, Prot. Eng. 4.155\u0026ndash;161.\u003c/li\u003e\n \u003cli\u003eM.W. Walter (2002) Structure-based design of agrochemicals, Nat. Prod. Rep. 19.278-291.\u003c/li\u003e\n \u003cli\u003eN. J. Hayden, R.B. Maude (1992) The role of seed-borne \u003cem\u003eAspergillus niger\u003c/em\u003e in the transmission of the black mould of onion, Plant Pathol. 41.573-581.\u003c/li\u003e\n \u003cli\u003eO. Trott, A.J. Olson (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. of comp. chem. 31.455-461.\u003c/li\u003e\n \u003cli\u003eOostenbrink, A. Villa, A.E. Mark, W.F. Gunsteren, (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6, J. Comput. Chem. 251.656-676.\u003c/li\u003e\n \u003cli\u003eP. Payal, H. Panchal (2016) Primary, secondary and tertiary structural analysis of disease resistance protein RGA4 of ZEA maize using bioinformatics tools, Int. J. of Ad. Res. in Comp. and Comm. Eng. 5.7\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eR.K. Pathak, M. Baunthiyal, R. Shukla, D. Pandey, G. Taj, A. Kumar (2017) In Silico identification of mimicking molecules as defense inducers triggering jasmonic acid mediated immunity against Alternaria blight disease in Brassica species, Fron. in pla. sci. 8.1-17.\u003c/li\u003e\n \u003cli\u003eRoy, A. Kucukural, Y. Zhang (2010) I-TASSER: A unified platform for automated protein structure and function prediction, Nat. Prot. 5.725\u0026ndash;738.\u003c/li\u003e\n \u003cli\u003eS. Wu, Y. Zhang (2007) LOMETS: A local meta-threading-server for protein structure prediction, Nuc. Aci. Res. 35.3375\u0026ndash;3382.\u003c/li\u003e\n \u003cli\u003eS.C. Gill, P.H. Hippel (1989) Calculation of protein extinction coefficients from amino acid sequence data, Anal. Biochem. 182.319\u0026ndash;326.\u003c/li\u003e\n \u003cli\u003eY. Shinbo, Y. Nakamura, M. Altaf-Ul-Amin, K. Asahi, M. Kuokawa, et. al. (2006) KNApSAcK: A comprehensive species\u0026ndash;metabolite relationship database, Biotech Agri. Fore. 57.165-181.\u003c/li\u003e\n \u003cli\u003eY. Zhang (2008) I-TASSER server for protein 3D structure prediction, BMC Bioinfo. 9.1-8.\u003c/li\u003e\n \u003cli\u003eDeckers, F., Rasim, K. \u0026amp; Schr\u0026ouml;der, C. (2022). Molecular dynamics simulation of polypropylene: diffusion and sorption of H2O, H2O2, H2, O2 and determination of the glass transition temperature. J Polym Res 29, 463. \u003cu\u003ehttps://doi.org/10.1007/s10965-022-0330\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eM.W. Walter (2002) Structure-based design of agrochemicals, Nat. Prod. Rep. 19.278-291.\u003c/li\u003e\n\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":"[email protected]","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":"Allium cepa, Phytoalexins, Nimbolide, Polygalacturonase, Molecular modeling, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-4521542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4521542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBlack mold disease provoked by \u003cem\u003eAspergillus niger\u003c/em\u003e is one of the major postharvest diseases in \u003cem\u003eAllium cepa\u003c/em\u003e. In the present study, efforts have been made to model the polygalacturonase protein of \u003cem\u003eAspergillus niger\u003c/em\u003e that is involved in disease progression as a promising molecular target for the identification of novel fungicides through computational approach. We used I-TASSER to determine the 3D structure of the target protein and docked it with naturally occurring phytoalexins which included nimbolide, nimbolin, Azadiradione, Quercetin and Azadirone. The result of present study revealed that nimbolide has the greatest affinity towards polygalacturonase as compared to other phytoalexins which binds the protein at amino acid residues Gln205, Gln261, Tyr262 with four hydrogen bonds and \u0026minus;\u0026thinsp;8.0 kcal/mol binding energy. Further, molecular dynamics simulation of protein and docked nimbolide-polyglacturonase complex was carried out to validate the stability of the system at the atomic level. Based on the study, this may lead to inhibition of pathogenic protein. Thus, it is of interest to consider the molecule for further validation at lab and field conditions for ensuring food and nutritional security.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Nimbolide as a natural fungicide against Black Mold disease of Allium cepa: A molecular docking and simulation-based study ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 10:59:48","doi":"10.21203/rs.3.rs-4521542/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"aea98843-f772-4c8a-b0bc-02affb933231","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-27T09:45:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 10:59:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4521542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4521542","identity":"rs-4521542","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}