PLANT BIOLOGY
ANIMAL BIOLOGY
SUBSCRIPTION
E-SUBSCRIPTION
 
MAP
MAIN PAGE

 

 

 

 

Tandon G., Prasad S.S., Singh A., Chester J.R.E., Singh P.K., Kaur S., Jaiswal S., Iquebal M.A., Rai A., Kumar D., Singh S. Molecular modelling, docking and dynamics simulations of Solanum lycopersicum PAD4 using EDS1 proteins. Sel'skokhozyaistvennaya Biologiya [Agricultural Biology], 2026, Vol. 61, № 1, p. 72-88.
(how to cite this article)

doi: 10.15389/agrobiology.2026.1.72eng

UDC: 635.64:58.071:577.29:51-76

Acknowledgements:
We gratefully acknowledge support from the Indian Council of Agricultural Research (ICAR), Government of India through its creation of the Advanced Supercomputing Hub for Omics Knowledge in Agriculture (ASHOKA) at ICAR-IASRI, New Delhi. GT and SK received fellowships from ICAR, and their work has been supported by the CABin Scheme program (Project Code: 1004936) of ICAR-IASRI, New Delhi. We also greatly appreciate the encouragement and constructive criticisms we received from HoD, Department of Computational Biology and Bioinformatics and Dean, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj.
There was an external funding received for the conduct of research from Indian Council of Agricultural Research (ICAR), Government of India through its creation of the Advanced Supercomputing Hub for Omics Knowledge in Agriculture (ASHOKA) at ICAR-IASRI, New Delhi. GT and SK received fellowships from ICAR, and their work has been supported by the CABin Scheme program (Project Code: 1004936) of ICAR-IASRI, New Delhi.

 

MOLECULAR MODELLING, DOCKING AND DYNAMICS SIMULATIONS OF Solanum lycopersicum PAD4 USING EDS1 PROTEINS

G. Tandon1, S.S. Prasad2, A. Singh2 ✉, J.R.E. Chester3, P.K. Singh4,
S. Kaur1, S. Jaiswal1, M.A. Iquebal1, A. Rai1, D. Kumar1, S. Singh5 ✉

1Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-11012, India,
e-mail: gitanjali.tandon@gmail.com, sukhii.deep@gmail.com,
sarika@icar.gov.in, ma.iquebal@icar.gov.in,anil.Rai@icar.gov.in,
dinesh.Kumar@icar.gov.in;
2Department of Agriculture, Koneru Lakshmaiah Education Foundation,
Green Fileds, Vaddeswaram, Andra Pradesh-522302, India,
e-mail: shivsaiprasad@kluniversity.in, atulsingh@kluniversity.in(✉ corresponding author);
3Department of Commerce, Koneru Lakshmaiah Education Foundation,
Green Fileds, Vaddeswaram, Andra Pradesh-522302, India,
e-mail: robertedwinchester@kluniversity.in;
4Department of Plant Pathology,Banda University
of Agriculture and Technology,
Banda, Uttar Pradesh-210001, India,
e-mail: praduman1311@gmail.com;
5Department of Computational Biology and Bioinformatics, JIBB, Sam Higginbottom University of Agriculture, Technology and Sciences,
Prayagraj-211007, India,
e-mail: satendra.singh@shiats.edu.in (✉ corresponding author)

ORCID:
Tandon G. orcid.org/0000-0002-9832-0488
Jaiswal S. orcid.org/0000-0002-9948-4994
Prasad S.S. orcid.org/0000-0002-5537-0565
Iquebal M.A. orcid.org/0000-0003-3787-5997
Singh A. orcid.org/0000-0002-1032-4442
Rai A. orcid.org/0000-0003-4454-6462
Chester J.R.E. orcid.org/0009-0008-9720-6932
Kumar D. orcid.org/0000-0001-5466-0686
Singh P.K. orcid.org/0009-0001-7608-4285
Singh S. orcid.org/0000-0002-6876-3376
Kaur S. orcid.org/0000-0001-7510-7636

Final revision received April 15, 2025
Accepted July 22, 2025

As biotic stress is one of the major impediments to food production, it is important to decipher systemic immunity-related pathways and genes. Enhanced Disease Susceptibility 1 (EDS1) and Phytoalexin Lacking 4 (PAD4) are two important proteins that together are essential for the accumulation of salicylic acid (SA). This accumulation, in turn, controls the expression of pathogenesis-related (PR) proteins, which in turn activates defense pathways against pathogen attacks. A computational approach can predict major protein-protein interactions and major signaling pathways possessing key genes. In this study, we conducted 3D modeling and examined the interaction of EDS1 and PAD4 in tomatoes (Solanum lycopersicum), using a homology modeling approach. The predicted models were further validated and then subjected to protein-protein docking using HADDOCK. Based on a comparative analysis of the spatial structures of proteins and potential interactions were revealing the presence of 13 hydrogen bonds and 5 hydrophobic interactions between both the proteins. Molecular dynamics simulations (MDSs) for 50ns were performed using GROMACS to observe the dynamic behavior of the proteins. Various parameters, including RMSD, potential energy, and PCA, were considered during MDS analysis. Docking analysis revealed the presence of EDS1 and PAD4 complexes in tomatoes, thus supporting the possibility that SA might operate as a defense pathway. Such pathway genes can be directly targeted for further studies required for new variety development.

Keywords: Solanum lycopersicum, salicylic acid pathway, EDS1, PAD4, systemic resistance, R-gene, molecular dynamics simulations, homology modeling, protein docking.

 

REFERENCES

  1. Mwangi R.W., Mustafa M., Charles K., Wagara I.W., Kappel N. Selected emerging and reemerging plant pathogens affecting the food basket: a threat to food security. Journal of Agriculture and Food Research, 2023, 14: 100827 CrossRef
  2. Tailor A., Bhatla S.C. R gene-mediated resistance in the management of plant diseases. Journal of Plant Biochemistry and Biotechnology, 2024, 33(1): 5-23 CrossRef
  3. Salguero-Linares J., Coll N.S. Plant proteases in the control of the hypersensitive response. Journal of Experimental Botany, 2019, 70(7): 2087-2095 CrossRef
  4. Dodds P.N., Chen J., Outram M.A. Pathogen perception and signaling in plant immunity. The Plant Cell, 2024, 36(5): 1465-1481 CrossRef
  5. Bauters L., Stojilković B., Gheysen G. Pathogens pulling the strings: effectors manipulating salicylic acid and phenylpropanoid biosynthesis in plants. Molecular Plant Pathology, 2021, 22(11): 1436-1448 CrossRef
  6. Joglekar S., Suliman M., Bartsch M., Halder V., Maintz J., Bautor J., Zeier J., Parker J.E., Kombrink E. Chemical activation of EDS1/PAD4 signaling leading to pathogen resistance in Arabidopsis. Plant and Cell Physiology, 2018, 59(8): 1592-607 CrossRef
  7. García A.V., Blanvillain-Baufumé S., Huibers R.P., Wiermer M., Li G., Gobbato E., Rietz S., Parker J.E. Balanced nuclear and cytoplasmic activities of EDS1 are required for a complete plant innate immune response. PLoS Pathogens, 2010, 6(7): e1000970 CrossRef
  8. Zschiesche W., Barth O., Daniel K., Böhme S., Rausche J., Humbeck K. The zinc-binding nuclear protein HIPP3 acts as an upstream regulator of the salicylate-dependent plant immunity pathway and of flowering time in Arabidopsis thaliana. The New Phytologist, 2015, 207(4): 1084-1096 CrossRef
  9. Bernoux M., Ve T., Williams S., Warren C., Hatters D., Valkov E., Zhang X., Ellis J.G., Kobe B., Dodds P.N. Structural and functional analysis of a plant resistance protein TIR domain reveals interfaces for self-association, signaling, and autoregulation. Cell Host & Microbe, 2011, 9(3): 200-211 CrossRef
  10. Maekawa T., Kufer T.A., Schulze-Lefert P. NLR functions in plant and animal immune systems: so far and yet so close. Nature Immunology, 2011, 12(9): 817-826 CrossRef
  11. Tandon G., Jaiswal S., Iquebal M.A., Kumar S., Kaur S., Rai A., Kumar D. Evidence of salicylic acid pathway with EDS1 and PAD4 proteins by molecular dynamics simulation for grape improvement. Journal of Biomolecular Structure and Dynamics, 2015, 33(10): 2180-2191 CrossRef
  12. Singh I., Shah K. In silico study of interaction between rice proteins enhanced disease susceptibility 1 and phytoalexin deficient 4, the regulators of salicylic acid signalling pathway. Journal of Biosciences, 2012, 37(3): 563-571 CrossRef
  13. Gao B., Chen D., Xue S., Chen C., Cui H., Song C.B.S. The contribution of transposable elements to size variations between four teleost genomes. Mobile DNA, 2016, 7(1): 4 CrossRef
  14. Osorio S., Alba R., Damasceno C.M.B., Lopez-Casado G., Lohse M., Zanor M.I., Tohge T., Usadel B., Rose J.K.C., Fei Z., Giovannoni J.J., Fernie A.R. Systems biology of tomato fruit development: combined transcript, protein, and metabolite analysis of tomato transcription factor (nor, rin) and ethylene receptor (Nr) mutants reveals novel regulatory interactions. Plant Physiology, 2011, 157(1): 405-425 CrossRef
  15. Gerszberg A., Hnatuszko-Konka K., Kowalczyk T., Kononowicz A.K. Tomato (Solanum lycopersicum L.) in the service of biotechnology. Plant Cell, Tissue and Organ Culture (PCTOC), 2015, 120(3): 881-902 CrossRef
  16. Chaudhary P., Sharma A., Singh B., Nagpal A.K. Bioactivities of phytochemicals present in tomato. Journal of Food Science and Technology, 2018, 55(8): 2833-2849 CrossRef
  17. Krishna R., Ansari W.A., Soumia P.S., Yadav A., Jaiswal D.K., Kumar S., Singh A.K., Singhm M., Verma J.P. Biotechnological interventions in tomato (Solanum lycopersicum) for drought stress tolerance: achievements and future prospects. BioTech, 2022, 11(4): 48 CrossRef
  18. Kumar R., Sharma A. Computational strategies and tools for protein tertiary structure prediction. In: Basic biotechniques for bioprocess and bioentrepreneurship. Elsevier, 2023: 225-242 CrossRef
  19. Sutradhar M., Singh B.K., Samanta S., Ali M.N., Mandal N. The overexpression of OsMed 37_6, a mediator complex subunit enhances salt stress tolerance in rice. Biocatalysis and Agricultural Biotechnology, 2024, 58: 103212 CrossRef
  20. Cooper P.S., Lipshultz D., Matten W.T., McGinnis S.D., Pechous S., Romiti M.L., Tao T., Valjavec-Gratian M., Sayers E.W. Education resources of the National Center for Biotechnology Information. Briefings in Bioinformatics, 2010, 11(6): 563-569 CrossRef
  21. Dorn M., E Silva M.B., Buriol L.S., Lamb L.C. Three-dimensional protein structure prediction: methods and computational strategies. Computational Biology and Chemistry, 2014, 53(Part B): 251-276 CrossRef
  22. Webb B., Sali A. Comparative protein structure modeling using MODELLER. Current Protocols in Bioinformatics, 2016, 54(1): 5.6.1-5.6.37 CrossRef
  23. Yang L., Li B., Zheng X.Y., Li J., Yang M., Dong X., He G., An C., Deng X.W. Salicylic acid biosynthesis is enhanced and contributes to increased biotrophic pathogen resistance in Arabidopsis hybrids. Nature Communications, 2015, 6: 7309 CrossRef
  24. Haddad Y., Adam V., Heger Z. Ten quick tips for homology modeling of high-resolution protein 3D structures. PLoS Computational Biology, 2020, 16(4): e1007449 CrossRef
  25. Heo L., Park H., Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Research, 2013, 41(W1): W384-W388 CrossRef
  26. Panigrahy S., Ray S.S., Manjunath K.R., Pandey P.S., Sharma S.K., Sood A., Yadav M., Gupta C., Kundu N., Parihar J.S. A spatial database of cropping system and its characteristics to aid climate change impact assessment studies. Journal of the Indian Society of Remote Sensing, 2011, 39(3): 355-364 CrossRef
  27. Singh A., Kaushik R., Jayaram B. Quality assessment of protein tertiary structures: past, present, and future. In: Bioinformatics: Sequences, Structures, Phylogeny. A. Shanker (ed.). Springer Singapore, Singapore, 2018: 271-288 CrossRef
  28. Abdollahi S., Raoufi Z., Fakoor M.H. Physicochemical and structural characterization, epitope mapping and vaccine potential investigation of a new protein containing Tetratrico Peptide Repeats of Acinetobacter baumannii: an in-silico and in-vivo approach. Molecular Immunology, 2021, 140: 22-34 CrossRef
  29. Bhattacharya S., Dhar S., Banerjee A., Ray S. Structural, functional, and evolutionary analysis of late embryogenesis abundant proteins (LEA) in Triticum aestivum: a detailed molecular level biochemistry using in silico approach. Computational Biology and Chemistry, 2019, 82: 9-24 CrossRef
  30. Dehury B., Patra M.C., Maharana J., Sahu J., Sen P., Modi M.K., Choudhury M.D., Barooah M. Structure-based computational study of two disease resistance gene homologues (Hm1 and Hm2) in maize (Zea mays L.) with implications in plant-pathogen interactions. PLoS ONE, 2014, 9(5): e97852 CrossRef
  31. Rangisetty P.T., Kilaparthi A., Akula S., Bhardwaj M., Singh S. RSAD2: An exclusive target protein for Zika virus comparative modeling, characterization, energy minimization and stabilization. International Journal of Health Sciences, 2023, 17(1): 12-17.
  32. Pronk S., Páll S., Schulz R., Larsson P., Bjelkmar P., Apostolov R., Shirts M.R., Smith J.C., Kasson P.M., van der Spoel D., Hess B., Lindahl E. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 2013, 29(7): 845-854 CrossRef
  33. Prasad S.S., Das U. Enhancing the throughput of design—build—test—learn cycle for the future perspective of synthetic biology in plants (review). Sel'skokhozyaistvennaya Biologiya [Agricultural Biology], 2024, 59(5): 831-46 CrossRef
  34. Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I., Mackerell Jr. A.D. CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields. Journal of Computational Chemistry, 2010, 31(4): 671-690 CrossRef
  35. MacCallum J.L., Hu S., Lenz S., Souza P.C.T., Corradi V., Tieleman D.P. An implementation of the Martini coarse-grained force field in OpenMM. Biophysical Journal, 2023, 122(14): 2864-2870 CrossRef
  36. Vaiwala R., Jadhav S., Thaokar R. Electrostatic interactions in dissipative particle dynamics—Ewald-like formalism, error analysis, and pressure computation. The Journal of Chemical Physics, 2017, 146(12): 124904 CrossRef
  37. Feng Z., Alqarni M.H., Yang P., Tong Q., Chowdhury A., Wang L., Xie X.-Q. Modeling, molecular dynamics simulation, and mutation validation for structure of cannabinoid receptor 2 based on known crystal structures of GPCRs. Journal of Chemical Information and Modeling, 2014, 54(9): 2483-2499 CrossRef
  38. Nayana P., Gollapalli P., Manjunatha H. Investigating the structural basis of piperine targeting AKT1 against prostate cancer through in vitro and molecular dynamics simulations. Journal of Biomolecular Structure & Dynamics, 2024, 13: 1-15 CrossRef
  39. Edmunds N.S., Alharbi S.M.A., Genc A.G., Adiyaman R., McGuffin L.J. Estimation of model accuracy in CASP15 using the ModFOLDdock server. Proteins: Structure, Function and Bioinformatics, 2023, 91(12): 1871-1878 CrossRef
  40. Chen M., Schliep M., Willows R.D., Cai Z.L., Neilan B.A., Scheer H. A red-shifted chlorophyll. Science, 2010, 329(5997): 1318-1319 CrossRef
  41. Banerjee A., Ray S. Molecular modeling, mutational analysis and conformational switching in IL27: an in silico structural insight towards AIDS research. Gene, 2016, 576(1): 72-78 CrossRef
  42. Fuglebakk E., Echave J., Reuter N. Measuring and comparing structural fluctuation patterns in large protein datasets. Bioinformatics (Oxford, England), 2012, 28(19): 2431-2440 CrossRef
  43. Yagi N., Fujita S., Nakamura M. Plant microtubule nucleating apparatus and its potential signaling pathway. Current Opinion in Plant Biology, 2024, 82: 102624 CrossRef
  44. Thakur A., Gizzio J., Levy R.M. Potts hamiltonian models and molecular dynamics free energy simulations for predicting the impact of mutations on protein kinase stability. The Journal of Physical Chemistry B, 2024, 128(7): 1656-1667 CrossRef
  45. Mishra S., Roychowdhury R., Ray S., Hada A., Kumar A., Sarker U., Sarker U., Aftab T., Das R. Salicylic acid (SA)-mediated plant immunity against biotic stresses: an insight on molecular components and signaling mechanism. Plant Stress, 2024, 11: 100427 CrossRef
  46. Sarangle Y., Bamel K., Purty R.S. Identification of acetylcholinesterase like gene family and its expression under salinity stress in Solanum lycopersicum. Journal of Plant Growth Regulation, 2024, 43(3): 940-960 CrossRef

back

 


CONTENTS

 

Full article PDF (Rus)