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In silico characterization, docking, and simulations to understand host–pathogen interactions in an effort to enhance crop production in date palms

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Abstract

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population’s needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host–pathogen protein interactions (HPIs) have a promising role in identifying the pathogens’ strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein–protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

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References

  1. Jones DL (1995) Palms - Google Scholar

  2. Purseglove JW (1972) Tropical crops: monocotyledons. - Google Scholar

  3. Peters HA, Pauw A, Silman MR, Terborgh JW (2004) Falling palm fronds structure amazonian rainforest sapling communities. Proc Biol Sci 7(271 Suppl 5):S367–S369

    Google Scholar 

  4. Baker WJ, Norup MV, Clarkson JJ, Couvreur TLP, Dowe JL, Lewis CE et al (2011) Phylogenetic relationships among arecoid palms (Arecaceae: Arecoideae). Ann Bot 108(8):1417–1432

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rugman-Jones PF, Hoddle CD, Hoddle MS, Stouthamer R (2013) The lesser of two weevils: molecular-genetics of pest palm weevil populations confirm Rhynchophorus vulneratus (Panzer 1798) as a valid species distinct from R. ferrugineus (Olivier 1790), and reveal the global extent of both. PLoS One 8(10):e78379

  6. Mckenna DD, Wild AL, Kanda K, Bellamy CL, Beutel RG, Caterino MS et al (2015) The beetle tree of life reveals that Coleoptera survived end-Permian mass extinction to diversify during the Cretaceous terrestrial revolution. Syst Entomol 40(4):835–880

    Article  Google Scholar 

  7. McKenna DD, Sequeira AS, Marvaldi AE, Farrell BD (2009) Temporal lags and overlap in the diversification of weevils and flowering plants. Proc Natl Acad Sci USA 106(17):7083–7088

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Abdel Farag El-Shafie H, Romeno Faleiro J (2020) Red palm weevil Rhynchophorus ferrugineus (Coleoptera: Curculionidae): global invasion, current management options, challenges and future prospects. In: El-Shafie H, editor. Invasive Species - introduction pathways, economic impact, and possible management options. IntechOpen

  9. Scientific Consultation and High-Level Meeting on Red Palm Weevil Management

  10. Host Palms [Internet]. [cited 2021 Jun 1]. Available from: https://www.savealgarvepalms.com/weevil-facts/host-palm-trees?lang=en

  11. Rochat D, Malosse C, Lettere M (1993) Identification of new pheromone-related compounds from volatiles produced by males of four Rhynchophorinae weevils (Coleoptera, Curculionidae). 316(12):1737–1742

  12. Dyer MD, Neff C, Dufford M, Rivera CG, Shattuck D, Bassaganya-Riera J, et al (2010) The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis. PLoS One 5(8):e12089

  13. König R, Zhou Y, Elleder D, Diamond TL, Bonamy GMC, Irelan JT et al (2008) Global analysis of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell 135(1):49–60

    Article  PubMed  PubMed Central  Google Scholar 

  14. Rizzetto L, Cavalieri D (2011) Friend or foe: using systems biology to elucidate interactions between fungi and their hosts. Trends Microbiol 19(10):509–515

    Article  CAS  PubMed  Google Scholar 

  15. Bonetta L (2010) Protein-protein interactions: interactome under construction. Nature 468(7325):851–854

    Article  CAS  PubMed  Google Scholar 

  16. Liu X, Liu B, Huang Z, Shi T, Chen Y, Zhang J (2012) SPPS: a sequence-based method for predicting probability of protein-protein interaction partners. PLoS One 7(1):e30938

  17. Durmuş S, Çakır T, Özgür A, Guthke R (2015) A review on computational systems biology of pathogen-host interactions. Front Microbiol 9(6):235

    Google Scholar 

  18. Zuck M, Austin LS, Danziger SA, Aitchison JD, Kaushansky A (2017) The promise of systems biology approaches for revealing host pathogen interactions in malaria. Front Microbiol 16(8):2183

    Article  Google Scholar 

  19. Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C (2013) Computational meta’omics for microbial community studies. Mol Syst Biol 14(9):666

    Article  Google Scholar 

  20. Sturdevant DE, Virtaneva K, Martens C, Bozinov D, Ogundare O, Castro N et al (2010) Host-microbe interaction systems biology: lifecycle transcriptomics and comparative genomics. Future Microbiol 5(2):205–219

    Article  CAS  PubMed  Google Scholar 

  21. Liu Z-P, Chen L (2012) Proteome-wide prediction of protein-protein interactions from high-throughput data. Protein Cell 3(7):508–520

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dix A, Vlaic S, Guthke R, Linde J (2016) Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers. Clin Microbiol Infect 22(7):600–606

    Article  CAS  PubMed  Google Scholar 

  23. Yeung A, Hale C, Clare S, Palmer S, Bartholdson Scott J, Baker S et al (2019) Using a systems biology approach to study host-pathogen interactions. Microbiol Spectr 7(2):1–11

  24. Shoemaker BA, Panchenko AR (2007) Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput Biol 3(4):e43

  25. Lewis ACF, Saeed R, Deane CM (2010) Predicting protein-protein interactions in the context of protein evolution. Mol Biosyst 6(1):55–64

    Article  CAS  PubMed  Google Scholar 

  26. Westermann AJ, Gorski SA, Vogel J (2012) Dual RNA-seq of pathogen and host. Nat Rev Microbiol 10(9):618–630

    Article  CAS  PubMed  Google Scholar 

  27. Galperin MY, Koonin EV (2000) Who’s your neighbor? New computational approaches for functional genomics. Nat Biotechnol 18(6):609–613

    Article  CAS  PubMed  Google Scholar 

  28. Overbeek R, Fonstein M, D’Souza M, Pusch GD, Maltsev N (1999) The use of gene clusters to infer functional coupling. Proc Natl Acad Sci USA 96(6):2896–2901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Liu Z-P, Wang J, Qiu Y-Q, Leung RKK, Zhang X-S, Tsui SKW et al (2012) Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs. BMC Bioinformatics 13(Suppl 7):S6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Aloy P, Ceulemans H, Stark A, Russell RB (2003) The relationship between sequence and interaction divergence in proteins. J Mol Biol 332(5):989–998

    Article  CAS  PubMed  Google Scholar 

  31. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO (1999) Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA 96(8):4285–4288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Barker D, Pagel M (2005) Predicting functional gene links from phylogenetic-statistical analyses of whole genomes. PLoS Comput Biol 1(1):e3

  33. Wang F, Liu M, Song B, Li D, Pei H, Guo Y et al (2012) Prediction and characterization of protein-protein interaction networks in swine. Proteome Sci 10(1):2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Shin CJ, Davis MJ, Ragan MA (2009) Towards the mammalian interactome: inference of a core mammalian interaction set in mouse. Proteomics 9(23):5256–5266

    Article  CAS  PubMed  Google Scholar 

  35. Schleker S, Garcia-Garcia J, Klein-Seetharaman J, Oliva B (2012) Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes. Chem Biodivers 9(5):991–1018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Krishnadev O, Srinivasan N (2011) Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic bacteria. Int J Biol Macromol 48(4):613–619

    Article  CAS  PubMed  Google Scholar 

  37. Pu Y-C, Ma T-L, Hou Y-M, Sun M (2017) An entomopathogenic bacterium strain, Bacillus thuringiensis, as a biological control agent against the red palm weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae). Pest Manag Sci 73(7):1494–1502

    Article  CAS  PubMed  Google Scholar 

  38. Hussain A, Rizwan-ul-Haq M, Al-Ayedh H, Ahmed S, Al-Jabr AM (2015) Effect of Beauveria bassiana infection on the feeding performance and antioxidant defence of red palm weevil. Rhynchophorus ferrugineus Biocontrol 60(6):849–859

    Article  CAS  Google Scholar 

  39. Jalinas J, Güerri-Agulló B, Mankin RW, López-Follana R, Lopez-Llorca LV (2015) Acoustic Assessment of Beauveria bassiana (Hypocreales: Clavicipitaceae) Effects on Rhynchophorus ferrugineus (Coleoptera: Dryophthoridae) larval activity and mortality. J Econ Entomol 108(2):444–453

    Article  CAS  PubMed  Google Scholar 

  40. Search: GCA_014462685.1 - NLM [Internet]. [cited 2021 May 30]. Available from: https://www.ncbi.nlm.nih.gov/search/all/?term=GCA_014462685.1

  41. Loaiza CD, Kaundal R (2021) PredHPI: an integrated web server platform for the detection and visualization of host-pathogen interactions using sequence-based methods. Bioinformatics 37(5):622–624

    Article  CAS  PubMed  Google Scholar 

  42. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291

    Article  CAS  Google Scholar 

  44. Lüthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83–85

    Article  PubMed  Google Scholar 

  45. Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170

    Article  CAS  PubMed  Google Scholar 

  46. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Weng G, Wang E, Wang Z, Liu H, Zhu F, Li D et al (2019) HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res 47(W1):W322–W330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Feng T, Chen F, Kang Y, Sun H, Liu H, Li D et al (2017) HawkRank: a new scoring function for protein-protein docking based on weighted energy terms. J Cheminform 9(1):66

    Article  PubMed  PubMed Central  Google Scholar 

  49. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kutzner C, Páll S, Fechner M, Esztermann A, de Groot BL, Grubmüller H (2019) More bang for your buck: improved use of GPU nodes for GROMACS 2018. J Comput Chem 40(27):2418–2431

    Article  CAS  PubMed  Google Scholar 

  51. Graham SC, Bond CS, Freeman HC, Guss JM (2005) Structural and functional implications of metal ion selection in aminopeptidase P, a metalloprotease with a dinuclear metal center. Biochemistry 44(42):13820–13836

    Article  CAS  PubMed  Google Scholar 

  52. Ramelot TA, Cort JR, Goldsmith-Fischman S, Kornhaber GJ, Xiao R, Shastry R et al (2004) Solution NMR structure of the iron-sulfur cluster assembly protein U (IscU) with zinc bound at the active site. J Mol Biol 344(2):567–583

    Article  CAS  PubMed  Google Scholar 

  53. Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7):845–854

  54. Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E (2015) Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Markidis S, Laure E (eds) Solving software challenges for exascale. Springer International Publishing, Cham, pp 3–27

    Chapter  Google Scholar 

  55. Huang Y, Chen W, Wallace JA, Shen J (2016) All-atom continuous constant pH molecular dynamics with particle mesh Ewald and titratable water. J Chem Theory Comput 12(11):5411–5421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X (2018) Molecular dynamics simulations and novel drug discovery. Expert Opin Drug Discov 13(1):23–37

    Article  CAS  PubMed  Google Scholar 

  57. Kutzner C, Páll S, Fechner M, Esztermann A, de Groot BL, Grubmüller H (2015) Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J Comput Chem 36(26):1990–2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Prakash A, Dixit G, Meena NK, Singh R, Vishwakarma P, Mishra S et al (2018) Elucidation of stable intermediates in urea-induced unfolding pathway of human carbonic anhydrase IX. J Biomol Struct Dyn 36(9):2391–2406

    Article  CAS  PubMed  Google Scholar 

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Funding

This research project has been funded by the Deanship of Scientific Research at the University of Ha’il, Ha’il, Kingdom of Saudi Arabia (fund number: RG-191352).

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Authors and Affiliations

  1. Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, P.O. Box 2440, Ha’il, 81411, Saudi Arabia

    Meshari Alazmi

  2. Department of Biology, College of Sciences, University of Ha’il, P.O. Box 2440, Ha’il, 81411, Saudi Arabia

    N. Alshammari, Naimah A. Alanazi & Abdel Moneim E. Sulieman

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  1. Meshari Alazmi
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  2. N. Alshammari
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  3. Naimah A. Alanazi
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  4. Abdel Moneim E. Sulieman
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Contributions

MA initiated and designed the project, MA and AN implemented the experiments, and MA, AN, AS, and NA wrote the manuscript.

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Correspondence to Meshari Alazmi.

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Alazmi, M., Alshammari, N., Alanazi, N.A. et al. In silico characterization, docking, and simulations to understand host–pathogen interactions in an effort to enhance crop production in date palms. J Mol Model 27, 339 (2021). https://doi.org/10.1007/s00894-021-04957-0

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