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Plant Science Today (PST)

Research Articles

Early Access

Comparative in-silico analysis of the bHLH protein family in rice Oryza sativa subsp. japonica cv. Nipponbare

DOI
https://doi.org/10.14719/pst.11864
Submitted
19 September 2025
Published
10-02-2026

Abstract

The basic Helix-Loop-Helix (bHLH) proteins are key regulators of gene expression, development and responses to environmental stimuli in plants. In this study, we performed a comprehensive computational analysis of six bHLH proteins (OsbHLH056, OsbHLH057, OsbHLH058, OsbHLH059, OsbHLH062 and OsbHLH063) in Oryza sativa subsp. japonica cv. Nipponbare. Their physicochemical properties, secondary and tertiary structures, domains, phylogenetic relationships, subcellular localisation and protein-protein interactions were investigated. Results showed that OsbHLH062 had the highest molecular weight (29,686.27 kilodalton (kDa) and amino acid number (265), while OsbHLH058 had the lowest (7,896.08 kDa; 77 amino acids). Isoelectric point analysis indicated five proteins were acidic, while OsbHLH058 was basic. All proteins were predicted to be unstable, reflecting the flexibility essential for regulation. Aliphatic index values (61.13–85.26) suggested moderate thermo-stability. The secondary structure was dominated by α-helices, which enhance structural stability and the extinction coefficients suggested enrichment of cysteine, tryptophan and tyrosine residues. Phylogenetic analysis showed OsbHLH057 as the earliest ancestor among the six proteins. Subcellular localisation predictions identified nuclear targeting for all proteins, with OsbHLH059 showing the highest nuclear localisation probability. Protein-protein interaction analysis highlighted potential partners, implying roles in diverse cellular pathways. This study provides valuable insights into the molecular characteristics, structure and interactions of rice bHLH proteins. These findings form a foundation for experimental validation and functional characterisation. Understanding these proteins may enable the development of innovative strategies to enhance abiotic stress tolerance and crop productivity in rice.

References

  1. 1. Mthiyane P, Aycan M, Mitsui T. Strategic advancements in rice cultivation: combating heat stress through genetic innovation and sustainable practices: A review. Stresses. 2024;4(3):452–80. https://doi.org/10.3390/biology13090659
  2. 2. Sun PW, Gao ZH, Lv FF, Yu CC, Jin Y, Xu YH, et al. Genome-wide analysis of basic helix–loop–helix (bHLH) transcription factors in Aquilaria sinensis. Sci Rep. 2022;12(1):7194. https://doi.org/10.1038/s41598-022-10785-w
  3. 3. Khan I, Asaf S, Jan R, Bilal S, Khan AL, Kim KM, et al. Genome-wide annotation and expression analysis of WRKY and bHLH transcription factor families reveal their involvement under cadmium stress in tomato (Solanum lycopersicum L.). Front Plant Sci. 2023; 14:1100895. https://doi.org/10.3389/fpls.2023.1100895
  4. 4. Berger N, Marin AJ, Stassen MJ, Lourenço T, Li M, Watanabe S, et al. Molecular regulation of iron homeostasis in plants. In: Lüttge U, Cánovas FM, Risueño Almeida MC, Leuschner C, Pretzsch H, editors. Progress in Botany Vol. 85. Cham: Springer; 2023. p. 75–103. https://doi.org/10.1007/124_2023_76
  5. 5. Xufeng X, Yuanfeng H, Ming Z, Shucheng S, Haonan Z, Weifeng Z, et al. Transcriptome profiling reveals the genes involved in tuberous root expansion in Pueraria montana var. thomsonii. BMC Plant Biol. 2023;23(1):1–19. https://doi.org/10.1186/s12870-023-04303-x
  6. 6. Zhang L, Xiang Z, Li J, Wang S, Chen Y, Liu Y, et al. bHLH57 confers chilling tolerance and grain yield improvement in rice. Plant Cell Environ. 2023;46(4):1402–18. https://doi.org/10.1111/pce.14513
  7. 7. Zou T, Wang X, Sun T, Rong H, Wu L, Deng J, et al. MYB transcription factor OsC1PLSr involves the regulation of purple leaf sheath in rice. Int J Mol Sci. 2023;24(7):6655. https://doi.org/10.3390/ijms24076655
  8. 8. Zuo ZF, Lee HY, Kang HG. Basic helix–loop–helix transcription factors: Regulators for plant growth, development and abiotic stress responses. Int J Mol Sci. 2023;24(2):1419. https://doi.org/10.3390/ijms24021419
  9. 9. Schmidt W, Thomine S, Buckhout TJ. Iron nutrition and interactions in plants. Front Plant Sci. 2020; 10:1670. https://doi.org/10.3389/fpls.2019.01670
  10. 10. Shekhawat PK, Ram H, Soni P. The regulatory circuit of iron homeostasis in rice: a tale of transcription factors. In: Srivastava V, Mishra S, Mehrotra S, Upadhyay SK, editors. Plant transcription factors. London: Academic Press; 2023. p. 251–68. https://doi.org/10.1016/B978-0-323-90613-5.00015-7
  11. 11. Zhang H, Li Y, Pu M, Xu P, Liang G, Yu D, et al. Oryza sativa positive regulator of iron deficiency response 2 (OsPRI2) and OsPRI3 are involved in the maintenance of Fe homeostasis. Plant Cell Environ. 2020;43(1):261–74. https://doi.org/10.1016/B978-0-323-90613-5.00015-7
  12. 12. Aycan M, Nahar L, Baslam M, Mitsui T. B-type response regulator hst1 controls salinity tolerance in rice by regulating transcription factors and antioxidant mechanisms. Plant Physiol Biochem. 2023; 196:542–55. https://doi.org/10.1016/j.plaphy.2023.02.008
  13. 13. Wang F, Itai RN, Nozoye T, Kobayashi T, Nishizawa NK, Nakanishi H. The bHLH protein OsIRO3 is critical for plant survival and iron homeostasis in rice (Oryza sativa L.) under Fe-deficient conditions. Soil Sci Plant Nutr. 2020;66(4):579–92. https://doi.org/10.1080/00380768.2020.1783966
  14. 14. Liu J, Shen Y, Cao H, He K, Chu Z, Li N. OsbHLH057 targets the AATCA cis-element to regulate disease resistance and drought tolerance in rice. Plant Cell Rep. 2022;41(5):1285–99. https://doi.org/10.1007/s00299-022-02859-w
  15. 15. Sakai H, Lee SS, Tanaka T, Numa H, Kim J, Kawahara Y, et al. Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics. Plant Cell Physiol. 2013;54(2): e6. https://doi.org/10.1093/pcp/pcs183
  16. 16. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. In: Walker JM, editor. The proteomics protocols handbook. Totowa (NJ): Humana Press; 2005.
  17. 17. Ikai A. Thermostability and aliphatic index of globular proteins. J Biochem. 1980;88(6):1895–98. https://doi.org/10.1093/oxfordjournals.jbchem.a133168
  18. 18. Gill SC, Von Hippel PH. Calculation of protein extinction coefficients from amino acid sequence data. Anal Biochem. 1989;182(2):319–26. https://doi.org/10.1016/0003-2697(89)90602-7
  19. 19. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982;157(1):105–32. https://doi.org/10.1016/0022-2836(82)90515-0
  20. 20. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics. 1995;11(6):681–84. https://doi.org/10.1093/bioinformatics/11.6.681
  21. 21. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics. 2008; 9:40. https://doi.org/10.1186/1471-2105-9-40
  22. 22. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2(9):1511–9. https://doi.org/10.1002/pro.5560020916
  23. 23. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The string database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011;39(Suppl_1): D561–8. https://doi.org/10.1093/nar/gkq973
  24. 24. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2012;41(D1): D808–15. https://doi.org/10.1093/nar/gks1094
  25. 25. Hirokawa T, Boon-Chieng S, Mitaku S. SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics. 1998;14(4):378–79. https://doi.org/10.1093/bioinformatics/14.4.378
  26. 26. Gardy JL, Laird MR, Chen F, Rey S, Walsh CJ, Ester M, et al. PSORTb v. 2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics. 2005;21(5):617–23. https://doi.org/10.1093/bioinformatics/bti057
  27. 27. Paysan-Lafosse T, Blum M, Chuguransky S, Grego T, Pinto BL, Salazar GA, et al. InterPro in 2022. Nucleic Acids Res. 2023;51(D1): D418–27. https://doi.org/10.1093/nar/gkac993
  28. 28. Tamura K, Stecher G, Kumar S. MEGA11: molecular evolutionary genetics analysis version 11. Mol Biol Evol. 2021;38(7):3022–7. https://doi.org/10.1093/molbev/msab120
  29. 29. Guan Y, Zhu Q, Huang D, Zhao S, Lo LJ, Peng J. An equation to estimate the difference between theoretically predicted and SDS PAGE-displayed molecular weights for an acidic peptide. Sci Rep. 2015; 5:13370. https://doi.org/10.1038/srep13370
  30. 30. Yang J, Zhang Y. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res. 2015;43: W174–81. https://doi.org/10.1093/nar/gkv342
  31. 31. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991; 253:164–70. DOI: 10.1126/science.1853201
  32. 32. Sohail R, Kalsoom S, Masood R, Tahir Y, Aftab AA, Muhammad I, et al. In silico analysis of four structural proteins of aphthovirus serotypes revealed significant B and T cell epitopes. Microb Pathog. 2019; 128:254–62. https://doi.org/10.1016/j.micpath.2019.01.007
  33. 33. Li J, Zhang M, Zhou L. Protein S-acyltransferases and acyl protein thioesterases, regulation executors of protein S-acylation in plants. Front Plant Sci. 2022; 13:956231. https://doi.org/10.3389/fpls.2022.956231
  34. 34. Zhang Y, Ge J, Huang B, Tong L, Shu Y, Qi C, et al. Genome-wide identification and expression analysis of the homeodomain–leucine zipper family in rice (Oryza sativa L.) under abiotic stress and in seed development. Preprint. 2022 Nov 30; Version. https://doi.org/10.21203/rs.3.rs-2324496/v1
  35. 35. Kobayashi T, Ogo Y, Nakanishi Itai R, Nakanishi H, Takahashi M, Mori S, et al. The novel transcription factor IDEF1 regulates iron-deficiency response and tolerance. Proc Int Plant Nutr Colloq XVI. 2009.
  36. 36. Liang G, Zhang H, Li Y, Pu M, Yang Y, Li C, et al. Fer-like fe deficiency-induced transcription factor (OsFIT) interacts with OsIRO2 to regulate iron homeostasis. bioRxiv. 2020;2020-03. https://doi.org/10.1101/2020.03.06.981126
  37. 37. Jang YH, Park JR, Kim EG, Kim KM. OsbHLHq11, the basic helix-loop-helix transcription factor, involved in regulation of chlorophyll content in rice. Biology. 2022;11(7):1000. https://doi.org/10.3390/biology11071000
  38. 38. Ebel C, BenFeki A, Hanin M, Solano R, Chini A. Characterization of wheat (Triticum aestivum) TIFY family and role of Triticum durum Td TIFY11a in salt stress tolerance. PLoS One. 2018;13(7): e0200566. https://doi.org/10.1371/journal.pone.0200566
  39. 39. Wang Y, Mostafa S, Zeng W, Jin B. Function and mechanism of jasmonic acid in plant responses to abiotic and biotic stresses. Int J Mol Sci. 2021;22(16):8568. https://doi.org/10.3390/ijms22168568
  40. 40. Hedin LE, Illergard K, Elofsson A. An introduction to membrane proteins. J Proteome Res. 2011;10(8):3324–31. https://doi.org/10.1021/pr200145a
  41. 41. Sakulsingharoj C, Inta P, Sukkasem R, Pongjaroenkit S, Chowpongpang S, Sangtong V. Cloning and characterization of OSB1 gene controlling anthocyanin biosynthesis from Thai black rice. Genomics Genet. 2016;9(1):7–18. https://doi.org/10.14456/gag.2016.2
  42. 42. Giri MK, Gautam JK, Babu Rajendra Prasad V, Chattopadhyay S, Nandi AK. Rice MYC2 (OsMYC2) modulates light-dependent seedling phenotype, disease defence but not ABA signalling. J Biosci. 2017; 42:501–08. https://doi.org/10.1007/s12038-017-9703-8
  43. 43. Ogo Y, Itai RN, Kobayashi T, Aung MS, Nakanishi H, Nishizawa NK. OsIRO2 is responsible for iron utilization in rice and improves growth and yield in calcareous soil. Plant Mol Biol. 2011; 75:593–605. https://doi.org/10.1007/s11103-011-9752-6
  44. 44. Wang YJ, Zhang ZG, He XJ, Zhou HL, Wen YX, Dai JX, et al. A rice transcription factor OsbHLH1 is involved in cold stress response. Theor Appl Genet. 2003; 107:1402–9. https://doi.org/10.1007/s00122-003-1378-x

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