Journals / Turkish Journal of Biology / 2016 / Cilt: 40 - Sayı: 1

Order-wide in silico comparative analysis and identification ofgrowth-regulating factor proteins in Malpighiales

Pages
26–42
Publication Date
2022-10-17
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed that QLQ, WRC, TQL, GGPL, RSRKPVE, FDV, and GFG domain motifs are conserved in rates between 34% and 82%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Malpighiales, containing approximately 16,000 species, is one of the largest and most diverse angiosperm orders in plants.
📊
Key Findings & Data:In the present study we conducted domain, two- and three-dimensional (2D, 3D) comparative, physicochemical, subcellular prediction, and sequence analysis of 87 putative GRF proteins belonging to 4 genera in Malpighiales using different bioinformatic tools.
💡
Conclusion & Impact:The results showed that QLQ, WRC, TQL, GGPL, RSRKPVE, FDV, and GFG domain motifs are conserved in rates between 34% and 82%.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Malpighiales, containing approximately 16,000 species, is one of the largest and most diverse angiosperm orders in plants. Growth-regulating factor (GRF) protein is a putative transcription factor and plays a regulatory role during plant growth and development processes such as stem elongation and cell expansion. The latest available protein data provide an opportunity to compare and understand the critical similarities and differentiation in GRF proteins among Malpighiales members. In the present study we conducted domain, two- and three-dimensional (2D, 3D) comparative, physicochemical, subcellular prediction, and sequence analysis of 87 putative GRF proteins belonging to 4 genera in Malpighiales using different bioinformatic tools. The results showed that QLQ, WRC, TQL, GGPL, RSRKPVE, FDV, and GFG domain motifs are conserved in rates between 34% and 82%. Comparative analysis proved that there is a common triangle-like shape among GRF proteins, and the conserved motifs are localized in similar regions of this triangle. The sequence lengths of GRFs change between 93 and 639 aa, and pH values differ between 5.28 and 10.50. According to subcellular prediction and domain analysis, only 9%-11% of GRFs are localized out of the nuclear region. Additionally, it was determined that 87 GRF proteins have monophyletic phylogeny. The average values of pairwise distance and nucleotide diversity (?) were 2.78 and 0.727, respectively. These results may contribute to an understanding of how structurally similar but functionally diverse GRF proteins execute various functions in different plant parts or in different plant species.

🔖

Cite This Article

AVCI, M. K., UYSAL, H., YAMANER, Ç., ÖRENAY, S. B., SEVİNDİK, E., AYVAZ, M. (2022). Order-wide in silico comparative analysis and identification ofgrowth-regulating factor proteins in Malpighiales. Turkish Journal of Biology, Cilt: 40 - Sayı: 1, 26–42. https://ajindex.com/articles/600445

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Adeloye AO, Ajibade PA (2011). A high molar extinction coefficient mono anthracenyl bipyridyl heteroleptic ruthenium(II) complex: synthesis, photophysical and electrochemical properties. Molecules 16: 4615–4631.
  2. Avci MK, Yamaner C, Ayvaz M, Yazgan-Karatas A (2014). In silico characterization and comparative analysis of Bacillus subtilis GntR type LutR transcription factor. Eurasia J Biosci 8: 12–28.
  3. Baloglu MC (2014). Genome-wide in silico identification and comparison of growth regulating factor (GRF) genes in Cucurbitaceae family. P Omic J 7: 260–270.
  4. Baum D (2008). Reading a phylogenetic tree: the meaning of monophyletic groups. Nature Educ 1: 190. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000). The Protein Data Bank. Nucleic Acids Res 28: 235–242.
  5. Best R, Henry G (1992). Report of the first meeting of the international network for cassava genetic resources. In: Roca WM, Thro AM, editors. Cassava Genetic Resources. Cali, CO, USA: Cent Int Agric Trop, pp. 3–11.
  6. Buchan DWA, Minneci F, Nugent TCO, Bryson K, Jones DT (2013). Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res 41: W340–W348. Buenavista MT, Roche DB, McGuffin LJ (2012). Improvement of 3D protein models using multiple templates guided by single template model quality assessment. Bioinformatics 28: 1851– 1857. Chase MW, Soltis DE, Olmstead RG, Morgan D, Les DH, Mishler BD, Duvall MR, Price RA, Hills HG, Qiu YL et al. (1993). Phylogenetics of seed plants-an analysis of nucleotide sequences from the plastid gene rbcL. Ann Missouri Bot Gard 80: 528–580.
  7. Choi D, Kim JH, Kende H (2004). Whole genome analysis of the OsGRF gene family encoding plant-specific putative transcription activators in rice (Oryza sativa L.). Plant Cell Physiol 45: 897–904.
  8. Cock JH (1982). Cassava—a basic energy-source in the tropics. Science 218: 755–762.
  9. Cock JH (1985). Cassava: New Potential for a Neglected Crop. London, UK: Westfield Press.
  10. Cowling RJ, Harberd NP (1999). Gibberellins control Arabidopsis hypocotyl growth via regulation of cellular elongation. J Exp Bot 50: 1351–1357.
  11. Cronk QC (2005). Plant eco-devo: the potential of poplar as a model organism. New Phytol 166: 39–48.
  12. Cronquist A (1981). An Integrated System of Classification of Flowering Plants. New York, NY, USA: Columbia University Press.
  13. Crooks GE, Hon G, Chandonia JM, Brenner SE (2004). WebLogo: a sequence logo generator. Genome Res 14: 1188–1190.
  14. Davis CC, Webb CO, Wurdac