In search of function for hypothetical proteins encoded by genes of SA-JA pathways in Oryza sativa by in silico comparison and structural modeling

  • Singh I
  • Agrawal P
  • Shah K
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Abstract

Knowledge of rice genome brings new dimensions to the management of abiotic stresses; however, gene sequences in the rice genome are yet to be assigned structure and function. Hydrogen peroxide, salicylates and jasmonates act as signal molecules in plants employing common machinery to manage abiotic stress. The present work is primarily focused to assign a structurefunction relationship by modeling of the hypothetical proteins of SA-JA signaling pathway known in Arabidopsis thaliana and compare them with corresponding proteins in rice in silico. Thirteen known gene sequences with their encoded proteins for SA/JA pathway in model plant A. thaliana were obtained and similar gene sequences from rice were retrieved at NCBI. Five rice gene sequences Os09g0392100, Os03g0233200, OsJ_33269, OsJ_23610 and Os01g0194300 resulted in hypothetical protein products with unknown structure and function. Modeling and comparison of 5 proteins from rice and Arabidopsis showed 73 - 98% identity with acceptable RMSD values of 0.6 - 1.7 upon superimposition. Results suggest conserved nature of these proteins during evolution. The hypothetical protein from rice contains similar functional protein domain as that in A. thaliana and therefore are likely to perform similar functions in rice. There is a cross talk between the genes in SA/JA pathway wherein Os09g0392100 or EDS1, Os03g0233200 or PR5, OsJ_33269 or PAD4 and OsJ_23610 or SFD-1 activates the pathway and Os01g0194300 or NPR1 inhibit the pathway. Further investigation through wet-lab experiments are in progress to look into suppression/activation of the genes of SAJA signaling in rice plants exposed to abiotic stress.

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Singh, I., Agrawal, P., & Shah, K. (2012). In search of function for hypothetical proteins encoded by genes of SA-JA pathways in Oryza sativa by in silico comparison and structural modeling. Bioinformation, 8(1), 1–5. https://doi.org/10.6026/97320630008001

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