The N-homologue LRR domain adopts a folding which explains the TMV-Cg-induced HR-like response in sensitive tobacco plants.

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作者:

Stange CMatus JTDomínguez CPerez-Acle TArce-Johnson P

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摘要:

Following leaf infection with the tobacco mosaic virus (TMV), Nicotiana species that carry the disease resistance N gene develop a hypersensitive response (HR) that blocks the systemic movement of the virus. TMV-sensitive tobacco plants that lack the N gene develop classical disease symptoms following infection with most of the tobamoviruses. However, upon infection with TMV-Cg, these plants display a HR-like response that is unable to limit viral spread. We previously identified the NH gene in sensitive plants; this gene is homologous to the resistance N gene and both belong to the TIR/NBS/LRR family. Isolation and analysis of the NH transcript enabled the prediction of the amino acid sequence in which we detected a leucine-rich repeat domain, proposed to be involved in pathogen recognition. This domain is found in four of five classes of pathogen resistant proteins, in which sequence and structural changes may generate different specificities. In order to study the possible functional role of the LRR domain in the HR-like response, we developed a comparative three-dimensional model for the NH and N gene products, by means of functional and structural domains recognition, secondary structure prediction, domain assignment through profile Hidden Markov Models (HMM) and molecular dynamics (MD) simulations. Based on our results we postulate that the NH protein could adopt a LRR fold with a functional role in the HR-like response. Our two reliable LRR three-dimensional models (N-LRR, NH-LRR) can be used as structural frameworks for future experiments in which the structure-function relationships regarding the protein-protein interaction process may be revealed. Evolutionary aspects of the N and NH genes in Nicotiana species are also discussed.

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DOI:

10.1016/j.jmgm.2007.05.006

被引量:

0

年份:

1970

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来源期刊

JOURNAL OF MOLECULAR GRAPHICS & MODELLING

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