Plants are constantly exposed to environmental stresses and in part due to their sessile nature, they have evolved signal perception and adaptive strategies that are distinct from those of other eukaryotes. This is reflected at the cellular level where receptors and signalling molecules cannot be identified using standard homology-based searches querying with proteins from prokaryotes and other eukaryotes. One of the reasons for this is the complex domain architecture of receptor molecules. In order to discover hidden plant signalling molecules, we have developed a motif-based approach designed specifically for the identification of functional centers in plant molecules. This has made possible the discovery of novel components involved in signalling and stimulus-response pathways; the molecules include cyclic nucleotide cyclases, a nitric oxide sensor and a novel target for the hormone abscisic acid. Here, we describe the major steps of the method and illustrate it with recent and experimentally confirmed molecules as examples. We foresee that carefully curated search motifs supported by structural and bioinformatic assessments will uncover many more structural and functional aspects, particularly of signalling molecules.
|Original language||English (US)|
|Number of pages||7|
|Journal||Computational and Structural Biotechnology Journal|
|State||Published - 2018|
Bibliographical noteFunding Information:
This work was funded by the Office of Research and Sponsored Programs of Wenzhou-Kean University . C.M. received funding from the European Union's Horizon 2020 research and innovation programme (H2020-MSCA-IF-2016, grant agreement no 752418 ).
This work was funded by the Office of Research and Sponsored Programs of Wenzhou-Kean University. C.M. received funding from the European Union's Horizon 2020 research and innovation programme (H2020-MSCA-IF-2016, grant agreement no 752418).
© 2017 The Authors
- Functional centers
- Hidden domains
- Molecular docking
- Search motif
- Structural modeling
ASJC Scopus subject areas
- Structural Biology
- Computer Science Applications