Unveiling orphan receptor-like kinases in plants: novel client discovery using high-confidence library predictions in the Kinase–Client (KiC) assay - Jorge, Gabriel Lemes; Kim, Daewon; Xu, Chunhui; Cho, Sung-Hwan; Su, Lingtao; Xu, Dong; Bartley, Laura E.; Stacey, Gary; Thelen, Jay J. (Frontiers in Plant Science, 2024)
Plants are remarkable in their ability to adapt to changing environments, with receptorlike kinases (RLKs) playing a pivotal role in perceiving and transmitting environmental cues into cellular responses. Despite extensive research on RLKs from the plant kingdom, the function and activity of many kinases, i.e., their substrates or "clients", remain uncharted. To validate a novel client prediction workflow and learn more about an important RLK, this study focuses on P2K1 (DORN1), which acts as a key receptor for extracellular ATP (eATP), playing a crucial role in plant stress resistance and immunity.We designed a Kinase-Client (KiC) assay library of 225 synthetic peptides, incorporating previously identified P2K phosphorylated peptides and novel predictions from a deeplearning phosphorylation site prediction model (MUsite) and a trained hidden Markov model (HMM) based tool, HMMER. Screening the library against purified P2K1-CD, we identified 46 putative substrates, including 34 novel clients, 27 of which may be novel peptides, not previously identified experimentally. Gene Ontology (GO) analysis among phosphopeptide candidates revealed proteins associated with important biological processes in metabolism, structure development, and response to stress, as well as molecular functions of kinase activity, catalytic activity, and transferase activity. We offer selection criteria for efficient further in vivo experiments to confirm these discoveries. This approach not only expands our knowledge of P2K1's substrates and functions but also highlights effective prediction algorithms for identifying additional potential substrates.Overall, the results support use of the KiC assay as a valuable tool in unraveling the complexities of plant phosphorylation and provide a foundation for predicting the phosphorylation landscape of plant species based on peptide library results.