Back

Structural Insights into Competitive Binding Dynamics between RALF23/33 and PCP-B in Brassicaceae Pollination

Bhalla, H.; Sudarsanam, K.; Srivastava, A.; Sankaranarayanan, S.

2024-12-03 plant biology
10.1101/2024.12.03.626569 bioRxiv
Show abstract

Ensuring successful fertilization, viable offspring production, genetic isolation, and maintaining species integrity is pivotal for the survival of flowering plants. Members of Brassicaceae employ a "gatekeeping mechanism" involving interaction between stigmatic membrane-bound Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) receptor, FERONIA, GPI anchored protein LLG2 (LORELEI-LIKE GLYCOPHOSPHATIDYLINOSITOL-ANCHORED PROTEIN 2) and autocrine secreted RALF23/33 (Rapid alkalinization factor) peptide. This binding establishes a barrier for pollen hydration by inducing ROS (Reactive Oxygen Species). Conversely, in the presence of compatible pollen, paracrine-secreted cysteine-rich peptides such as PCP-B{gamma} compete with RALF23/33 for binding to the FERONIA-LLG2 complex, thus reducing ROS levels, ensuring successful pollen hydration and germination. Despite its crucial role, the structural basis of this competitive binding dynamics remains elusive owing to the lack of structural data and the inherent flexibility of these peptides. Using structural modeling, molecular docking, and simulations, this study reveals that PCP-B{gamma} binds to the same negatively charged pocket in the FERONIA-LLG2 complex as RALF23, displacing and interrupting the heterodimerized structure, thus reducing ROS levels to promote pollination. Our study unveils the experimental data-based predicted models, competitive binding dynamics, and mechanism behind this "gatekeeping mechanism," shedding light on the molecular mechanism underlying this pollen hydration barrier in Brassicaceae.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.