S2-PepAnalyst: A Web Tool for Predicting Plant Small Signalling Peptides

Abstract

Small signalling peptides mediate cell-to-cell communication playing essential roles in plant growth, development, and stress responses. They specifically bind to the extracellular domain of receptors to trigger biochemical and physiological responses. Despite their significance, accurately identifying novel signalling peptides remains challenging due to their structural diversity, low abundance, and highly specific expression patterns. Here, we present S{superscript 2}-PepAnalyst, a web tool integrating plant-specific datasets and machine learning to predict SSPs with 99.5% accuracy and low false-negative rates. S{superscript 2}-PepAnalyst outperforms existing tools (e.g., SignalP 6.0) by combining protein language models, geometric-topological analysis, and reinforcement learning, enabling robust classification of small signalling peptide families (e.g., CLE, RALF). The tool is freely available at https://www.s2-pepanalyst.uma.es.