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

Area of Science:

• Plant Biology
• Computational Biology
• Genomics

Background:

• Small signalling peptides (SSPs) are vital for plant cell communication, regulating development and stress responses.
• Identifying novel SSPs is difficult due to their small size, sequence variability, and transient expression.

Purpose of the Study:

• To develop a computational tool, S2-PepAnalyst, for efficient prediction and classification of plant SSPs.
• To overcome limitations in identifying and characterizing small signalling peptides in plant genomes.

Main Methods:

• Developed S2-PepAnalyst, a web tool using machine learning, protein language models, and geometric-topological features.
• Integrated plant-specific datasets and advanced computational approaches for SSP analysis.
• Validated the tool against experimentally confirmed peptides.

Main Results:

• S2-PepAnalyst achieved 99.5% predictive accuracy in identifying small signalling peptides.
• The tool successfully classified peptides into known families (e.g., CLE, RALF) and identified non-canonical SSPs.
• Demonstrated robust performance across diverse plant species, including model and crop plants.

Conclusions:

• S2-PepAnalyst is a powerful, freely available resource for exploring plant small signalling peptides.
• Facilitates discoveries in plant cell signalling and aids in crop improvement strategies.
• Empowers researchers to systematically investigate the largely untapped SSP repertoire.