Abstract
ABSTRACT Small signalling peptides (SSPs) serve as crucial mediators of cell‐to‐cell communication in plants, orchestrating diverse physiological processes from development to stress responses. While recent advances in sequencing technologies have improved genome annotation, the identification of novel SSPs remains challenging due to their small size, sequence diversity, and often transient expression patterns. To address this bottleneck, we developed S 2 ‐PepAnalyst, a machine learning‐powered web tool that integrates plant‐specific datasets with advanced computational approaches for SSP prediction and classification. Our platform combines protein language models with geometric‐topological feature analysis to capture both sequence and structural characteristics of known SSP families. When validated against experimentally confirmed peptides, S 2 ‐PepAnalyst achieved high predictive accuracy (99.5%) while maintaining low false‐negative rates. The tool successfully classified peptides into functionally distinct families (e.g., CLE, RALF) and identified non‐canonical SSPs that lack traditional signal peptides. Importantly, S 2 ‐PepAnalyst demonstrated robust performance across both model plants and agriculturally important species. As a freely available resource ( https://www.s2‐pepanalyst.uma.es ), this tool will empower plant biologists to systematically explore the largely untapped repertoire of plant SSPs, facilitating discoveries in plant cell signalling and potential applications in crop improvement.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc-nd. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
The SSP prediction tool achieved high predictive accuracy of 99.5% when validated against experimentally confirmed peptides, while maintaining low false-negative rates.
Verbatim
e0“When validated against experimentally confirmed peptides”
e1“achieved high predictive accuracy (99.5%)”
Claim · model-written summary
The tool successfully classified peptides into functionally distinct families such as CLE and RALF.
- Outcome
- classified peptides into functionally distinct familiese0
Not reportedValue
Verbatim
e0“The tool successfully classified peptides into functionally distinct families (e.g., CLE, RALF)”
Claim · model-written summary
The tool identified non-canonical SSPs that lack traditional signal peptides.
- Outcome
- SSPs that lack traditional signal peptidese0
Not reportedValue
Verbatim
e0“SSPs that lack traditional signal peptides”
Claim · model-written summary
The tool performed robustly across both model plants and agriculturally important species.
Not reportedValue
Verbatim
e0“demonstrated robust performance across both model plants and agriculturally important species”
Claim · model-written summary
The tool is provided as a freely available web resource.
- Outcome
- freely available resourcee0
Not reportedValue
Verbatim
e0“As a freely available resource”