LSPpred Suite: Tools for Leaderless Secretory Protein Prediction in Plants

Plant proteins that are secreted without a classical signal peptide leader sequence are termed leaderless secretory proteins (LSPs) and are implicated in both plant development and (a)biotic stress responses. In plant proteomics experimental workflows, identification of LSPs is hindered by the possibility of contamination from other subcellar compartments upon purification of the secretome. Applying machine learning algorithms to predict LSPs in plants is also challenging due to the rarity of experimentally validated examples for training purposes. This work attempts to address this issue by establishing criteria for identifying potential plant LSPs based on experimental observations and training random forest classifiers on the putative datasets. The resultant plant protein database LSPDB and bioinformatic prediction tools LSPpred and SPLpred are available at lsppred.lspdb.org. The LSPpred and SPLpred modules are internally validated on the training dataset, with false positives controlled at 5%, and are also able to classify the limited number of established plant LSPs (SPLpred (3/4, LSPpred 4/4). Until such time as a larger set of bona fide (independently experimentally validated) LSPs is established using imaging technologies (light/fluorescence/electron microscopy) to confirm sub-cellular location, these tools represent a bridging method for predicting and identifying plant putative LSPs for subsequent experimental validation.

Automated summary

Plant proteins that lack classical signal peptide leader sequences, known as leaderless secretory proteins (LSPs), play roles in plant development and stress responses. Identifying LSPs in plant proteomics experiments is challenging due to possible contamination during purification, and machine learning algorithms also lack sufficient validated examples. This study establishes criteria for identifying potential plant LSPs based on experimental observations and trains random forest classifiers to predict LSPs. The resulting database LSPDB and bioinformatic prediction tools LSPpred and SPLpred provide a method for predicting and identifying plant putative LSPs until more validated LSPs are established.