{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:00:54Z","timestamp":1760148054415,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ARC Centre of Excellence in Plant Walls","award":["CE110100410","PID2020-115856RB-100"],"award-info":[{"award-number":["CE110100410","PID2020-115856RB-100"]}]},{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["CE110100410","PID2020-115856RB-100"],"award-info":[{"award-number":["CE110100410","PID2020-115856RB-100"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/plants12071428","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T05:19:24Z","timestamp":1679548764000},"page":"1428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LSPpred Suite: Tools for Leaderless Secretory Protein Prediction in Plants"],"prefix":"10.3390","volume":"12","author":[{"given":"Andrew","family":"Lonsdale","sequence":"first","affiliation":[{"name":"ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7223-1719","authenticated-orcid":false,"given":"Laura","family":"Ceballos-Laita","sequence":"additional","affiliation":[{"name":"Plant Stress Physiology Group, Plant Nutrition Department, Aula Dei Experimental Station, CSIC, P.O. Box 13034, 50080 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9503-614X","authenticated-orcid":false,"given":"Daisuke","family":"Takahashi","sequence":"additional","affiliation":[{"name":"United Graduate School of Agricultural Sciences, Iwate University, Morioka 020-8550, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0436-2976","authenticated-orcid":false,"given":"Matsuo","family":"Uemura","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Iwate University, Morioka 020-8550, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5470-5901","authenticated-orcid":false,"given":"Javier","family":"Abad\u00eda","sequence":"additional","affiliation":[{"name":"Plant Stress Physiology Group, Plant Nutrition Department, Aula Dei Experimental Station, CSIC, P.O. Box 13034, 50080 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melissa J.","family":"Davis","sequence":"additional","affiliation":[{"name":"Bioinformatics, Walter and Eliza Hall Institute for Medical Research, Melbourne, VIC 3052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antony","family":"Bacic","sequence":"additional","affiliation":[{"name":"ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-2725","authenticated-orcid":false,"given":"Monika S.","family":"Doblin","sequence":"additional","affiliation":[{"name":"ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1093\/bioinformatics\/btx431","article-title":"DeepLoc: Prediction of protein subcellular localization using deep learning","volume":"33","author":"Armenteros","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1093\/jxb\/erx286","article-title":"The endoplasmic reticulum is a hub to sort proteins toward unconventional traffic pathways and endosymbiotic organelles","volume":"69","author":"Bellucci","year":"2017","journal-title":"J. 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