{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:21:08Z","timestamp":1791519668142,"version":"4.3.4"},"reference-count":70,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T00:00:00Z","timestamp":1513296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T00:00:00Z","timestamp":1513296000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"},{"start":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T00:00:00Z","timestamp":1513296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100000943","name":"Commonwealth Scientific and Industrial Research Organisation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000943","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000943","name":"Commonwealth Scientific and Industrial Research Organisation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000943","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["wiley.com"],"crossmark-restriction":true},"short-container-title":["New Phytologist"],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>\n                    <jats:list list-type=\"bullet\">\n                      <jats:list-item>\n                        <jats:p>The plant apoplast is integral to intercellular signalling, transport and plant\u2013pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>\n                          We present\n                          <jats:sc>ApoplastP<\/jats:sc>\n                          , the first method for predicting whether an effector or plant protein localizes to the apoplast.\n                        <\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>\n                          <jats:sc>ApoplastP<\/jats:sc>\n                          uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids.\n                          <jats:sc>ApoplastP<\/jats:sc>\n                          predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine\u2010rich classifiers by &gt; 13%.\n                          <jats:sc>ApoplastP<\/jats:sc>\n                          does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors.\n                        <\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>\n                          <jats:sc>ApoplastP<\/jats:sc>\n                          pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells.\n                        <\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:p>","DOI":"10.1111\/nph.14946","type":"journal-article","created":{"date-parts":[[2017,12,16]],"date-time":"2017-12-16T16:32:04Z","timestamp":1513441924000},"page":"1764-1778","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":179,"title":["<scp>ApoplastP<\/scp>\n                    : prediction of effectors and plant proteins in the apoplast using machine learning"],"prefix":"10.1111","volume":"217","author":[{"given":"Jana","family":"Sperschneider","sequence":"first","affiliation":[{"name":"Centre for Environment and Life Sciences CSIRO Agriculture and Food Perth WA 6014 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter N.","family":"Dodds","sequence":"additional","affiliation":[{"name":"Black Mountain Laboratories CSIRO Agriculture and Food Canberra ACT 2601 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karam B.","family":"Singh","sequence":"additional","affiliation":[{"name":"Centre for Environment and Life Sciences CSIRO Agriculture and Food Perth WA 6014 Australia"},{"name":"Centre for Crop and Disease Management Department of Environment and Agriculture Curtin University Bentley WA 6102 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer M.","family":"Taylor","sequence":"additional","affiliation":[{"name":"Black Mountain Laboratories CSIRO Agriculture and Food Canberra ACT 2601 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,12,15]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1000261"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkp335"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/protein\/gzh037"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2004.05.028"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.0020050"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkq477"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-8137.2010.03362.x"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.14144"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2014.00249"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts635"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2812"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.12277"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2007.131"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.2000.3903"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti299"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1094\/MPMI-05-15-0097-R"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr367"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2229-8-129"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0031435"},{"key":"e_1_2_7_21_1","volume-title":"The WEKA Workbench. 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