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Here, we introduce Plasmodium Function Predictor (PlasmoFP), deep learning models designed specifically for species of genus\n                  <jats:italic>Plasmodium<\/jats:italic>\n                  . Innovatively, PlasmoFP models are trained on structure-function relationships of the phylogenetically relevant SAR (Stramenopiles, Alveolate, and Rhizarians) supergroup proteins, addressing challenges to annotating\n                  <jats:italic>Plasmodium<\/jats:italic>\n                  proteins due to their low sequence similarity to well-characterized model organism proteins. PlasmoFP models estimate epistemic uncertainty, control false discovery rates in model predictions, and are validated using proteins with manually curated GO terms and experimentally characterized proteins. Integrating PlasmoFP predictions with current protein annotations, we reduced the proportion of unannotated proteins without Gene Ontology terms from 15-59% to 3-28% across 19\n                  <jats:italic>Plasmodium<\/jats:italic>\n                  species, and improved the proportion of fully annotated proteins from 7-42% to 36-68%. PlasmoFP predictions advance\n                  <jats:italic>Plasmodium<\/jats:italic>\n                  basic research, an important component of global malaria R&amp;D.\n                <\/jats:p>","DOI":"10.1101\/2025.09.12.675843","type":"posted-content","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T19:35:17Z","timestamp":1758137717000},"source":"Crossref","is-referenced-by-count":1,"title":["PlasmoFP: leveraging deep learning to predict protein function of uncharacterized proteins across the malaria parasite genus"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7295-6613","authenticated-orcid":false,"given":"Harsh R.","family":"Srivastava","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Berenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Qassab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4354-7906","authenticated-orcid":false,"given":"Richard","family":"Bonneau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9340-5020","authenticated-orcid":false,"given":"Jane M.","family":"Carlton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025102210151939000_2025.09.12.675843v2.1","unstructured":"World Health Organization, World Malaria Report 2024. 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You , Z. Zhang , S. Dai , S. Yao , W. Tian , R. Cao , C. Chandler , M. Amezola , D. Johnson , J. M. Chang , W. H. Liao , Y. W. Liu , S. Pascarelli , Y. Frank , R. Hoehndorf , M. Kulmanov , I. Boudellioua , G. Politano , S. Di Carlo , A. Benso , K. Hakala , F. Ginter , F. Mehryary , S. Kaewphan , J. Bjorne , H. Moen , M. E. E. Tolvanen , T. Salakoski , D. Kihara , A. Jain , T. Smuc , A. Altenhoff , A. Ben-Hur , B. Rost , S. E. Brenner , C. A. Orengo , C. J. Jeffery , G. Bosco , D. A. Hogan , M. J. Martin , C. O\u2019Donovan , S. D. Mooney , C. S. Greene , P. Radivojac , I. Friedberg , The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. 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