{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T06:51:29Z","timestamp":1776927089526,"version":"3.51.2"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010511","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000}}],"reference-count":50,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFA0908700"],"award-info":[{"award-number":["2020YFA0908700"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072003, 11835014, U19A2064"],"award-info":[{"award-number":["62072003, 11835014, U19A2064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Academic and Technology Leaders and Backup Candidate of Anhui Province","award":["2020H237"],"award-info":[{"award-number":["2020H237"]}]},{"DOI":"10.13039\/501100018628","name":"Scientific Research Foundation of Education Department of Anhui Province of China","doi-asserted-by":"publisher","award":["KJ2020A0047"],"award-info":[{"award-number":["KJ2020A0047"]}],"id":[{"id":"10.13039\/501100018628","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Prediction of therapeutic peptide is a significant step for the discovery of promising therapeutic drugs. Most of the existing studies have focused on the mono-functional therapeutic peptide prediction. However, the number of multi-functional therapeutic peptides (MFTP) is growing rapidly, which requires new computational schemes to be proposed to facilitate MFTP discovery. In this study, based on multi-head self-attention mechanism and class weight optimization algorithm, we propose a novel model called PrMFTP for MFTP prediction. PrMFTP exploits multi-scale convolutional neural network, bi-directional long short-term memory, and multi-head self-attention mechanisms to fully extract and learn informative features of peptide sequence to predict MFTP. In addition, we design a class weight optimization scheme to address the problem of label imbalanced data. Comprehensive evaluation demonstrate that PrMFTP is superior to other state-of-the-art computational methods for predicting MFTP. We provide a user-friendly web server of PrMFTP, which is available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/bioinfo.ahu.edu.cn\/PrMFTP%20\" xlink:type=\"simple\">http:\/\/bioinfo.ahu.edu.cn\/PrMFTP<\/jats:ext-link>.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010511","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T17:40:30Z","timestamp":1663004430000},"page":"e1010511","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":47,"title":["PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization"],"prefix":"10.1371","volume":"18","author":[{"given":"Wenhui","family":"Yan","sequence":"first","affiliation":[]},{"given":"Wending","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Lihua","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-5930","authenticated-orcid":true,"given":"Yannan","family":"Bin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-1705","authenticated-orcid":true,"given":"Junfeng","family":"Xia","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"pcbi.1010511.ref001","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/s41573-020-00135-8","article-title":"Trends in peptide drug discovery","volume":"20","author":"Muttenthaler Markus","year":"2021","journal-title":"Nature Reviews Drug 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