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The majority of previous studies have focused on mono-functional peptides, but an increasing number of multi-functional peptides have been discovered. Although there have been enormous experimental efforts to assay multi-functional peptides, only a small portion of millions of known peptides has been explored. The development of effective and accurate techniques for identifying multi-functional peptides can facilitate their discovery and mechanistic understanding. In this study, we presented iMFP-LG, a method for multi-functional peptide identification based on protein language models (pLMs) and graph attention networks (GATs). Our comparative analyses demonstrated that iMFP-LG outperformed the state-of-the-art methods in identifying both multi-functional bioactive peptides and multi-functional therapeutic peptides. The interpretability of iMFP-LG was also illustrated by visualizing attention patterns in pLMs and GATs. Regarding the outstanding performance of iMFP-LG on the identification of multi-functional peptides, we employed iMFP-LG to screen novel peptides with both anti-microbial and anti-cancer functions from millions of known peptides in the UniRef90 database. As a result, eight candidate peptides were identified, among which one candidate was validated to process both anti-bacterial and anti-cancer properties through molecular structure alignment and biological experiments. We anticipate that iMFP-LG can assist in the discovery of multi-functional peptides and contribute to the advancement of peptide drug design.<\/jats:p>","DOI":"10.1093\/gpbjnl\/qzae084","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T21:38:03Z","timestamp":1732570683000},"source":"Crossref","is-referenced-by-count":11,"title":["iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9965-296X","authenticated-orcid":false,"given":"Jiawei","family":"Luo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen 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518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4461-1378","authenticated-orcid":false,"given":"Fuchuan","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0888-6575","authenticated-orcid":false,"given":"Yumeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University , Shenzhen 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-3309","authenticated-orcid":false,"given":"Xiaopeng","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University , Shenzhen 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