{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T03:19:37Z","timestamp":1780629577078,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["22A520013"],"award-info":[{"award-number":["22A520013"]}]},{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["23B520004"],"award-info":[{"award-number":["23B520004"]}]},{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["232102210020"],"award-info":[{"award-number":["232102210020"]}]},{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["202102210144"],"award-info":[{"award-number":["202102210144"]}]},{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["2019GGJS132"],"award-info":[{"award-number":["2019GGJS132"]}]},{"name":"Key Science and Technology Development Program of Henan Province","award":["22A520013"],"award-info":[{"award-number":["22A520013"]}]},{"name":"Key Science and Technology Development Program of Henan Province","award":["23B520004"],"award-info":[{"award-number":["23B520004"]}]},{"name":"Key Science and Technology Development Program of Henan Province","award":["232102210020"],"award-info":[{"award-number":["232102210020"]}]},{"name":"Key Science and Technology Development Program of Henan Province","award":["202102210144"],"award-info":[{"award-number":["202102210144"]}]},{"name":"Key Science and Technology Development Program of Henan Province","award":["2019GGJS132"],"award-info":[{"award-number":["2019GGJS132"]}]},{"name":"Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province","award":["22A520013"],"award-info":[{"award-number":["22A520013"]}]},{"name":"Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province","award":["23B520004"],"award-info":[{"award-number":["23B520004"]}]},{"name":"Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province","award":["232102210020"],"award-info":[{"award-number":["232102210020"]}]},{"name":"Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province","award":["202102210144"],"award-info":[{"award-number":["202102210144"]}]},{"name":"Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province","award":["2019GGJS132"],"award-info":[{"award-number":["2019GGJS132"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models\u2014ESM-2, ProtBert, and UniRep\u2014to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field.<\/jats:p>","DOI":"10.3390\/sym16040464","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T03:29:04Z","timestamp":1712806144000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["UniproLcad: Accurate Identification of Antimicrobial Peptide by Fusing Multiple Pre-Trained Protein Language Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3113-5149","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"Henan Provincial Key Laboratory of Data Intelligence for Food Safety, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7817-6786","authenticated-orcid":false,"given":"Xu","family":"Gao","sequence":"additional","affiliation":[{"name":"National Supercomputing Center in Zhengzhou, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis","volume":"399","author":"Murray","year":"2022","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1111\/jam.15314","article-title":"Antimicrobial peptides (AMPs): A promising class of antimicrobial compounds","volume":"132","author":"Kesmen","year":"2022","journal-title":"J. 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