{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T19:37:52Z","timestamp":1784921872147,"version":"3.55.0"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22373051"],"award-info":[{"award-number":["22373051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22374082"],"award-info":[{"award-number":["22374082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Haihe Laboratory of Sustainable Chemical Transformations","award":["LHGG202303"],"award-info":[{"award-number":["LHGG202303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The emergence of drug-resistant pathogens represents a formidable challenge to global health. Using computational methods to identify the antibacterial peptides (ABPs), an alternative antimicrobial agent, has demonstrated advantages in further drug design studies. Most of the current approaches, however, rely on handcrafted features and underutilize structural information, which may affect prediction performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To present an ultra-accurate model for ABP identification, we propose a novel deep learning approach, PGAT-ABPp. PGAT-ABPp leverages structures predicted by AlphaFold2 and a pretrained protein language model, ProtT5-XL-U50 (ProtT5), to construct graphs. Then the graph attention network (GAT) is adopted to learn global discriminative features from the graphs. PGAT-ABPp outperforms the other fourteen state-of-the-art models in terms of accuracy, F1-score and Matthews Correlation Coefficient on the independent test dataset. The results show that ProtT5 has significant advantages in the identification of ABPs and the introduction of spatial information further improves the prediction performance of the model. The interpretability analysis of key residues in known active ABPs further underscores the superiority of PGAT-ABPp.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The datasets and source codes for the PGAT-ABPp model are available at https:\/\/github.com\/moonseter\/PGAT-ABPp\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae497","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T20:59:03Z","timestamp":1723237143000},"source":"Crossref","is-referenced-by-count":27,"title":["PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5703-0800","authenticated-orcid":false,"given":"Yuelei","family":"Hao","sequence":"first","affiliation":[{"name":"Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University , Tianjin 300071, China"},{"name":"Haihe Laboratory of Sustainable Chemical Transformations , Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University , Tianjin 300071, China"},{"name":"Haihe Laboratory of Sustainable Chemical Transformations , Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haohao","family":"Fu","sequence":"additional","affiliation":[{"name":"Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University , Tianjin 300071, China"},{"name":"Haihe Laboratory of Sustainable Chemical Transformations , Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueguang","family":"Shao","sequence":"additional","affiliation":[{"name":"Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University , Tianjin 300071, China"},{"name":"Haihe Laboratory of Sustainable Chemical Transformations , Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6457-7058","authenticated-orcid":false,"given":"Wensheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University , Tianjin 300071, China"},{"name":"Haihe Laboratory of Sustainable Chemical Transformations , Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"key":"2024082201242633000_btae497-B1","doi-asserted-by":"crossref","first-page":"bbaa153","DOI":"10.1093\/bib\/bbaa153","article-title":"AntiCP 2.0: an updated model for predicting anticancer peptides","volume":"22","author":"Agrawal","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024082201242633000_btae497-B2","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1021\/acs.jcim.3c00958","article-title":"Explainable graph neural networks with data augmentation for predicting pKa of C-H acids","volume":"64","author":"An","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2024082201242633000_btae497-B3","doi-asserted-by":"crossref","first-page":"5480","DOI":"10.1021\/acs.jcim.4c00449","article-title":"AttenGpKa: a universal predictor of solvation acidity using graph neural network and molecular topology","volume":"64","author":"An","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2024082201242633000_btae497-B4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.drup.2016.04.002","article-title":"Mechanisms and consequences of bacterial resistance to antimicrobial peptides","volume":"26","author":"Andersson","year":"2016","journal-title":"Drug Resist Updat"},{"key":"2024082201242633000_btae497-B5","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.cosrev.2021.100378","article-title":"Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, Ica, t-SNE)","volume":"40","author":"Anowar","year":"2021","journal-title":"Comput Sci Rev"},{"key":"2024082201242633000_btae497-B6","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1002\/bip.21499","article-title":"The role of disulfide bonds and N-terminus in the structural properties of hepcidins: insights from molecular dynamics simulations","volume":"93","author":"Aschi","year":"2010","journal-title":"Biopolymers"},{"key":"2024082201242633000_btae497-B7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2018.11.008","article-title":"Ensembles for feature selection: a review and future trends","volume":"52","author":"Bol\u00f3n-Canedo","year":"2019","journal-title":"Inf Fusion"},{"key":"2024082201242633000_btae497-B8","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1021\/acs.jcim.1c01518","article-title":"Mechanisms of binding of antimicrobial peptide PGLa to DMPC\/DMPG membrane","volume":"62","author":"Bowers","year":"2022","journal-title":"J Chem Inf Model"},{"key":"2024082201242633000_btae497-B9","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1038\/nrmicro1098","article-title":"Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria?","volume":"3","author":"Brogden","year":"2005","journal-title":"Nat Rev Microbiol"},{"key":"2024082201242633000_btae497-B10","doi-asserted-by":"crossref","first-page":"bbad058","DOI":"10.1093\/bib\/bbad058","article-title":"Designing antimicrobial peptides using deep learning and molecular dynamic simulations","volume":"24","author":"Cao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024082201242633000_btae497-B11","author":"Chen","year":"2024"},{"key":"2024082201242633000_btae497-B12","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1016\/j.tetlet.2018.07.045","article-title":"The natural and synthetic indole weaponry against bacteria","volume":"59","author":"Ciulla","year":"2018","journal-title":"Tetrahedron Lett"},{"key":"2024082201242633000_btae497-B13","doi-asserted-by":"crossref","first-page":"bbad135","DOI":"10.1093\/bib\/bbad135","article-title":"UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity","volume":"24","author":"Du","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024082201242633000_btae497-B14","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing","volume":"44","author":"Elnaggar","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2024082201242633000_btae497-B15","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1038\/s42256-023-00721-6","article-title":"A method for multiple-sequence-alignment-free protein structure prediction using a protein language model","volume":"5","author":"Fang","year":"2023","journal-title":"Nat Mach Intell"},{"key":"2024082201242633000_btae497-B16","doi-asserted-by":"crossref","first-page":"bbac606","DOI":"10.1093\/bib\/bbac606","article-title":"AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning","volume":"24","author":"Fang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024082201242633000_btae497-B17","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1039\/D1CS90109E","article-title":"The multifaceted nature of antimicrobial peptides: current synthetic chemistry approaches and future directions","volume":"51","author":"Gan","year":"2021","journal-title":"Chem Soc Rev"},{"key":"2024082201242633000_btae497-B18","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.jmr.2004.02.008","article-title":"Orientation of the antimicrobial peptide PGLa in lipid membranes determined from 19F-NMR dipolar couplings of 4-CF3-phenylglycine labels","volume":"168","author":"Glaser","year":"2004","journal-title":"J Magn Reson"},{"key":"2024082201242633000_btae497-B19","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1038\/s41467-021-23303-9","article-title":"Structure-based protein function prediction using graph convolutional networks","volume":"12","author":"Gligorijevi\u0107","year":"2021","journal-title":"Nat Commun"},{"key":"2024082201242633000_btae497-B20","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1038\/s41579-021-00585-w","article-title":"Antibiofilm activity of host defence peptides: complexity provides opportunities","volume":"19","author":"Hancock","year":"2021","journal-title":"Nat Rev Microbiol"},{"key":"2024082201242633000_btae497-B21","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1021\/acs.langmuir.7b04219","article-title":"Mechanism of initial stage of pore formation induced by antimicrobial peptide Magainin 2","volume":"34","author":"Hasan","year":"2018","journal-title":"Langmuir"},{"key":"2024082201242633000_btae497-B22","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1038\/s41467-019-08853-3","article-title":"Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage","volume":"10","author":"Hendriksen","year":"2019","journal-title":"Nat Commun"},{"key":"2024082201242633000_btae497-B23","author":"Hu","year":"2022"},{"key":"2024082201242633000_btae497-B24","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2024082201242633000_btae497-B25","doi-asserted-by":"crossref","first-page":"4691","DOI":"10.1021\/acs.jcim.0c00841","article-title":"IAMPE: NMR-assisted computational prediction of antimicrobial peptides","volume":"60","author":"Kavousi","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2024082201242633000_btae497-B26","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1002\/prot.26237","article-title":"Critical assessment of methods of protein structure prediction (CASP)\u2014round XIV","volume":"89","author":"Kryshtafovych","year":"2021","journal-title":"Proteins"},{"key":"2024082201242633000_btae497-B27","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1111\/j.1432-1033.1997.00938.x","article-title":"Structural aspects of the interaction of peptidyl-glycylleucine-carboxyamide, a highly potent antimicrobial peptide from frog skin, with lipids","volume":"248","author":"Latal","year":"1997","journal-title":"Eur J Biochem"},{"key":"2024082201242633000_btae497-B28","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1093\/bioinformatics\/btaa917","article-title":"amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool","volume":"37","author":"Lawrence","year":"2021","journal-title":"Bioinformatics"},{"key":"2024082201242633000_btae497-B29","first-page":"475062","article-title":"A large-scale structural classification of antimicrobial peptides","volume":"2015","author":"Lee","year":"2015","journal-title":"Biomed Res Int"},{"key":"2024082201242633000_btae497-B30","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1021\/acs.jcim.3c01017","article-title":"AMPpred-MFA: an interpretable antimicrobial peptide predictor with a stacking architecture, multiple features, and multihead attention","volume":"64","author":"Li","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2024082201242633000_btae497-B31","doi-asserted-by":"crossref","first-page":"160461","DOI":"10.1016\/j.scitotenv.2022.160461","article-title":"Bacterial resistance to antibacterial agents: mechanisms, control strategies, and implications for global health","volume":"860","author":"Li","year":"2023","journal-title":"Sci Total Environ"},{"key":"2024082201242633000_btae497-B32","author":"Lin","year":"2014"},{"key":"2024082201242633000_btae497-B33","doi-asserted-by":"crossref","first-page":"e4928","DOI":"10.1002\/pro.4928","article-title":"Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow","volume":"33","author":"Mart\u00ednez-Mauricio","year":"2024","journal-title":"Protein Sci"},{"key":"2024082201242633000_btae497-B34","doi-asserted-by":"crossref","first-page":"42362","DOI":"10.1038\/srep42362","article-title":"Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou\u2019s general PseAAC","volume":"7","author":"Meher","year":"2017","journal-title":"Sci Rep"},{"key":"2024082201242633000_btae497-B35","author":"Mikolov","year":"2013"},{"key":"2024082201242633000_btae497-B36","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/s41592-022-01488-1","article-title":"ColabFold: making protein folding accessible to all","volume":"19","author":"Mirdita","year":"2022","journal-title":"Nat Methods"},{"key":"2024082201242633000_btae497-B37","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/s41573-019-0058-8","article-title":"Antimicrobial host defence peptides: functions and clinical potential","volume":"19","author":"Mookherjee","year":"2020","journal-title":"Nat Rev Drug Discov"},{"key":"2024082201242633000_btae497-B38","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1038\/s41587-019-0222-z","article-title":"Design of stapled antimicrobial peptides that are stable, nontoxic and kill antibiotic-resistant bacteria in mice","volume":"37","author":"Mourtada","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2024082201242633000_btae497-B39","doi-asserted-by":"crossref","first-page":"183","DOI":"10.3389\/fmed.2017.00183","article-title":"Infections caused by antimicrobial drug-resistant saprophytic Gram-negative bacteria in the environment","volume":"4","author":"Raphael","year":"2017","journal-title":"Front Med (Lausanne)"},{"key":"2024082201242633000_btae497-B40","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024082201242633000_btae497-B41","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.nmni.2015.02.007","article-title":"The global threat of antimicrobial resistance: science for intervention","volume":"6","author":"Roca","year":"2015","journal-title":"New Microbes New Infect"},{"key":"2024082201242633000_btae497-B42","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.foodres.2014.01.025","article-title":"Antibiotic resistance among commercially available probiotics","volume":"57","author":"Sharma","year":"2014","journal-title":"Food Res Int"},{"key":"2024082201242633000_btae497-B43","doi-asserted-by":"crossref","first-page":"bbab065","DOI":"10.1093\/bib\/bbab065","article-title":"Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec","volume":"22","author":"Sharma","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024082201242633000_btae497-B44","doi-asserted-by":"crossref","first-page":"7607","DOI":"10.1038\/s41598-022-11684-w","article-title":"Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment","volume":"12","author":"Singh","year":"2022","journal-title":"Sci Rep"},{"key":"2024082201242633000_btae497-B45","doi-asserted-by":"crossref","first-page":"bbab439","DOI":"10.1093\/bib\/bbab439","article-title":"StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides","volume":"23","author":"Singh","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024082201242633000_btae497-B46","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/0014-5793(88)80027-9","article-title":"Antimicrobial properties of peptides from Xenopus granular gland secretions","volume":"228","author":"Soravia","year":"1988","journal-title":"FEBS Lett"},{"key":"2024082201242633000_btae497-B47","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","article-title":"A comparison of random Forest variable selection methods for classification prediction modeling","volume":"134","author":"Speiser","year":"2019","journal-title":"Expert Syst Appl"},{"key":"2024082201242633000_btae497-B48","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/s41592-019-0437-4","article-title":"Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold","volume":"16","author":"Steinegger","year":"2019","journal-title":"Nat Methods"},{"key":"2024082201242633000_btae497-B49","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1038\/s41467-018-04964-5","article-title":"Clustering huge protein sequence sets in linear time","volume":"9","author":"Steinegger","year":"2018","journal-title":"Nat Commun"},{"key":"2024082201242633000_btae497-B50","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","article-title":"UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches","volume":"31","author":"Suzek","year":"2015","journal-title":"Bioinformatics"},{"key":"2024082201242633000_btae497-B51","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1038\/s41467-023-36994-z","article-title":"Discovering highly potent antimicrobial peptides with deep generative model HydrAMP","volume":"14","author":"Szymczak","year":"2023","journal-title":"Nat Commun"},{"key":"2024082201242633000_btae497-B52","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1021\/acs.jcim.2c01551","article-title":"Bacteria-specific feature selection for enhanced antimicrobial peptide activity predictions using machine-learning methods","volume":"63","author":"Teimouri","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2024082201242633000_btae497-B53","doi-asserted-by":"crossref","first-page":"4820","DOI":"10.1182\/blood.V128.22.4820.4820","article-title":"High sensitivity Hepcidin-25 bioactive elisas: manual and fully automated system for the quantification of Hepcidin-25 in human serum and plasma","volume":"128","author":"Uelker","year":"2016","journal-title":"Blood"},{"key":"2024082201242633000_btae497-B54","first-page":"5998","author":"Vaswani","year":"2017"},{"key":"2024082201242633000_btae497-B55","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"2024082201242633000_btae497-B56","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","article-title":"Deep learning improves antimicrobial peptide recognition","volume":"34","author":"Veltri","year":"2018","journal-title":"Bioinformatics"},{"key":"2024082201242633000_btae497-B57","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/0959-440X(95)80038-7","article-title":"Structure, function, and membrane integration of defensins","volume":"5","author":"White","year":"1995","journal-title":"Curr Opin Struct Biol"},{"key":"2024082201242633000_btae497-B58","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/s41586-023-06887-8","article-title":"Discovery of a structural class of antibiotics with explainable deep learning","volume":"626","author":"Wong","year":"2024","journal-title":"Nature"},{"key":"2024082201242633000_btae497-B59","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1038\/s41467-023-38192-3","article-title":"Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking","volume":"14","author":"Wu","year":"2023","journal-title":"Nat Commun"},{"key":"2024082201242633000_btae497-B60","doi-asserted-by":"crossref","first-page":"e0086021","DOI":"10.1128\/spectrum.00860-21","article-title":"Recombinant HNP-1 produced by Escherichia coli triggers bacterial apoptosis and exhibits antibacterial activity against drug-resistant bacteria","volume":"10","author":"Xie","year":"2022","journal-title":"Microbiol Spectr"},{"key":"2024082201242633000_btae497-B61","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.3390\/antibiotics11101451","article-title":"Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning","volume":"11","author":"Yan","year":"2022","journal-title":"Antibiotics"},{"key":"2024082201242633000_btae497-B62","doi-asserted-by":"crossref","first-page":"btac715","DOI":"10.1093\/bioinformatics\/btac715","article-title":"sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure","volume":"39","author":"Yan","year":"2023","journal-title":"Bioinformatics"},{"key":"2024082201242633000_btae497-B63","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.1073\/pnas.84.15.5449","article-title":"Magainins, a class of antimicrobial peptides from Xenopus skin: isolation, characterization of two active forms, and partial cDNA sequence of a precursor","volume":"84","author":"Zasloff","year":"1987","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024082201242633000_btae497-B64","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1021\/acsmedchemlett.1c00556","article-title":"Large-scale screening of antifungal peptides based on quantitative structure\u2013activity relationship","volume":"13","author":"Zhang","year":"2022","journal-title":"ACS Med Chem Lett"},{"key":"2024082201242633000_btae497-B65","doi-asserted-by":"crossref","first-page":"5630","DOI":"10.3390\/ijms22115630","article-title":"Prediction of anticancer peptides with high efficacy and low toxicity by hybrid model based on 3D structure of peptides","volume":"22","author":"Zhao","year":"2021","journal-title":"Int J Mol Sci"},{"key":"2024082201242633000_btae497-B66","doi-asserted-by":"crossref","first-page":"bbac462","DOI":"10.1093\/bib\/bbac462","article-title":"ACP_MS: prediction of anticancer peptides based on feature extraction","volume":"23","author":"Zhou","year":"2022","journal-title":"Brief Bioinform"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae497\/58789309\/btae497.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/8\/btae497\/58883948\/btae497.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/8\/btae497\/58883948\/btae497.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:27:34Z","timestamp":1724300854000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae497\/7731002"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":66,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae497","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,8]]},"published":{"date-parts":[[2024,8]]},"article-number":"btae497"}}