{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T16:55:09Z","timestamp":1793120109117,"version":"4.5.0"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cellular machinery is built upon proteins and their functional interrelationships. Their network evaluation is essential for a comprehensive insight into biological processes and may establish a foundation for predicting antivirulence. Antiviral peptides (AVPs) have robust, broad-spectrum anti-virulence capabilities. Nevertheless, the existing predicted AVPs database is insufficient and necessitates more precise, reliable annotations. This study aimed to screen differentially expressed proteins and peptides of healthy and porcine reproductive and respiratory syndrome virus (PRRSV)-infected tissues and to predict AVP\u2019s using Machine learning and Deep learning based computational methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Lungs, small intestine and large intestine samples were collected to validate and quantify proteins and peptides through proteomics, and followed by predicting AVPs by employing machine learning (ML) and deep learning (DL). Models were developed exploiting significant features based on physicochemical characteristics, encompassing amino acid composition (AAC), secondary structure, and hydrophilicity. Proteomics analysis facilitated peptide qualification through GO, KEGG, COG, and PPI analysis. To predict AVPs, we employed a DL graph neural network (GNN) by making its inaugural implication in this domain and benchmarked its efficacy against conventional ML random forest (RF) and support vector machine (SVM) models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Findings demonstrated that lysine, arginine, and leucine were ranked nearly 0.1, highlighting their significant importance in prediction. Additionally, the correlation heatmap showed that lysine and glutamate exhibited the strongest positive association (0.57). RF model achieved an area under the curve (AUC) of 0.95\u202f\u00b1\u202f2, verified via 5-fold cross-validation. In contrast, GNN and SVM models yielded 0.94\u202f\u00b1\u202f1 AUC, demonstrating comparable performance across models, and revealed that the RF model outperformed compared to the others.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Integrating proteomics with computational modeling revealed peptides with antiviral potential against PRRSV. The RF model demonstrated the best discriminative power, and amino acid composition played a key predictive role. Consequently, these comparative predictive results may serve as revolutionized and distinctive resources for the experimental validation and identification of PRRSV AVPs as prospective therapeutics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fvets.2025.1681083","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T05:13:53Z","timestamp":1764911633000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models"],"prefix":"10.3389","volume":"12","author":[{"given":"Wafa","family":"Yousaf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Haseeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongheng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongquan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuohai","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panpan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaogui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huizhen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenbiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nairui","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40813-020-00183-x","article-title":"Application of an economic calculator to determine the cost of porcine reproductive and respiratory syndrome at farm-level in 21 pig herds in Germany","volume":"7","author":"Renken","year":"2021","journal-title":"Porcine Health Manag"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1024720","DOI":"10.3389\/fvets.2022.1024720","article-title":"The economic impact of porcine reproductive and respiratory syndrome outbreak in four Chinese farms: based on cost and revenue analysis","volume":"9","author":"Zhang","year":"2022","journal-title":"Front Vet Sci"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/bs.aivir.2021.06.001","article-title":"A great ape perspective on the origins and evolution of human viruses","volume":"110","author":"Calvignac-Spencer","year":"2021","journal-title":"Adv Virus Res"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.compbiomed.2019.02.011","article-title":"AntiVPP 1.0: a portable tool for prediction of antiviral peptides","volume":"107","author":"Lissabet","year":"2019","journal-title":"Comput Biol Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3390\/v17030379","article-title":"Genome and pathogenicity analysis of an NADC30-like PRRSV strain in China\u2019s Xinjiang Province","volume":"17","author":"Li","year":"2025","journal-title":"Viruses"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"110210","DOI":"10.1016\/j.vetmic.2024.110210","article-title":"Assessing the antiviral activity of antimicrobial peptides Caerin1. 1 against PRRSV in vitro and in vivo","volume":"297","author":"Ruan","year":"2024","journal-title":"Vet Microbiol"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"104682","DOI":"10.1016\/j.chemolab.2022.104682","article-title":"Prediction of antiviral peptides using transform evolutionary & SHAP analysis based descriptors by incorporation with ensemble learning strategy","volume":"230","author":"Akbar","year":"2022","journal-title":"Chemom Intell Lab Syst"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10989-020-10072-0","article-title":"Antiviral peptides: identification and validation","volume":"27","author":"Agarwal","year":"2021","journal-title":"Int J Pept Res Ther"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"9898241","DOI":"10.34133\/2022\/9898241","article-title":"An overview of antiviral peptides and rational biodesign considerations","volume":"2022","author":"LeeYing-Chiang","year":"2022","journal-title":"Biodes Res"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.3390\/ijms21082873","article-title":"Bioinformatics methods for mass spectrometry-based proteomics data analysis","volume":"21","author":"Chen","year":"2020","journal-title":"Int J Mol Sci"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1016\/j.molimm.2008.01.028","article-title":"Porcine reproductive and respiratory syndrome virus (PRRSV) suppresses interferon-beta production by interfering with the RIG-I signaling pathway","volume":"45","author":"Luo","year":"2008","journal-title":"Mol Immunol"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1186\/s13567-024-01392-w","article-title":"Porcine reproductive and respiratory syndrome virus degrades TANK-binding kinase 1 via chaperon-mediated autophagy to suppress type I interferon production and facilitate viral proliferation","volume":"55","author":"Zhao","year":"2024","journal-title":"Vet Res"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"15000","DOI":"10.1038\/s41598-018-32984-0","article-title":"Porcine reproductive and respiratory syndrome virus strains with higher virulence cause marked protein profile changes in MARC-145 cells","volume":"8","author":"Chen","year":"2018","journal-title":"Sci Rep"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1128\/JVI.00767-19","article-title":"Porcine reproductive and respiratory syndrome virus E protein degrades porcine cholesterol 25-hydroxylase via the ubiquitin-proteasome pathway","volume":"93","author":"Ke","year":"2019","journal-title":"J Virol"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"224","DOI":"10.3389\/fnmol.2019.00224","article-title":"Systematic affinity purification coupled to mass spectrometry identified p62 as part of the cannabinoid receptor CB2 interactome","volume":"12","author":"Sharaf","year":"2019","journal-title":"Front Mol Neurosci"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1093\/intbio\/zyz025","article-title":"Substrate-based kinase activity inference identifies MK2 as driver of colitis","volume":"11","author":"Strasser","year":"2019","journal-title":"Integr Biol (Camb)"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"27294","DOI":"10.1038\/s41598-025-12704-1","article-title":"Salidroside ameliorates diabetic amyotrophy by targeting Caspase-3 to inhibit apoptosis","volume":"15","author":"Wang","year":"2025","journal-title":"Sci Rep"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e3001144","DOI":"10.1371\/journal.pbio.3001144","article-title":"Quantitative proteome comparison of human hearts with those of model organisms","volume":"19","author":"Linscheid","year":"2021","journal-title":"PLoS Biol"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"7548","DOI":"10.1038\/s41598-024-58301-6","article-title":"Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus","volume":"14","author":"Zheng","year":"2024","journal-title":"Sci Rep"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1074\/mcp.TIR118.000853","article-title":"A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics","volume":"17","author":"Bache","year":"2018","journal-title":"Mol Cell Proteomics"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cels.2020.05.012","article-title":"Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection","volume":"11","author":"Messner","year":"2020","journal-title":"Cell Syst"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1038\/s41587-021-00860-4","article-title":"Ultra-fast proteomics with scanning SWATH","volume":"39","author":"Messner","year":"2021","journal-title":"Nat Biotechnol"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1021\/acs.jproteome.9b00640","article-title":"Skyline for small molecules: a unifying software package for quantitative metabolomics","volume":"19","author":"Adams","year":"2020","journal-title":"J Proteome Res"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1038\/nmeth.3901","article-title":"The Perseus computational platform for comprehensive analysis of (prote) omics data","volume":"13","author":"Tyanova","year":"2016","journal-title":"Nat Methods"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1038\/nmeth.4256","article-title":"MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry\u2013based proteomics","volume":"14","author":"Kong","year":"2017","journal-title":"Nat Methods"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1002\/pmic.201400392","article-title":"Visualization of proteomics data using R and bioconductor","volume":"15","author":"Gatto","year":"2015","journal-title":"Proteomics"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"e16968","DOI":"10.1371\/journal.pone.0016968","article-title":"Connecting peptide physicochemical and antimicrobial properties by a rational prediction model","volume":"6","author":"Torrent","year":"2011","journal-title":"PLoS One"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1007\/s11030-025-11173-y","article-title":"Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools","volume":"29","author":"Nawaz","year":"2025","journal-title":"Mol Divers"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"25554","DOI":"10.1038\/s41598-025-11328-9","article-title":"A hybrid framework of generative deep learning for antiviral peptide discovery","volume":"15","author":"Duy","year":"2025","journal-title":"Sci Rep"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"W199","DOI":"10.1093\/nar\/gks450","article-title":"AVPpred: collection and prediction of highly effective antiviral peptides","volume":"40","author":"Thakur","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1109\/TCBB.2012.89","article-title":"ClassAMP: a prediction tool for classification of antimicrobial peptides","volume":"9","author":"Joseph","year":"2012","journal-title":"Bioinformatics"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.ab.2013.01.019","article-title":"iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types","volume":"436","author":"Xiao","year":"2013","journal-title":"Anal Biochem"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"743","DOI":"10.3390\/ijms20225743","article-title":"Meta-iAVP: a sequence-based Meta-predictor for improving the prediction of antiviral peptides using effective feature representation","volume":"20","author":"Schaduangrat","year":"2019","journal-title":"Int J Mol Sci"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1093\/bib\/bbab209","article-title":"iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types","volume":"22","author":"Xiao","year":"2021","journal-title":"Brief Bioinform"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1093\/bioadv\/vbac080","article-title":"AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation","volume":"2","author":"Lin","year":"2022","journal-title":"Bioinform Adv"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/ijms26136159","article-title":"Deep generative models for the discovery of antiviral peptides targeting dengue virus: a systematic review","volume":"26","author":"Duy","year":"2025","journal-title":"Int J Mol Sci"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"5067","DOI":"10.1109\/JBHI.2021.3130825","article-title":"Deep-AVPpred: artificial intelligence driven discovery of peptide drugs for viral infections","volume":"26","author":"Sharma","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1007\/s11030-023-10718-3","article-title":"Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides","volume":"28","author":"Lefin","year":"2024","journal-title":"Mol Divers"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"e70166","DOI":"10.1371\/journal.pone.0070166","article-title":"Analysis and prediction of highly effective antiviral peptides based on random forests","volume":"8","author":"Chang","year":"2013","journal-title":"PLoS One"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1016\/B978-0-323-88506-5.50312-0","article-title":"Data-driven prediction of antiviral peptides based on periodicities of amino acid properties","volume":"50","author":"Kieslich","year":"2021","journal-title":"Comput Aided Chem Eng"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10822-022-00442-9","article-title":"Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery","volume":"36","author":"Rodr\u00edguez-P\u00e9rez","year":"2022","journal-title":"J Comput Aided Mol Des"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"100576","DOI":"10.1016\/j.mlwa.2024.100576","article-title":"Supervised machine learning in drug discovery and development: algorithms, applications, challenges, and prospects","volume":"17","author":"Obaido","year":"2024","journal-title":"Mach Learn Appl"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"19260","DOI":"10.1038\/s41598-020-76161-8","article-title":"Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance","volume":"10","author":"Chowdhury","year":"2020","journal-title":"Sci Rep"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1007\/s10462-021-10058-4","article-title":"Machine learning in drug discovery: a review","volume":"55","author":"Dara","year":"2022","journal-title":"Artif Intell Rev"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"e17729","DOI":"10.7717\/peerj.17729","article-title":"DeepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model","volume":"12","author":"Zhao","year":"2024","journal-title":"PeerJ"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2008","journal-title":"IEEE Trans Neural Netw"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"bbab340","DOI":"10.1093\/bib\/bbab340","article-title":"Graph representation learning in bioinformatics: trends, methods and applications","volume":"23","author":"Yi","year":"2022","journal-title":"Brief Bioinform"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TCBB.2024.3489614","article-title":"Bioinformatics. iAnOxPep: a machine learning model for the identification of anti-oxidative peptides using ensemble learning","volume":"22","author":"Hassan","year":"2024","journal-title":"IEEE Trans Comput Biol Bioinform"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s00204-024-03888-y","article-title":"Possum: identification and interpretation of potassium ion inhibitors using probabilistic feature vectors","volume":"99","author":"Hassan","year":"2025","journal-title":"Arch Toxicol"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1002\/mas.21804","article-title":"Advances in mass spectrometry imaging for spatial cancer metabolomics","volume":"43","author":"Ma","year":"2024","journal-title":"Mass Spectrom Rev"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1002\/cbic.201800650","article-title":"A review on quantitative multiplexed proteomics","volume":"20","author":"Pappireddi","year":"2019","journal-title":"Chembiochem"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1631433","DOI":"10.3389\/fmicb.2025.1631433","article-title":"Parvoviruses NS1 oncolytic attributes: mechanistic insights and synergistic anti-tumor therapeutic strategies","volume":"16","author":"Haseeb","year":"2025","journal-title":"Front Microbiol"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1038\/s41579-018-0020-5","article-title":"ISG15 in antiviral immunity and beyond","volume":"16","author":"Perng","year":"2018","journal-title":"Nat Rev Microbiol"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1093\/jmcb\/mjac015","article-title":"The molecular regulation of autophagy in antimicrobial immunity","volume":"14","author":"Qin","year":"2022","journal-title":"J Mol Cell Biol"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/s00726-024-03427-0","article-title":"The application and prospects of antimicrobial peptides in antiviral therapy","volume":"56","author":"Yang","year":"2024","journal-title":"Amino Acids"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"1505571","DOI":"10.3389\/fmicb.2024.1505571","article-title":"Immunomodulatory peptides: new therapeutic horizons for emerging and re-emerging infectious diseases","volume":"15","author":"Chatterjee","year":"2024","journal-title":"Front Microbiol"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.2174\/1381612826666201102105827","article-title":"In silico approaches for the prediction and analysis of antiviral peptides: a review","volume":"27","author":"Charoenkwan","year":"2021","journal-title":"Curr Pharm Des"},{"key":"ref58","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1039\/D3CB00166K","article-title":"Helix-based screening with structure prediction using artificial intelligence has potential for the rapid development of peptide inhibitors targeting class I viral fusion","volume":"5","author":"Suzuki","year":"2024","journal-title":"RSC Chem Biol"},{"key":"ref59","doi-asserted-by":"publisher","first-page":"715","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":"Bioinf (Oxf)"},{"key":"ref60","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1016\/j.csbj.2024.08.029","article-title":"Identification and biophysical characterization of epitope atlas of porcine reproductive and respiratory syndrome virus","volume":"23","author":"Dey","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"ref61","doi-asserted-by":"publisher","first-page":"110102","DOI":"10.1016\/j.virol.2024.110102","article-title":"Identification of B-cell epitope on the N protein of type 1 porcine reproductive and respiratory syndrome virus (PRRSV) using monoclonal antibody and construction of epitope-mutated virus","volume":"596","author":"Liu","year":"2024","journal-title":"Virology"},{"key":"ref62","doi-asserted-by":"publisher","first-page":"e18103","DOI":"10.1111\/jcmm.18103","article-title":"Epitope screening and vaccine molecule design of PRRSV GP3 and GP5 protein based on immunoinformatics","volume":"28","author":"Liu","year":"2024","journal-title":"J Cell Mol Med"}],"container-title":["Frontiers in Veterinary Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2025.1681083\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T05:13:54Z","timestamp":1764911634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2025.1681083\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,5]]},"references-count":62,"alternative-id":["10.3389\/fvets.2025.1681083"],"URL":"https:\/\/doi.org\/10.3389\/fvets.2025.1681083","relation":{},"ISSN":["2297-1769"],"issn-type":[{"value":"2297-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,5]]},"article-number":"1681083"}}