{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:02:14Z","timestamp":1790326934079,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"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":["62372090"],"award-info":[{"award-number":["62372090"]}],"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":["82130112"],"award-info":[{"award-number":["82130112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62372090"],"award-info":[{"award-number":["62372090"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82130112"],"award-info":[{"award-number":["82130112"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Science Research and Innovation Fund for Fiscal Year 2025"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Plant ATP-binding proteins play a central role in various physiological processes in plants, including growth and development, energy metabolism, signal transduction, and environmental adaptation. Accurately identifying ATP-binding proteins from plant protein sequences is crucial for a deeper understanding of plant physiology and their applications in agricultural biotechnology. In this study, we propose ATPPred, a tool based on the protein large language model Evolutionary Scale Modeling 2 (ESM2) and a 1-dimensional convolutional neural network for the high-precision prediction of plant ATP-binding proteins. The tool extracts global features of plant ATP-binding proteins using ESM2 and then further extracts feature information with the 1-dimensional convolutional neural network. Comprehensive performance metrics demonstrate ATPPred\u2019s competitive prediction capability. A systematic evaluation of the impact of protein sequence similarity thresholds on ATPPred yielded an average AUC ranging from 0.9191 \u00b1 0.0161 to 0.9669 \u00b1 0.0030 across thresholds from 0.4 to 0.9, highlighting its robustness. Cross-dataset benchmarking of ATPPred further validated the effectiveness of our framework, showcasing superior performance compared to existing methods. In summary, ATPPred provides a feasible computational approach for sequence-based prediction of plant ATP-binding proteins and may serve as a useful auxiliary tool for the preliminary screening and prioritization of candidate ATP-binding proteins.<\/jats:p>","DOI":"10.3390\/ijms27198566","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T08:09:02Z","timestamp":1790323742000},"page":"8566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5214-0855","authenticated-orcid":false,"given":"Hong-Qi","family":"Zhang","sequence":"first","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5311-7179","authenticated-orcid":false,"given":"Zi-Xuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Yi-Xuan","family":"Qi","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3282-8892","authenticated-orcid":false,"given":"Jian","family":"Huang","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6265-2862","authenticated-orcid":false,"given":"Hao","family":"Lin","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0411-549X","authenticated-orcid":false,"given":"Ke-Jun","family":"Deng","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Juan","family":"Feng","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70782","DOI":"10.1111\/tpj.70782","article-title":"Mechanistic basis of plant hormone transport by ABC transporters: Insights from cryo-EM structures","volume":"125","author":"An","year":"2026","journal-title":"Plant J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1093\/aob\/mcq120","article-title":"\u2018And then there were three\u2019: Highly efficient uptake of potassium by foliar trichomes of epiphytic bromeliads","volume":"106","author":"Winkler","year":"2010","journal-title":"Ann. Bot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100834","DOI":"10.1016\/j.xplc.2024.100834","article-title":"ATP homeostasis and signaling in plants","volume":"5","author":"Xiao","year":"2024","journal-title":"Plant Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5664","DOI":"10.1111\/pce.70540","article-title":"Plant Flavonoids: Biosynthesis, Regulation, and Roles in Biotic and Abiotic Stresses","volume":"49","author":"Ren","year":"2026","journal-title":"Plant Cell Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s10265-026-01719-y","article-title":"ATP-dependent binding cassette transporter ClABCI6 promotes the expression of citrus yellow vein clearing virus via inhibiting salicylic acid pathway","volume":"139","author":"Song","year":"2026","journal-title":"J. Plant Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.1073\/pnas.061437498","article-title":"14-3-3 protein is a regulator of the mitochondrial and chloroplast ATP synthase","volume":"98","author":"Bunney","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1038\/s41467-021-22854-1","article-title":"S-acylation of P2K1 mediates extracellular ATP-induced immune signaling in Arabidopsis","volume":"12","author":"Chen","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1038\/s41467-022-28150-w","article-title":"Activation of the plant mevalonate pathway by extracellular ATP","volume":"13","author":"Cho","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"123371","DOI":"10.1016\/j.jhazmat.2020.123371","article-title":"An ATP binding cassette transporter HvABCB25 confers aluminum detoxification in wild barley","volume":"401","author":"Liu","year":"2021","journal-title":"J. Hazard. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"eadj4591","DOI":"10.1126\/science.adj4591","article-title":"Structure and function of the Arabidopsis ABC transporter ABCB19 in brassinosteroid export","volume":"383","author":"Ying","year":"2024","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2206151","DOI":"10.1002\/advs.202206151","article-title":"Explainable deep hypergraph learning modeling the peptide secondary structure prediction","volume":"10","author":"Jiang","year":"2023","journal-title":"Adv. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jare.2023.10.012","article-title":"P450Rdb: A manually curated database of reactions catalyzed by cytochrome P450 enzymes","volume":"63","author":"Zhang","year":"2024","journal-title":"J. Adv. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/nar\/gkad055","article-title":"DeepBIO: An automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis","volume":"51","author":"Wang","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"154274","DOI":"10.1016\/j.bbrc.2026.154274","article-title":"Crystal structure of a fungal dihydroxyacetone kinase reveals a non-canonical ATP-binding site","volume":"830","author":"Wei","year":"2026","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_15","first-page":"221","article-title":"Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives","volume":"22","author":"Mahapatra","year":"2024","journal-title":"Curr. Drug Targets"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"bbae118","DOI":"10.1093\/bib\/bbae118","article-title":"Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration","volume":"25","author":"Chen","year":"2024","journal-title":"Brief. Bioinform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chauhan, J.S., Mishra, N.K., and Raghava, G.P. (2009). Identification of ATP binding residues of a protein from its primary sequence. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-434"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"S4","DOI":"10.1186\/1477-5956-9-S1-S4","article-title":"ATPsite: Sequence-based prediction of ATP-binding residues","volume":"9","author":"Chen","year":"2011","journal-title":"Proteome Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Y.N., Yu, D.J., Li, S.S., Fan, Y.X., Huang, Y., and Shen, H.B. (2012). Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-118"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Song, J., Liu, G., Jiang, J., Zhang, P., and Liang, Y. (2021). Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22020939"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"bbaa275","DOI":"10.1093\/bib\/bbaa275","article-title":"Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework","volume":"22","author":"Wei","year":"2020","journal-title":"Brief. Bioinform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Song, J., Jiang, J., Zhang, C., and Guo, S. (2026). A Novel Weighted Ensemble Framework of Transformer and Deep Q-Network for ATP-Binding Site Prediction Using Protein Language Model Features. Int. J. Mol. Sci., 27.","DOI":"10.3390\/ijms27073097"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"212106","DOI":"10.1007\/s11432-024-4171-9","article-title":"SBSM-Pro: Support bio-sequence machine for proteins","volume":"67","author":"Wang","year":"2024","journal-title":"Sci. China-Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"91","DOI":"10.2174\/1574893618666230417104543","article-title":"SVM-Root: Identification of Root-Associated Proteins in Plants by Employing the Support Vector Machine with Sequence-Derived Features","volume":"19","author":"Meher","year":"2024","journal-title":"Curr. Bioinform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, T., Chen, J.M., Zhang, D., Zhang, Q., Peng, B., Xu, L., and Tang, H. (2020). ApoPred: Identification of Apolipoproteins and Their Subfamilies with Multifarious Features. Front. Cell Dev. Biol., 8.","DOI":"10.3389\/fcell.2020.621144"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"130638","DOI":"10.1016\/j.ijbiomac.2024.130638","article-title":"Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy","volume":"264","author":"Liu","year":"2024","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"W633","DOI":"10.1093\/nar\/gkac415","article-title":"RaacFold: A webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets","volume":"50","author":"Zheng","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"134798","DOI":"10.1016\/j.ijbiomac.2024.134798","article-title":"Review and new insights into the catalytic structural domains of the Fe(ll) and 2-Oxoglutarate families","volume":"278","author":"Yang","year":"2024","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"581245","DOI":"10.1155\/2014\/581245","article-title":"Application of Hybrid Functional Groups to Predict ATP Binding Proteins","volume":"2014","author":"Mbah","year":"2014","journal-title":"ISRN Comput. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hou, R., Wang, L., and Wu, Y.J. (2020). Predicting ATP-Binding Cassette Transporters Using the Random Forest Method. Front. Genet., 11.","DOI":"10.3389\/fgene.2020.00156"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ab.2014.04.032","article-title":"Predicting peroxidase subcellular location by hybridizing different descriptors of Chou\u2019 pseudo amino acid patterns","volume":"458","author":"Zuo","year":"2014","journal-title":"Anal. Biochem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"D609","DOI":"10.1093\/nar\/gkae1010","article-title":"UniProt: The Universal Protein Knowledgebase in 2025","volume":"53","author":"UniProt","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103348","DOI":"10.1016\/j.rineng.2024.103348","article-title":"Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction","volume":"24","author":"Almusallam","year":"2024","journal-title":"Results Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102448","DOI":"10.1016\/j.jocs.2024.102448","article-title":"VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN","volume":"83","author":"Ali","year":"2024","journal-title":"J. Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"136475","DOI":"10.1016\/j.ijbiomac.2024.136475","article-title":"An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP","volume":"282","author":"Almusallam","year":"2024","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zulfiqar, H., Guo, Z., Ahmad, R.M., Ahmed, Z., Cai, P., Chen, X., Zhang, Y., Lin, H., and Shi, Z. (2024). Deep-STP: A deep learning-based approach to predict snake toxin proteins by using word embeddings. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1291352"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.2174\/0113894501330963240905083020","article-title":"Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures","volume":"25","author":"Abdelkader","year":"2024","journal-title":"Curr. Drug Targets"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ymeth.2024.12.005","article-title":"Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory peptides","volume":"234","author":"Charoenkwan","year":"2025","journal-title":"Methods"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"115550","DOI":"10.1016\/j.ab.2024.115550","article-title":"Improving protein-protein interaction prediction using protein language model and protein network features","volume":"693","author":"Hu","year":"2024","journal-title":"Anal. Biochem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3191","DOI":"10.1007\/s11831-025-10237-4","article-title":"Comprehensive Analysis of Computational Models for Prediction of Anticancer Peptides Using Machine Learning and Deep Learning","volume":"32","author":"Ali","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1049\/syb2.12108","article-title":"Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy","volume":"18","author":"Zouari","year":"2024","journal-title":"IET Syst. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/TCBB.2024.3422288","article-title":"AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles","volume":"22","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2306329","DOI":"10.1002\/advs.202306329","article-title":"Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching","volume":"11","author":"Guo","year":"2024","journal-title":"Adv. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhu, H., Hao, H., and Yu, L. (2024). Identification of microbe\u2013disease signed associations via multi-scale variational graph autoencoder based on signed message propagation. BMC Biol., 22.","DOI":"10.1186\/s12915-024-01968-0"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"122498","DOI":"10.1016\/j.eswa.2023.122498","article-title":"Geometric Deep Learning for Drug Discovery","volume":"240","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"212105","DOI":"10.1007\/s11432-024-4147-8","article-title":"TPpred-SC: Multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning","volume":"67","author":"Yan","year":"2024","journal-title":"Sci. China Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e129","DOI":"10.1093\/nar\/gkab829","article-title":"BioSeq-BLM: A platform for analyzing DNA, RNA, and protein sequences based on biological language models","volume":"49","author":"Li","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"btae561","DOI":"10.1093\/bioinformatics\/btae561","article-title":"RetroCaptioner: Beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation","volume":"40","author":"Liu","year":"2024","journal-title":"Bioinformatics"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ai, C., Yang, H., Liu, X., Dong, R., Ding, Y., and Guo, F. (2024). MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning. PLoS Comput. Biol., 20.","DOI":"10.1371\/journal.pcbi.1012229"},{"key":"ref_51","first-page":"8439","article-title":"MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier","volume":"9","author":"Zhang","year":"2024","journal-title":"ACS Omega"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4762","DOI":"10.1038\/s41598-024-81807-y","article-title":"Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy","volume":"15","author":"Shoombuatong","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107455","DOI":"10.1016\/j.future.2024.07.033","article-title":"M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy","volume":"162","author":"Charoenkwan","year":"2025","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"124228","DOI":"10.1016\/j.ijbiomac.2023.124228","article-title":"Pretoria: An effective computational approach for accurate and high-throughput identification of CD8+ t-cell epitopes of eukaryotic pathogens","volume":"238","author":"Charoenkwan","year":"2023","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zou, X., Ren, L., Cai, P., Zhang, Y., Ding, H., Deng, K., Yu, X., Lin, H., and Huang, C. (2023). Accurately identifying hemagglutinin using sequence information and machine learning methods. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1281880"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T08:12:01Z","timestamp":1790323921000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":55,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["ijms27198566"],"URL":"https:\/\/doi.org\/10.3390\/ijms27198566","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]}}}