{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T13:12:57Z","timestamp":1779973977606,"version":"3.53.1"},"reference-count":78,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":132,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Grants Council","award":["14212525"],"award-info":[{"award-number":["14212525"]}]},{"name":"Research Grants Council","award":["17204124"],"award-info":[{"award-number":["17204124"]}]},{"name":"Research Grants Council","award":["17209021"],"award-info":[{"award-number":["17209021"]}]},{"name":"Research Grants Council","award":["17210023"],"award-info":[{"award-number":["17210023"]}]},{"name":"Research Grants Council","award":["14100122"],"award-info":[{"award-number":["14100122"]}]},{"name":"Research Grants Council","award":["C6027-19GF"],"award-info":[{"award-number":["C6027-19GF"]}]},{"name":"Research Grants Council","award":["C7074-21GF"],"award-info":[{"award-number":["C7074-21GF"]}]},{"name":"Research Grants Council","award":["C4026-21G"],"award-info":[{"award-number":["C4026-21G"]}]},{"name":"Research Grants Council","award":["STG1\/E-401\/23-N"],"award-info":[{"award-number":["STG1\/E-401\/23-N"]}]},{"name":"Research Grants Council","award":["AoE\/E-407\/24-N"],"award-info":[{"award-number":["AoE\/E-407\/24-N"]}]},{"name":"Research Grants Council","award":["AoE\/M-604\/16"],"award-info":[{"award-number":["AoE\/M-604\/16"]}]},{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","award":["21200872"],"award-info":[{"award-number":["21200872"]}],"id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Identifying the enzyme functions of proteins and their catalytic residues is vital to our understanding of diverse cellular processes. However, existing frameworks that can concurrently determine the enzymatic functions and active sites of proteins are scarce, and still have much room for improvement in prediction performance. In this study, we present EC-LMGraph, a protein language model- and graph convolutional network-based framework to predict enzyme commission (EC) numbers from protein sequence features and structures, and saliency mapping to score representative residues attribute to the enzymatic functions. EC-LMGraph attained an average F1 score of 0.77 in 3rd-level EC number prediction, and 0.76 in 4th-level prediction, outperforming numerous other algorithms that were either sequence-based only, or additionally incorporated structural information. Benchmarking on the Mechanism and Catalytic Site Atlas dataset and a set of Parkinson\u2019s disease-related proteins, we showed that EC-LMGraph showed a stronger emphasis on catalytic sites than the current state-of-the-art algorithm DeepFRI. Combining EC-LMGraph with AlphaFold2, our framework correctly determined the 3rd-level EC numbers of 229,160 proteins based purely on their predicted structures. We show that EC-LMGraph is capable of accurately predicting the 3rd\/4th-level EC numbers, and pinpointing the key amino acid residues for many enzymes. EC-LMGraph is implemented and freely available at https:\/\/github.com\/ngyuilun\/EC-LMGraph.<\/jats:p>","DOI":"10.1093\/gigascience\/giag056","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:31:30Z","timestamp":1778585490000},"source":"Crossref","is-referenced-by-count":0,"title":["Accurate proteome-wide prediction of enzymes and catalytic sites using graph deep learning and protein language model"],"prefix":"10.1093","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5750-4897","authenticated-orcid":false,"given":"Yui-Lun","family":"Ng","sequence":"first","affiliation":[{"name":"The University of Hong Kong Department of Mechanical Engineering, Faculty of Engineering, , Pok Fu Lam ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomei","family":"Wang","sequence":"additional","affiliation":[{"name":"The University of Hong Kong Department of Mechanical Engineering, Faculty of Engineering, , Pok Fu Lam ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6730-4338","authenticated-orcid":false,"given":"Yingqi","family":"Li","sequence":"additional","affiliation":[{"name":"The University of Hong Kong Department of Mechanical Engineering, Faculty of Engineering, , Pok Fu Lam ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzhe","family":"Huang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, , Shatin ,","place":["Hong Kong"]},{"name":"Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hei Ming","family":"Lai","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, , Shatin ,","place":["Hong Kong"]},{"name":"Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"The Chinese University of Hong Kong Department of Psychiatry, Faculty of Medicine, , Shatin ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason Ying-Kuen","family":"Chan","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong Department of Otorhinolaryngology, Head and Neck Surgery, Faculty of Medicine, , Shatin ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Billy Wai-Lung","family":"Ng","sequence":"additional","affiliation":[{"name":"Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho","family":"Ko","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, , Shatin ,","place":["Hong Kong"]},{"name":"Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong , Shatin ,","place":["Hong Kong"]},{"name":"The Chinese University of Hong Kong Department of Psychiatry, Faculty of Medicine, , Shatin ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1879-9730","authenticated-orcid":false,"given":"Ka-Wai","family":"Kwok","sequence":"additional","affiliation":[{"name":"The University of Hong Kong Department of Mechanical Engineering, Faculty of Engineering, , Pok Fu Lam ,","place":["Hong Kong"]},{"name":"The Chinese University of Hong Kong Department of Mechanical and Automation Engineering, Faculty of Engineering, , Shatin ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"2026052808514675100_bib1","doi-asserted-by":"publisher","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"UniProt Consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib2","doi-asserted-by":"publisher","first-page":"4643","DOI":"10.1093\/bioinformatics\/btaa485","article-title":"UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase","volume":"36","author":"MacDougall","year":"2020","journal-title":"Bioinformatics"},{"key":"2026052808514675100_bib3","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J Mol Biol"},{"key":"2026052808514675100_bib4","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1126\/science.adf2465","article-title":"Enzyme function prediction using contrastive learning","volume":"379","author":"Yu","year":"2023","journal-title":"Science"},{"key":"2026052808514675100_bib5","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1038\/s42003-024-07359-z","article-title":"Improved enzyme functional annotation prediction using contrastive learning with structural inference","volume":"7","author":"Yang","year":"2024","journal-title":"Commun Biol"},{"key":"2026052808514675100_bib6","doi-asserted-by":"publisher","first-page":"7370","DOI":"10.1038\/s41467-023-43216-z","article-title":"Functional annotation of enzyme-encoding genes using deep learning with transformer layers","volume":"14","author":"Kim","year":"2023","journal-title":"Nat Commun"},{"key":"2026052808514675100_bib7","doi-asserted-by":"publisher","first-page":"e80942","DOI":"10.7554\/eLife.80942","article-title":"ProteInfer, deep neural networks for protein functional inference","volume":"12","author":"Sanderson","year":"2023","journal-title":"eLife"},{"key":"2026052808514675100_bib8","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1186\/s12859-018-2368-y","article-title":"ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature","volume":"19","author":"Dalkiran","year":"2018","journal-title":"BMC Bioinf"},{"key":"2026052808514675100_bib9","doi-asserted-by":"publisher","first-page":"13996","DOI":"10.1073\/pnas.1821905116","article-title":"Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers","volume":"116","author":"Ryu","year":"2019","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026052808514675100_bib10","doi-asserted-by":"publisher","first-page":"8180","DOI":"10.1038\/s41467-024-52533-w","article-title":"Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures","volume":"15","author":"Song","year":"2024","journal-title":"Nat Commun"},{"key":"2026052808514675100_bib11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbad401","article-title":"Evidential deep learning for trustworthy prediction of enzyme commission number","volume":"25","author":"Han","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026052808514675100_bib12","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.sbi.2017.10.010","article-title":"Protein structure-based drug design: from docking to molecular dynamics","volume":"48","author":"\u015aled\u017a","year":"2018","journal-title":"Curr Opin Struct Biol"},{"key":"2026052808514675100_bib13","doi-asserted-by":"publisher","first-page":"13384","DOI":"10.3390\/molecules200713384","article-title":"Molecular docking and structure-based drug design strategies","volume":"20","author":"Ferreira","year":"2015","journal-title":"Molecules"},{"key":"2026052808514675100_bib14","doi-asserted-by":"publisher","first-page":"146","DOI":"10.2174\/157340911795677602","article-title":"Molecular docking: a powerful approach for structure-based drug discovery","volume":"7","author":"Meng","year":"2011","journal-title":"Curr Comput Aided-Drug Des"},{"key":"2026052808514675100_bib15","doi-asserted-by":"publisher","first-page":"W291","DOI":"10.1093\/nar\/gkx366","article-title":"COFACTOR: improved protein function prediction by combining structure, sequence and protein\u2013protein interaction information","volume":"45","author":"Zhang","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib16","doi-asserted-by":"publisher","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":"2026052808514675100_bib17","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1016\/j.gpb.2021.02.001","article-title":"QAUST: protein function prediction using structure similarity, protein interaction, and functional motifs","volume":"19","author":"Smaili","year":"2021","journal-title":"Genomics, Proteomics Bioinforma"},{"key":"2026052808514675100_bib18","doi-asserted-by":"publisher","first-page":"132360","DOI":"10.1109\/ACCESS.2020.3010028","article-title":"Prediction of enzyme function based on a structure relation network","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"2026052808514675100_bib19","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: a review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"2026052808514675100_bib20","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"5th Int Conf Learn Represent ICLR 2017\u2013Conf Track Proc","author":"Kipf","year":"2017"},{"key":"2026052808514675100_bib21","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: toward understanding the language of life through self-supervised learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026052808514675100_bib22","doi-asserted-by":"publisher","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":"2026052808514675100_bib23","article-title":"Attention is all you need","volume-title":"Proc 31st Int Conf Neural Inf Process Syst","author":"Vaswani A","year":"2017"},{"key":"2026052808514675100_bib24","doi-asserted-by":"publisher","first-page":"D271","DOI":"10.1093\/nar\/gkw1000","article-title":"The RCSB protein data bank: integrative view of protein, gene and 3D structural information","volume":"45","author":"Altunkaya","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib25","doi-asserted-by":"publisher","first-page":"D618","DOI":"10.1093\/nar\/gkx1012","article-title":"Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites","volume":"46","author":"Ribeiro","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib26","doi-asserted-by":"publisher","first-page":"129D","DOI":"10.1093\/nar\/gkh028","article-title":"The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data","volume":"32","author":"Porter","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib27","doi-asserted-by":"publisher","first-page":"D485","DOI":"10.1093\/nar\/gkt1243","article-title":"The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes","volume":"42","author":"Furnham","year":"2014","journal-title":"Nucl Acids Res"},{"key":"2026052808514675100_bib28","author":"EC-LMGraph repository"},{"key":"2026052808514675100_bib29","first-page":"1","article-title":"How attentive are graph attention networks?","volume-title":"ICLR 2022\u201310th Int Conf Learn Represent","author":"Brody","year":"2022"},{"key":"2026052808514675100_bib30","doi-asserted-by":"publisher","first-page":"107637","DOI":"10.1016\/j.patcog.2020.107637","article-title":"Hypergraph convolution and hypergraph attention","volume":"110","author":"Bai","year":"2021","journal-title":"Pattern Recognit"},{"key":"2026052808514675100_bib31","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925","article-title":"Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks","volume-title":"Proc ACM SIGKDD Int Conf Knowl Discov Data Min","author":"Chiang","year":"2019"},{"key":"2026052808514675100_bib32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5997","article-title":"ASAP: adaptive structure aware pooling for learning hierarchical graph representations","volume-title":"AAAI 2020\u201334th AAAI Conf Artif Intell","author":"Ranjan","year":"2020"},{"key":"2026052808514675100_bib33","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.ymeth.2019.04.008","article-title":"Deep learning in bioinformatics: introduction, application, and perspective in the big data era","volume":"166","author":"Li","year":"2019","journal-title":"Methods"},{"key":"2026052808514675100_bib34","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1002\/prot.25832","article-title":"Machine learning techniques for protein function prediction","volume":"88","author":"Bonetta","year":"2020","journal-title":"Proteins"},{"key":"2026052808514675100_bib35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J Big Data"},{"key":"2026052808514675100_bib36","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026052808514675100_bib37","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","article-title":"ProteinBERT: a universal deep-learning model of protein sequence and function","volume":"38","author":"Brandes","year":"2022","journal-title":"Bioinformatics"},{"key":"2026052808514675100_bib38","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2026052808514675100_bib39","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/978-3-642-23808-6_10","article-title":"On the Stratification of Multi-label Data","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Sechidis","year":"2011"},{"key":"2026052808514675100_bib40","first-page":"1","article-title":"Deep inside convolutional networks: visualising image classification models and saliency maps","volume-title":"2nd Int Conf Learn Represent ICLR 2014\u2013Work Track Proc","author":"Simonyan","year":"2014"},{"key":"2026052808514675100_bib41","first-page":"4844","article-title":"Learning important features through propagating activation differences","volume-title":"34th Int Conf Mach Learn ICML","author":"Shrikumar","year":"2017"},{"key":"2026052808514675100_bib42","first-page":"1","article-title":"Striving for simplicity: the all convolutional net","volume-title":"3rd Int Conf Learn Represent ICLR 2015\u2013Work Track Proc","author":"Springenberg","year":"2015"},{"key":"2026052808514675100_bib43","first-page":"818","article-title":"Visualizing and understanding convolutional networks","volume-title":"ECCV 2014","author":"Zeiler","year":"2014"},{"key":"2026052808514675100_bib44","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1002\/pro.3730","article-title":"RCSB Protein Data Bank: enabling biomedical research and drug discovery","volume":"29","author":"Goodsell","year":"2020","journal-title":"Protein Sci"},{"key":"2026052808514675100_bib45","doi-asserted-by":"publisher","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","article-title":"AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models","volume":"50","author":"Varadi","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib46","doi-asserted-by":"publisher","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":"2026052808514675100_bib47","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"2026052808514675100_bib48","doi-asserted-by":"publisher","first-page":"eabm4805","DOI":"10.1126\/science.abm4805","article-title":"Computed structures of core eukaryotic protein complexes","volume":"374","author":"Humphreys","year":"2021","journal-title":"Science"},{"key":"2026052808514675100_bib49","doi-asserted-by":"publisher","first-page":"648588","DOI":"10.3389\/fneur.2021.648588","article-title":"Genotype-phenotype correlations in monogenic Parkinson disease: a review on clinical and molecular findings","volume":"12","author":"Guadagnolo","year":"2021","journal-title":"Front Neurol"},{"key":"2026052808514675100_bib50","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.3390\/genes12071006","article-title":"The genetics of parkinson\u2019s disease and implications for clinical practice","volume":"12","author":"Day","year":"2021","journal-title":"Genes"},{"key":"2026052808514675100_bib51","doi-asserted-by":"publisher","first-page":"R48","DOI":"10.1093\/hmg\/ddp012","article-title":"Parkinson\u2019s disease: from monogenic forms to genetic susceptibility factors","volume":"18","author":"Lesage","year":"2009","journal-title":"Hum Mol Genet"},{"key":"2026052808514675100_bib52","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2026052808514675100_bib53","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1093\/bioinformatics\/bts510","article-title":"EFICAz2.5: application of a high-precision enzyme function predictor to 396 proteomes","volume":"28","author":"Kumar","year":"2012","journal-title":"Bioinformatics"},{"key":"2026052808514675100_bib54","doi-asserted-by":"publisher","first-page":"giae093","DOI":"10.1093\/gigascience\/giae093","article-title":"Learning a generalized graph transformer for protein function prediction in dissimilar sequences","volume":"13","author":"Fu","year":"2024","journal-title":"Gigascience"},{"key":"2026052808514675100_bib55","doi-asserted-by":"publisher","first-page":"8360","DOI":"10.1038\/s41598-022-12201-9","article-title":"Prediction of protein\u2013protein interaction using graph neural networks","volume":"12","author":"Jha","year":"2022","journal-title":"Sci Rep"},{"key":"2026052808514675100_bib56","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1021\/acs.jcim.7b00028","article-title":"DeepPPI: boosting prediction of protein\u2013Protein interactions with deep neural networks","volume":"57","author":"Du","year":"2017","journal-title":"J Chem Inf Model"},{"key":"2026052808514675100_bib57","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1186\/s12859-017-1700-2","article-title":"Sequence-based prediction of protein\u2013protein interaction using a deep-learning algorithm","volume":"18","author":"Sun","year":"2017","journal-title":"BMC Bioinf"},{"key":"2026052808514675100_bib58","doi-asserted-by":"publisher","first-page":"giaf114","DOI":"10.1093\/gigascience\/giaf114","article-title":"MGMA-PPIS: predicting the protein\u2013protein interaction site with multiview graph embedding and multiscale attention fusion","volume":"14","author":"Han","year":"2025","journal-title":"Gigascience"},{"key":"2026052808514675100_bib59","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/s41592-022-01490-7","article-title":"ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction","volume":"19","author":"Tubiana","year":"2022","journal-title":"Nat Methods"},{"key":"2026052808514675100_bib60","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1186\/s12864-020-07291-6","article-title":"RBPsuite: rNA-protein binding sites prediction suite based on deep learning","volume":"21","author":"Pan","year":"2020","journal-title":"Bmc Genomics [Electronic Resource]"},{"key":"2026052808514675100_bib61","doi-asserted-by":"publisher","first-page":"bbab336","DOI":"10.1093\/bib\/bbab336","article-title":"DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026052808514675100_bib62","doi-asserted-by":"publisher","first-page":"bbab521","DOI":"10.1093\/bib\/bbab521","article-title":"DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning","volume":"23","author":"Zhang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026052808514675100_bib63","doi-asserted-by":"publisher","first-page":"giae080","DOI":"10.1093\/gigascience\/giae080","article-title":"Protein\u2013protein and protein\u2013nucleic acid binding site prediction via interpretable hierarchical geometric deep learning","volume":"13","author":"Zhang","year":"2024","journal-title":"Gigascience"},{"key":"2026052808514675100_bib64","doi-asserted-by":"publisher","first-page":"giaf083","DOI":"10.1093\/gigascience\/giaf083","article-title":"DeepAnnotation: a novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations","volume-title":"Gigascience","author":"Ma","year":"2025"},{"key":"2026052808514675100_bib65","doi-asserted-by":"publisher","first-page":"8210","DOI":"10.1038\/s41467-023-44113-1","article-title":"UniKP: a unified framework for the prediction of enzyme kinetic parameters","volume":"14","author":"Yu","year":"2023","journal-title":"Nat Commun"},{"key":"2026052808514675100_bib66","doi-asserted-by":"publisher","first-page":"bbaf187","DOI":"10.1093\/bib\/bbaf187","article-title":"DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks","volume":"26","author":"Wang","year":"2025","journal-title":"Brief Bioinform"},{"key":"2026052808514675100_bib67","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1109\/TCBB.2019.2911677","article-title":"Amino acid encoding methods for protein sequences: a comprehensive review and assessment","volume":"17","author":"Jing","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol and Bioinf"},{"key":"2026052808514675100_bib68","doi-asserted-by":"publisher","first-page":"6530","DOI":"10.5555\/3295222.3295399","article-title":"Protein interface prediction using graph convolutional networks","author":"Fout","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026052808514675100_bib69","doi-asserted-by":"crossref","DOI":"10.1109\/ICTAI50040.2020.00060","article-title":"DTIGCCN: prediction of drug-target interactions based on GCN and CNN","volume-title":"2020 IEEE 32nd Int Conf Tools with Artif Intell","author":"Shao","year":"2020"},{"key":"2026052808514675100_bib70","doi-asserted-by":"publisher","first-page":"giae108","DOI":"10.1093\/gigascience\/giae108","article-title":"scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data","volume":"13","author":"Lin","year":"2024","journal-title":"Gigascience"},{"key":"2026052808514675100_bib71","article-title":"Empirical evaluation of rectified activations in convolutional network","volume-title":"arXiv 150500853","author":"Xu","year":"2015"},{"key":"2026052808514675100_bib72","first-page":"1","article-title":"Adam: a method for stochastic optimization","volume-title":"3rd Int Conf Learn Represent ICLR 2015\u2013Conf Track Proc.","author":"Kingma","year":"2015"},{"key":"2026052808514675100_bib73","first-page":"1","article-title":"Fast graph representation learning with PyTorch geometric","volume-title":"ICLR 2019 Work Represent Learn Graphs Manifolds","author":"Fey","year":"2015"},{"key":"2026052808514675100_bib74","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1002\/jcc.20084","article-title":"UCSF Chimera\u2014a visualization system for exploratory research and analysis","volume":"25","author":"Pettersen","year":"2004","journal-title":"J Comput Chem"},{"key":"2026052808514675100_bib75","author":"wwPDB"},{"key":"2026052808514675100_bib76","author":"UniProt Database"},{"key":"2026052808514675100_bib77","author":"M-CSA Database"},{"key":"2026052808514675100_bib78","author":"AlphaFold DB"}],"container-title":["GigaScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/gigascience\/advance-article-pdf\/doi\/10.1093\/gigascience\/giag056\/68279319\/giag056.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gigascience\/article-pdf\/doi\/10.1093\/gigascience\/giag056\/68279319\/giag056.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gigascience\/article-pdf\/doi\/10.1093\/gigascience\/giag056\/68279319\/giag056.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T12:51:57Z","timestamp":1779972717000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gigascience\/article\/doi\/10.1093\/gigascience\/giag056\/8677297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":78,"URL":"https:\/\/doi.org\/10.1093\/gigascience\/giag056","relation":{},"ISSN":["2047-217X"],"issn-type":[{"value":"2047-217X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026]]},"published":{"date-parts":[[2026]]},"article-number":"giag056"}}