{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T09:02:39Z","timestamp":1775379759733,"version":"3.50.1"},"reference-count":74,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF1203100"],"award-info":[{"award-number":["2022YFF1203100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12126610"],"award-info":[{"award-number":["12126610"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Supercomputing facilities of Shenzhen Bay Laboratory"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Determining intrinsically disordered regions of proteins is essential for elucidating protein biological functions and the mechanisms of their associated diseases. As the gap between the number of experimentally determined protein structures and the number of protein sequences continues to grow exponentially, there is a need for developing an accurate and computationally efficient disorder predictor. However, current single-sequence-based methods are of low accuracy, while evolutionary profile-based methods are computationally intensive. Here, we proposed a fast and accurate protein disorder predictor LMDisorder that employed embedding generated by unsupervised pretrained language models as features. We showed that LMDisorder performs best in all single-sequence-based methods and is comparable or better than another language-model-based technique in four independent test sets, respectively. Furthermore, LMDisorder showed equivalent or even better performance than the state-of-the-art profile-based technique SPOT-Disorder2. In addition, the high computation efficiency of LMDisorder enabled proteome-scale analysis of human, showing that proteins with high predicted disorder content were associated with specific biological functions. The datasets, the source codes, and the trained model are available at https:\/\/github.com\/biomed-AI\/LMDisorder.<\/jats:p>","DOI":"10.1093\/bib\/bbad173","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T12:39:16Z","timestamp":1684499956000},"source":"Crossref","is-referenced-by-count":19,"title":["Fast and accurate protein intrinsic disorder prediction by using a pretrained language model"],"prefix":"10.1093","volume":"24","author":[{"given":"Yidong","family":"Song","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering at Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianmu","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering at Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering at Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering at Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering at Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"2023072020023264200_ref1","first-page":"437","article-title":"Thousands of proteins likely to have long disordered regions","volume":"3","author":"Romero","year":"1998","journal-title":"Pac Symp Biocomput"},{"key":"2023072020023264200_ref2","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1093\/nar\/19.suppl.2247","article-title":"The SWISS-PROT protein sequence data bank","volume":"19","author":"Bairoch","year":"1991","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref3","first-page":"60","article-title":"Functions of short lifetime biological structures at large: the case of intrinsically disordered proteins","volume":"19","author":"Uversky","year":"2020","journal-title":"Brief Funct Genomics"},{"key":"2023072020023264200_ref4","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.jmb.2007.07.004","article-title":"Molecular principles of the interactions of disordered proteins","volume":"372","author":"M\u00e9sz\u00e1ros","year":"2007","journal-title":"J Mol Biol"},{"key":"2023072020023264200_ref5","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1021\/pr0701411","article-title":"Characterization of molecular recognition features, MoRFs, and their binding partners","volume":"6","author":"Vacic","year":"2007","journal-title":"J Proteome Res"},{"key":"2023072020023264200_ref6","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nrm1589","article-title":"Intrinsically unstructured proteins and their functions","volume":"6","author":"Dyson","year":"2005","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2023072020023264200_ref7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1002\/prot.20750","article-title":"Assessing protein disorder and induced folding, proteins: structure","volume":"62","author":"Receveur-Br\u00e9chot","year":"2006","journal-title":"Function, and Bioinformatics"},{"key":"2023072020023264200_ref8","doi-asserted-by":"crossref","first-page":"2949","DOI":"10.1007\/s00018-016-2138-9","article-title":"Natural protein sequences are more intrinsically disordered than random sequences","volume":"73","author":"Yu","year":"2016","journal-title":"Cell Mol Life Sci"},{"key":"2023072020023264200_ref9","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1007\/s00018-017-2554-5","article-title":"Intrinsic disorder here, there, and everywhere, and nowhere to escape from it","volume":"74","author":"Uversky","year":"2017","journal-title":"Cell Mol Life Sci"},{"key":"2023072020023264200_ref10","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.jmr.2013.11.011","article-title":"NMR contributions to structural dynamics studies of intrinsically disordered proteins","volume":"241","author":"Konrat","year":"2014","journal-title":"J Magn Reson"},{"key":"2023072020023264200_ref11","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1002\/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3","article-title":"Sequence complexity of disordered protein, proteins: structure","volume":"42","author":"Romero","year":"2001","journal-title":"Function, and Bioinformatics"},{"key":"2023072020023264200_ref12","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1093\/bioinformatics\/btr682","article-title":"ESpritz: accurate and fast prediction of protein disorder","volume":"28","author":"Walsh","year":"2012","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref13","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1093\/bioinformatics\/btw678","article-title":"Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks","volume":"33","author":"Hanson","year":"2017","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref14","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1002\/prot.25674","article-title":"NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning","volume":"87","author":"Klausen","year":"2019","journal-title":"Proteins: Structure, Function, and Bioinformatics"},{"key":"2023072020023264200_ref15","doi-asserted-by":"crossref","first-page":"i672","DOI":"10.1093\/bioinformatics\/btw446","article-title":"AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields","volume":"32","author":"Wang","year":"2016","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref16","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1080\/073911012010525022","article-title":"SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method","volume":"29","author":"Zhang","year":"2012","journal-title":"Journal of Biomolecular Structure and Dynamics"},{"key":"2023072020023264200_ref17","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.gpb.2019.01.004","article-title":"SPOT-Disorder2: improved protein intrinsic disorder prediction by ensembled deep learning","volume":"17","author":"Hanson","year":"2019","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2023072020023264200_ref18","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.1093\/bioinformatics\/bti541","article-title":"IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content","volume":"21","author":"Doszt\u00e1nyi","year":"2005","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref19","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.1093\/nar\/gkg519","article-title":"GlobPlot: exploring protein sequences for globularity and disorder","volume":"31","author":"Linding","year":"2003","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref20","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1093\/bioinformatics\/bti537","article-title":"FoldIndex\u00a9: a simple tool to predict whether a given protein sequence is intrinsically unfolded","volume":"21","author":"Prilusky","year":"2005","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref21","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1093\/bib\/bbx126","article-title":"A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction","volume":"20","author":"Liu","year":"2019","journal-title":"Brief Bioinform"},{"key":"2023072020023264200_ref22","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1093\/bioinformatics\/btx590","article-title":"A comprehensive assessment of long intrinsic protein disorder from the DisProt database","volume":"34","author":"Necci","year":"2018","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref23","doi-asserted-by":"crossref","first-page":"i489","DOI":"10.1093\/bioinformatics\/btq373","article-title":"Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources","volume":"26","author":"Mizianty","year":"2010","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref24","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1126\/science.aah4043","article-title":"Protein structure determination using metagenome sequence data","volume":"355","author":"Ovchinnikov","year":"2017","journal-title":"Science"},{"key":"2023072020023264200_ref25","volume":"32"},{"key":"2023072020023264200_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3220-8","article-title":"Modeling aspects of the language of life through transfer-learning protein sequences","volume":"20","author":"Heinzinger","year":"2019","journal-title":"BMC bioinformatics"},{"key":"2023072020023264200_ref27","volume-title":"bioRxiv"},{"key":"2023072020023264200_ref28","article-title":"ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing","volume":"44","author":"Elnaggar","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2023072020023264200_ref29","doi-asserted-by":"crossref","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"},{"key":"2023072020023264200_ref30","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/s42256-022-00457-9","article-title":"Learning functional properties of proteins with language models","volume":"4","author":"Unsal","year":"2022","journal-title":"Nature Machine Intelligence"},{"key":"2023072020023264200_ref31","first-page":"1","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":"2023072020023264200_ref32","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1093\/bioinformatics\/btac053","article-title":"SPOT-contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model","volume":"38","author":"Singh","year":"2022","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref33","volume-title":"Briefings in Bioinformatics"},{"key":"2023072020023264200_ref34","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1021\/acs.jcim.8b00636","article-title":"Accurate single-sequence prediction of protein intrinsic disorder by an ensemble of deep recurrent and convolutional architectures","volume":"58","author":"Hanson","year":"2018","journal-title":"J Chem Inf Model"},{"key":"2023072020023264200_ref35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1093\/bioinformatics\/bth476","article-title":"DisProt: a database of protein disorder","volume":"21","author":"Vucetic","year":"2005","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref36","doi-asserted-by":"crossref","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":"2023072020023264200_ref37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2164-11-S1-S15","article-title":"Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset","volume":"11","author":"Sirota","year":"2010","journal-title":"BMC Genomics"},{"key":"2023072020023264200_ref38","doi-asserted-by":"crossref","first-page":"D471","DOI":"10.1093\/nar\/gkx1071","article-title":"MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins","volume":"46","author":"Piovesan","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref39","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"Journal of machine learning research"},{"key":"2023072020023264200_ref40","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1093\/bioinformatics\/btm098","article-title":"UniRef: comprehensive and non-redundant UniProt reference clusters","volume":"23","author":"Suzek","year":"2007","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref41","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/nmeth.1818","article-title":"HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment","volume":"9","author":"Remmert","year":"2012","journal-title":"Nat Methods"},{"key":"2023072020023264200_ref42","doi-asserted-by":"crossref","first-page":"D170","DOI":"10.1093\/nar\/gkw1081","article-title":"Uniclust databases of clustered and deeply annotated protein sequences and alignments","volume":"45","author":"Mirdita","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref43","doi-asserted-by":"crossref","first-page":"2842","DOI":"10.1093\/bioinformatics\/btx218","article-title":"Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility","volume":"33","author":"Heffernan","year":"2017","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref44","volume-title":"Advances in neural information processing systems"},{"key":"2023072020023264200_ref45","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"He","year":"2016"},{"key":"2023072020023264200_ref46","first-page":"21","article-title":"Layer normalization","volume":"1050","author":"Ba","year":"2016","journal-title":"Stat"},{"key":"2023072020023264200_ref47","volume-title":"arXiv"},{"key":"2023072020023264200_ref48","first-page":"8026","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"2023072020023264200_ref49","doi-asserted-by":"crossref","first-page":"W329","DOI":"10.1093\/nar\/gky384","article-title":"IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding","volume":"46","author":"M\u00e9sz\u00e1ros","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref50","volume":"50","journal-title":"Nucleic acids research"},{"key":"2023072020023264200_ref51","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/333210a0","article-title":"Acid blobs and negative noodles","volume":"333","author":"Sigler","year":"1988","journal-title":"Nature"},{"key":"2023072020023264200_ref52","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1093\/bioinformatics\/bti534","article-title":"RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins","volume":"21","author":"Yang","year":"2005","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref53","doi-asserted-by":"crossref","first-page":"e24428","DOI":"10.4161\/idp.24428","article-title":"MFDp2: accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles","volume":"1","author":"Mizianty","year":"2013","journal-title":"Intrinsically disordered proteins"},{"key":"2023072020023264200_ref54","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1093\/bioinformatics\/btu744","article-title":"DISOPRED3: precise disordered region predictions with annotated protein-binding activity","volume":"31","author":"Jones","year":"2015","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref55","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1093\/bioinformatics\/btx015","article-title":"MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins","volume":"33","author":"Necci","year":"2017","journal-title":"Bioinformatics"},{"key":"2023072020023264200_ref56","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1038\/s41592-021-01117-3","article-title":"Critical assessment of protein intrinsic disorder prediction","volume":"18","author":"Necci","year":"2021","journal-title":"Nat Methods"},{"key":"2023072020023264200_ref57","doi-asserted-by":"crossref","first-page":"D204","DOI":"10.1093\/nar\/gku989","article-title":"UniProt: a hub for protein information","volume":"43","author":"Consortium","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref58","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat Genet"},{"key":"2023072020023264200_ref59","doi-asserted-by":"crossref","first-page":"W191","DOI":"10.1093\/nar\/gkz369","article-title":"G: profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)","volume":"47","author":"Raudvere","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023072020023264200_ref60","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/S0092-8674(00)80463-8","article-title":"Solution structure of the KIX domain of CBP bound to the transactivation domain of CREB: a model for activator: coactivator interactions","volume":"91","author":"Radhakrishnan","year":"1997","journal-title":"Cell"},{"key":"2023072020023264200_ref61","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1006\/jmbi.1999.3110","article-title":"Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm","volume":"293","author":"Wright","year":"1999","journal-title":"J Mol Biol"},{"key":"2023072020023264200_ref62","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.tibs.2007.10.003","article-title":"Fuzzy complexes: polymorphism and structural disorder in protein\u2013protein interactions","volume":"33","author":"Tompa","year":"2008","journal-title":"Trends Biochem Sci"},{"key":"2023072020023264200_ref63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2011-12-2-r14","article-title":"Bringing order to protein disorder through comparative genomics and genetic interactions","volume":"12","author":"Bellay","year":"2011","journal-title":"Genome Biol"},{"key":"2023072020023264200_ref64","doi-asserted-by":"crossref","first-page":"e1003030","DOI":"10.1371\/journal.pcbi.1003030","article-title":"Distinct types of disorder in the human proteome: functional implications for alternative splicing","volume":"9","author":"Colak","year":"2013","journal-title":"PLoS Comput Biol"},{"key":"2023072020023264200_ref65","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/07391102.2012.675145","article-title":"Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life","volume":"30","author":"Xue","year":"2012","journal-title":"Journal of Biomolecular Structure and Dynamics"},{"key":"2023072020023264200_ref66","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s00018-014-1661-9","article-title":"Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life","volume":"72","author":"Peng","year":"2015","journal-title":"Cell Mol Life Sci"},{"key":"2023072020023264200_ref67","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1146\/annurev.biophys.37.032807.125924","article-title":"Intrinsically disordered proteins in human diseases: introducing the D2 concept","volume":"37","author":"Uversky","year":"2008","journal-title":"Annu Rev Biophys"},{"key":"2023072020023264200_ref68","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/jb\/mvx056","article-title":"Common molecular pathogenesis of disease-related intrinsically disordered proteins revealed by NMR analysis","volume":"163","author":"Shigemitsu","year":"2018","journal-title":"The Journal of Biochemistry"},{"key":"2023072020023264200_ref69","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1002\/jmr.747","article-title":"Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling","volume":"18","author":"Uversky","year":"2005","journal-title":"Journal of Molecular Recognition: An Interdisciplinary Journal"},{"key":"2023072020023264200_ref70","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/S0022-2836(02)00969-5","article-title":"Intrinsic disorder in cell-signaling and cancer-associated proteins","volume":"323","author":"Iakoucheva","year":"2002","journal-title":"J Mol Biol"},{"key":"2023072020023264200_ref71","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":"2023072020023264200_ref72","doi-asserted-by":"crossref","first-page":"bbab564","DOI":"10.1093\/bib\/bbab564","article-title":"AlphaFold2-aware protein\u2013DNA binding site prediction using graph transformer","volume":"23","author":"Yuan","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023072020023264200_ref73","volume-title":"bioRxiv"},{"key":"2023072020023264200_ref74","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","article-title":"Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network","volume":"38","author":"Yuan","year":"2021","journal-title":"Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/4\/bbad173\/50917086\/bbad173.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/4\/bbad173\/50917086\/bbad173.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T20:05:01Z","timestamp":1689883501000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad173\/7171415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":74,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad173","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,7]]},"published":{"date-parts":[[2023,5,18]]},"article-number":"bbad173"}}