{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T19:55:59Z","timestamp":1793649359847,"version":"4.5.1"},"reference-count":83,"publisher":"Elsevier BV","issue":"15","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"am","delay-in-days":307,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007548","name":"Northwest A and F University","doi-asserted-by":"publisher","award":["Z1090124074"],"award-info":[{"award-number":["Z1090124074"]}],"id":[{"id":"10.13039\/501100007548","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2146027"],"award-info":[{"award-number":["2146027"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2125218"],"award-info":[{"award-number":["2125218"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012338","name":"Xinyang Normal University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012337","name":"Nanhu Scholars Program for Young Scholars of Xinyang Normal University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012337","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Henan Province Natural Science Foundation","doi-asserted-by":"publisher","award":["242300421410"],"award-info":[{"award-number":["242300421410"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Molecular Biology"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1016\/j.jmb.2024.168872","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T23:23:17Z","timestamp":1732058597000},"page":"168872","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning"],"prefix":"10.1016","volume":"437","author":[{"given":"Jian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushmita","family":"Basu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukasz","family":"Kurgan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jmb.2024.168872_b0005","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1110\/ps.21302","article-title":"Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations","volume":"11","author":"Ma","year":"2002","journal-title":"Protein Sci."},{"key":"10.1016\/j.jmb.2024.168872_b0010","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1038\/nbt1519","article-title":"Protein promiscuity and its implications for biotechnology","volume":"27","author":"Nobeli","year":"2009","journal-title":"Nature Biotechnol."},{"key":"10.1016\/j.jmb.2024.168872_b0015","series-title":"Intrinsically Disordered Proteins","first-page":"1","article-title":"Introduction to intrinsically disordered proteins and regions","author":"Oldfield","year":"2019"},{"key":"10.1016\/j.jmb.2024.168872_b0020","doi-asserted-by":"crossref","DOI":"10.1080\/21690707.2016.1259708","article-title":"How disordered is my protein and what is its disorder for? A guide through the \u201cdark side\u201d of the protein universe","volume":"4","author":"Lieutaud","year":"2016","journal-title":"Intrinsically Disord. Proteins"},{"key":"10.1016\/j.jmb.2024.168872_b0025","doi-asserted-by":"crossref","first-page":"4191","DOI":"10.2174\/1381612811319230005","article-title":"Intrinsic disorder-based protein interactions and their modulators","volume":"19","author":"Uversky","year":"2013","journal-title":"Curr. Pharm Design."},{"key":"10.1016\/j.jmb.2024.168872_b0030","doi-asserted-by":"crossref","DOI":"10.3390\/ijms17111874","article-title":"p53 proteoforms and intrinsic disorder: an illustration of the protein structure-function continuum concept","volume":"17","author":"Uversky","year":"2016","journal-title":"Int. J. Mol. Sci."},{"issue":"Suppl 1","key":"10.1016\/j.jmb.2024.168872_b0035","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1186\/1471-2164-9-S1-S1","article-title":"Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners","volume":"9","author":"Oldfield","year":"2008","journal-title":"BMC Genomics"},{"key":"10.1016\/j.jmb.2024.168872_b0040","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.3389\/fphar.2019.01027","article-title":"Non-functional trace amine-associated receptor 1 variants in patients with mental disorders","volume":"10","author":"Rutigliano","year":"2019","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.jmb.2024.168872_b0045","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.2174\/138161211796355056","article-title":"Molecular recognition in the case of flexible targets","volume":"17","author":"Ivetac","year":"2011","journal-title":"Curr. Pharm. Des."},{"key":"10.1016\/j.jmb.2024.168872_b0050","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrm3884","article-title":"The structure, function and evolution of proteins that bind DNA and RNA","volume":"15","author":"Hudson","year":"2014","journal-title":"Nature Rev. Mol. Cell Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0055","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0968-0004(03)00135-X","article-title":"Conformational diversity and protein evolution\u2013a 60-year-old hypothesis revisited","volume":"28","author":"James","year":"2003","journal-title":"Trends Biochem. Sci"},{"key":"10.1016\/j.jmb.2024.168872_b0060","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.jmb.2004.07.001","article-title":"Dynamics of ATP-binding cassette contribute to allosteric control, nucleotide binding and energy transduction in ABC transporters","volume":"342","author":"Wang","year":"2004","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.bpc.2021.106742","article-title":"Targeting disorders in unstructured and structured proteins in various diseases","volume":"281","author":"Choudhary","year":"2022","journal-title":"Biophys. Chem."},{"key":"10.1016\/j.jmb.2024.168872_b0070","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.cbpa.2021.02.009","article-title":"Intrinsically disordered proteins and biomolecular condensates as drug targets","volume":"62","author":"Biesaga","year":"2021","journal-title":"Curr. Opin. Chem. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0075","doi-asserted-by":"crossref","DOI":"10.3390\/ijms17020144","article-title":"Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods","volume":"17","author":"Du","year":"2016","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.jmb.2024.168872_b0080","doi-asserted-by":"crossref","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","article-title":"UniProt: the universal protein knowledgebase in 2023","volume":"51","author":"UniProt","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0085","article-title":"RefSeq and the prokaryotic genome annotation pipeline in the age of metagenomes","author":"Haft","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0090","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1093\/bfgp\/elac009","article-title":"A survey on protein-DNA-binding sites in computational biology","volume":"21","author":"Zhang","year":"2022","journal-title":"Brief. Funct. Genomics"},{"key":"10.1016\/j.jmb.2024.168872_b0095","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1093\/bib\/bbx022","article-title":"Review and comparative assessment of sequence-based predictors of protein-binding residues","volume":"19","author":"Zhang","year":"2018","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0100","article-title":"Comprehensive survey and comparative assessment of RNA-binding residue predictions with analysis by RNA type","volume":"21","author":"Wang","year":"2020","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.jmb.2024.168872_b0105","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1007\/s10822-019-00235-7","article-title":"Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies","volume":"33","author":"Macari","year":"2019","journal-title":"J. Comput. Aided Mol. Des."},{"key":"10.1016\/j.jmb.2024.168872_b0110","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1093\/bib\/bbv023","article-title":"A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues","volume":"17","author":"Yan","year":"2016","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0115","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab476","article-title":"Artificial intelligence in the prediction of protein\u2013ligand interactions: recent advances and future directions","volume":"23","author":"Dhakal","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0120","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1093\/bib\/bbx168","article-title":"Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains","volume":"20","author":"Zhang","year":"2019","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0125","doi-asserted-by":"crossref","first-page":"e51","DOI":"10.1093\/nar\/gkab044","article-title":"GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues","volume":"49","author":"Xia","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0130","doi-asserted-by":"crossref","first-page":"5069","DOI":"10.1021\/acs.jcim.1c00799","article-title":"DeepPocket: ligand binding site detection and segmentation using 3D convolutional neural networks","volume":"62","author":"Aggarwal","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.jmb.2024.168872_b0135","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab564","article-title":"AlphaFold2-aware protein-DNA binding site prediction using graph transformer","volume":"23","author":"Yuan","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2023.168091","article-title":"LigBind: identifying binding residues for over 1000 ligands with relation-aware graph neural networks","volume":"435","author":"Xia","year":"2023","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0145","doi-asserted-by":"crossref","first-page":"e60","DOI":"10.1093\/nar\/gkad288","article-title":"GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning","volume":"51","author":"Li","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0150","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad718","article-title":"DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model","volume":"39","author":"Fang","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0155","doi-asserted-by":"crossref","first-page":"i343","DOI":"10.1093\/bioinformatics\/btz324","article-title":"SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences","volume":"35","author":"Zhang","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0160","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1093\/bioinformatics\/btaa573","article-title":"Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0165","doi-asserted-by":"crossref","first-page":"i735","DOI":"10.1093\/bioinformatics\/btaa806","article-title":"PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0170","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1093\/bioinformatics\/btaa750","article-title":"DELPHI: accurate deep ensemble model for protein interaction sites prediction","volume":"37","author":"Li","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0175","doi-asserted-by":"crossref","first-page":"12814","DOI":"10.3390\/ijms232112814","article-title":"PITHIA: protein interaction site prediction using multiple sequence alignments and attention","volume":"23","author":"Hosseini","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.jmb.2024.168872_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2023.167945","article-title":"Modular deep learner that accurately predicts structure and disorder-annotated protein binding residues","author":"Zhang","year":"2023","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0185","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac480","article-title":"HN-PPISP: a hybrid network based on MLP-Mixer for protein\u2013protein interaction site prediction","volume":"24","author":"Kang","year":"2023","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2023.167963","article-title":"ISPRED-SEQ: deep neural networks and embeddings for predicting interaction sites in protein sequences","volume":"435","author":"Manfredi","year":"2023","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0195","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1109\/TCBB.2016.2616469","article-title":"Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs","volume":"14","author":"Hu","year":"2016","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0200","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1021\/acs.jcim.8b00749","article-title":"DNAPred: accurate identification of DNA-binding sites from protein sequence by ensembled hyperplane-distance-based support vector machines","volume":"59","author":"Zhu","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.jmb.2024.168872_b0205","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1093\/nar\/gkad1131","article-title":"HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins","volume":"52","author":"Zhang","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0210","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0158445","article-title":"FastRNABindR: fast and accurate prediction of protein-RNA interface residues","volume":"11","author":"El-Manzalawy","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.jmb.2024.168872_b0215","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12859-017-1879-2","article-title":"A boosting approach for prediction of protein-RNA binding residues","volume":"18","author":"Tang","year":"2017","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0220","doi-asserted-by":"crossref","first-page":"e25","DOI":"10.1093\/nar\/gkac1253","article-title":"HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins","volume":"51","author":"Zhang","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0225","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TCBB.2013.104","article-title":"Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering","volume":"10","author":"Yu","year":"2013","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0230","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-15-297","article-title":"Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble","volume":"15","author":"Yu","year":"2014","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0235","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TCBB.2016.2615010","article-title":"HEMEsPred: structure-based ligand-specific heme binding residues prediction by using fast-adaptive ensemble learning scheme","volume":"15","author":"Zhang","year":"2016","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.ab.2021.114241","article-title":"Accurate prediction of protein-ATP binding residues using position-specific frequency matrix","volume":"626","author":"Hu","year":"2021","journal-title":"Anal. Biochem."},{"key":"10.1016\/j.jmb.2024.168872_b0245","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1109\/TCBB.2022.3173437","article-title":"SCAMPER: accurate type-specific prediction of calcium-binding residues using sequence-derived features","volume":"20","author":"Zhang","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0250","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac444","article-title":"Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning","volume":"23","author":"Yuan","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0255","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad782","article-title":"M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings","volume":"40","author":"Shenoy","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0260","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1752-0509-4-S2-S1","article-title":"BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features","volume":"4","author":"Wang","year":"2010","journal-title":"BMC Syst. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0265","first-page":"e84","article-title":"DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues","volume":"45","author":"Yan","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0270","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1093\/bioinformatics\/bty756","article-title":"Improving the prediction of protein-nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods","volume":"35","author":"Su","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0275","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac244","article-title":"TSNAPred: predicting type-specific nucleic acid binding residues via an ensemble approach","volume":"23","author":"Nie","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0280","article-title":"iNucRes-ASSH: Identifying nucleic acid-binding residues in proteins by using self-attention-based structure-sequence hybrid neural network","author":"Zhang","year":"2023","journal-title":"Proteins"},{"key":"10.1016\/j.jmb.2024.168872_b0285","article-title":"MucLiPred: multi-level contrastive learning for predicting nucleic acid binding residues of proteins","author":"Zhang","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.jmb.2024.168872_b0290","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkv585","article-title":"High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder","volume":"43","author":"Peng","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0295","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1016\/j.jmb.2020.02.026","article-title":"ProNA2020 predicts protein\u2013DNA, protein\u2013RNA, and protein\u2013protein binding proteins and residues from sequence","volume":"432","author":"Qiu","year":"2020","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.jmb.2024.168872_b0300","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":"10.1016\/j.jmb.2024.168872_b0305","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/TCBB.2021.3118916","article-title":"To improve prediction of binding residues with DNA, RNA, carbohydrate, and peptide via multi-task deep neural networks","volume":"19","author":"Sun","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0310","doi-asserted-by":"crossref","first-page":"D1096","DOI":"10.1093\/nar\/gks966","article-title":"BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions","volume":"41","author":"Yang","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0315","article-title":"BioLiP2: an updated structure database for biologically relevant ligand\u2013protein interactions","author":"Zhang","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0320","doi-asserted-by":"crossref","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":"10.1016\/j.jmb.2024.168872_b0325","article-title":"NCBRPred: predicting nucleic acid binding residues in proteins based on multilabel learning","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168872_b0330","first-page":"PP","article-title":"Deep multi-label joint learning for RNA and DNA-binding proteins prediction","author":"Du","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0335","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/TCBB.2021.3118916","article-title":"To improve prediction of binding residues with DNA, RNA, carbohydrate, and peptide via multi-task deep neural networks","volume":"19","author":"Sun","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0340","doi-asserted-by":"crossref","first-page":"D520","DOI":"10.1093\/nar\/gky949","article-title":"Protein Data Bank: the single global archive for 3D macromolecular structure data","volume":"47","author":"wwPDB consortium","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0345","article-title":"HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins","volume":"51","author":"Zhang","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0350","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":"2011","journal-title":"Nature Methods"},{"key":"10.1016\/j.jmb.2024.168872_b0355","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1186\/1471-2105-8-211","article-title":"Composition Profiler: a tool for discovery and visualization of amino acid composition differences","volume":"8","author":"Vacic","year":"2007","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0360","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1186\/1471-2105-7-208","article-title":"Length-dependent prediction of protein intrinsic disorder","volume":"7","author":"Peng","year":"2006","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.jmb.2024.168872_b0365","doi-asserted-by":"crossref","DOI":"10.3390\/biom10121636","article-title":"Comparative assessment of intrinsic disorder predictions with a focus on protein and nucleic acid-binding proteins","volume":"10","author":"Katuwawala","year":"2020","journal-title":"Biomolecules"},{"key":"10.1016\/j.jmb.2024.168872_b0370","doi-asserted-by":"crossref","first-page":"W402","DOI":"10.1093\/nar\/gkz297","article-title":"The PSIPRED protein analysis workbench: 20 years on","volume":"47","author":"Buchan","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jmb.2024.168872_b0375","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1002\/prot.24682","article-title":"Accurate single-sequence prediction of solvent accessible surface area using local and global features","volume":"82","author":"Faraggi","year":"2014","journal-title":"Proteins"},{"key":"10.1016\/j.jmb.2024.168872_b0380","doi-asserted-by":"crossref","first-page":"4438","DOI":"10.1038\/s41467-021-24773-7","article-title":"flDPnn: accurate intrinsic disorder prediction with putative propensities of disorder functions","volume":"12","author":"Hu","year":"2021","journal-title":"Nature Commun."},{"key":"10.1016\/j.jmb.2024.168872_b0385","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":"Nature Methods"},{"key":"10.1016\/j.jmb.2024.168872_b0390","doi-asserted-by":"crossref","DOI":"10.1002\/prot.26582","article-title":"Critical assessment of protein intrinsic disorder prediction (CAID) \u2013 results of round 2","author":"Conte","year":"2023","journal-title":"Proteins"},{"key":"10.1016\/j.jmb.2024.168872_b0395","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1002\/prot.25585","article-title":"Evolutionary couplings and sequence variation effect predict protein binding sites","volume":"86","author":"Schelling","year":"2018","journal-title":"Proteins"},{"key":"10.1016\/j.jmb.2024.168872_b0400","doi-asserted-by":"crossref","first-page":"13891","DOI":"10.1038\/s41598-022-18205-9","article-title":"S-Pred: protein structural property prediction using MSA transformer","volume":"12","author":"Hong","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jmb.2024.168872_b0405","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad122","article-title":"Accurate and efficient protein sequence design through learning concise local environment of residues","volume":"39","author":"Huang","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168872_b0410","doi-asserted-by":"crossref","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":"10.1016\/j.jmb.2024.168872_b0415","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"}],"container-title":["Journal of Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022283624005023?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022283624005023?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T03:55:57Z","timestamp":1777694157000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022283624005023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":83,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["S0022283624005023"],"URL":"https:\/\/doi.org\/10.1016\/j.jmb.2024.168872","relation":{},"ISSN":["0022-2836"],"issn-type":[{"value":"0022-2836","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning","name":"articletitle","label":"Article Title"},{"value":"Journal of Molecular Biology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jmb.2024.168872","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"168872"}}