{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T14:23:46Z","timestamp":1778768626707,"version":"3.51.4"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["113\u20132311-B-007-007-MY3"],"award-info":[{"award-number":["113\u20132311-B-007-007-MY3"]}]},{"name":"National Science and Technology Council, Taiwan","award":["114\u20132311-B-007-002"],"award-info":[{"award-number":["114\u20132311-B-007-002"]}]},{"DOI":"10.13039\/501100005057","name":"National Tsing Hua University, Taiwan","doi-asserted-by":"crossref","award":["114QF003E1"],"award-info":[{"award-number":["114QF003E1"]}],"id":[{"id":"10.13039\/501100005057","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>X-ray crystallography is a central technique for high-resolution protein structure determination; however, the production of diffraction-quality crystals remains a major bottleneck due to high experimental cost and low success rates. Computational prediction of protein crystallization propensity offers a promising strategy to prioritize targets and reduce unnecessary experimental screening. While numerous computational predictors have been proposed, many rely primarily on sequence-derived features or provide limited interpretability, thereby offering little practical guidance for experimental design. Here, we present Shoebill, an interpretable protein crystallization propensity predictor that integrates AlphaFold2 (AF2)-derived structural descriptors with an XGBoost framework to assess whether a protein\u2014assuming successful expression and purification\u2014is likely to form diffraction-quality crystals. Shoebill leverages a comprehensive feature set extracted directly from AF2-predicted structures, capturing complementary structural information beyond sequence alone, including structural disorder, AF2 confidence metrics, molecular geometry, and surface physicochemical properties. On our primary independent benchmark, Shoebill outperforms existing nondeep-learning crystallization propensity predictors while maintaining balanced sensitivity and specificity, improving the area under the receiver operating characteristic curve from 0.700 to 0.804 and more than doubling the Matthews correlation coefficient from 0.123 to 0.297 relative to DCFCrystal, the best-performing nondeep-learning method. Although the deep learning-based predictor SADeepcry achieves higher overall predictive accuracy, Shoebill provides feature-level explanations for individual predictions through SHAP analysis, highlighting biologically meaningful features associated with crystallization propensity that may help guide rational strategies for construct optimization. The Shoebill source code and a user-friendly web server are publicly available at https:\/\/github.com\/KJ-Liao\/Shoebill.<\/jats:p>","DOI":"10.1093\/bib\/bbag233","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T11:41:13Z","timestamp":1776944473000},"source":"Crossref","is-referenced-by-count":0,"title":["Shoebill: an interpretable AlphaFold2-informed predictor of protein crystallization propensity using XGBoost"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0475-6459","authenticated-orcid":false,"given":"Kuan-Ju","family":"Liao","sequence":"first","affiliation":[{"name":"Institute of Bioinformatics and Structural Biology, National Tsing Hua University , No. 101, Section 2, Kuang-Fu Road, Hsinchu 300044,","place":["Taiwan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4734-7848","authenticated-orcid":false,"given":"Yuh-Ju","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics and Structural Biology, National Tsing Hua University , No. 101, Section 2, Kuang-Fu Road, Hsinchu 300044,","place":["Taiwan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"2026051410042631700_ref1","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1038\/80784","article-title":"From structure to function: approaches and limitations","volume":"7","author":"Thornton","year":"2000","journal-title":"Nat Struct Biol"},{"key":"2026051410042631700_ref2","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/S1359-6446(05)03484-7","article-title":"Structural biology and drug discovery","volume":"10","author":"Congreve","year":"2005","journal-title":"Drug Discov Today"},{"key":"2026051410042631700_ref3","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1016\/j.drudis.2013.10.027","article-title":"Modelling three-dimensional protein structures for applications in drug design","volume":"19","author":"Schmidt","year":"2014","journal-title":"Drug Discov Today"},{"key":"2026051410042631700_ref4","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3390\/molecules25051030","article-title":"Protein X-ray crystallography and drug discovery","volume":"25","author":"Maveyraud","year":"2020","journal-title":"Molecules"},{"key":"2026051410042631700_ref5","doi-asserted-by":"publisher","first-page":"167127","DOI":"10.1016\/j.jmb.2021.167127","article-title":"Integrative structural biology in the era of accurate structure prediction","volume":"433","author":"Masrati","year":"2021","journal-title":"J Mol Biol"},{"key":"2026051410042631700_ref6","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1146\/annurev-biochem-013118-111947","article-title":"Biophysical techniques in structural biology","volume":"88","author":"Dobson","year":"2019","journal-title":"Annu Rev Biochem"},{"key":"2026051410042631700_ref7","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":"ww PDBc.","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026051410042631700_ref8","doi-asserted-by":"publisher","first-page":"i24","DOI":"10.1093\/bioinformatics\/btr229","article-title":"Sequence-based prediction of protein crystallization, purification and production propensity","volume":"27","author":"Mizianty","year":"2011","journal-title":"Bioinformatics"},{"key":"2026051410042631700_ref9","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/nmeth.f.203","article-title":"Protein crystallization: from purified protein to diffraction-quality crystal","volume":"5","author":"Chayen","year":"2008","journal-title":"Nat Methods"},{"key":"2026051410042631700_ref10","doi-asserted-by":"publisher","first-page":"102572","DOI":"10.1016\/j.xpro.2023.102572","article-title":"A concise guide to choosing suitable gene expression systems for recombinant protein production","volume":"4","author":"Schutz","year":"2023","journal-title":"STAR Protoc"},{"key":"2026051410042631700_ref11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-1-62703-977-2_1","article-title":"Protein purification: an overview","volume":"1129","author":"Labrou","year":"2014","journal-title":"Methods Mol Biol"},{"key":"2026051410042631700_ref12","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1107\/S2053230X14011601","article-title":"Automation in biological crystallization","volume":"70","author":"Stewart","year":"2014","journal-title":"Acta Crystallogr F Struct Biol Commun"},{"key":"2026051410042631700_ref13","doi-asserted-by":"publisher","first-page":"5046","DOI":"10.1038\/srep05046","article-title":"Nanoliter-scale protein crystallization and screening with a microfluidic droplet robot","volume":"4","author":"Zhu","year":"2014","journal-title":"Sci Rep"},{"key":"2026051410042631700_ref14","doi-asserted-by":"publisher","first-page":"9514","DOI":"10.3390\/ijms13089514","article-title":"Correlation between protein sequence similarity and crystallization reagents in the biological macromolecule crystallization database","volume":"13","author":"Lu","year":"2012","journal-title":"Int J Mol Sci"},{"key":"2026051410042631700_ref15","doi-asserted-by":"publisher","first-page":"100024","DOI":"10.1016\/j.patter.2020.100024","article-title":"A searchable database of crystallization cocktails in the PDB: Analyzing the chemical condition space","volume":"1","author":"Lynch","year":"2020","journal-title":"Patterns"},{"key":"2026051410042631700_ref16","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1002\/prot.26844","article-title":"Using AlphaFold and symmetrical docking to predict protein-protein interactions for exploring potential crystallization conditions","volume":"93","author":"Liao","year":"2025","journal-title":"Proteins"},{"key":"2026051410042631700_ref17","doi-asserted-by":"publisher","first-page":"557","DOI":"10.3934\/biophy.2017.4.557","article-title":"Protein crystallization: eluding the bottleneck of X-ray crystallography","volume":"4","author":"Holcomb","year":"2017","journal-title":"AIMS Biophys"},{"key":"2026051410042631700_ref18","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1146\/annurev.biophys.050708.133740","article-title":"Lessons from structural genomics","volume":"38","author":"Terwilliger","year":"2009","journal-title":"Annu Rev Biophys"},{"key":"2026051410042631700_ref19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/nbt.1514","article-title":"Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data","volume":"27","author":"Price","year":"2009","journal-title":"Nat Biotechnol"},{"key":"2026051410042631700_ref20","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1107\/S2053230X15014892","article-title":"Analysis of crystallization data in the protein data Bank","volume":"71","author":"Kirkwood","year":"2015","journal-title":"Acta Crystallogr F Struct Biol Commun"},{"key":"2026051410042631700_ref21","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1007\/s00726-016-2274-4","article-title":"TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM","volume":"48","author":"Hu","year":"2016","journal-title":"Amino Acids"},{"key":"2026051410042631700_ref22","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1093\/bioinformatics\/btz762","article-title":"BCrystal: an interpretable sequence-based protein crystallization predictor","volume":"36","author":"Elbasir","year":"2020","journal-title":"Bioinformatics"},{"key":"2026051410042631700_ref23","doi-asserted-by":"publisher","first-page":"i709","DOI":"10.1093\/bioinformatics\/btaa791","article-title":"CLPred: a sequence-based protein crystallization predictor using BLSTM neural network","volume":"36","author":"Xuan","year":"2020","journal-title":"Bioinformatics"},{"key":"2026051410042631700_ref24","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1186\/s12859-017-1995-z","article-title":"fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization","volume":"18","author":"Meng","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2026051410042631700_ref25","doi-asserted-by":"publisher","first-page":"bbaa076","DOI":"10.1093\/bib\/bbaa076","article-title":"Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features","volume":"22","author":"Zhu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026051410042631700_ref26","doi-asserted-by":"publisher","first-page":"bbac32","DOI":"10.1093\/bib\/bbac352","article-title":"SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026051410042631700_ref27","doi-asserted-by":"publisher","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J R Soc Interface"},{"key":"2026051410042631700_ref28","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat Mach Intell"},{"key":"2026051410042631700_ref29","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":"2026051410042631700_ref30","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2026051410042631700_ref31","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":"2026051410042631700_ref32","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":"2026051410042631700_ref33","doi-asserted-by":"publisher","first-page":"e101123","DOI":"10.1371\/journal.pone.0101123","article-title":"Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms","volume":"9","author":"Fusco","year":"2014","journal-title":"PloS One"},{"key":"2026051410042631700_ref34","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"2026051410042631700_ref35","first-page":"4768","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017)","author":"Lundberg","year":"2017"},{"key":"2026051410042631700_ref36","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10969-011-9106-2","article-title":"The structural biology knowledgebase: a portal to protein structures, sequences, functions, and methods","volume":"12","author":"Gabanyi","year":"2011","journal-title":"J Struct Funct Genomics"},{"key":"2026051410042631700_ref37","doi-asserted-by":"publisher","first-page":"115020","DOI":"10.1016\/j.ab.2022.115020","article-title":"GCmapCrys: integrating graph attention network with predicted contact map for multi-stage protein crystallization propensity prediction","volume":"663","author":"Wang","year":"2023","journal-title":"Anal Biochem"},{"key":"2026051410042631700_ref38","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1007\/s12539-024-00639-6","article-title":"PLMC: language model of protein sequences enhances protein crystallization prediction","volume":"16","author":"Xiong","year":"2024","journal-title":"Interdiscip Sci"},{"key":"2026051410042631700_ref39","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2026051410042631700_ref40","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1093\/bioinformatics\/btab801","article-title":"NetSolP: predicting protein solubility in Escherichia coli using language models","volume":"38","author":"Thumuluri","year":"2022","journal-title":"Bioinformatics"},{"key":"2026051410042631700_ref41","doi-asserted-by":"publisher","first-page":"D202","DOI":"10.1093\/nar\/gkm998","article-title":"AAindex: amino acid index database, progress report 2008","volume":"36","author":"Kawashima","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2026051410042631700_ref42","doi-asserted-by":"publisher","first-page":"e8140","DOI":"10.1371\/journal.pone.0008140","article-title":"Generating triangulated macromolecular surfaces by Euclidean distance transform","volume":"4","author":"Xu","year":"2009","journal-title":"PloS One"},{"key":"2026051410042631700_ref43","doi-asserted-by":"publisher","first-page":"D411","DOI":"10.1093\/nar\/gkq1105","article-title":"A series of PDB related databases for everyday needs","volume":"39","author":"Joosten","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2026051410042631700_ref44","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1002\/bip.360221211","article-title":"Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features","volume":"22","author":"Kabsch","year":"1983","journal-title":"Biopolymers"},{"key":"2026051410042631700_ref45","doi-asserted-by":"publisher","first-page":"167208","DOI":"10.1016\/j.jmb.2021.167208","article-title":"AlphaFold and implications for intrinsically disordered proteins","volume":"433","author":"Ruff","year":"2021","journal-title":"J Mol Biol"},{"key":"2026051410042631700_ref46","doi-asserted-by":"publisher","first-page":"3248","DOI":"10.1016\/j.csbj.2023.06.001","article-title":"Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins","volume":"21","author":"Zhao","year":"2023","journal-title":"Comput Struct Biotechnol J"},{"key":"2026051410042631700_ref47","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1038\/s41594-022-00849-w","article-title":"A structural biology community assessment of AlphaFold2 applications","volume":"29","author":"Akdel","year":"2022","journal-title":"Nat Struct Mol Biol"},{"key":"2026051410042631700_ref48","doi-asserted-by":"publisher","first-page":"406","DOI":"10.3390\/a15110406","article-title":"Stable evaluation of 3D Zernike moments for surface meshes","volume":"15","author":"J\u00e9r\u00f4me Houdayer","year":"2022","journal-title":"Algorithms"},{"key":"2026051410042631700_ref49","doi-asserted-by":"publisher","first-page":"3013","DOI":"10.1093\/bioinformatics\/btz026","article-title":"KORP: knowledge-based 6D potential for fast protein and loop modeling","volume":"35","author":"Lopez-Blanco","year":"2019","journal-title":"Bioinformatics"},{"key":"2026051410042631700_ref50","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","article-title":"A review of feature selection methods based on mutual information","volume":"24","author":"Jorge","year":"2014","journal-title":"Neural Comput Applic"},{"key":"2026051410042631700_ref51","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1107\/S0907444905035237","article-title":"Entropy and surface engineering in protein crystallization","volume":"62","author":"Derewenda","year":"2006","journal-title":"Acta Crystallogr D Biol Crystallogr"},{"key":"2026051410042631700_ref52","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1146\/annurev-biochem-072711-164947","article-title":"Intrinsically disordered proteins and intrinsically disordered protein regions","volume":"83","author":"Oldfield","year":"2014","journal-title":"Annu Rev Biochem"},{"key":"2026051410042631700_ref53","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1002\/pro.3041","article-title":"Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe","volume":"25","author":"Necci","year":"2016","journal-title":"Protein Sci"},{"key":"2026051410042631700_ref54","doi-asserted-by":"publisher","first-page":"3213","DOI":"10.1021\/acs.biochem.1c00501","article-title":"Glycine-rich peptides from FUS have an intrinsic ability to self-assemble into fibers and networked fibrils","volume":"60","author":"Kar","year":"2021","journal-title":"Biochemistry"},{"key":"2026051410042631700_ref55","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1021\/acs.jctc.2c01027","article-title":"Effective molecular dynamics from neural network-based structure prediction models","volume":"19","author":"Jussupow","year":"2023","journal-title":"J Chem Theory Comput"},{"key":"2026051410042631700_ref56","doi-asserted-by":"publisher","first-page":"11643","DOI":"10.3390\/ijms140611643","article-title":"An overview of biological macromolecule crystallization","volume":"14","author":"Russo Krauss","year":"2013","journal-title":"Int J Mol Sci"},{"key":"2026051410042631700_ref57","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1126\/science.add2187","article-title":"Robust deep learning-based protein sequence design using ProteinMPNN","volume":"378","author":"Dauparas","year":"2022","journal-title":"Science"},{"key":"2026051410042631700_ref58","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/S0065-3233(05)70003-6","article-title":"The structure of alpha-helical coiled coils","volume":"70","author":"Lupas","year":"2005","journal-title":"Adv Protein Chem"},{"key":"2026051410042631700_ref59","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-1-4939-7000-1_4","article-title":"The \"sticky patch\" model of crystallization and modification of proteins for enhanced crystallizability","volume":"1607","author":"Derewenda","year":"2017","journal-title":"Methods Mol Biol"},{"key":"2026051410042631700_ref60","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1074\/mcp.O111.013706","article-title":"A human ubiquitin conjugating enzyme (E2)-HECT E3 ligase structure-function screen","volume":"11","author":"Sheng","year":"2012","journal-title":"Mol Cell Proteomics"},{"key":"2026051410042631700_ref61","doi-asserted-by":"publisher","first-page":"906437","DOI":"10.3389\/fmolb.2022.906437","article-title":"AlphaFold 2 and NMR spectroscopy: partners to understand protein structure","volume":"9","author":"Laurents","year":"2022","journal-title":"Dynamics and Function, Front Mol Biosci"},{"key":"2026051410042631700_ref62","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1038\/s42003-023-05610-7","article-title":"Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning","volume":"6","author":"Xia","year":"2023","journal-title":"Commun Biol"},{"key":"2026051410042631700_ref63","doi-asserted-by":"publisher","first-page":"21383","DOI":"10.1038\/srep21383","article-title":"Crysalis: an integrated server for computational analysis and design of protein crystallization","volume":"6","author":"Wang","year":"2016","journal-title":"Sci Rep"},{"key":"2026051410042631700_ref64","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1110\/ps.072914007","article-title":"Toward rational protein crystallization: a web server for the design of crystallizable protein variants","volume":"16","author":"Goldschmidt","year":"2007","journal-title":"Protein Sci"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/3\/bbag233\/68284959\/bbag233.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/3\/bbag233\/68284959\/bbag233.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T14:04:43Z","timestamp":1778767483000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag233\/8678072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":64,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag233","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,5]]},"published":{"date-parts":[[2026,5]]},"article-number":"bbag233"}}