{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:37:59Z","timestamp":1777606679329,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010851","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000}}],"reference-count":62,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01GM122845"],"award-info":[{"award-number":["R01GM122845"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AD057555"],"award-info":[{"award-number":["R01AD057555"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2226183"],"award-info":[{"award-number":["2226183"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain \u201cdark\u201d\u2014i.e., their small-molecule ligands are undiscovered due to experimental limitations or human\/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010851","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T18:45:15Z","timestamp":1674067515000},"page":"e1010851","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":20,"title":["End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins"],"prefix":"10.1371","volume":"19","author":[{"given":"Tian","family":"Cai","sequence":"first","affiliation":[]},{"given":"Li","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9497-6263","authenticated-orcid":true,"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Muge","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Di","family":"He","sequence":"additional","affiliation":[]},{"given":"Amitesh","family":"Badkul","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hari Krishna","family":"Namballa","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Dorogan","sequence":"additional","affiliation":[]},{"given":"Wayne W.","family":"Harding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7985-2561","authenticated-orcid":true,"given":"Cameron","family":"Mura","sequence":"additional","affiliation":[]},{"given":"Philip E.","family":"Bourne","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9051-2111","authenticated-orcid":true,"given":"Lei","family":"Xie","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"issue":"4","key":"pcbi.1010851.ref001","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1021\/acs.jcim.0c01285","article-title":"MSA-Regularized Protein Sequence Transformer toward Predicting Genome-Wide Chemical-Protein Interactions: Application to GPCRome Deorphanization","volume":"61","author":"T Cai","year":"2021","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"2","key":"pcbi.1010851.ref002","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/s43018-020-00169-2","article-title":"Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients","volume":"2","author":"J Ma","year":"2021","journal-title":"Nature Cancer"},{"key":"pcbi.1010851.ref003","first-page":"1","article-title":"A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening","author":"D He","year":"2022","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"pcbi.1010851.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-21511-x","article-title":"Improved protein structure refinement guided by deep learning based accuracy estimation","volume":"12","author":"N Hiranuma","year":"2021","journal-title":"Nature communications"},{"key":"pcbi.1010851.ref005","first-page":"1","article-title":"Highly accurate protein structure prediction with AlphaFold","author":"J Jumper","year":"2021","journal-title":"Nature"},{"key":"pcbi.1010851.ref006","article-title":"Accurate prediction of protein structures and interactions using a 3-track network","author":"M Baek","year":"2021","journal-title":"bioRxiv"},{"issue":"3","key":"pcbi.1010851.ref007","first-page":"1","article-title":"Identifying cell types from single-cell data based on similarities and dissimilarities between cells","volume":"22","author":"Y Li","year":"2021","journal-title":"BMC bioinformatics"},{"issue":"5","key":"pcbi.1010851.ref008","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/JPROC.2021.3058954","article-title":"Toward causal representation learning","volume":"109","author":"B Sch\u00f6lkopf","year":"2021","journal-title":"Proceedings of the IEEE"},{"key":"pcbi.1010851.ref009","unstructured":"Chen W, Yu Z, Wang Z, Anandkumar A. Automated synthetic-to-real generalization. In: International Conference on Machine Learning. PMLR; 2020. p. 1746\u20131756."},{"key":"pcbi.1010851.ref010","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:190911942. 2019;."},{"key":"pcbi.1010851.ref011","article-title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","author":"C Finn","year":"2017","journal-title":"CoRR"},{"key":"pcbi.1010851.ref012","article-title":"Meta-Learning in Neural Networks: A Survey","author":"TM Hospedales","year":"2020","journal-title":"CoRR"},{"issue":"7","key":"pcbi.1010851.ref013","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1007\/s00335-019-09809-0","article-title":"Exploring the dark genome: implications for precision medicine","volume":"30","author":"TI Oprea","year":"2019","journal-title":"Mammalian Genome"},{"key":"pcbi.1010851.ref014","first-page":"1","article-title":"Understudied proteins: opportunities and challenges for functional proteomics","author":"G Kustatscher","year":"2022","journal-title":"Nature Methods"},{"key":"pcbi.1010851.ref015","first-page":"1","article-title":"An open invitation to the Understudied Proteins Initiative","author":"G Kustatscher","year":"2022","journal-title":"Nature Biotechnology"},{"key":"pcbi.1010851.ref016","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1146\/annurev-pharmtox-010611-134630","article-title":"Novel computational approaches to polypharmacology as a means to define responses to individual drugs","volume":"52","author":"L Xie","year":"2012","journal-title":"Annual review of pharmacology and toxicology"},{"issue":"D1","key":"pcbi.1010851.ref017","first-page":"D845","article-title":"The DisGeNET knowledge platform for disease genomics: 2019 update","volume":"48","author":"J Pi\u00f1ero","year":"2020","journal-title":"Nucleic Acids Research"},{"issue":"18","key":"pcbi.1010851.ref018","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1093\/bioinformatics\/btz111","article-title":"DeepAffinity: interpretable deep learning of compound\u2013protein affinity through unified recurrent and convolutional neural networks","volume":"35","author":"M Karimi","year":"2019","journal-title":"Bioinformatics"},{"issue":"17","key":"pcbi.1010851.ref019","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: deep drug\u2013target binding affinity prediction","volume":"34","author":"H \u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"pcbi.1010851.ref020","doi-asserted-by":"crossref","first-page":"138","DOI":"10.3389\/fchem.2018.00138","article-title":"Reverse screening methods to search for the protein targets of chemopreventive compounds","volume":"6","author":"H Huang","year":"2018","journal-title":"Frontiers in chemistry"},{"key":"pcbi.1010851.ref021","doi-asserted-by":"crossref","first-page":"102372","DOI":"10.1016\/j.sbi.2022.102372","article-title":"AlphaFold illuminates half of the dark human proteins","volume":"74","author":"JL Binder","year":"2022","journal-title":"Current Opinion in Structural Biology"},{"issue":"7","key":"pcbi.1010851.ref022","doi-asserted-by":"crossref","first-page":"10150","DOI":"10.3390\/molecules190710150","article-title":"Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design","volume":"19","author":"SZ Grinter","year":"2014","journal-title":"Molecules"},{"issue":"3","key":"pcbi.1010851.ref023","doi-asserted-by":"crossref","first-page":"e1007680","DOI":"10.1371\/journal.pcbi.1007680","article-title":"Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity","volume":"16","author":"M Jaiteh","year":"2020","journal-title":"PLoS computational biology"},{"key":"pcbi.1010851.ref024","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805. 2018;."},{"key":"pcbi.1010851.ref025","first-page":"1","article-title":"Single-sequence protein structure prediction using a language model and deep learning","author":"R Chowdhury","year":"2022","journal-title":"Nature Biotechnology"},{"key":"pcbi.1010851.ref026","article-title":"Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model","author":"S Sledzieski","year":"2021","journal-title":"bioRxiv"},{"issue":"15","key":"pcbi.1010851.ref027","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"A Rives","year":"2021","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"14","key":"pcbi.1010851.ref028","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.1073\/pnas.0704422105","article-title":"Detecting evolutionary relationships across existing fold space, using sequence order-independent profile\u2013profile alignments","volume":"105","author":"L Xie","year":"2008","journal-title":"Proceedings of the National Academy of sciences"},{"issue":"10","key":"pcbi.1010851.ref029","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1038\/s41592-021-01283-4","article-title":"Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms","volume":"18","author":"M AlQuraishi","year":"2021","journal-title":"Nature methods"},{"issue":"D1","key":"pcbi.1010851.ref030","doi-asserted-by":"crossref","first-page":"D412","DOI":"10.1093\/nar\/gkaa913","article-title":"Pfam: The protein families database in 2021","volume":"49","author":"J Mistry","year":"2021","journal-title":"Nucleic Acids Research"},{"issue":"D1","key":"pcbi.1010851.ref031","doi-asserted-by":"crossref","first-page":"D945","DOI":"10.1093\/nar\/gkw1074","article-title":"The ChEMBL database in 2017","volume":"45","author":"A Gaulton","year":"2016","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1010851.ref032","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading","volume":"31","author":"O Trott","year":"2010","journal-title":"Journal of Computational Chemistry"},{"key":"pcbi.1010851.ref033","doi-asserted-by":"crossref","unstructured":"Li S, Zhou J, Xu T, Huang L, Wang F, Xiong H, et al. Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining; 2021. p. 975\u2013985.","DOI":"10.1145\/3447548.3467311"},{"issue":"14","key":"pcbi.1010851.ref034","doi-asserted-by":"crossref","first-page":"6582","DOI":"10.1021\/jm300687e","article-title":"Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking","volume":"55","author":"MM Mysinger","year":"2012","journal-title":"Journal of medicinal chemistry"},{"issue":"1","key":"pcbi.1010851.ref035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1097\/FBP.0b013e3283242f05","article-title":"Genetics of dopamine receptors and drug addiction: a comprehensive review","volume":"20","author":"B Le Foll","year":"2009","journal-title":"Behavioural pharmacology"},{"key":"pcbi.1010851.ref036","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neulet.2018.09.043","article-title":"Alteration of dopamine receptors subtypes in the brain of opioid abusers: a postmortem study in Iran","volume":"687","author":"MS Sadat-Shirazi","year":"2018","journal-title":"Neuroscience letters"},{"issue":"4","key":"pcbi.1010851.ref037","doi-asserted-by":"crossref","first-page":"e12988","DOI":"10.1111\/adb.12988","article-title":"Low-dose polypharmacology targeting dopamine D1 and D3 receptors reduces cue-induced relapse to heroin seeking in rats","volume":"26","author":"ST Ewing","year":"2021","journal-title":"Addiction Biology"},{"issue":"1","key":"pcbi.1010851.ref038","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neuron.2016.06.014","article-title":"Parkinsonism driven by antipsychotics originates from dopaminergic control of striatal cholinergic interneurons","volume":"91","author":"G Kharkwal","year":"2016","journal-title":"Neuron"},{"key":"pcbi.1010851.ref039","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neubiorev.2018.03.020","article-title":"Dopamine D1 and D3 receptor polypharmacology as a potential treatment approach for substance use disorder","volume":"89","author":"E Galaj","year":"2018","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"pcbi.1010851.ref040","unstructured":"Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, et al. Strategies For Pre-training Graph Neural Networks. 2020;."},{"issue":"383","key":"pcbi.1010851.ref041","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aag1166","article-title":"The druggable genome and support for target identification and validation in drug development","volume":"9","author":"C Finan","year":"2017","journal-title":"Science translational medicine"},{"issue":"D1","key":"pcbi.1010851.ref042","doi-asserted-by":"crossref","first-page":"D1334","DOI":"10.1093\/nar\/gkaa993","article-title":"UTCRD and Pharos 2021: mining the human proteome for disease biology","volume":"49","author":"TK Sheils","year":"2021","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1010851.ref043","doi-asserted-by":"crossref","first-page":"eaag1166","DOI":"10.1126\/scitranslmed.aag1166","article-title":"The druggable genome and support for target identification and validation in drug development","volume":"9","author":"C Finan","year":"2017","journal-title":"Science Translational Medicine"},{"issue":"4","key":"pcbi.1010851.ref044","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1038\/nm.4306","article-title":"The Drug Repurposing Hub: a next-generation drug library and information resource","volume":"23","author":"SM Corsello","year":"2017","journal-title":"Nature medicine"},{"issue":"13","key":"pcbi.1010851.ref045","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1093\/bioinformatics\/bts251","article-title":"DAVID-WS: a stateful web service to facilitate gene\/protein list analysis","volume":"28","author":"X Jiao","year":"2012","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1010851.ref046","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1124\/pr.115.011239","article-title":"Pharmacology of modulators of alternative splicing","volume":"69","author":"DO Bates","year":"2017","journal-title":"Pharmacological reviews"},{"issue":"10","key":"pcbi.1010851.ref047","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1038\/aps.2015.43","article-title":"Alternative splicing as a biomarker and potential target for drug discovery","volume":"36","author":"Kq Le","year":"2015","journal-title":"Acta Pharmacologica Sinica"},{"issue":"2","key":"pcbi.1010851.ref048","article-title":"Alternative splicing in Alzheimer\u2019s disease","volume":"2","author":"JE Love","year":"2015","journal-title":"Journal of Parkinson\u2019s disease and Alzheimer\u2019s disease"},{"issue":"1","key":"pcbi.1010851.ref049","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep31222","article-title":"Insulin receptor alternative splicing is regulated by insulin signaling and modulates beta cell survival","volume":"6","author":"P Malakar","year":"2016","journal-title":"Scientific reports"},{"issue":"6223","key":"pcbi.1010851.ref050","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1126\/science.aaa0314","article-title":"A small-molecule inhibitor of the aberrant transcription factor CBF\u03b2-SMMHC delays leukemia in mice","volume":"347","author":"A Illendula","year":"2015","journal-title":"Science"},{"key":"pcbi.1010851.ref051","unstructured":"Zhang S, Liu Y, Xie L. Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes. arXiv preprint arXiv:220602789. 2022;."},{"issue":"3","key":"pcbi.1010851.ref052","first-page":"1","article-title":"Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding","volume":"23","author":"Y Liu","year":"2022","journal-title":"BMC bioinformatics"},{"key":"pcbi.1010851.ref053","article-title":"COVID-19 multi-targeted drug repurposing using few-shot learning","volume":"1","author":"Y Liu","year":"2021","journal-title":"Frontiers in Bioinformatics"},{"key":"pcbi.1010851.ref054","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? arXiv preprint arXiv:181000826. 2018;."},{"issue":"1","key":"pcbi.1010851.ref055","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"HM Berman","year":"2000","journal-title":"Nucleic Acids Research"},{"issue":"D1","key":"pcbi.1010851.ref056","doi-asserted-by":"crossref","first-page":"D1096","DOI":"10.1093\/nar\/gks966","article-title":"BioLiP: a semi-manually curated database for biologically relevant ligand\u2013protein interactions","volume":"41","author":"J Yang","year":"2012","journal-title":"Nucleic acids research"},{"issue":"W1","key":"pcbi.1010851.ref057","doi-asserted-by":"crossref","first-page":"W200","DOI":"10.1093\/nar\/gky448","article-title":"HMMER web server: 2018 update","volume":"46","author":"SC Potter","year":"2018","journal-title":"Nucleic acids research"},{"key":"pcbi.1010851.ref058","doi-asserted-by":"crossref","DOI":"10.1017\/9781108583664","volume-title":"Introduction to applied linear algebra: vectors, matrices, and least squares","author":"S Boyd","year":"2018"},{"key":"pcbi.1010851.ref059","unstructured":"Santos Cd, Tan M, Xiang B, Zhou B. Attentive pooling networks. arXiv preprint arXiv:160203609. 2016;."},{"key":"pcbi.1010851.ref060","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"pcbi.1010851.ref061","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/bib\/bbz157","article-title":"Machine learning approaches and databases for prediction of drug\u2013target interaction: a survey paper","volume":"22","author":"M Bagherian","year":"2021","journal-title":"Briefings in bioinformatics"},{"issue":"12","key":"pcbi.1010851.ref062","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1021\/jm030580l","article-title":"The PDBbind database: Collection of binding affinities for protein- ligand complexes with known three-dimensional structures","volume":"47","author":"R Wang","year":"2004","journal-title":"Journal of medicinal chemistry"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010851","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T23:02:17Z","timestamp":1701730937000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010851"}},"subtitle":[],"editor":[{"given":"Jeffrey","family":"Skolnick","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,1,18]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010851","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1010851","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,18]]}}}