{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:53:57Z","timestamp":1773356037729,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12104295"],"award-info":[{"award-number":["12104295"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31630002"],"award-info":[{"award-number":["31630002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872094"],"award-info":[{"award-number":["61872094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61832019"],"award-info":[{"award-number":["61832019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872094"],"award-info":[{"award-number":["61872094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (&gt;\u20094 mutation sites), when finetuned by using only a small number of experimental mutation data (&lt;\u200950). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.<\/jats:p>","DOI":"10.1186\/s13321-023-00688-x","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T15:03:57Z","timestamp":1675436637000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering"],"prefix":"10.1186","volume":"15","author":[{"given":"Mingchen","family":"Li","sequence":"first","affiliation":[]},{"given":"Liqi","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Yu Guang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guisheng","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Pan","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Hong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"issue":"3","key":"688_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1021\/ar960017f","volume":"31","author":"FH Arnold","year":"1998","unstructured":"Arnold FH (1998) Design by directed evolution. Acc Chem Res 31(3):125\u2013131","journal-title":"Acc Chem Res"},{"issue":"18","key":"688_CR2","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1073\/pnas.1901979116","volume":"116","author":"Z Wu","year":"2019","unstructured":"Wu Z et al (2019) Machine learning-assisted directed protein evolution with combinatorial libraries. Proc Natl Acad Sci 116(18):8852\u20138858","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"688_CR3","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1021\/acscatal.0c05126","volume":"11","author":"Y Cui","year":"2021","unstructured":"Cui Y et al (2021) Computational redesign of a PETase for plastic biodegradation under ambient condition by the GRAPE strategy. ACS Catal 11(3):1340\u20131350","journal-title":"ACS Catal"},{"issue":"6526","key":"688_CR4","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1126\/science.abd7331","volume":"371","author":"B Hie","year":"2021","unstructured":"Hie B et al (2021) Learning the language of viral evolution and escape. Science 371(6526):284\u2013288","journal-title":"Science"},{"key":"688_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2022.01.003","author":"BL Hie","year":"2022","unstructured":"Hie BL, Yang KK, Kim PS (2022) Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins. Cell Syst. https:\/\/doi.org\/10.1016\/j.cels.2022.01.003","journal-title":"Cell Syst"},{"issue":"15","key":"688_CR6","doi-asserted-by":"publisher","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","volume":"118","author":"A Rives","year":"2021","unstructured":"Rives A et al (2021) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci 118(15):e2016239118","journal-title":"Proc Natl Acad Sci"},{"key":"688_CR7","first-page":"9689","volume":"32","author":"R Rao","year":"2019","unstructured":"Rao R et al (2019) Evaluating protein transfer learning with TAPE. Adv Neural Inf Process Syst 32:9689","journal-title":"Adv Neural Inf Process Syst"},{"key":"688_CR8","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2206.13517","author":"E Nijkamp","year":"2022","unstructured":"Nijkamp E et al (2022) ProGen2: exploring the boundaries of protein language models. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.2206.13517","journal-title":"arXiv Preprint"},{"key":"688_CR9","doi-asserted-by":"publisher","DOI":"10.1101\/2021.07.09.450648","author":"J Meier","year":"2021","unstructured":"Meier J et al (2021) Language models enable zero-shot prediction of the effects of mutations on protein function. bioRxiv. https:\/\/doi.org\/10.1101\/2021.07.09.450648","journal-title":"bioRxiv"},{"key":"688_CR10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2007.06225","author":"A Elnaggar","year":"2020","unstructured":"Elnaggar A et al (2020) ProtTrans: towards cracking the language of Life\u2019s code through self-supervised deep learning and high performance computing. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.2007.06225","journal-title":"arXiv Preprint"},{"issue":"8","key":"688_CR11","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","volume":"38","author":"N Brandes","year":"2022","unstructured":"Brandes N et al (2022) ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38(8):2102\u20132110","journal-title":"Bioinformatics"},{"issue":"12","key":"688_CR12","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","volume":"16","author":"EC Alley","year":"2019","unstructured":"Alley EC et al (2019) Unified rational protein engineering with sequence-based deep representation learning. Nat Methods 16(12):1315\u20131322","journal-title":"Nat Methods"},{"issue":"6502","key":"688_CR13","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1126\/science.aba3304","volume":"369","author":"WP Russ","year":"2020","unstructured":"Russ WP et al (2020) An evolution-based model for designing chorismate mutase enzymes. Science 369(6502):440\u2013445","journal-title":"Science"},{"issue":"10","key":"688_CR14","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1038\/s41592-018-0138-4","volume":"15","author":"AJ Riesselman","year":"2018","unstructured":"Riesselman AJ, Ingraham JB, Marks DS (2018) Deep generative models of genetic variation capture the effects of mutations. Nat Methods 15(10):816\u2013822","journal-title":"Nat Methods"},{"key":"688_CR15","unstructured":"Rao RM et al (2021) MSA transformer, in proceedings of the 38th international conference on machine learning. In: Marina M, Tong Z (eds). PMLR: proceedings of machine learning research. p. 8844-8856."},{"issue":"2","key":"688_CR16","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1038\/nbt.3769","volume":"35","author":"TA Hopf","year":"2017","unstructured":"Hopf TA et al (2017) Mutation effects predicted from sequence co-variation. Nat Biotechnol 35(2):128\u2013135","journal-title":"Nat Biotechnol"},{"issue":"1","key":"688_CR17","doi-asserted-by":"publisher","first-page":"5743","DOI":"10.1038\/s41467-021-25976-8","volume":"12","author":"Y Luo","year":"2021","unstructured":"Luo Y et al (2021) ECNet is an evolutionary context-integrated deep learning framework for protein engineering. Nat Commun 12(1):5743","journal-title":"Nat Commun"},{"issue":"4","key":"688_CR18","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s41592-021-01100-y","volume":"18","author":"S Biswas","year":"2021","unstructured":"Biswas S et al (2021) Low-N protein engineering with data-efficient deep learning. Nat Methods 18(4):389\u2013396","journal-title":"Nat Methods"},{"issue":"7873","key":"688_CR19","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583\u2013589","journal-title":"Nature"},{"issue":"6557","key":"688_CR20","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1126\/science.abj8754","volume":"373","author":"M Baek","year":"2021","unstructured":"Baek M et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373(6557):871","journal-title":"Science"},{"issue":"D1","key":"688_CR21","doi-asserted-by":"publisher","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","volume":"50","author":"M Varadi","year":"2022","unstructured":"Varadi M et al (2022) AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50(D1):D439\u2013D444","journal-title":"Nucleic Acids Res"},{"key":"688_CR22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2203.06125","author":"Z Zhang","year":"2022","unstructured":"Zhang Z et al (2022) Protein representation learning by geometric structure pretraining. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.2203.06125","journal-title":"arXiv Preprint"},{"key":"688_CR23","doi-asserted-by":"publisher","DOI":"10.1101\/2022.04.10.487779","author":"C Hsu","year":"2022","unstructured":"Hsu C et al (2022) Learning inverse folding from millions of predicted structures. bioRxiv. https:\/\/doi.org\/10.1101\/2022.04.10.487779","journal-title":"bioRxiv"},{"issue":"7907","key":"688_CR24","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/s41586-022-04599-z","volume":"604","author":"H Lu","year":"2022","unstructured":"Lu H et al (2022) Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604(7907):662\u2013667","journal-title":"Nature"},{"issue":"1","key":"688_CR25","doi-asserted-by":"publisher","first-page":"6832","DOI":"10.1038\/s41598-022-10775-y","volume":"12","author":"Z Wang","year":"2022","unstructured":"Wang Z et al (2022) LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction. Sci Rep 12(1):6832","journal-title":"Sci Rep"},{"issue":"48","key":"688_CR26","doi-asserted-by":"publisher","first-page":"e2104878118","DOI":"10.1073\/pnas.2104878118","volume":"118","author":"S Gelman","year":"2021","unstructured":"Gelman S et al (2021) Neural networks to learn protein sequence\u2013function relationships from deep mutational scanning data. Proc Natl Acad Sci 118(48):e2104878118","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"688_CR27","doi-asserted-by":"publisher","first-page":"012707","DOI":"10.1103\/PhysRevE.87.012707","volume":"87","author":"M Ekeberg","year":"2013","unstructured":"Ekeberg M et al (2013) Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys Rev E 87(1):012707","journal-title":"Phys Rev E"},{"issue":"11","key":"688_CR28","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.1021\/acssynbio.0c00345","volume":"9","author":"R Shroff","year":"2020","unstructured":"Shroff R et al (2020) Discovery of novel gain-of-function mutations guided by structure-based deep learning. ACS Synth Biol 9(11):2927\u20132935","journal-title":"ACS Synth Biol"},{"issue":"7603","key":"688_CR29","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1038\/nature17995","volume":"533","author":"KS Sarkisyan","year":"2016","unstructured":"Sarkisyan KS et al (2016) Local fitness landscape of the green fluorescent protein. Nature 533(7603):397\u2013401","journal-title":"Nature"},{"issue":"3","key":"688_CR30","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1021\/cr010142r","volume":"102","author":"M Zimmer","year":"2002","unstructured":"Zimmer M (2002) Green fluorescent protein (GFP): applications, structure, and related photophysical behavior. Chem Rev 102(3):759\u2013782","journal-title":"Chem Rev"},{"issue":"11","key":"688_CR31","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1261\/rna.040709.113","volume":"19","author":"D Melamed","year":"2013","unstructured":"Melamed D et al (2013) Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly (A)-binding protein. RNA 19(11):1537\u20131551","journal-title":"RNA"},{"key":"688_CR32","doi-asserted-by":"publisher","DOI":"10.1101\/2022.03.08.483422","author":"M Minot","year":"2022","unstructured":"Minot M, Reddy ST (2022) Nucleotide augmentation for machine learning-guided protein engineering. bioRxiv. https:\/\/doi.org\/10.1101\/2022.03.08.483422","journal-title":"bioRxiv"},{"issue":"7","key":"688_CR33","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1038\/s41587-021-01146-5","volume":"40","author":"C Hsu","year":"2022","unstructured":"Hsu C et al (2022) Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40(7):1114\u20131122","journal-title":"Nat Biotechnol"},{"issue":"3","key":"688_CR34","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.cell.2015.09.055","volume":"163","author":"CD Aakre","year":"2015","unstructured":"Aakre CD et al (2015) Evolving new protein-protein interaction specificity through promiscuous intermediates. Cell 163(3):594\u2013606","journal-title":"Cell"},{"key":"688_CR35","doi-asserted-by":"publisher","DOI":"10.1101\/2021.04.16.440236","author":"S Sinai","year":"2021","unstructured":"Sinai S et al (2021) Generative AAV capsid diversification by latent interpolation. bioRxiv. https:\/\/doi.org\/10.1101\/2021.04.16.440236","journal-title":"bioRxiv"},{"issue":"11","key":"688_CR36","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579","journal-title":"J Mach Learn Res"},{"key":"688_CR37","unstructured":"Rao R et al (2021) Msa transformer. In international conference on machine learning. PMLR."},{"issue":"7883","key":"688_CR38","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/s41586-021-04043-8","volume":"599","author":"J Frazer","year":"2021","unstructured":"Frazer J et al (2021) Disease variant prediction with deep generative models of evolutionary data. Nature 599(7883):91\u201395","journal-title":"Nature"},{"issue":"1","key":"688_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-25976-8","volume":"12","author":"Y Luo","year":"2021","unstructured":"Luo Y et al (2021) ECNet is an evolutionary context-integrated deep learning framework for protein engineering. Nat Commun 12(1):1\u201314","journal-title":"Nat Commun"},{"issue":"1","key":"688_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3019-7","volume":"20","author":"M Steinegger","year":"2019","unstructured":"Steinegger M et al (2019) HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20(1):1\u201315","journal-title":"BMC Bioinformatics"},{"key":"688_CR41","doi-asserted-by":"publisher","first-page":"e03430","DOI":"10.7554\/eLife.03430","volume":"3","author":"TA Hopf","year":"2014","unstructured":"Hopf TA et al (2014) Sequence co-evolution gives 3D contacts and structures of protein complexes. elife 3:e03430","journal-title":"elife"},{"issue":"21","key":"688_CR42","doi-asserted-by":"publisher","first-page":"3128","DOI":"10.1093\/bioinformatics\/btu500","volume":"30","author":"S Seemayer","year":"2014","unstructured":"Seemayer S, Gruber M, S\u00f6ding J (2014) CCMpred\u2014fast and precise prediction of protein residue\u2013residue contacts from correlated mutations. Bioinformatics 30(21):3128\u20133130","journal-title":"Bioinformatics"},{"key":"688_CR43","doi-asserted-by":"crossref","unstructured":"Rao R et al (2019) Evaluating protein transfer learning with TAPE. Advances in neural information processing systems. 32.","DOI":"10.1101\/676825"},{"key":"688_CR44","first-page":"29287","volume":"34","author":"J Meier","year":"2021","unstructured":"Meier J et al (2021) Language models enable zero-shot prediction of the effects of mutations on protein function. Adv Neural Inf Process Syst 34:29287\u201329303","journal-title":"Adv Neural Inf Process Syst"},{"key":"688_CR45","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805","author":"J Devlin","year":"2018","unstructured":"Devlin J et al (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.1810.04805","journal-title":"arXiv Preprint"},{"key":"688_CR46","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1607.06450","author":"JL Ba","year":"2016","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.1607.06450","journal-title":"arXiv Preprint"},{"issue":"3","key":"688_CR47","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/BF00342633","volume":"20","author":"K Fukushima","year":"1975","unstructured":"Fukushima K (1975) Cognitron: a self-organizing multilayered neural network. Biol Cybern 20(3):121\u2013136","journal-title":"Biol Cybern"},{"issue":"7","key":"688_CR48","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1038\/s41588-019-0432-9","volume":"51","author":"NJ Rollins","year":"2019","unstructured":"Rollins NJ et al (2019) Inferring protein 3D structure from deep mutation scans. Nat Genet 51(7):1170\u20131176","journal-title":"Nat Genet"},{"issue":"1","key":"688_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-12101-z","volume":"10","author":"B Bolognesi","year":"2019","unstructured":"Bolognesi B et al (2019) The mutational landscape of a prion-like domain. Nat Commun 10(1):1\u201312","journal-title":"Nat Commun"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00688-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00688-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00688-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T14:11:49Z","timestamp":1679407909000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00688-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["688"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00688-x","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,3]]},"assertion":[{"value":"3 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The Supplementary information has been updated","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12"}}