{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:11Z","timestamp":1772138051759,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"United States National Science Foundation","award":["1948117"],"award-info":[{"award-number":["1948117"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a protein sequence, there are no methods to predict which SA may lead to more accurate models without actually building the models. In this work, we describe a method to predict the quality of a protein\u2019s SA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We created our own dataset by generating a variety of SAs for a set of 1351 representative proteins and investigated various deep learning architectures to predict the local distance difference test (lDDT) scores of distance maps predicted with SAs as the input. These lDDT scores serve as indicators of the quality of the SAs.<\/jats:p>\n                    <jats:p>Using two independent test datasets consisting of CASP13 and CASP14 targets, we show that our method is effective for scoring and ranking SAs when a pool of SAs is available for a protein sequence. With an example, we further discuss that SA selection using our method can lead to improved structure prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code and the data underlying this article are available at https:\/\/github.com\/ba-lab\/Alignment-Score\/.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac210","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T09:44:44Z","timestamp":1649238284000},"page":"2988-2995","source":"Crossref","is-referenced-by-count":0,"title":["Scoring protein sequence alignments using deep learning"],"prefix":"10.1093","volume":"38","author":[{"given":"Bikash","family":"Shrestha","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Missouri-St. Louis , St. Louis, MO 63132, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1547-0238","authenticated-orcid":false,"given":"Badri","family":"Adhikari","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Missouri-St. Louis , St. Louis, MO 63132, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"2023041402572387700_","author":"Adhikari","year":"2020"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1002\/prot.24829","article-title":"Confold: residue\u2013residue contact-guided ab initio protein folding","volume":"83","author":"Adhikari","year":"2015","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12859-020-03938-z","article-title":"Disteval: a web server for evaluating predicted protein distances","volume":"22","author":"Adhikari","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1186\/1471-2105-7-484","article-title":"A statistical score for assessing the quality of multiple sequence alignments","volume":"7","author":"Ahola","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped blast and psi-blast: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"7353","DOI":"10.1093\/nar\/gkq625","article-title":"Issues in bioinformatics benchmarking: the case study of multiple sequence alignment","volume":"38","author":"Aniba","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1002\/prot.26194","article-title":"Protein tertiary structure prediction and refinement using deep learning and rosetta in casp14","volume":"89","author":"Anishchenko","year":"2021","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","author":"Billings","year":"2019"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"D751","DOI":"10.1093\/nar\/gkaa939","article-title":"The IMG\/M data management and analysis system v. 6.0: new tools and advanced capabilities","volume":"49","author":"Chen","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","first-page":"1251","author":"Chollet","year":"2017"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"Uniprot: a worldwide hub of protein knowledge","volume":"47","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1093\/nar\/gkh340","article-title":"Muscle: multiple sequence alignment with high accuracy and high throughput","volume":"32","author":"Edgar","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1002\/prot.10043","article-title":"A study on protein sequence alignment quality","volume":"46","author":"Elofsson","year":"2002","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1093\/bioinformatics\/btv592","article-title":"Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments","volume":"32","author":"Fox","year":"2016","journal-title":"Bioinformatics"},{"key":"2023041402572387700_","first-page":"770","author":"He","year":"2016"},{"key":"2023041402572387700_","first-page":"4700","author":"Huang","year":"2017"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1186\/1471-2105-11-431","article-title":"Hidden Markov model speed heuristic and iterative HMM search procedure","volume":"11","author":"Johnson","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1093\/bioinformatics\/btw840","article-title":"Protein multiple sequence alignment benchmarking through secondary structure prediction","volume":"33","author":"Le","year":"2017","journal-title":"Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"e1008865","DOI":"10.1371\/journal.pcbi.1008865","article-title":"Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks","volume":"17","author":"Li","year":"2021","journal-title":"PLoS Comput. Biol"},{"key":"2023041402572387700_","first-page":"58","article-title":"Improving protein tertiary structure prediction by deep learning and distance prediction in casp14","volume-title":"Proteins Struct. Funct. Bioinf","author":"Liu","year":"2022"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1093\/bioinformatics\/btt473","article-title":"LDDT: a local superposition-free score for comparing protein structures and models using distance difference tests","volume":"29","author":"Mariani","year":"2013","journal-title":"Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"D170","DOI":"10.1093\/nar\/gkw1081","article-title":"Uniclust databases of clustered and deeply annotated protein sequences and alignments","volume":"45","author":"Mirdita","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","first-page":"D570","article-title":"MGnify: the microbiome analysis resource in 2020","volume":"48","author":"Mitchell","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1002\/prot.25784","article-title":"High-accuracy refinement using Rosetta in casp13","volume":"87","author":"Park","year":"2019","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1093\/nar\/gkg062","article-title":"The cath database: an extended protein family resource for structural and functional genomics","volume":"31","author":"Pearl","year":"2003","journal-title":"Nucleic Acids Res"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1002\/prot.26171","article-title":"High-accuracy protein structure prediction in casp14","volume":"89","author":"Pereira","year":"2021","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","author":"Petti","year":"2021"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/1471-2105-4-47","article-title":"Oxbench: a benchmark for evaluation of protein multiple sequence alignment accuracy","volume":"4","author":"Raghava","year":"2003","journal-title":"BMC Bioinformatics"},{"key":"2023041402572387700_","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":"Nat. Methods"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1002\/prot.25834","article-title":"Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (casp13)","volume":"87","author":"Senior","year":"2019","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved protein structure prediction using potentials from deep learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1002\/prot.26232","article-title":"When homologous sequences meet structural decoys: accurate contact prediction by tfold in casp14\u2014(tfold for casp14 contact prediction","volume":"89","author":"Shen","year":"2021","journal-title":"Proteins Struct. Funct. Bioinformatics"},{"key":"2023041402572387700_","author":"Simonyan","year":"2014"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/s41592-019-0437-4","article-title":"Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold","volume":"16","author":"Steinegger","year":"2019","journal-title":"Nat. Methods"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1471-2105-6-66","article-title":"Dialign-t: an improved algorithm for segment-based multiple sequence alignment","volume":"6","author":"Subramanian","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2023041402572387700_","first-page":"2818","author":"Szegedy","year":"2016"},{"key":"2023041402572387700_","first-page":"6105","author":"Tan","year":"2019"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1002\/prot.20527","article-title":"Balibase 3.0: latest developments of the multiple sequence alignment benchmark","volume":"61","author":"Thompson","year":"2005","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1093\/bioinformatics\/bth493","article-title":"Sabmark\u2014a benchmark for sequence alignment that covers the entire known fold space","volume":"21","author":"Van Walle","year":"2005","journal-title":"Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1186\/s12859-018-2524-4","article-title":"A benchmark study of sequence alignment methods for protein clustering","volume":"19","author":"Wang","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"16856","DOI":"10.1073\/pnas.1821309116","article-title":"Distance-based protein folding powered by deep learning","volume":"116","author":"Xu","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/nmeth.3213","article-title":"The i-tasser suite: protein structure and function prediction","volume":"12","author":"Yang","year":"2015","journal-title":"Nat. Methods"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1073\/pnas.1914677117","article-title":"Improved protein structure prediction using predicted interresidue orientations","volume":"117","author":"Yang","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1093\/bioinformatics\/btz863","article-title":"DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"2023041402572387700_","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1002\/prot.26193","article-title":"Protein structure prediction using deep learning distance and hydrogen-bonding restraints in casp14","volume":"89","author":"Zheng","year":"2021","journal-title":"Proteins Struct. Funct. Bioinf"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac210\/43503592\/btac210.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/11\/2988\/49878758\/btac210.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/11\/2988\/49878758\/btac210.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T13:31:09Z","timestamp":1700400669000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/11\/2988\/6564224"}},"subtitle":[],"editor":[{"given":"Karsten","family":"Borgwardt","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,4,6]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,5,26]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac210","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.08.14.456366","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,6,1]]},"published":{"date-parts":[[2022,4,6]]}}}