{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:50:55Z","timestamp":1777881055742,"version":"3.51.4"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T00:00:00Z","timestamp":1544140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australia Research Council","doi-asserted-by":"crossref","award":["DP180102060"],"award-info":[{"award-number":["DP180102060"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["1121629"],"award-info":[{"award-number":["1121629"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015449","name":"Queensland Cyber Infrastructure Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100015449","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (\u03b8, \u03c4, \u03d5 and \u03c8), half-sphere exposure, contact numbers and solvent accessible surface area (ASA).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The new method, named SPOT-1D, achieves similar, high performance on a large validation set and test set (\u22481000 proteins in each set), suggesting robust performance for unseen data. For the large test set, it achieves 87% and 77% in 3- and 8-state secondary structure prediction and 0.82 and 0.86 in correlation coefficients between predicted and measured ASA and contact numbers, respectively. Comparison to current state-of-the-art techniques reveals substantial improvement in secondary structure and backbone angle prediction. In particular, 44% of 40-residue fragment structures constructed from predicted backbone C\u03b1-based \u03b8 and \u03c4 angles are less than 6\u2009\u00c5 root-mean-squared-distance from their native conformations, nearly 20% better than the next best. The method is expected to be useful for advancing protein structure and function prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>SPOT-1D and its data is available at: http:\/\/sparks-lab.org\/.<\/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\/bty1006","type":"journal-article","created":{"date-parts":[[2018,12,6]],"date-time":"2018-12-06T22:37:40Z","timestamp":1544135860000},"page":"2403-2410","source":"Crossref","is-referenced-by-count":178,"title":["Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-6748","authenticated-orcid":false,"given":"Jack","family":"Hanson","sequence":"first","affiliation":[{"name":"Signal Processing Laboratory, Griffith University, Brisbane, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuldip","family":"Paliwal","sequence":"additional","affiliation":[{"name":"Signal Processing Laboratory, Griffith University, Brisbane, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Litfin","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6782-2813","authenticated-orcid":false,"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9958-5699","authenticated-orcid":false,"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia"},{"name":"Institute for Glycomics, Griffith University, Gold Coast, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,12,7]]},"reference":[{"key":"2023062712305730400_bty1006-B2","first-page":"7","article-title":"DNCON2: improved protein contact prediction using two-level deep convolutional neural networks","volume":"1","author":"Adhikari","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062712305730400_bty1006-B3","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":"2023062712305730400_bty1006-B4","first-page":"1899","volume-title":"IEEE IJCNN","author":"Ceroni","year":"2004"},{"key":"2023062712305730400_bty1006-B5","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.neunet.2005.07.001","article-title":"Learning protein secondary structure from sequential and relational data","volume":"18","author":"Ceroni","year":"2005","journal-title":"Neural Netw"},{"key":"2023062712305730400_bty1006-B6","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TCBB.2006.17","article-title":"Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction","volume":"3","author":"Chu","year":"2006","journal-title":"IEEE ACM Trans. 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