{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T10:30:29Z","timestamp":1781087429923,"version":"3.54.1"},"reference-count":101,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"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\/100020628","name":"Information and Communication Technology Division","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100020628","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>SAINT is freely available as an open-source project at https:\/\/github.com\/SAINTProtein\/SAINT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa531","type":"journal-article","created":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T15:11:35Z","timestamp":1589641895000},"page":"4599-4608","source":"Crossref","is-referenced-by-count":68,"title":["SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction"],"prefix":"10.1093","volume":"36","author":[{"given":"Mostofa Rafid","family":"Uddin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology , Dhaka 1205, Bangladesh"},{"name":"Department of Computer Science and Engineering, East West University , Dhaka 1212, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sazan","family":"Mahbub","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology , Dhaka 1205, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9887-4456","authenticated-orcid":false,"given":"M Saifur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology , Dhaka 1205, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5640-0615","authenticated-orcid":false,"given":"Md Shamsuzzoha","family":"Bayzid","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology , Dhaka 1205, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"2023062213552886600_btaa531-B1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.18653\/v1\/W17-4711","volume-title":"Proceedings of the Second Conference on Machine Translation","author":"Alkhouli","year":"2017"},{"key":"2023062213552886600_btaa531-B2","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":"2023062213552886600_btaa531-B3","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1126\/science.181.4096.223","article-title":"Principles that govern the folding of protein chains","volume":"181","author":"Anfinsen","year":"1973","journal-title":"Science"},{"key":"2023062213552886600_btaa531-B4","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1093\/bioinformatics\/9.2.141","article-title":"Prediction of protein secondary structure by the hidden Markov model","volume":"9","author":"Asai","year":"1993","journal-title":"Bioinformatics"},{"key":"2023062213552886600_btaa531-B5","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1186\/1471-2105-7-178","article-title":"Protein secondary structure prediction for a single-sequence using hidden semi-Markov models","volume":"7","author":"Aydin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023062213552886600_btaa531-B6","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv preprint arXiv: 1409.0473"},{"key":"2023062213552886600_btaa531-B7","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1126\/science.1065659","article-title":"Protein structure prediction and structural genomics","volume":"294","author":"Baker","year":"2001","journal-title":"Science"},{"key":"2023062213552886600_btaa531-B8","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1093\/bioinformatics\/15.11.937","article-title":"Exploiting the past and the future in protein secondary structure prediction","volume":"15","author":"Baldi","year":"1999","journal-title":"Bioinformatics"},{"key":"2023062213552886600_btaa531-B9","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. 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