{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T13:04:47Z","timestamp":1773147887871,"version":"3.50.1"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The prediction of \u03b2-turns is an important element of protein secondary structure prediction. Recently, a highly accurate neural network based method Betatpred2 has been developed for predicting \u03b2-turns in proteins using position-specific scoring matrices (PSSM) generated by PSI-BLAST and secondary structure information predicted by PSIPRED. However, the major limitation of Betatpred2 is that it predicts only \u03b2-turn and non-\u03b2-turn residues and does not provide any information of different \u03b2-turn types. Thus, there is a need to predict \u03b2-turn types using an approach based on multiple sequence alignment, which will be useful in overall tertiary structure prediction.<\/jats:p>\n               <jats:p>Results: In the present work, a method has been developed for the prediction of \u03b2-turn types I, II, IV and VIII. For each turn type, two consecutive feed-forward back-propagation networks with a single hidden layer have been used where the first sequence-to-structure network has been trained on single sequences as well as on PSI-BLAST PSSM. The output from the first network along with PSIPRED predicted secondary structure has been used as input for the second-level structure-to-structure network. The networks have been trained and tested on a non-homologous dataset of 426 proteins chains by 7-fold cross-validation. It has been observed that the prediction performance for each turn type is improved significantly by using multiple sequence alignment. The performance has been further improved by using a second level structure-to-structure network and PSIPRED predicted secondary structure information. It has been observed that Type I and II \u03b2-turns have better prediction performance than Type IV and VIII \u03b2-turns. The final network yields an overall accuracy of 74.5, 93.5, 67.9 and 96.5% with MCC values of 0.29, 0.29, 0.23 and 0.02 for Type I, II, IV and VIII \u03b2-turns, respectively, and is better than random prediction.<\/jats:p>\n               <jats:p>Availability: A web server for prediction of \u03b2-turn types I, II, IV and VIII based on above approach is available at http:\/\/www.imtech.res.in\/raghava\/betaturns\/ and http:\/\/bioinformatics.uams.edu\/mirror\/betaturns\/ (mirror site).<\/jats:p>","DOI":"10.1093\/bioinformatics\/bth322","type":"journal-article","created":{"date-parts":[[2004,5,18]],"date-time":"2004-05-18T00:43:56Z","timestamp":1084841036000},"page":"2751-2758","source":"Crossref","is-referenced-by-count":83,"title":["A neural network method for prediction of \u03b2-turn types in proteins using evolutionary information"],"prefix":"10.1093","volume":"20","author":[{"given":"Harpreet","family":"Kaur","sequence":"first","affiliation":[{"name":"Institute of Microbial Technology, Sector-39A, Chandigarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. P. S.","family":"Raghava","sequence":"additional","affiliation":[{"name":"Institute of Microbial Technology, Sector-39A, Chandigarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2004,5,14]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/16\/2751\/48906371\/bioinformatics_20_16_2751.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/16\/2751\/48906371\/bioinformatics_20_16_2751.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T16:38:51Z","timestamp":1674664731000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/20\/16\/2751\/236899"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,5,14]]},"references-count":0,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2004,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bth322","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2004,11,1]]},"published":{"date-parts":[[2004,5,14]]}}}