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CSSP2: An improved method for predicting contact-dependent secondary structure propensity

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dc.contributor.authorYoon, Sukjoon-
dc.contributor.authorWelsh, William J.-
dc.contributor.authorJung, Heeyoung-
dc.contributor.authorYoo, Young Do-
dc.date.available2021-02-22T15:02:01Z-
dc.date.issued2007-10-
dc.identifier.issn1476-9271-
dc.identifier.issn1476-928X-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14631-
dc.description.abstractThe calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native beta-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANN's) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i +/- 4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i +/- 4) and >(i, i +/- 4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information. (C) 2007 Elsevier Ltd. All rights reserved.-
dc.format.extent5-
dc.language영어-
dc.language.isoENG-
dc.publisherELSEVIER SCI LTD-
dc.titleCSSP2: An improved method for predicting contact-dependent secondary structure propensity-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1016/j.compbiolchem.2007.06.002-
dc.identifier.scopusid2-s2.0-35148821507-
dc.identifier.wosid000250912400008-
dc.identifier.bibliographicCitationCOMPUTATIONAL BIOLOGY AND CHEMISTRY, v.31, no.5-6, pp 373 - 377-
dc.citation.titleCOMPUTATIONAL BIOLOGY AND CHEMISTRY-
dc.citation.volume31-
dc.citation.number5-6-
dc.citation.startPage373-
dc.citation.endPage377-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaLife Sciences & Biomedicine - Other Topics-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryBiology-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.subject.keywordPlusPROTEIN-STRUCTURE-
dc.subject.keywordPlusAMYLOID FIBRIL-
dc.subject.keywordAuthoramyloid fibril formation-
dc.subject.keywordAuthorsecondary structure prediction-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorartificial neural network-
dc.subject.keywordAuthorenergy decomposition-
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