CSSP2: An improved method for predicting contact-dependent secondary structure propensity
DC Field | Value | Language |
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dc.contributor.author | Yoon, Sukjoon | - |
dc.contributor.author | Welsh, William J. | - |
dc.contributor.author | Jung, Heeyoung | - |
dc.contributor.author | Do Yoo, Young | - |
dc.date.available | 2021-02-22T15:02:01Z | - |
dc.date.issued | 2007-10 | - |
dc.identifier.issn | 1476-9271 | - |
dc.identifier.issn | 1476-928X | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14631 | - |
dc.description.abstract | The 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.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | CSSP2: An improved method for predicting contact-dependent secondary structure propensity | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1016/j.compbiolchem.2007.06.002 | - |
dc.identifier.scopusid | 2-s2.0-35148821507 | - |
dc.identifier.wosid | 000250912400008 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL BIOLOGY AND CHEMISTRY, v.31, no.5-6, pp 373 - 377 | - |
dc.citation.title | COMPUTATIONAL BIOLOGY AND CHEMISTRY | - |
dc.citation.volume | 31 | - |
dc.citation.number | 5-6 | - |
dc.citation.startPage | 373 | - |
dc.citation.endPage | 377 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.subject.keywordPlus | PROTEIN-STRUCTURE | - |
dc.subject.keywordPlus | AMYLOID FIBRIL | - |
dc.subject.keywordAuthor | amyloid fibril formation | - |
dc.subject.keywordAuthor | secondary structure prediction | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | artificial neural network | - |
dc.subject.keywordAuthor | energy decomposition | - |
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