Folding small proteins via annealing stochastic approximation Monte Carlo
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cheon, Sooyoung | - |
dc.contributor.author | Liang, Faming | - |
dc.date.accessioned | 2021-09-07T08:40:55Z | - |
dc.date.available | 2021-09-07T08:40:55Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2011-09 | - |
dc.identifier.issn | 0303-2647 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/111631 | - |
dc.description.abstract | Recently, the stochastic approximation Monte Carlo algorithm has been proposed by Liang et al. (2007) as a general-purpose stochastic optimization and simulation algorithm. An annealing version of this algorithm was developed for real small protein folding problems. The numerical results indicate that it outperforms simulated annealing and conventional Monte Carlo algorithms as a stochastic optimization algorithm. We also propose one method for the use of secondary structures in protein folding. The predicted protein structures are rather close to the true structures. (C) 2011 Elsevier Ireland Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | SECONDARY STRUCTURE | - |
dc.subject | GLOBULAR-PROTEINS | - |
dc.subject | FOLDED STATES | - |
dc.subject | ALGORITHM | - |
dc.subject | OPTIMIZATION | - |
dc.subject | PREDICTION | - |
dc.subject | KINETICS | - |
dc.subject | MODEL | - |
dc.subject | NUCLEATION | - |
dc.subject | PATHWAYS | - |
dc.title | Folding small proteins via annealing stochastic approximation Monte Carlo | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cheon, Sooyoung | - |
dc.identifier.doi | 10.1016/j.biosystems.2011.05.015 | - |
dc.identifier.scopusid | 2-s2.0-79961128515 | - |
dc.identifier.wosid | 000295603700010 | - |
dc.identifier.bibliographicCitation | BIOSYSTEMS, v.105, no.3, pp.243 - 249 | - |
dc.relation.isPartOf | BIOSYSTEMS | - |
dc.citation.title | BIOSYSTEMS | - |
dc.citation.volume | 105 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 243 | - |
dc.citation.endPage | 249 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | SECONDARY STRUCTURE | - |
dc.subject.keywordPlus | GLOBULAR-PROTEINS | - |
dc.subject.keywordPlus | FOLDED STATES | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | KINETICS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | NUCLEATION | - |
dc.subject.keywordPlus | PATHWAYS | - |
dc.subject.keywordAuthor | Protein folding | - |
dc.subject.keywordAuthor | Secondary structure | - |
dc.subject.keywordAuthor | Markov chain Monte Carlo | - |
dc.subject.keywordAuthor | Annealing stochastic approximation Monte Carlo | - |
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