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De novo protein structure prediction by dynamic fragment assembly and conformational space annealing

DC Field Value Language
dc.contributor.authorLee, Ju Yong-
dc.contributor.authorLee, Jin Hyuk-
dc.contributor.authorSasaki, Takeshi N.-
dc.contributor.authorSasai, Masaki-
dc.contributor.authorSeok, Chaok-
dc.contributor.authorLee, Joo Young-
dc.date.accessioned2024-05-13T05:01:55Z-
dc.date.available2024-05-13T05:01:55Z-
dc.date.created2021-06-16-
dc.date.issued2011-08-
dc.identifier.citationPROTEINS : Structure, Function, and Bioinformatics, Vol.79 No.8, pp.2403-2417-
dc.identifier.issn0887-3585-
dc.identifier.urihttps://hdl.handle.net/10371/201546-
dc.description.abstractAb initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short-and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods.-
dc.language영어-
dc.publisherWiley-Liss Inc-
dc.titleDe novo protein structure prediction by dynamic fragment assembly and conformational space annealing-
dc.typeArticle-
dc.identifier.doi10.1002/prot.23059-
dc.citation.journaltitlePROTEINS : Structure, Function, and Bioinformatics-
dc.identifier.wosid000292925200005-
dc.identifier.scopusid2-s2.0-79960101915-
dc.citation.endpage2417-
dc.citation.number8-
dc.citation.startpage2403-
dc.citation.volume79-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorLee, Ju Yong-
dc.contributor.affiliatedAuthorSeok, Chaok-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusPOTENTIAL-ENERGY FUNCTIONS-
dc.subject.keywordPlusGLOBAL OPTIMIZATION-
dc.subject.keywordPlusACCURACY-
dc.subject.keywordPlusALGORITHMS-
dc.subject.keywordPlusMODELS-
dc.subject.keywordPlusCASP8-
dc.subject.keywordPlusFORCE-
dc.subject.keywordPlusSIMULATIONS-
dc.subject.keywordPlusPARAMETERS-
dc.subject.keywordPlusDOCKING-
dc.subject.keywordAuthortemplate-free modeling-
dc.subject.keywordAuthorab initio protein structure prediction-
dc.subject.keywordAuthorfragment assembly-
dc.subject.keywordAuthorenergy parameter optimization-
dc.subject.keywordAuthorconformational space annealing-
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

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