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BetaSCP2: A program for the optimal prediction of side-chains in proteins

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dc.contributor.authorRyu, Joonghyun-
dc.contributor.authorLee, Mokwon-
dc.contributor.authorCha, Jehyun-
dc.contributor.authorSong, Chanyoung-
dc.contributor.authorKim, Deok-Soo-
dc.date.accessioned2022-07-16T03:46:03Z-
dc.date.available2022-07-16T03:46:03Z-
dc.date.created2021-05-11-
dc.date.issued2014-08-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/159465-
dc.description.abstractThe side-chain prediction problem (SCP-problem), is a computational problem to predict the optimal structure of proteins by finding the optimal dihedral angles. The SCP-problem is one of key computational cornerstones for many important problems such as protein design, flexible docking of proteins, homology modeling, etc. The SCP-problem can be formulated as a minimization problem of an integer linear program which is NP-hard thus inevitably invites heuristic approach to find the solution. In this paper, we report a heuristic algorithm, called BetaSCP2, which quickly finds an excellent solution of the SCP-problem. The solution process of the BetaSCP2 is facilitated by the Voronoi diagram and its dual structure called the quasi-triangulation. The BetaSCP2 is entirely implemented using the Molecular Geometry engine called BULL! which has been developed by Voronoi Diagram Research Center (VDRC) in C++ programming language. The benchmark test of the BetaSCP2 with other programs is also provided. The BetaSCP2 program is available as both a stand-alone and a web server program from VDRC.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleBetaSCP2: A program for the optimal prediction of side-chains in proteins-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Deok-Soo-
dc.identifier.doi10.1007/978-3-662-44199-2_52-
dc.identifier.scopusid2-s2.0-84905842170-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.8592 LNCS, pp.333 - 340-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume8592 LNCS-
dc.citation.startPage333-
dc.citation.endPage340-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusBenchmarking-
dc.subject.keywordPlusC++ (programming language)-
dc.subject.keywordPlusChains-
dc.subject.keywordPlusComputational geometry-
dc.subject.keywordPlusForecasting-
dc.subject.keywordPlusGraphic methods-
dc.subject.keywordPlusHeuristic algorithms-
dc.subject.keywordPlusHeuristic methods-
dc.subject.keywordPlusInteger programming-
dc.subject.keywordPlusProteins-
dc.subject.keywordPlusStructural optimization-
dc.subject.keywordPlusTriangulation-
dc.subject.keywordPlusBeta complexes-
dc.subject.keywordPlusBetaSCP2-
dc.subject.keywordPlusProtein structures-
dc.subject.keywordPlusQuasi triangulations-
dc.subject.keywordPlusSide-chains-
dc.subject.keywordPlusVoronoi diagrams-
dc.subject.keywordPlusProblem oriented languages-
dc.subject.keywordAuthorbeta-complex-
dc.subject.keywordAuthorBetaSCP2-
dc.subject.keywordAuthorprotein structure/function-
dc.subject.keywordAuthorquasi-triangulation-
dc.subject.keywordAuthorside-chain prediction-
dc.subject.keywordAuthorVoronoi diagram-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007%2F978-3-662-44199-2_52-
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