{"title":"用合并解耦法扩展蛋白质侧链构象的死角消除","authors":"K. F. Chong, H. Leong","doi":"10.1145/1141277.1141320","DOIUrl":null,"url":null,"abstract":"A two-phase strategy is widely adopted to solve the side-chain conformation prediction (SCCP) problem. Phase one is a fast reduction phase removing large numbers of rotamers not existing in the GMEC. Phase two (optimization phase) uses heuristics or exhaustive search to find a good/optimal solution. Presently, DEE (Dead End Elimination) is the only deterministic reduction method for phase one. However, to achieve convergence in phase two using DEE, the strategy of forming super-residues is used. This quickly leads to a combinatorial explosion, and becomes inefficient In this paper, an improvement of the DEE process by forming super-residues efficiently is proposed for phase one. The method basically merges residues into pairs based on some merging criteria. Simple Goldstein is then applied until no more elimination is possible. A decoupling process then reforms the original residues sans removed rotamers and rotamer pairs. The process of merging and elimination is repeated until no more elimination is possible. Initial experiments have shown the method, called Merge-Decoupling DEE, can fix up to 25% of the unfixed residues coming out of Simple Goldstein DEE.","PeriodicalId":269830,"journal":{"name":"Proceedings of the 2006 ACM symposium on Applied computing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An extension of dead end elimination for protein side-chain conformation using merge-decoupling\",\"authors\":\"K. F. Chong, H. Leong\",\"doi\":\"10.1145/1141277.1141320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A two-phase strategy is widely adopted to solve the side-chain conformation prediction (SCCP) problem. Phase one is a fast reduction phase removing large numbers of rotamers not existing in the GMEC. Phase two (optimization phase) uses heuristics or exhaustive search to find a good/optimal solution. Presently, DEE (Dead End Elimination) is the only deterministic reduction method for phase one. However, to achieve convergence in phase two using DEE, the strategy of forming super-residues is used. This quickly leads to a combinatorial explosion, and becomes inefficient In this paper, an improvement of the DEE process by forming super-residues efficiently is proposed for phase one. The method basically merges residues into pairs based on some merging criteria. Simple Goldstein is then applied until no more elimination is possible. A decoupling process then reforms the original residues sans removed rotamers and rotamer pairs. The process of merging and elimination is repeated until no more elimination is possible. Initial experiments have shown the method, called Merge-Decoupling DEE, can fix up to 25% of the unfixed residues coming out of Simple Goldstein DEE.\",\"PeriodicalId\":269830,\"journal\":{\"name\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1141277.1141320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2006 ACM symposium on Applied computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1141277.1141320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
侧链构象预测(SCCP)问题广泛采用两阶段策略。第一阶段是快速还原阶段,去除GMEC中不存在的大量转子。第二阶段(优化阶段)使用启发式或穷举搜索来找到一个好的/最优的解决方案。目前,DEE (Dead End Elimination)是第一阶段唯一的确定性约简方法。然而,为了在第二阶段使用DEE实现收敛,使用了形成超残数的策略。本文提出了一种通过有效地形成超残基来改进DEE过程的方法。该方法基本上是根据一些合并准则将残基合并成对。然后应用简单的戈尔茨坦直到不可能再消除为止。然后进行解耦过程,在不去除转子和转子对的情况下对原始残留物进行改造。重复合并和消除的过程,直到不能再消除为止。最初的实验表明,这种被称为“合并-解耦DEE”的方法可以固定简单戈尔茨坦DEE中高达25%的未固定残留物。
An extension of dead end elimination for protein side-chain conformation using merge-decoupling
A two-phase strategy is widely adopted to solve the side-chain conformation prediction (SCCP) problem. Phase one is a fast reduction phase removing large numbers of rotamers not existing in the GMEC. Phase two (optimization phase) uses heuristics or exhaustive search to find a good/optimal solution. Presently, DEE (Dead End Elimination) is the only deterministic reduction method for phase one. However, to achieve convergence in phase two using DEE, the strategy of forming super-residues is used. This quickly leads to a combinatorial explosion, and becomes inefficient In this paper, an improvement of the DEE process by forming super-residues efficiently is proposed for phase one. The method basically merges residues into pairs based on some merging criteria. Simple Goldstein is then applied until no more elimination is possible. A decoupling process then reforms the original residues sans removed rotamers and rotamer pairs. The process of merging and elimination is repeated until no more elimination is possible. Initial experiments have shown the method, called Merge-Decoupling DEE, can fix up to 25% of the unfixed residues coming out of Simple Goldstein DEE.