Suppr超能文献

使用隐马尔可夫模型和β链对势进行折叠识别和从头结构预测。

Fold recognition and ab initio structure predictions using hidden Markov models and beta-strand pair potentials.

作者信息

Hubbard T J, Park J

机构信息

Centre for Protein Engineering (CPE), MRC Centre, Cambridge, UK.

出版信息

Proteins. 1995 Nov;23(3):398-402. doi: 10.1002/prot.340230313.

Abstract

Protein structure predictions were submitted for 9 of the target sequences in the competition that ran during 1994. Targets sequences were selected that had no known homology with any sequence of known structure and were members of a reasonably sized family of related but divergent sequences. The objective was either to recognize a compatible fold for the target sequence in the database of known structures or to predict ab initio its rough 3D topology. The main tools used were Hidden Markov models (HMM) for fold recognition, a beta-strand pair potential to predict beta-sheet topology, and the PHD server for secondary structure prediction. Compatible folds were correctly identified in a number of cases and the beta-strand pair potential was shown to be a useful tool for ab initio topology prediction.

摘要

1994年进行的竞赛中,针对9个目标序列提交了蛋白质结构预测结果。所选择的目标序列与任何已知结构的序列均无已知同源性,并且是一个规模适中的相关但不同的序列家族的成员。目标是在已知结构数据库中识别与目标序列兼容的折叠,或者从头预测其大致的三维拓扑结构。使用的主要工具包括用于折叠识别的隐马尔可夫模型(HMM)、用于预测β-折叠拓扑结构的β-链对势,以及用于二级结构预测的PHD服务器。在许多情况下都正确识别出了兼容的折叠,并且β-链对势被证明是用于从头拓扑预测的有用工具。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验