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An evaluation of automated homology modelling methods at low target–template sequence similarity

机译:对低靶标-模板序列相似性的自动同源建模方法的评估

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摘要

Motivation: There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities ≤50%.
机译:动机:同源性建模有两个主要困难领域,当靶标和模板之间的序列同一性低于5​​0%时特别重要:序列比对和环构建。由于没有人为纠正错误的输入,这些问题在自动建模过程中变得更加严重。因此,我们已经对几种独立策略进行了基准测试,这些策略可以在工作流程中实施以实现自动化的高通量同源性建模。其中包括三个新的序列结构比对程序:3D-Coffee,Staccato和SAlign,以及五个同源性建模程序及其各自的循环构建方法:Builder,Nest,Modeller,SegMod / ENCAD和Swiss-Model。 SABmark数据库为123个靶标提供了至少5个来自同一SCOP系列的模板,且序列同一性≤50%。

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  • 来源
    《Bioinformatics》 |2007年第15期|1901-1908|共8页
  • 作者单位

    Institute of Molecular and Cellular Biology Faculty of Biological Sciences University of Leeds Leeds LS2 9JT UK;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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