Gene Center and Center for Integrated Protein Science Munich, Ludwig-Maximilians Universität München, Munich, Germany.
Nat Methods. 2011 Dec 25;9(2):173-5. doi: 10.1038/nmeth.1818.
Sequence-based protein function and structure prediction depends crucially on sequence-search sensitivity and accuracy of the resulting sequence alignments. We present an open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM-based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/). Compared to the sequence-search tool PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50-100% higher sensitivity and generates more accurate alignments.
基于序列的蛋白质功能和结构预测，关键取决于序列搜索的灵敏度和所得序列比对的准确性。我们提供了一个开源的通用工具，该工具通过轮廓隐马尔可夫模型（HMM）表示查询和数据库序列：“基于 HMM-HMM 的闪电般快速迭代序列搜索”（HHblits；http://toolkit.genzentrum.lmu.de/hhblits/）。与序列搜索工具 PSI-BLAST 相比，HHblits 由于其离散轮廓预过滤器而更快，其灵敏度提高了 50-100%，并且生成了更准确的比对。
