SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent.
SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent.
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Slimdisc：简短的线性图案发现，校正共同进化下降。
DOI：
10.1093/nar/gkl486
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发表时间：
2006
影响因子：
14.9
通讯作者：
Edwards RJ
中科院分区：
文献类型：
--
作者：
Davey NE;Shields DC;Edwards RJ
许多重要的蛋白质相互作用是由蛋白质一级序列中的短线性基序（SLiM）促进的。我们的目的是建立强大的方法来发现推定的功能基序。当相同的基序出现在不相关的蛋白质中，通过趋同进化时，获得了这种基序的最强有力的证据。在实践中，对这些基序的搜索经常被在相关蛋白质中共享的基序淹没，这些基序在血统上是相同的。使用TEIRESIAS算法预测生物相关蛋白质组之间的基序，包括具有和不具有可检测相似性的基序。基于BLAST局部比对的处理，将通过共同进化血统产生的基序出现的数量标准化。根据标准化出现次数和信息内容的乘积得出的分数对基序进行排名。该方法被证明是显着优于方法，不折扣进化相关性，当应用于已知的SLiM从真核线性基序（ELM）数据库的一个子集。在各种设置中，多生成树加权的实现优于其他两种加权方案。
Many important interactions of proteins are facilitated by short, linear motifs (SLiMs) within a protein's primary sequence. Our aim was to establish robust methods for discovering putative functional motifs. The strongest evidence for such motifs is obtained when the same motifs occur in unrelated proteins, evolving by convergence. In practise, searches for such motifs are often swamped by motifs shared in related proteins that are identical by descent. Prediction of motifs among sets of biologically related proteins, including those both with and without detectable similarity, were made using the TEIRESIAS algorithm. The number of motif occurrences arising through common evolutionary descent were normalized based on treatment of BLAST local alignments. Motifs were ranked according to a score derived from the product of the normalized number of occurrences and the information content. The method was shown to significantly outperform methods that do not discount evolutionary relatedness, when applied to known SLiMs from a subset of the eukaryotic linear motif (ELM) database. An implementation of Multiple Spanning Tree weighting outperformed two other weighting schemes, in a variety of settings.