Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge

Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid–nucleotide recognition preferences. These are used to predict binding sites for novel transcription factors from the same structural family. We demonstrate our approach on the Cys2His2 Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with experimental results. We use these preferences to perform a genome-wide scan for direct targets of Drosophila melanogaster Cys2His2 transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.

　    目前，用来检测转录因子结合位点的计算方法大都需要知道该转录因子的目标基因。然而这些计算机方法有一个与生俱来的“缺陷“：（1）转录因子结合位点在搜集的数据中必需重复出现；（2）假设所有的TFBS被等概率使用也是不正确的。文章另辟蹊径，提出一个基于转录因子结构信息的方法，在没有先验的目标基因数据的情况下，预测转录因子的结合位点。这种新的方法只用到了转录因子的序列和结构信息，来推导位置特异的氨基酸残基和碱基相互识别的偏好性。作者将这种方法用于“正规”结构的锌指环转录因子家族，预测的结果和实验数据相吻合。

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