Prediction of TF target sites based on atomistic models of protein-DNA complexes
Prediction of TF target sites based on atomistic models of protein-DNA complexes
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DOI：
10.1186/1471-2105-9-436
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发表时间：
2008-10-16
影响因子：
3
通讯作者：
Contreras-Moreira, Bruno
中科院分区：
文献类型：
--
作者：
Espinosa Angarica, Vladimir;Gonzalez Perez, Abel;Contreras-Moreira, Bruno
背景资料：转录因子对基因组顺式调控元件的特异性识别在协调基因表达的调控中起着重要作用。因此，研究蛋白质-DNA相互作用中决定结合特异性的机制是一个重要的目标。目前大多数建模TF特异性识别的方法依赖于知识的大集合的同源靶位点，并考虑只包含在其primary sequence.Results的信息：在这里，我们描述了一个基于结构的方法，从TF-DNA复合物的坐标开始预测序列基序。我们的算法结合信息的直接和间接读出的DNA到一个原子的统计模型，这是用来估计的相互作用潜力。我们首先测量我们的方法正确估计属于不同结构超家族的8个原核和真核TF的结合特异性的能力。其次，该方法被应用到两个同源模型，发现接口侧链旋转异构体的采样显着改善的结果。第三，该算法进行了比较与参考结构的方法的基础上接触计数，获得可比的预测实验复合物和更准确的序列基序的同源models.Conclusion：我们的研究结果表明，原子细节的结构信息可以切实可行地用于预测TF结合位点。本文提出的计算方法具有普适性，并可应用于其他涉及蛋白质-DNA识别的系统。
Background: The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence.Results: Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models.Conclusion: Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition.