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From sequence to structure and back again: approaches for predicting protein-DNA binding

机译:从序列到结构再返回:预测蛋白质-DNA结合的方法

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摘要

Gene regulation in higher organisms is achieved by a complex network of transcription factors (TFs). Modulating gene expression and exploring gene function are major aims in molecular biology. Furthermore, the identification of putative target genes for a certain TF serve as powerful tools for specific targeting of rational drugs.Detecting the short and variable transcription factor binding sites (TFBSs) in genomic DNA is an intriguing challenge for computational and structural biologists. Fast and reliable computational methods for predicting TFBSs on a whole-genome scale offer several advantages compared to the current experimental methods that are rather laborious and slow. Two main approaches are being explored, advanced sequence-based algorithms and structure-based methods.The aim of this review is to outline the computational and experimental methods currently being applied in the field of protein-DNA interactions. With a focus on the former, the current state of the art in modeling these interactions is discussed. Surveying sequence and structure-based methods for predicting TFBSs, we conclude that in order to achieve a sound and specific method applicable on genomic sequences it is desirable and important to bring these two approaches together.
机译:高等生物的基因调控是通过复杂的转录因子(TFs)网络实现的。调节基因表达和探索基因功能是分子生物学的主要目标。此外,确定特定TF的推定靶基因是特异性靶向合理药物的有力工具。检测基因组DNA中的短和可变转录因子结合位点(TFBS)对计算和结构生物学家来说是一个有趣的挑战。与目前相当费力且缓慢的实验方法相比,在全基因组规模上预测TFBS的快速而可靠的计算方法具有许多优势。目前正在探索两种主要的方法,高级的基于序列的算法和基于结构的方法。本文的目的是概述目前在蛋白质-DNA相互作用领域中应用的计算和实验方法。以前者为重点,讨论了对这些交互进行建模的最新技术。通过调查基于序列和基于结构的方法来预测TFBS,我们得出结论,为了实现适用于基因组序列的合理且特定的方法,将这两种方法结合在一起是理想且重要的。

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