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噬菌体 186 的双重用途重组方向因子和裂解-溶原调节蛋白 Apl 蛋白的定量结合模型。

A quantitative binding model for the Apl protein, the dual purpose recombination-directionality factor and lysis-lysogeny regulator of bacteriophage 186.

机构信息

Department of Molecular and Biomedical Science, The University of Adelaide, Adelaide 5005, Australia.

出版信息

Nucleic Acids Res. 2020 Sep 18;48(16):8914-8926. doi: 10.1093/nar/gkaa655.

Abstract

The Apl protein of bacteriophage 186 functions both as an excisionase and as a transcriptional regulator; binding to the phage attachment site (att), and also between the major early phage promoters (pR-pL). Like other recombination directionality factors (RDFs), Apl binding sites are direct repeats spaced one DNA helix turn apart. Here, we use in vitro binding studies with purified Apl and pR-pL DNA to show that Apl binds to multiple sites with high cooperativity, bends the DNA and spreads from specific binding sites into adjacent non-specific DNA; features that are shared with other RDFs. By analysing Apl's repression of pR and pL, and the effect of operator mutants in vivo with a simple mathematical model, we were able to extract estimates of binding energies for single specific and non-specific sites and for Apl cooperativity, revealing that Apl monomers bind to DNA with low sequence specificity but with strong cooperativity between immediate neighbours. This model fit was then independently validated with in vitro data. The model we employed here is a simple but powerful tool that enabled better understanding of the balance between binding affinity and cooperativity required for RDF function. A modelling approach such as this is broadly applicable to other systems.

摘要

噬菌体 186 的 Apl 蛋白兼具内切酶和转录调控因子的功能;既能与噬菌体附着位点(att)结合,也能在主要早期噬菌体启动子(pR-pL)之间结合。与其他重组方向性因子(RDF)一样,Apl 结合位点是彼此之间间隔一个 DNA 螺旋的直接重复序列。在这里,我们使用纯化的 Apl 和 pR-pL DNA 的体外结合研究表明,Apl 以高协同性结合多个位点,使 DNA 发生弯曲,并从特定结合位点扩展到相邻的非特异性 DNA;这些特征与其他 RDF 共享。通过分析 Apl 对 pR 和 pL 的抑制作用,以及体内带有简单数学模型的操纵子突变体的影响,我们能够提取单个特异性和非特异性位点以及 Apl 协同性的结合能估计值,揭示 Apl 单体与 DNA 的结合具有较低的序列特异性,但相邻单体之间具有很强的协同性。然后,通过体外数据对该模型拟合进行了独立验证。我们在这里使用的模型是一个简单但强大的工具,使我们能够更好地理解 RDF 功能所需的结合亲和力和协同性之间的平衡。这种建模方法广泛适用于其他系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d601/7498355/5e90fa0948ea/gkaa655fig1.jpg

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