Suppr超能文献

与DNA结合的EGR蛋白的定量分析:评估结合位点和蛋白中的加和性。

Quantitative analysis of EGR proteins binding to DNA: assessing additivity in both the binding site and the protein.

作者信息

Liu Jiajian, Stormo Gary D

机构信息

Department of Genetics, Washington University School of Medicine, St, Louis, MO 63110, USA.

出版信息

BMC Bioinformatics. 2005 Jul 13;6:176. doi: 10.1186/1471-2105-6-176.

Abstract

BACKGROUND

Recognition codes for protein-DNA interactions typically assume that the interacting positions contribute additively to the binding energy. While this is known to not be precisely true, an additive model over the DNA positions can be a good approximation, at least for some proteins. Much less information is available about whether the protein positions contribute additively to the interaction.

RESULTS

Using EGR zinc finger proteins, we measure the binding affinity of six different variants of the protein to each of six different variants of the consensus binding site. Both the protein and binding site variants include single and double mutations that allow us to assess how well additive models can account for the data. For each protein and DNA alone we find that additive models are good approximations, but over the combined set of data there are context effects that limit their accuracy. However, a small modification to the purely additive model, with only three additional parameters, improves the fit significantly.

CONCLUSION

The additive model holds very well for every DNA site and every protein included in this study, but clear context dependence in the interactions was detected. A simple modification to the independent model provides a better fit to the complete data.

摘要

背景

蛋白质与DNA相互作用的识别密码通常假定相互作用位点对结合能的贡献是可加性的。虽然已知这并不完全准确,但至少对于某些蛋白质来说,DNA位点上的加性模型可以是一个很好的近似。关于蛋白质位点对相互作用的贡献是否具有可加性,目前所知的信息要少得多。

结果

使用早期生长反应(EGR)锌指蛋白,我们测量了该蛋白的六种不同变体与共有结合位点的六种不同变体各自的结合亲和力。蛋白质和结合位点变体都包括单突变和双突变,这使我们能够评估加性模型对数据的解释程度。对于单独的每种蛋白质和DNA,我们发现加性模型是很好的近似,但在整个数据集上存在上下文效应,限制了它们的准确性。然而,对纯加性模型进行一个小的修改,只需增加三个参数,就能显著改善拟合效果。

结论

加性模型对于本研究中包含的每个DNA位点和每种蛋白质都非常适用,但在相互作用中检测到了明显的上下文依赖性。对独立模型进行简单修改能更好地拟合完整数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c8/1184061/87500390d257/1471-2105-6-176-1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验