Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Ul, Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
BMC Bioinformatics. 2011 Aug 18;12:348. doi: 10.1186/1471-2105-12-348.
Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.
We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.
In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/
蛋白质与 RNA 的相互作用在许多生物过程中起着至关重要的作用。了解蛋白质与 RNA 识别的分子机制以及蛋白质-RNA 复合物的形成是结构生物学的主要挑战。不幸的是，通过 X 射线晶体学和 NMR 实验确定蛋白质-RNA 复合物既繁琐又困难。对于许多相互作用的蛋白质和 RNA，其单个结构是可用的，这使得通过计算对接来预测复合物结构成为可能。然而，蛋白质-RNA 对接的方法仍然很少，特别是与众多蛋白质-蛋白质对接方法相比。
我们开发了两种基于知识的中等分辨率的评分势能，用于打分对接得到的蛋白质-RNA 模型：准化学势能（QUASI-RNP）和诱饵作为参考状态势能（DARS-RNP）。这两种势能都使用了粗粒化表示，适用于具有转录后修饰残基的 RNA 结构。我们比较了 DARS-RNP 和 QUASI-RNP 与 Varani 和 Fernandez 小组先前开发的势能在选择刚性对接构象方面的判别能力。
在结合和未结合的对接测试中，DARS-RNP 均表现出识别天然样结构的最高能力。DARS-RNP 和 QUASI-RNP 的 Python 实现可在 http://iimcb.genesilico.pl/RNP/ 免费下载。
