Shao Qi, Gong Weikang, Li Chunhua
College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Biophys Chem. 2020 Sep;264:106393. doi: 10.1016/j.bpc.2020.106393. Epub 2020 May 11.
The allosteric regulation during the binding interactions between small nuclear RNAs (snRNAs) and the associated protein factors is critical to the function of spliceosomes in alternative RNA splicing. Although network models combined with molecular dynamics simulations have shown to be powerful tools for the analysis of protein allostery, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems. In this work, we use a residual network model combined with a coarse-grained Gaussian network model (GNM) to investigate the binding interactions between the snRNA and the human U1A protein which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle, and to identify the residues that play an important role in the allosteric communication in U1A during this process. We also utilize the Girvan-Newman method to detect the structural organization in U1A-snRNA recognition and interactions. Our results reveal that: (Ι) not only the residues at the binding sites that are traditionally considered to play a major role in U1A-snRNA association, but those residues that are far away from the RNA binding interface participate in the U1A's allosteric signal transmission induced by the RNA binding; (Π) the structure of U1A protein is well organized with different communities acting different roles for its RNA binding and allosteric regulation. The study demonstrates that the combination of the residual network and elastic network models is an effective and efficient method which can be readily extended to the investigation of the allosteric communication for other macromolecular interaction systems.
小核RNA(snRNA)与相关蛋白质因子结合相互作用过程中的变构调节对于剪接体在RNA可变剪接中的功能至关重要。尽管结合分子动力学模拟的网络模型已被证明是分析蛋白质变构的强大工具,但对于大型系统而言,原子水平的模拟成本过高且精度有限。在这项工作中,我们使用残差网络模型结合粗粒度高斯网络模型(GNM)来研究snRNA与人类U1A蛋白之间的结合相互作用,U1A蛋白是剪接体U1小核核糖核蛋白颗粒的主要成分,并识别在此过程中在U1A变构通讯中起重要作用的残基。我们还利用Girvan-Newman方法检测U1A-snRNA识别和相互作用中的结构组织。我们的结果表明:(Ι)不仅传统上认为在U1A-snRNA结合中起主要作用的结合位点处的残基,而且远离RNA结合界面的那些残基也参与了由RNA结合诱导的U1A变构信号传递;(Π)U1A蛋白的结构组织良好,不同的群落对其RNA结合和变构调节发挥不同作用。该研究表明,残差网络和弹性网络模型的结合是一种有效且高效的方法,可轻松扩展到其他大分子相互作用系统的变构通讯研究。