论文标题
GLN-一种揭示蛋白质中拉索型拓扑独特特性的方法
GLN -- a method to reveal unique properties of lasso type topology in proteins
论文作者
论文摘要
几何和拓扑是决定蛋白质功能特性的主要因素。在这项工作中,我们展示了如何以矩阵图的形式使用高斯连接积分(GLN) - 用于一对环和尾巴 - 研究具有封闭环的蛋白质的几何和拓扑结构和拓扑。拉索斯。我们表明,与其他方法相比,GLN方法是一种检测Lasso蛋白质纠缠的技术更快的技术。基于GLN技术,我们对沉积在PDB中的所有蛋白质进行全面分析,并将其与聚合物的统计特性进行比较。我们发现,与蛋白质中具有正阳性的横梁相比,MaxGln的平均值(环和尾巴之间的最大GLN)的平均值取决于尾巴的长度与聚合物相似的尾巴长度。接下来,我们展示GLN值与蛋白质的内部质疑的相关性以及如何使用基质图形式的GLN来研究折叠和展开路线。最后,我们讨论如何将GLN方法应用于两个结构之间的纠缠,它们都不是封闭循环。由于这种方法比其他链接不变式要快得多,因此下一步将评估较长的分子(例如RNA或单个染色体中的环路)中的套索。
Geometry and topology are the main factors that determine the functional properties of proteins. In this work, we show how to use the Gauss linking integral (GLN) in the form of a matrix diagram - for a pair of a loop and a tail - to study both the geometry and topology of proteins with closed loops e.g. lassos. We show that the GLN method is a significantly faster technique to detect entanglement in lasso proteins in comparison with other methods. Based on the GLN technique, we conduct comprehensive analysis of all proteins deposited in the PDB and compare it to the statistical properties of the polymers. We found that there are significantly more lassos with negative crossings than those with positive ones in proteins, the average value of maxGLN (maximal GLN between loop and pieces of tail) depends logarithmically on the length of a tail similarly as in the polymers. Next, we show the how high and low GLN values correlate with the internal exibility of proteins, and how the GLN in the form of a matrix diagram can be used to study folding and unfolding routes. Finally, we discuss how the GLN method can be applied to study entanglement between two structures none of which are closed loops. Since this approach is much faster than other linking invariants, the next step will be evaluation of lassos in much longer molecules such as RNA or loops in a single chromosome.