GLN --一种揭示蛋白质套索拓扑结构独特性质的方法
GLN -- a method to reveal unique properties of lasso type topology in proteins
摘要：
几何和拓扑结构是决定蛋白质功能性质的主要因素。在这项工作中，我们展示了如何使用高斯连接积分（GLN）的矩阵图的形式-一对一个环和一个尾巴-研究的几何形状和拓扑结构的蛋白质与闭环，如套索。我们表明，GLN方法是一个显着更快的技术来检测套索蛋白质的纠缠相比，其他方法。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。我们发现蛋白质中负交叉的套索明显多于正交叉的套索，maxGLN的平均值（环和尾段之间的最大GLN）与聚合物中的情况类似，依赖于尾的长度。接下来，我们展示了高和低GLN值与蛋白质内部弹性的相关性，以及如何以矩阵图的形式使用GLN来研究折叠和解折叠路线。最后，我们讨论了GLN方法如何应用于研究两个非闭环结构之间的纠缠。由于这种方法比其他连接不变量快得多，下一步将是评估更长分子中的套索，如RNA或单个染色体中的环。
英文摘要：
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.
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