用随机几何图识别蛋白关键残基，三种方法提升预测准确性。
Identifying critical residues of a protein using meaningfully-thresholded Random Geometric Graphs
- 基于残基状态相关性构建随机几何图，用Cramer's V计算关联度。
- 三种关键性指标：节点度、概率差值排名、动态度数变化，均优于传统阈值法。
- 结果与实验确认的关键残基高度一致，适合结构生物学研究者参考。
识别蛋白质中的关键残基是功能研究的核心，因其对维持蛋白活性至关重要。本文以156个残基的蛋白为例，通过分子动力学模拟追踪其演化过程，提出三种识别关键残基的方法。首先，构建随机几何图（RGG），每个残基对应一个节点，利用残基对间状态变量的相关性矩阵学习图结构，采用Cramer's V处理分类变量间的相关性，并引入自适应阈值法优化图生成。其次，通过比较包含全部156个残基的完整图与缺失任一残基的图的后验概率差异，定义新的关键性度量。第三，评估节点度随时间演化的动态变化作为关键性指标。最后，将三类参数所得结果与实验验证的关键残基进行对比，验证了方法的有效性。
原文摘要 · Abstract (English)
Identification of critical residues of a protein is actively pursued, since such residues are essential for protein function. We present three ways of recognising critical residues of an example protein, the evolution of which is tracked via molecular dynamical simulations. Our methods are based on learning a Random Geometric Graph (RGG) variable, where the state variable of each of 156 residues, is attached to a node of this graph, with the RGG learnt using the matrix of correlations between state variables of each residue-pair. Given the categorical nature of the state variable, correlation between a residue pair is computed using Cramer's V. We advance an organic thresholding to learn an RGG, and compare results against extant thresholding techniques, when parametrising criticality as the nodal degree in the learnt RGG. Secondly, we develop a criticality measure by ranking the computed differences between the posterior probability of the full graph variable defined on all 156 residues, and that of the graph with all but one residue omitted. A third parametrisation of criticality informs on the dynamical variation of nodal degrees as the protein evolves during the simulation. Finally, we compare results obtained with the three distinct criticality parameters, against experimentally-ascertained critical residues.
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