Algebra and Geometry Department, Mathematics Faculty, Universitat de Barcelona, Spain.
Proteins. 2010 Dec;78(16):3376-85. doi: 10.1002/prot.22844. Epub 2010 Sep 16.
Docking algorithms predict the structure of protein-protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near-native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein-protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near-native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair-potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step.
对接算法可预测蛋白质-蛋白质相互作用的结构。它们会对两个未结合的蛋白质的方向进行采样，从而对它们的相互作用产生各种预测，然后进行评分步骤对这些预测进行排序。我们对用于对近天然构象进行排序的评分函数进行了统计评估，将我们的统计分析应用于蛋白质-蛋白质复合物的配体数据集，并在预测相互作用的关键评估 (CAPRI) 评分实验中评估结果的统计显著性。分配了一个 P 值，该值取决于采样中近天然结构的数量。我们研究了过滤掉冗余结构的效果，并测试了使用 ZDock 和 ZRank 生成的对势能的使用。我们的结果表明，对于许多目标，无法确定评分函数执行成功的重新排序仅仅是由于随机选择。该分析表明，应该在 CAPRI 评分实验的设计中进行更改。我们建议在预处理或评估步骤中包含该统计评估。
