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Integrating cross-linking experiments with ab initio protein-protein docking

机译:将交联实验与从头算蛋白-蛋白对接相结合

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摘要

Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases the rank of the top-scoring near-native prediction was improved by at least two fold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
机译:从头算蛋白质对蛋白质的算法通常依赖于实验数据来确定最可能的复杂结构。我们将蛋白质-蛋白质对接与化学交联然后进行质谱分析的实验数据进行了集成。我们使用19个案例对我们的方法进行了测试,这些案例是通过彻底搜索蛋白质数据库中的蛋白质复合物(在我们的实验中确定具有交叉链接)而得出的。我们基于欧几里得距离或空隙体积距离将交叉链接作为约束来实现。对于大多数测试用例,与没有交叉链接信息的对接相比,得分最高的本机预测的等级至少提高了两倍,并且前5位预测的成功率几乎提高了两倍。我们的结果表明,在保持正确的预测与消除误报之间存在微妙的平衡。几个测试用例包含具有不同接口的多个组件,我们提供了一种将交叉链接分配给接口的方法。针对这些情况采用对称信息可以进一步提高复杂结构预测的性能。

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