Finding correct protein-protein docking models using ProQDock.
Finding correct protein-protein docking models using ProQDock.
复制标题
DOI：
10.1093/bioinformatics/btw257
复制
发表时间：
2016-06-15
期刊：
影响因子：
--
通讯作者：
Wallner B
中科院分区：
文献类型：
--
作者：
Basu S;Wallner B
动机：蛋白质-蛋白质相互作用几乎是所有生物过程的关键。为了详细了解生物过程，蛋白质复合物的结构至关重要。鉴于目前用于结构测定的实验技术，绝大多数蛋白质复合物永远无法通过实验技术来解决。在缺乏实验数据的情况下，可以使用计算对接方法来预测蛋白质复合物的结构。一种常见的策略是生成许多替代的对接解决方案（原子模型），然后使用评分函数来选择最佳的。计算对接技术的成功在很大程度上取决于评分函数对许多替代对接模型进行准确排名和评分的能力。结果：在这里，我们提出了 ProQDock，这是一种评分函数，可以预测通过新型蛋白质对接质量评分 (DockQ) 测量的对接模型的绝对质量。 ProQDock 使用经过训练的支持向量机来使用可从对接模型本身计算出的特征来预测蛋白质对接模型的质量。通过结合描述蛋白质-蛋白质界面和整体物理化学的不同类型的特征，可以将与 DockQ 的相关性从最佳个体特征（静电互补性）的 0.25 提高到 ProQDock 最终版本的 0.49。在相关性、排名和在独立测试集上找到正确模型方面，ProQDock 的表现优于最先进的方法 ZRANK 和 ZRANK2。最后，我们还证明可以将 ProQDock 与 ZRANK 和 ZRANK2 结合起来以进一步提高性能。可用性和实施：http://bioinfo.ifm.liu.se/ProQDock 联系方式：bjornw@ifm.liu.se 补充信息：补充数据可在生物信息学在线获取。
Motivation: Protein–protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models. Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein–protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further. Availability and implementation: http://bioinfo.ifm.liu.se/ProQDock Contact: bjornw@ifm.liu.se Supplementary information: Supplementary data are available at Bioinformatics online.