Raymond and Beverly Sackler Faculty of Exact Sciences, Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
Proteins. 2010 Nov 15;78(15):3197-204. doi: 10.1002/prot.22790.
The CAPRI experiment (Critical Assessment of Predicted Interactions) simulates realistic and diverse docking challenges, each case having specific properties that may be exploited by docking algorithms. Motivated by the different CAPRI challenges, we developed and implemented a comprehensive suite of docking algorithms. These were incorporated into a dynamic docking protocol, consisting of four main stages: (1) Biological and bioinformatics research aiming to predict the binding site residues, to define distance constraints between interface atoms and to analyze the flexibility of molecules; (2) Rigid or flexible docking, performed by the PatchDock or FlexDock method, which utilizes the information gathered in the previous step. Symmetric complexes are predicted by the SymmDock method; (3) Flexible refinement and reranking of the rigid docking solution candidates, performed by FiberDock; and finally, (4) clustering and filtering the results based on energy funnels. We analyzed the performance of our docking protocol on a large benchmark and on recent CAPRI targets. The analysis has demonstrated the importance of biological information gathering prior to docking, which significantly increased the docking success rate, and of the refinement and rescoring stage that significantly improved the ranking of the rigid docking solutions. Our failures were mostly a result of mishandling backbone flexibility, inaccurate homology modeling, or incorrect biological assumptions. Most of the methods are available at http://bioinfo3d.cs.tau.ac.il/.
CAPRI 实验(预测相互作用的关键评估)模拟了现实和多样化的对接挑战,每个案例都具有特定的性质,这些性质可能被对接算法利用。受不同的 CAPRI 挑战的启发,我们开发并实现了一套全面的对接算法。这些算法被纳入一个动态对接协议中,该协议由四个主要阶段组成:(1)旨在预测结合位点残基、定义界面原子之间的距离约束以及分析分子灵活性的生物和生物信息学研究;(2)通过 PatchDock 或 FlexDock 方法进行刚性或柔性对接,该方法利用前一步骤中收集的信息。SymmDock 方法预测对称复合物;(3)通过 FiberDock 对刚性对接解决方案候选者进行灵活的细化和重新排序;最后,(4)根据能量漏斗对结果进行聚类和过滤。我们在一个大型基准测试和最近的 CAPRI 目标上分析了我们的对接协议的性能。分析表明,在对接之前收集生物信息的重要性,这大大提高了对接成功率,以及细化和重新评分阶段的重要性,这显著提高了刚性对接解决方案的排名。我们的失败主要是由于错误处理骨架灵活性、不准确的同源建模或不正确的生物学假设。大多数方法都可在 http://bioinfo3d.cs.tau.ac.il/ 获取。