Life Sciences, Barcelona Supercomputing Center, Barcelona, Spain.
Proteins. 2010 Nov 15;78(15):3182-8. doi: 10.1002/prot.22773.
We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology-based modeling of the interacting subunits, domain-domain assembling, and protein-RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound-bound target T29, the second best model among scorers for the protein-RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking.
我们在这里描述了在最近一次 CAPRI 版本中的结果。我们使用 pyDock 对接方法参与了所有的靶标,既作为预测者,也作为评分者。新的挑战(相互作用亚基的基于同源建模、结构域-结构域组装和蛋白质-RNA 相互作用)将我们的计算机工具推向了极限,并促使我们设计了新的对接方法。总的来说,结果与之前的版本相当成功,尤其是考虑到一些靶标的高难度。我们的对接方法在五个靶标中作为预测者或评分者取得了成功(T29、T34、T35、T41 和 T42)。此外,通过纳入关于预期参与相互作用的残基的可用信息,我们的方案在另外两个案例中也会成功(T32 和 T40)。在其余的靶标中(除了 T37),对于大多数组来说,结果同样较差。我们提交了针对无约束-约束靶标 T29 的评分者中最佳的配体 RMSD 模型,针对蛋白质-RNA 靶标 T34 的评分者中第二好的模型,以及针对结构域组装靶标 T35 的预测者中唯一正确的模型。总之,我们在本次 CAPRI 版本中对新提出的挑战的出色表现展示了我们方法的局限性和适用性,并鼓励我们继续开发自动化生物分子对接的方法。