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.

译文

:我们在上一版CAPRI中描述了我们的结果。使用pyDock对接方法,我们参与了所有目标的预测和得分。新的挑战(基于相互作用的亚基的同源性建模,域-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,并鼓励我们设计新的对接方法。总体而言,与以前的版本一致,结果是相当成功的,尤其是考虑到某些目标的高难度。我们的对接方法成功地在五个目标中用作预测指标或得分指标(T29,T34,T35,T41和T42)。此外,由于包含了预期参与相互作用的残基的可用信息,因此我们的方案在另外两种情况(T32和T40)中也将获得成功。在其余目标中(T37除外),大多数组的结果同样差。我们提交了未绑定目标T29的评分器中最佳的模型(配体RMSD中),蛋白质RNA靶T34的评分器中第二好的模型,以及结构域组装目标T35的预测因子中唯一正确的模型。总而言之,我们在此CAPRI版本中针对新提出的挑战所获得的出色结果表明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。

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