Our information‐driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38‐45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template‐based refinement/CA‐CA restraint‐guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse‐grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher‐quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

中文翻译：

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CAPRI第38至45轮概述了数据驱动的HADDOCK策略。

自从参与CAPRI以来，我们的信息驱动对接方法HADDOCK就表现出持续的性能。这部分是由于其将数据集成到建模过程中的能力以及其评分功能的稳定性。我们以服务器和手动预测器的形式参与了CAPRI。在CAPRI第38至45轮中，我们根据可用信息使用了各种策略。这些范围从施加限制到从文献中确定的对相互作用很重要的一些残基到结合同源物或基于模板的提纯/ CA-CA限制引导的已识别模板对接识别的结合口袋。当相关时，使用对称约束来限制构象采样。我们还针对大型弯靶测试了HADDOCK中MARTINI粗粒度力场的新实现。总体而言，我们从22个界面中分别获得了13个和11个服务器和手动提交的可接受或更好的预测。我们的服务器性能（考虑前十名时，可接受的或更高质量的模型）要好于手动（50％）（59％），在这种情况下，我们通常会尝试各种协议和数据源的组合。同样，我们基于分子间范德华力和静电能的线性组合以及经验去溶剂化条件的简单评分功能在评分实验中表现出良好的性能，所有22个界面的成功率均为63％。对模型质量的分析表明，