Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset to predict the binding modes and ranking of ligands. The macrocyclic nature of the BACE ligands brought additional challenges, which were dealt with by a careful preparation of the three-dimensional input structures for ligands. This provided top-performing predictions for BACE, in contrast with CatS, where the predictions in the absence of guiding constraints provided poor results. These results highlight the importance of previous structural knowledge that is needed for correct predictions on some challenging targets. After the end of the challenge, we also carried out free energy calculations (i.e. in a non-blinded manner) for CatS using the pmx software and several force fields (AMBER, Charmm). Using knowledge-based starting pose construction allowed reaching remarkable accuracy for the CatS free energy estimates. Interestingly, we show that the use of a consensus result, by averaging the results from different force fields, increases the prediction accuracy.

中文翻译：

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使用D3R Grand Challenge 4数据集进行分子对接和自由能计算协议的性能评估。

使用包含Beta-分泌酶1（BACE）和组织蛋白酶S（CatS）抑制剂的D3R Grand Challenge 4数据集，我们评估了我们内部对接工作流程的性能，该工作涉及第一步，即选择最适合的对接软件基于公共数据库中可用的结构和功能信息构建感兴趣的系统，然后对接数据集以预测结合模式和配体排名。BACE配体的大环性质带来了其他挑战，这些挑战可以通过精心准备配体的三维输入结构来解决。与CatS相比，这提供了BACE的最佳性能预测，而CatS在缺乏指导性约束的情况下提供的预测效果较差。这些结果突出了对某些具有挑战性的目标进行正确预测所需要的先前结构知识的重要性。挑战结束后，我们还使用pmx软件和多个力场（AMBER，Charmm）对CatS进行了自由能计算（即，非盲式）。使用基于知识的起始姿势构造，可以使CatS自由能估算达到极高的准确性。有趣的是，我们表明，通过对来自不同力场的结果求平均，可以使用共识结果来提高预测精度。使用基于知识的起始姿势构造，可以使CatS自由能估算达到显着的准确性。有趣的是，我们表明，通过对来自不同力场的结果求平均，可以使用共识结果来提高预测精度。使用基于知识的起始姿势构造，可以使CatS自由能估算达到显着的准确性。有趣的是，我们表明使用共识结果，通过平均不同力场的结果，可以提高预测的准确性。