The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins’ inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

中文翻译：

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建立以形状为中心的药效团模型以进行有效的对接筛选


通过比较灵活采样姿势与目标蛋白倒置结合腔的形状相似性，可以提高分子对接的性能。通过执行富集驱动的优化，进一步提高了这些伪配体或基于负图像的模型在对接重新评分中的有效性。在这里，我们介绍了一种新颖的以形状为中心的药效团建模算法 O-LAP，该算法通过成对距离图聚类将重叠的原子内容聚集在一起，生成一类新型空腔填充模型。灵活对接的活性配体的排名最高的姿势被用作建模输入，并且使用随机训练/测试划分，对五个要求严格的药物靶点对多个替代聚类设置进行了彻底的基准测试。在对接重新评分中，O-LAP 建模通常在默认对接丰富的基础上大幅改进；此外，结果表明聚类模型在刚性对接中效果良好。基于 C++/Qt5 的算法 O-LAP 在 GNU 通用公共许可证 v3.0 下通过 GitHub (https://github.com/jvlehtonen/overlap-toolkit) 发布。本研究介绍了 O-LAP，这是一种基于 C++/Qt5 的图形聚类软件，用于生成新型的以形状为中心的药效团模型。在 O-LAP 建模中，目标蛋白空腔充满了灵活对接的活性配体，重叠的配体原子聚集在一起，并将所得模型的形状/静电势与灵活采样的分子对接姿势进行比较。 O-LAP 建模可确保基于综合基准测试的对接重新评分和刚性对接的高度丰富。