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局部相互作用密度(LID),一种快速有效的对接构象优先级排序工具。

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.

机构信息

Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.

出版信息

Molecules. 2019 Jul 18;24(14):2610. doi: 10.3390/molecules24142610.

Abstract

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

摘要

通过优先考虑与配体/蛋白质复合物的晶体结构中发现的经过验证的结合模式相似的构象,可以改善配体在蛋白质位点上的对接。在预测模型中形成的相互作用在每个单独的参考 3D 结构中进行搜索。我们建议合并所有参考资料提供的信息,创建所有已知结合模式的单一表示形式。该方法称为 LID,是 Local Interaction Density 的缩写。LID 在使用 PLANTS 作为对接软件的 19 种蛋白质和 1382 种配体的构象预测练习中进行了基准测试。它还在 8 种蛋白质的虚拟筛选挑战中进行了测试,数据集来自 DUD-E 和 PubChem 的 140,000 种化合物。LID 显著提高了对接程序在构象预测和虚拟筛选中的性能。与基于参考结合模式的个体比较的重新评分方法(GRIM 方法)相比,这种收益相当。重要的是,LID 对少量参考资料有效。与对接时间相比,LID 的计算时间可以忽略不计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fced/6681060/9288a119b9b2/molecules-24-02610-g001.jpg

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