Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano , Via Mangiagalli, 25, I-20133 Milano, Italy.
Department of Pharmacy, University Hospital Centre (CHUV) , Rue du Bugnon, CH-1011 Lausanne, Switzerland.
J Chem Inf Model. 2017 Jul 24;57(7):1691-1702. doi: 10.1021/acs.jcim.7b00121. Epub 2017 Jul 10.
Docking simulations are very popular approaches able to assess the capacity of a given ligand to interact with a target. Docking simulations are usually focused on a single best complex even though many studies showed that ligands retain a significant mobility within a binding pocket by assuming different binding modes all of which may contribute to the monitored ligand affinity. The present study describes an innovative concept, the binding space, which allows an exploration of the ligand mobility within the binding pocket by simultaneously considering several ligand poses as generated by docking simulations. The multiple poses and the relative docking scores can then be analyzed by taking advantage of the same concepts already used in the property space analysis; hence the binding space can be parametrized by (a) mean scores, (b) score ranges, and (c) score sensitivity values. The first parameter represents a very simple procedure to account for the contribution of the often neglected alternative binding modes, while the last two descriptors encode the degree of mobility which a given ligand retains within the binding cavity (score range) as well as the ease with which a ligand explores such a mobility (score sensitivity). Here, the binding space concept is applied to the prediction of the hydrolytic activity of BChE by synergistically considering multiple poses and multiple protein structures. The obtained results shed light on the remarkable potential of the binding space concept, whose parameters allow a significant increase of the predictive power of the docking results as revealed by extended correlative analyses. Mean scores are the parameters affording the largest statistical improvement, and all the here proposed docking-based descriptors show enhancing effects in developing predictive models. Finally, the study describes a new score function (Contacts score) simply based on the number of surrounding residues which appears to be particularly productive in the framework of the binding space.
对接模拟是一种非常流行的方法，能够评估给定配体与靶标相互作用的能力。对接模拟通常集中在单个最佳复合物上，尽管许多研究表明，配体通过采用不同的结合模式在结合口袋中保持显著的移动性，所有这些模式都可能有助于监测配体亲和力。本研究描述了一种创新概念，即结合空间，它允许通过同时考虑对接模拟生成的几个配体构象来探索配体在结合口袋中的移动性。然后可以利用已经在性质空间分析中使用的相同概念来分析多个构象和相对对接分数；因此，结合空间可以通过以下参数进行参数化：(a) 平均分数，(b) 分数范围，和 (c) 分数灵敏度值。第一个参数代表一种非常简单的程序，可以说明经常被忽视的替代结合模式的贡献，而最后两个描述符编码了给定配体在结合腔中保持的移动性程度（分数范围）以及配体探索这种移动性的容易程度（分数灵敏度）。在这里，结合空间概念应用于通过协同考虑多个构象和多个蛋白质结构来预测 BChE 的水解活性。获得的结果阐明了结合空间概念的显著潜力，其参数允许对接结果的预测能力显著提高，这通过扩展的相关分析得到了证实。平均分数是提供最大统计改进的参数，这里提出的所有基于对接的描述符都在开发预测模型方面显示出增强效果。最后，该研究描述了一种新的分数函数（接触分数），该函数仅基于周围残基的数量，在结合空间框架中似乎特别有效。
