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基于分子对接的结合能预测不同结合位点二元混合物的毒性。

Using molecular docking-based binding energy to predict toxicity of binary mixture with different binding sites.

机构信息

State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

出版信息

Chemosphere. 2013 Aug;92(9):1169-76. doi: 10.1016/j.chemosphere.2013.01.081. Epub 2013 Feb 26.

Abstract

The flood of chemical substances in the environment result in the complexity of chemical mixtures, and one of the reasons for complexity is that their individual chemicals bind to different binding sites on different (or same) target proteins within the organism. A general approaches therefore are proposed in this study to predict the toxicity of chemical mixtures with different binding sites by using molecular docking-based binding energy (Ebinding). Aldehydes and cyanogenic toxicants were selected as the example of chemical mixtures with same binding site. Triazines and urea herbicide were selected as the example of chemical mixtures with different binding sites but on same target protein. Sulfonamides and trimethoprim toxicants were selected as the example of chemical mixtures with different target proteins. Although these chemical mixtures bind to their binding sites by different ways, there is a general relationship between their binary mixture toxicity (EC50M) and their corresponding Ebinding of individual chemicals and logKow(mix). By using the Ebinding to describe how the individual chemicals work in the different binding sites, the approach may provide a general and simply model to predict mixture toxicity to microorganism.

摘要

环境中化学物质的大量涌入导致了化学混合物的复杂性,其中一个原因是它们的单个化学物质会与生物体中不同(或相同)靶蛋白上的不同结合位点结合。因此,本研究提出了一种通用方法,通过基于分子对接的结合能(Ebinding)来预测具有不同结合位点的化学混合物的毒性。本研究选择醛类和氰基毒物作为具有相同结合位点的化学混合物的示例,选择三嗪类和尿素类除草剂作为具有不同结合位点但作用于同一靶蛋白的化学混合物的示例,选择磺胺类和甲氧苄啶类毒物作为具有不同靶蛋白的化学混合物的示例。尽管这些化学混合物通过不同的方式与它们的结合位点结合,但它们的二元混合物毒性(EC50M)与其单个化学物质的相应 Ebinding 和 logKow(mix)之间存在一般关系。通过使用 Ebinding 来描述单个化学物质在不同结合位点的作用方式,该方法可以为预测混合物对微生物的毒性提供一种通用且简单的模型。

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