Departamento de Química Orgánica, Grupo de Química Biológica y Computacional, Universidad de Concepción, Edmundo Larenas 129, Concepción, Chile.
Enzyme Microb Technol. 2013 Jan 10;52(1):68-76. doi: 10.1016/j.enzmictec.2012.10.009. Epub 2012 Nov 1.
A Structure Activity Relationship (SAR) study for laccase mediator systems was performed in order to correctly classify different natural phenolic mediators. Decision tree (DT) classification models with a set of five quantum-chemical calculated molecular descriptors were used. These descriptors included redox potential (ɛ°), ionization energy (E(i)), pK(a), enthalpy of formation of radical (Δ(f)H), and OH bond dissociation energy (D(O-H)). The rationale for selecting these descriptors is derived from the laccase-mediator mechanism. To validate the DT predictions, the kinetic constants of different compounds as laccase substrates, their ability for pesticide transformation as laccase-mediators, and radical stability were experimentally determined using Coriolopsis gallica laccase and the pesticide dichlorophen. The prediction capability of the DT model based on three proposed descriptors showed a complete agreement with the obtained experimental results.
为了正确分类不同的天然酚类介体，我们对漆酶介体系统进行了构效关系（SAR）研究。使用了具有一组五个量子化学计算分子描述符的决策树（DT）分类模型。这些描述符包括氧化还原电位（ɛ°）、电离能（E(i)）、pK(a)、自由基生成焓（Δ(f)H）和 OH 键离解能（D(O-H)）。选择这些描述符的原理来自于漆酶-介体机制。为了验证 DT 的预测结果，使用 Coriolopsis gallica 漆酶和农药二氯苯，通过实验测定了不同化合物作为漆酶底物的动力学常数、作为漆酶-介体转化农药的能力以及自由基稳定性。基于三个提出的描述符的 DT 模型的预测能力与获得的实验结果完全一致。
