Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.
Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México.
J Chem Inf Model. 2019 Mar 25;59(3):1109-1120. doi: 10.1021/acs.jcim.9b00034. Epub 2019 Mar 4.
Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN ) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.
由于抗生素的不同敏感性，预测具有不同代谢反应网络 (MRN) 的致病微生物对新化合物的活性是一个重要目标。ChEMBL 数据库包含 55931 种化合物的 160000 多个临床前抗菌活性测定结果，这些化合物具有 >365 个活性参数（MIC、IC 等）和 >90 种细菌菌株，超过 25 种细菌。此外，Leong 和 Barabási 数据集包括 >40 种微生物的 MRN。然而，目前还没有能够同时考虑药物和 MRN 结构来预测多种测定方法的抗菌活性的模型。在这项工作中，我们结合了扰动理论、机器学习和信息融合技术来开发第一个 PTMLIF 模型。在训练/验证系列中，发现的最佳线性模型的特异性 = 90.31/90.40，灵敏度 = 88.14/88.07。我们与非线性人工神经网络 (ANN) 技术和文献中的以前模型进行了比较。接下来，我们通过一个实验案例研究说明了模型的实际用途。我们首次报道了从植物 Cissus incisa 中分离和表征萜类化合物的情况。萜类化合物的抗菌活性通过实验确定。更具活性的化合物是植醇和 α-香树素，对万古霉素耐药粪肠球菌和耐碳青霉烯类的鲍曼不动杆菌的 MIC = 100 μg/mL。这些化合物已经从其他来源中得到了报道。然而，它们是首次在这里被分离并针对包括世界卫生组织（WHO）优先病原体在内的多种多药耐药菌进行评估。最后，我们使用该模型预测这些化合物对具有不同 MRN 的其他微生物的活性，以寻找其他潜在的靶标。
