Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal.
UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, Blue Biotechnology and Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal.
Mar Drugs. 2022 Feb 8;20(2):129. doi: 10.3390/md20020129.
Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure-activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar's website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.
生物污损是指微/宏观生物在人工水浸表面的不良生长，这导致水产养殖、航运和其他依赖沿海和近海基础设施的行业在预防和维护这一过程方面的成本高昂（每年数十亿欧元）。迄今为止，仍然没有可持续、经济和环境安全的解决方案来克服这一具有挑战性的现象。本研究采用计算机辅助药物设计（CADD）方法，包括配体和基于结构的方法，来预测海洋天然产物（MNPs）的防污活性。在 CADD 配体方法中，使用从 ChEMBL 数据库和文献中提取的 141 种具有防污筛选数据的有机分子，来构建定量构效关系（QSAR）分类模型。最佳 QSAR 模型的外部和内部验证的预测准确性总评分高达 71%，使用测试集和训练集。还使用开发的最佳 QSAR 模型对来自 Encinar 网站的 14,492 种 MNPs 和目前处于临床管道的 14 种 MNPs 进行了虚拟筛选。在 CADD 基于结构的方法中，使用 QSAR 方法选择的 125 种 MNPs 进行了分子对接实验，以对抗乙酰胆碱酯酶。总体而言，有 16 种 MNPs 被提出作为最有前途的海洋类药物先导化合物，作为防污剂，例如，大环内酯、大环生物碱、吲哚和吡啶衍生物。
