LAQV-REQUIMTE/Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, India.
Biomolecules. 2021 Nov 10;11(11):1670. doi: 10.3390/biom11111670.
The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box-Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries-i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.
两种丝裂原活化蛋白激酶相互作用激酶（即 MNK-1 和 MNK-2）抑制剂被认为可用于治疗多种疾病，包括癌症。本研究首次报道了一种多靶标（或多任务）的计算建模方法（mt-QSAR），用于探测这些同工酶对 MNKs 的抑制潜力。根据 Box-Jenkins 移动平均方法，结合一系列特征选择算法和机器学习工具，从在各种测定条件下测试的 1892 种化学物质的大型数据集构建线性和非线性 mt-QSAR 分类模型，其中最具预测性的模型（>90%的整体准确性）用于对 MNK-1 和 MNK-2 的可能抑制作用进行机制解释。考虑到该后一种模型适用于化学文库的虚拟筛选，即商业、非商业和内部文库，我们将其构建为一个基于 Flask 的可公开访问的即用型应用程序。此外，本研究还使用 mt-QSAR 模型对聚焦激酶文库进行了虚拟筛选。在这个过程中确定的虚拟命中物进一步通过相似性搜索、药物相似性的计算预测、ADME 特征以及合成可及性工具进行筛选。最后，进行分子动力学模拟以识别和选择最有前途的虚拟命中物。本工作中收集的信息可为发现新型 MNK-1/2 抑制剂作为潜在治疗剂提供重要指导。
