Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Department of Functional Food and Biotechnology, College of Medical Sciences, Jeonju University, Jeonju 55069, Republic of Korea.
Int J Mol Sci. 2024 Mar 27;25(7):3747. doi: 10.3390/ijms25073747.
Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of .
克氏锥虫抑制剂在治疗恰加斯病的药物之后是必需的，因为需要更安全、更有效的治疗方法。克氏锥虫是克氏锥虫的来源，克氏锥虫是一种重要的半胱氨酸蛋白酶，这激发了人们使用计算方法来创建更有效的抑制剂的兴趣。我们使用了一个包含 36 种已知抑制剂的数据集和一个药效团模型来建立 3D-QSAR 模型，以鉴定克氏锥虫的潜在抑制剂。我们还使用 Deep purpose 库构建了一个深度学习模型，该模型基于 204 种活性化合物进行训练，并使用特定的测试集进行了验证。在对 Drug Bank 数据库中的 8533 种分子进行全面筛选后，药效团和深度学习模型分别识别出 1012 种和 340 种类药性分子。这些分子进一步通过分子对接进行评估，然后进行诱导契合对接。最终，对表现出强结合相互作用的最终有效抑制剂进行了分子动力学模拟。这些结果提出了四种新型的克氏锥虫抑制剂，可抑制 的克氏锥虫蛋白。
