Enejoh Ojochenemi A, Okonkwo Chinelo H, Nortey Hector, Kemiki Olalekan A, Moses Ainembabazi, Mbaoji Florence N, Yusuf Abdulrazak S, Awe Olaitan I
Genetics, Genomics and Bioinformatics Department, National Biotechnology Research and Development Agency, Abuja, Nigeria.
Department of Pharmacy, National Hospital Abuja, Abuja, Nigeria.
Front Chem. 2025 Jan 9;12:1503593. doi: 10.3389/fchem.2024.1503593. eCollection 2024.
Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure. TGR5 agonists are attractive candidates for T2D therapy because of their ability to improve glycemic control. This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics simulations (MDS) to explore novel small molecules as potential TGR5 agonists.
Bioactivity data for known TGR5 agonists were obtained from the ChEMBL database. The dataset was cleaned and molecular descriptors based on Lipinski's rule of five were selected as input features for the ML model, which was built using the Random Forest algorithm. The optimized ML model was used to screen the COCONUT database and predict potential TGR5 agonists based on their molecular features. 6,656 compounds predicted from the COCONUT database were docked within the active site of TGR5 to calculate their binding energies. The four top-scoring compounds with the lowest binding energies were selected and their activities were compared to those of the co-crystallized ligand. A 100 ns MDS was used to assess the binding stability of the compounds to TGR5.
Molecular docking results showed that the lead compounds had a stronger affinity for TGR5 than the cocrystallized ligand. MDS revealed that the lead compounds were stable within the TGR5 binding pocket.
The combination of ML, MD, and MDS provides a powerful approach for predicting new TGR5 agonists that can be optimised for T2D treatment.
2型糖尿病（T2D）的治疗仍然是一项重大挑战，因为其具有多因素性质和复杂的代谢途径。人们越来越有兴趣寻找新的治疗靶点，以带来更安全、更有效的治疗选择。武田G蛋白偶联受体5（TGR5）是一个有前景的抗糖尿病靶点，在代谢调节中起关键作用，尤其是在葡萄糖稳态和能量消耗方面。TGR5激动剂因其改善血糖控制的能力，成为T2D治疗的有吸引力的候选药物。本研究使用基于机器学习的模型（ML）、分子对接（MD）和分子动力学模拟（MDS）来探索作为潜在TGR5激动剂的新型小分子。
从ChEMBL数据库获得已知TGR5激动剂的生物活性数据。对数据集进行清理，并选择基于Lipinski五规则的分子描述符作为ML模型的输入特征，该模型使用随机森林算法构建。优化后的ML模型用于筛选COCONUT数据库，并根据其分子特征预测潜在的TGR5激动剂。将从COCONUT数据库预测的6656种化合物对接至TGR5的活性位点，以计算它们的结合能。选择结合能最低的四种得分最高的化合物，并将它们的活性与共结晶配体的活性进行比较。使用100纳秒的MDS评估化合物与TGR5的结合稳定性。
分子对接结果表明，先导化合物对TGR5的亲和力比共结晶配体更强。MDS显示，先导化合物在TGR5结合口袋内是稳定的。
ML、MD和MDS的结合为预测可优化用于T2D治疗的新型TGR5激动剂提供了一种强大的方法。
