Sherwani Zaid Anis, Tariq Syeda Sumayya, Mushtaq Mamona, Siddiqui Ali Raza, Nur-E-Alam Mohammad, Ahmed Aftab, Ul-Haq Zaheer
Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
H.E.J Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
Sci Rep. 2024 Apr 24;14(1):9398. doi: 10.1038/s41598-024-60056-z.
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein-ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein-ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4's functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.
游离脂肪酸受体4（FFAR4）是一种G蛋白偶联受体，负责触发调节各种生理过程的细胞内信号通路。FFAR4激动剂与增强胰岛素释放以及减轻通常与结合到FFAR4的游离脂肪酸相关的致动脉粥样硬化、致肥胖、促癌和促糖尿病作用有关。在本研究中，采用基于分子结构的机器学习技术来评估化合物作为FFAR4潜在激动剂的可能性。分子结构被编码为位阵列，用作分子指纹，随后使用贝叶斯网络算法进行分析以识别用于筛选数据的模式。通过机器学习协议获得的入围命中化合物通过分子对接以及ADME和毒性预测进一步验证。然后对入围化合物进行膜结合FFAR4-配体复合物的分子动力学模拟，每个模拟时长100纳秒。进行了包括结合相互作用、均方根偏差（RMSD）、均方根波动（RMSF）、回转半径（RoG）、主成分分析（PCA）和自由能景观（FEL）在内的分子分析，以在原子水平上仔细研究蛋白质-配体复合物。分析揭示了入围化合物与先前记录的FFAR4关键残基之间的显著相互作用。作为复合物一部分的FFAR4表现出一致的RMSD，范围为3.57至3.64，残基波动最小，为5.27至6.03纳米，表明复合物稳定。回转半径值在22.8至23.5纳米之间波动，表明所研究系统的结构紧凑性和有序性。此外，在每个复合物中观察到明显的构象运动，在整个模拟过程中能量轮廓转移到更宽的能量盆地，表明蛋白质-配体复合物在热力学上稳定。基于这些见解和深入分析，两种化合物CHEMBL2012662和CHEMBL64616被提出作为潜在的FFAR4激动剂。总体而言，这些发现推进了我们对FFAR4功能和机制的理解，突出了这些化合物作为潜在的FFAR4激动剂，值得作为代谢和免疫相关疾病的创新治疗方法进行进一步探索。
