Sabitu Babatunde Olasupo, A. Uzairu, Gideon Adamu Shallangwa, S. Uba
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The genetic function Algorithm (GFA) approach was used to generate a highly predictive and statistically significant model with good correlation coefficient R2 Train = 0.952, Cross validated coefficient Q2cv = 0.870 and adjusted squared correlation coefficient R2adj = 0.898. The predictability and accuracy of the developed model was evaluated through external validation using test set molecules, Y-randomization and applicability domain techniques. The results of Molecular docking simulation by using two neurotransmitter transporters PDB ID 2A65 (resolution = 1.65 A ) and PDB ID 4M48 (resolution = 2.955 A) showed that two of the ligands (compound numbers 12 and 44) having higher binding affinity were observed to inhibit the targets by forming hydrogen bonds and hydrophobic interactions with amino acids of the two receptors respectively. 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引用次数: 5
摘要
去甲肾上腺素转运体(NET)是一种Na+/Cl-偶联的神经递质转运体,负责将释放的去甲肾上腺素(NE)再摄取到大脑的神经末梢,是治疗精神疾病的重要药物。采用定量构效关系(QSAR)研究了50种NET抑制剂作为抗精神病药物对去甲肾上腺素转运体的抑制作用。采用密度泛函理论(DFT),以B3LYP/6-31G*为基集对分子进行优化。采用遗传函数算法(genetic function Algorithm, GFA)生成的模型具有良好的相关系数R2 Train = 0.952,交叉验证系数Q2cv = 0.870,调整后的平方相关系数R2adj = 0.898,具有较高的预测性和统计学显著性。通过使用测试集分子、y随机化和适用域技术进行外部验证,评估了所开发模型的可预测性和准确性。利用两种神经递质转运体PDB ID 2A65(分辨率= 1.65 A)和PDB ID 4M48(分辨率= 2.955 A)进行分子对接模拟,结果表明,两种具有较高结合亲和力的配体(化合物编号12和44)分别通过与两种受体的氨基酸形成氢键和疏水相互作用来抑制靶标。这项研究的结果预计将为分子基础和结构要求提供非常重要的新见解,这将有助于设计更有效和更特异性的治疗性抗精神病药物。
Quantitative Structure-Activity Relationship (QSAR) Studies and Molecular docking Simulation of Norepinephrine Transporter (NET) Inhibitors as Anti-psychotic Therapeutic Agents
The Norepinephrine transporter (NET) is a Na+/Cl- coupled neurotransmitter transporter responsible for reuptake of released norepinephrine (NE) into neural terminals in the brain, an important therapeutic agent used in the treatment of psychiatric disorders. A quantitative structural activity relationship (QSAR) investigation was carried out on 50 Molecules of NET Inhibitors to investigate their inhibitory potencies against norepinephrine transporter as novel agents for anti-psychotic disorders. The molecules were optimized by employing Density functional theory (DFT) with basis set of B3LYP/6-31G*. The genetic function Algorithm (GFA) approach was used to generate a highly predictive and statistically significant model with good correlation coefficient R2 Train = 0.952, Cross validated coefficient Q2cv = 0.870 and adjusted squared correlation coefficient R2adj = 0.898. The predictability and accuracy of the developed model was evaluated through external validation using test set molecules, Y-randomization and applicability domain techniques. The results of Molecular docking simulation by using two neurotransmitter transporters PDB ID 2A65 (resolution = 1.65 A ) and PDB ID 4M48 (resolution = 2.955 A) showed that two of the ligands (compound numbers 12 and 44) having higher binding affinity were observed to inhibit the targets by forming hydrogen bonds and hydrophobic interactions with amino acids of the two receptors respectively. The results of this study are envisaged to provide very important new insights into the molecular basis and structural requirements that would help in designing more potent and more specific therapeutic anti-psychotic agents.