D-[(3R,5S)-3,5-bis(azidomethyl)-1-(tert-butoxycarbonyl)]prolyl-L-valine | 1480697-22-0

• 分子结构分类: 有机化合物 - 有机酸及其衍生物 - 羧酸及其衍生物
• 英文名称: D-[(3R,5S)-3,5-bis(azidomethyl)-1-(tert-butoxycarbonyl)]prolyl-L-valine
• CAS: 1480697-22-0
• 化学式: C₁₇H₂₈N₈O₅
• 分子量: 424.46
• InChiKey: ZPTHCPHRVCMFJU-QNWHQSFQSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  2.83
• 重原子数：
  30.0
• 可旋转键数：
  8.0
• 环数：
  1.0
• sp3杂化的碳原子比例：
  0.82
• 拓扑面积：
  193.46
• 氢给体数：
  2.0
• 氢受体数：
  6.0

反应信息

• 作为反应物：
  描述：

  D-[(3R,5S)-3,5-bis(azidomethyl)-1-(tert-butoxycarbonyl)]prolyl-L-valine 在 O-(benzotriazol-1-yl)-N,N,N',N'-tetramethyluronium tetrafluoroborate 、 lithium hydroxide 作用下， 以 四氢呋喃 、 水 、 乙腈 为溶剂， 反应 41.5h， 生成 D-[(3S,5R)-3,5-bis(azidomethyl)-1-(tert-butoxycarbonyl)]prolyl-L-valylglycine
  参考文献：
  名称：

  Synthesis and allosteric modulation of the dopamine receptor by peptide analogs of l-prolyl-l-leucyl-glycinamide (PLG) modified in the l-proline or l-proline and l-leucine scaffolds

  摘要：

  Novel analogs of L-prolyl-L-leucylglycinamide (PLC) were synthesized wherein the prolyl residue was replaced with other amino acids based on a 3,5-disubstituted proline scaffold. In some examples, the L-leucyl residue was also replaced by L-valine. These analogs were tested for their ability to enhance the binding of [H-3]-N-propylnorapomorphine to short isoform of human dopamine D-2 receptors.Compounds 18b and 19b, increased [H-3] NPA binding at concentrations between 10(-12) and 10(-9) M, which is similar to the effect of PLG in this assay and, provides evidences that these compounds are acting as allosteric modulators of dopamine D-2 receptors. (C) 2013 Elsevier Masson SAS. All rights reserved.

  DOI：

  10.1016/j.ejmech.2013.08.001
• 作为产物：
  描述：

  (±)-3,5-bis(azidomethyl)-1-(tert-butoxycarbonyl)pyrrolidine-2-carboxylic acid 在 O-(benzotriazol-1-yl)-N,N,N',N'-tetramethyluronium tetrafluoroborate 、 lithium hydroxide 作用下， 以 四氢呋喃 、 二氯甲烷 、 水 为溶剂， 反应 21.5h， 生成 D-[(3R,5S)-3,5-bis(azidomethyl)-1-(tert-butoxycarbonyl)]prolyl-L-valine
  参考文献：
  名称：

  Synthesis and allosteric modulation of the dopamine receptor by peptide analogs of l-prolyl-l-leucyl-glycinamide (PLG) modified in the l-proline or l-proline and l-leucine scaffolds

  摘要：

  Novel analogs of L-prolyl-L-leucylglycinamide (PLC) were synthesized wherein the prolyl residue was replaced with other amino acids based on a 3,5-disubstituted proline scaffold. In some examples, the L-leucyl residue was also replaced by L-valine. These analogs were tested for their ability to enhance the binding of [H-3]-N-propylnorapomorphine to short isoform of human dopamine D-2 receptors.Compounds 18b and 19b, increased [H-3] NPA binding at concentrations between 10(-12) and 10(-9) M, which is similar to the effect of PLG in this assay and, provides evidences that these compounds are acting as allosteric modulators of dopamine D-2 receptors. (C) 2013 Elsevier Masson SAS. All rights reserved.

  DOI：

  10.1016/j.ejmech.2013.08.001

文献信息

• Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New <scp>l</scp>-Prolyl-<scp>l</scp>-leucyl-glycinamide Peptidomimetics
  作者：Joana Ferreira da Costa、David Silva、Olga Caamaño、José M. Brea、Maria Isabel Loza、Cristian R. Munteanu、Alejandro Pazos、Xerardo García-Mera、Humbert González-Díaz

  DOI：10.1021/acschemneuro.8b00083

  日期：2018.11.21

  series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental

  预测涉及多巴胺途径的靶蛋白的药物-蛋白相互作用（DPI）是药物化学中非常重要的目标。我们可以使用分子对接或机器学习（ML）模型针对一种特定蛋白质来解决此问题。不幸的是，这些模型无法解释公共数据库中报告的临床前测定的大而复杂的大数据集。这包括多种测定条件，例如不同的实验参数，生物学测定，靶蛋白，细胞系，靶标生物或测定生物。另一方面，微扰理论（PT）模型使我们能够基于先前已知的参考案例，在具有多个边界条件的实验分析中预测查询化合物或分子系统的性质。在这项工作中，我们报告了针对多巴胺途径蛋白的化合物的临床前测定的大型ChEMBL数据集的首次PTML（PT + ML）研究。在培训和外部验证系列中，发现的最佳PTML模型可以预测50000例病例，准确率达到70-91％。我们还将线性PTML模型与通过多种非线性方法（人工神经网络（ANN），随机森林，深度学习等）训练的替代PTML模型进行了比较。一些非