RXR药效团模建和分子对接研究
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摘要
RXR能以形成同源二聚体(RXR:RXR)或与其它核受体(RAR、TR、VDR、LXR、FXR、PPARs、NGFIB)形成异源二聚体的形式调节多个基因转录。因此对多种核受体有调节作用,成为近几年药物开发的重要靶点之一。
本实验利用Catalyst软件生成并研究了RXR(α,β,γ)激动剂和RXRα拮抗剂的三维药效团模型。并利用DOCK软件对RXR(α,β)激动剂与相应受体进行了对接研究。
RXRα激动剂最佳模型包括四个药效团元素(3个疏水团,1个氢键受体)和8个排除体积。分子对接结果显示配体分子绝大部分处于受体的疏水区域,周围有较多体积较大的氨基酸形成空间位阻,限制配体分子与受体结合,配体分子能与Ala332间形成氢键。药效团模型能与分子对接结果很好地拟合,互相印证了结果的可靠性。
RXRα拮抗剂的三维药效团模型包括四个药效团元素(3个疏水团,1个氢键受体,1个芳香环)和5个排除体积。
RXRβ激动剂的三维药效团模型包括四个药效团元素(1个氢键受体,2个脂肪疏水团,1个芳香疏水团)和9个排除体积。分子对接结果显示配体分子绝大部分处于受体的疏水区域,周围有较多体积较大的氨基酸形成空间位阻,限制配体分子与受体结合,配体分子能与Ala398、Arg387间形成氢键。药效团模型能与分子对接结果很好地拟合,互相印证了结果的可靠性。
RXRγ激动剂的三维药效团模型包括五个药效团元素(1个氢键受体,3个脂肪疏水团,1个芳香疏水团)和5个排除体积。
这些在三维空间分布的排除体积有利于限制配体的柔性旋转,使其采取最佳的构象与受体的配体结合区匹配。
这些模型都用数十个测试集分子进行了预测活性检测,表明本实验生成的药效团模型能预测多种不同结构类型RXR配体活性的高低。本实验结果可以用于指导研发和优化RXR(α,β,γ)各亚型的选择性激动剂和RXRα拮抗剂。
RXRs regulate gene transcription either as homodimers (RXR:RXR) or as heterodimers with other nuclear receptors such as RARs, TR, VDR, LXR, FXR, PPARs, and NGFIB. RXRs are important targets for drug discovery, for their unique capability to interact with a variety of other nuclear receptors.
Three-dimensional pharmacophore models were generated for RXR (α,β,γ) agonists and RXRαantagonists using quantitative approach (Catalyst HypoRefine). Molecular Docking for RXR (α,β) were made using DOCK software.
The best quantitative model for RXRαagonists has four features and eight excluded volumes: three hydrophobic groups and one hydrogen bond receptor. DOCK result shows that a majority of the ligand is in the hydrophobic region and there are some bulk amino acids around the ligand. Ligands can form hydrogen bond with Ala332 in receptor. The pharmacophore model matches the DOCK result well.
The best quantitative model for RXRαantagonists has five features and five excluded volumes: three hydrophobic groups, one aromatic ring, and one hydrogen bond receptor.
The best quantitative model for RXRβagonists had four features and nine excluded volumes: two hydrophobic groups and one hydrophobic aromatic, one hydrogen bond receptor. DOCK result shows that a majority of the ligand is in the hydrophobic region and there are some bulk amino acids around the ligand. Ligands can form hydrogen bond with Ala398、Arg387 in receptor. The pharmacophore model matches the DOCK result well.
The best quantitative model for RXRγagonists had five features and five excluded volumes: three hydrophobic aliphatic groups, one hydrophobic aromatic ring, and one hydrogen bond receptor.
These EVs’combined effort restricts the flexibility of the compounds and thus could reflect the best conformation with which the ligands interact with the receptor.
These models were validated using a wide range of test molecules. It could predict agonist or antagonist activity and identify highly potent molecules. The present results are valuable to discover and develop specific RXR (α,β,γ) agonists and RXRαantagonists with desired biological activities.
本实验利用Catalyst软件生成并研究了RXR(α,β,γ)激动剂和RXRα拮抗剂的三维药效团模型。并利用DOCK软件对RXR(α,β)激动剂与相应受体进行了对接研究。
RXRα激动剂最佳模型包括四个药效团元素(3个疏水团,1个氢键受体)和8个排除体积。分子对接结果显示配体分子绝大部分处于受体的疏水区域,周围有较多体积较大的氨基酸形成空间位阻,限制配体分子与受体结合,配体分子能与Ala332间形成氢键。药效团模型能与分子对接结果很好地拟合,互相印证了结果的可靠性。
RXRα拮抗剂的三维药效团模型包括四个药效团元素(3个疏水团,1个氢键受体,1个芳香环)和5个排除体积。
RXRβ激动剂的三维药效团模型包括四个药效团元素(1个氢键受体,2个脂肪疏水团,1个芳香疏水团)和9个排除体积。分子对接结果显示配体分子绝大部分处于受体的疏水区域,周围有较多体积较大的氨基酸形成空间位阻,限制配体分子与受体结合,配体分子能与Ala398、Arg387间形成氢键。药效团模型能与分子对接结果很好地拟合,互相印证了结果的可靠性。
RXRγ激动剂的三维药效团模型包括五个药效团元素(1个氢键受体,3个脂肪疏水团,1个芳香疏水团)和5个排除体积。
这些在三维空间分布的排除体积有利于限制配体的柔性旋转,使其采取最佳的构象与受体的配体结合区匹配。
这些模型都用数十个测试集分子进行了预测活性检测,表明本实验生成的药效团模型能预测多种不同结构类型RXR配体活性的高低。本实验结果可以用于指导研发和优化RXR(α,β,γ)各亚型的选择性激动剂和RXRα拮抗剂。
RXRs regulate gene transcription either as homodimers (RXR:RXR) or as heterodimers with other nuclear receptors such as RARs, TR, VDR, LXR, FXR, PPARs, and NGFIB. RXRs are important targets for drug discovery, for their unique capability to interact with a variety of other nuclear receptors.
Three-dimensional pharmacophore models were generated for RXR (α,β,γ) agonists and RXRαantagonists using quantitative approach (Catalyst HypoRefine). Molecular Docking for RXR (α,β) were made using DOCK software.
The best quantitative model for RXRαagonists has four features and eight excluded volumes: three hydrophobic groups and one hydrogen bond receptor. DOCK result shows that a majority of the ligand is in the hydrophobic region and there are some bulk amino acids around the ligand. Ligands can form hydrogen bond with Ala332 in receptor. The pharmacophore model matches the DOCK result well.
The best quantitative model for RXRαantagonists has five features and five excluded volumes: three hydrophobic groups, one aromatic ring, and one hydrogen bond receptor.
The best quantitative model for RXRβagonists had four features and nine excluded volumes: two hydrophobic groups and one hydrophobic aromatic, one hydrogen bond receptor. DOCK result shows that a majority of the ligand is in the hydrophobic region and there are some bulk amino acids around the ligand. Ligands can form hydrogen bond with Ala398、Arg387 in receptor. The pharmacophore model matches the DOCK result well.
The best quantitative model for RXRγagonists had five features and five excluded volumes: three hydrophobic aliphatic groups, one hydrophobic aromatic ring, and one hydrogen bond receptor.
These EVs’combined effort restricts the flexibility of the compounds and thus could reflect the best conformation with which the ligands interact with the receptor.
These models were validated using a wide range of test molecules. It could predict agonist or antagonist activity and identify highly potent molecules. The present results are valuable to discover and develop specific RXR (α,β,γ) agonists and RXRαantagonists with desired biological activities.
引文
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