Testing physical models of passive membrane permeation.
Testing physical models of passive membrane permeation.
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DOI：
10.1021/ci200583t
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发表时间：
2012-06-25
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
--
作者：
被动膜渗透性的生物物理基础是很好理解的，但大多数用于在药物设计的背景下预测膜渗透性的方法是基于间接捕获关键物理方面的统计关系。在这里，我们研究了基于分子力学的被动膜渗透性模型，并根据不同类型的实验数据评估其性能，包括平行人工膜渗透性测定（PAMPA），基于细胞的测定，体内测量和其他计算机预测。我们在这些测试中使用的实验数据集是多样化的，包括肽模拟物、同类系列和各种FDA批准的药物。物理模型没有针对任何这些数据集进行专门训练;相反，输入参数基于标准分子力学力场，例如部分电荷和隐式溶剂模型。采用系统的方法来分析基于物理的渗透率模型中各个组分的贡献。确定被动膜渗透速率的主要因素是分子去溶剂化的构象依赖性自由能，并且在许多情况下，仅此措施就提供了与实验渗透性测量的良好一致性。提高与实验数据的一致性的其他因素包括去离子和配体和膜的熵损失的估计，这导致渗透速率的大小依赖性。
The biophysical basis of passive membrane permeability is well understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.
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影响因子：
4.3
作者：
Dolghih E;Bryant C;Renslo AR;Jacobson MP
通讯作者：
Jacobson MP
影响因子：
5.5
作者：
De Nicola, Antonio;Zhao, Ying;Milano, Giuseppe
通讯作者：
Milano, Giuseppe
DOI：
10.1126/science.1168750
发表时间：
2009-03-27
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Aller SG;Yu J;Ward A;Weng Y;Chittaboina S;Zhuo R;Harrell PM;Trinh YT;Zhang Q;Urbatsch IL;Chang G
通讯作者：
Chang G
影响因子：
5.5
作者：
Bennett, W. F. Drew;Tieleman, D. Peter
通讯作者：
Tieleman, D. Peter
影响因子：
5.5
作者：
Eriksson, Emma S. E.;dos Santos, Daniel J. V. A.;Eriksson, Leif A.
通讯作者：
Eriksson, Leif A.