Predicting binding to p-glycoprotein by flexible receptor docking.
Predicting binding to p-glycoprotein by flexible receptor docking.
复制标题
DOI：
10.1371/journal.pcbi.1002083
复制
发表时间：
2011-06
影响因子：
4.3
通讯作者：
Jacobson MP
中科院分区：
文献类型：
--
作者：
Dolghih E;Bryant C;Renslo AR;Jacobson MP
P-糖蛋白 (P-gp) 是一种 ATP 依赖性转运蛋白，选择性地在外源物的入口点表达，作为外排泵，阻止外源物进入敏感器官。该蛋白还在许多药物的吸收和血脑屏障渗透中发挥着关键作用，而它在癌细胞中的过度表达与肿瘤的多药耐药性有关。最近发表的小鼠 P-gp 晶体结构揭示了一个大的疏水性结合腔，没有明确定义的子位点支持“诱导拟合”配体结合模型。我们采用灵活的受体对接来开发一种新的 P-gp 结合特异性预测算法。我们使用来自 P-gp 流出和钙黄绿素抑制测定的一致测量的实验数据测试了该方法区分 P-gp 结合剂和非结合剂的能力。我们还对该模型进行了一系列肽类半胱氨酸蛋白酶抑制剂的盲测，证实了预测更可能是 P-gp 底物的化合物的能力。最后，我们使用该方法预测可能是 P-gp 底物的细胞代谢物。总的来说，我们的结果表明，许多 P-gp 底物在空腔中的结合比晶体结构中环肽的结合更深，并且 P-gp 的特异性可以根据配体（和结合位点）的理化性质更好地理解，而不是由特定的子位点定义。由于 ADMETox（吸收、分布、代谢、排泄和毒性）特性不良，许多药物在药物发现的临床前阶段失败，在过程早期改善这些特性以及优化化合物活性，正在成为制药领域的新焦点。影响许多临床相关化合物的药代动力学特征的关键因素之一是活性外排转运蛋白 P-糖蛋白。它主要在各种生理屏障中表达，可以影响药物吸收（肠上皮、结肠）、药物消除（肾近曲小管）和药物穿透血脑屏障（内皮脑细胞）。此外，它在癌细胞中表达的增加与肿瘤对多种药物的耐药性有关。在这项研究中，我们描述了一种计算方法，可以预测哪些化合物更有可能与 P-gp 相互作用。我们通过使用一致测量的体外实验数据测试了该方法区分 P-gp 结合剂和非结合剂的能力。我们还对一系列肽类半胱氨酸蛋白酶抑制剂进行了盲测，结果令人鼓舞。总体而言，我们的结果表明，该方法提供了一种定性、快速且廉价的方法来评估药物开发早期阶段潜在的药物流出问题。
P-glycoprotein (P-gp) is an ATP-dependent transport protein that is selectively expressed at entry points of xenobiotics where, acting as an efflux pump, it prevents their entering sensitive organs. The protein also plays a key role in the absorption and blood-brain barrier penetration of many drugs, while its overexpression in cancer cells has been linked to multidrug resistance in tumors. The recent publication of the mouse P-gp crystal structure revealed a large and hydrophobic binding cavity with no clearly defined sub-sites that supports an “induced-fit” ligand binding model. We employed flexible receptor docking to develop a new prediction algorithm for P-gp binding specificity. We tested the ability of this method to differentiate between binders and nonbinders of P-gp using consistently measured experimental data from P-gp efflux and calcein-inhibition assays. We also subjected the model to a blind test on a series of peptidic cysteine protease inhibitors, confirming the ability to predict compounds more likely to be P-gp substrates. Finally, we used the method to predict cellular metabolites that may be P-gp substrates. Overall, our results suggest that many P-gp substrates bind deeper in the cavity than the cyclic peptide in the crystal structure and that specificity in P-gp is better understood in terms of physicochemical properties of the ligands (and the binding site), rather than being defined by specific sub-sites. With many drugs failing in the preclinical stages of drug discovery due to undesirable ADMETox (absorption, distribution, metabolism, excretion and toxicity) properties, improvement of these properties early on in the process, alongside the optimization of the compound activity, is emerging as a new focus in the pharmaceutical field. One of the key players affecting pharmacokinetic profiles of many clinically relevant compounds is an active efflux transporter, P-glycoprotein. Expressed predominantly at various physiological barriers, it can influence drug absorption (intestinal epithelium, colon), drug elimination (kidney proximal tubules) and drug penetration of the blood-brain barrier (endothelial brain cells). Moreover, its increased expression in cancer cells has been linked to resistance to multiple drugs in tumors. In this study we describe a computational approach that allows prediction of which compounds are more likely to interact with P-gp. We have tested the ability of this method to differentiate between binders and nonbinders of P-gp by using consistently measured in vitro experimental data. We also implemented a blind test on a series of peptidic cysteine protease inhibitors with encouraging outcome. Overall, our results suggest that this method provides a qualitative, quick, and inexpensive way of evaluating potential drug efflux problem at the early stages of drug development.
登录
查看更多内容
影响因子：
3.8
作者：
Cabrera, MA;González, I;Bermejo, M
通讯作者：
Bermejo, M
影响因子：
3.6
作者：
Garrigues, A;Loiseau, N;Orlowski, S
通讯作者：
Orlowski, S
影响因子：
3.9
作者：
Feng, Bo;Mills, Jessica B.;de Morais, Sonia M.
通讯作者：
de Morais, Sonia M.
DOI：
10.1124/jpet.108.149815
发表时间：
2009-08-01
影响因子：
3.5
作者：
Fan, Jianghong;Liu, Shanjun;Pang, K. Sandy
通讯作者：
Pang, K. Sandy
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