Modeling G protein‐coupled receptors for structure‐based drug discovery using low‐frequency normal modes for refinement of homology models: Application to H3 antagonists
Modeling G protein‐coupled receptors for structure‐based drug discovery using low‐frequency normal modes for refinement of homology models: Application to H3 antagonists
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使用低频正态模式对 G 蛋白偶联受体进行建模，以进行基于结构的药物发现，以细化同源模型：在 H3 拮抗剂中的应用
DOI：
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
C. Humblet
中科院分区：
文献类型：
--
作者：
B. Rai;G. Tawa;A. Katz;C. Humblet
G蛋白偶联受体（GPCRs）是一种在调节关键生理功能中发挥重要作用的膜蛋白，并且是所有最近推出的药物中约50%的靶点。高分辨率实验结构仅适用于极少数GPCR。因此，GPCR的基于结构的药物设计工作继续依赖于计算机建模，这被认为是一项极其困难的任务，尤其是对于这些受体。在这里，我们描述了Gmodel，这是一种使用基于正常模式的同源模型细化来构建GPCR的3D原子模型的新方法。Gmodel使用一小部分来自随机弹性网络模型的相关低频振动模式，有效地对大规模受体构象变化进行采样，并生成替代模型的集合。这些用于通过将已知活性物对接到每个替代模型中来组装受体-配体复合物。接下来使用源自已知突变和结合亲和力数据的限制来过滤这些中的每一个，并且在活性配体的存在下进行精制。本研究应用Gmodel建立组胺3受体拮抗剂模型。这种新的建模方法的有效性证明了执行虚拟筛选（使用细化模型），始终产生高度丰富的命中列表。通过分析与经典H3拮抗剂相关的SAR，进一步验证了模型，并发现与现有的实验数据吻合良好，从而为受体-配体相互作用提供了新的见解。Proteins 2010.© 2009 Wiley利斯公司
G Protein‐Coupled Receptors (GPCRs) are integral membrane proteins that play important role in regulating key physiological functions, and are targets of about 50% of all recently launched drugs. High‐resolution experimental structures are available only for very few GPCRs. As a result, structure‐based drug design efforts for GPCRs continue to rely on in silico modeling, which is considered to be an extremely difficult task especially for these receptors. Here, we describe Gmodel, a novel approach for building 3D atomic models of GPCRs using a normal mode‐based refinement of homology models. Gmodel uses a small set of relevant low‐frequency vibrational modes derived from Random Elastic Network model to efficiently sample the large‐scale receptor conformation changes and generate an ensemble of alternative models. These are used to assemble receptor–ligand complexes by docking a known active into each of the alternative models. Each of these is next filtered using restraints derived from known mutation and binding affinity data and is refined in the presence of the active ligand. In this study, Gmodel was applied to generate models of the antagonist form of histamine 3 (H3) receptor. The validity of this novel modeling approach is demonstrated by performing virtual screening (using the refined models) that consistently produces highly enriched hit lists. The models are further validated by analyzing the available SAR related to classical H3 antagonists, and are found to be in good agreement with the available experimental data, thus providing novel insights into the receptor–ligand interactions. Proteins 2010. © 2009 Wiley‐Liss, Inc.
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影响因子：
7.3
作者：
Furse, KE;Lybrand, TP
通讯作者：
Lybrand, TP
影响因子：
7.3
作者：
Halgren, TA;Murphy, RB;Banks, JL
通讯作者：
Banks, JL
影响因子：
7.3
作者：
Sherman, W;Day, T;Farid, R
通讯作者：
Farid, R
影响因子：
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作者：
I. Visiers;J. Ballesteros;H. Weinstein
通讯作者：
I. Visiers;J. Ballesteros;H. Weinstein
DOI：
10.1073/pnas.0400100101
发表时间：
2004-03-16
影响因子：
11.1
作者：
Kalani, MYS;Vaidehi, N;Goddard, WA
通讯作者：
Goddard, WA