PREDICT modeling and in‐silico screening for G‐protein coupled receptors
PREDICT modeling and in‐silico screening for G‐protein coupled receptors
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DOI：
10.1002/prot.20195
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发表时间：
2004-10
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通讯作者：
S. Shacham;Y. Marantz;S. Bar-Haim;O. Kalid;D. Warshaviak;N. Avisar;B. Inbal;Alexander Heifetz;M. Fichman;M. Topf;Z. Naor;S. Noiman;O. Becker
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作者：
S. Shacham;Y. Marantz;S. Bar-Haim;O. Kalid;D. Warshaviak;N. Avisar;B. Inbal;Alexander Heifetz;M. Fichman;M. Topf;Z. Naor;S. Noiman;O. Becker
G蛋白偶联受体（gpcr）是一组主要的药物靶点，只有一种x射线结构是已知的（不可药物的视紫红质），这限制了基于结构的药物发现在gpcr中的应用。在本文中，我们介绍了PREDICT的细节，这是一种新的算法方法，可以在不依赖于视紫质同源性的情况下对gpcr的3D结构进行建模。PREDICT专注于gpcr的跨膜结构域，从受体的初级序列开始，同时优化蛋白质的多个“诱饵”构象，以找到其最稳定的结构，最终形成虚拟受体-配体复合物。在本文中，我们提出了三种预测模型的多巴胺D2，神经激肽NK1和神经肽yy1受体的综合分析。对CCR3受体模型的简短讨论也包括在内。所有的模型都与大量的实验数据很好地吻合。PREDICT模型的质量，至少对于药物发现的目的，是通过它们在计算机筛选中的成功应用来评估的。使用所有三个PREDICT模型的虚拟筛选产生的富集因子比随机筛选好9 - 44倍。也就是说，PREDICT模型可用于识别嵌入在大型化合物文库中的活性小分子配体，其效率与使用非GPCR目标的晶体结构获得的效率相当。2004的蛋白质。©2004 Wiley‐Liss, Inc。
G‐protein coupled receptors (GPCRs) are a major group of drug targets for which only one x‐ray structure is known (the nondrugable rhodopsin), limiting the application of structure‐based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple ‘decoy’ conformations of the protein in order to find its most stable structure, culminating in a virtual receptor‐ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in‐silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9‐fold to 44‐fold better than random screening. Namely, the PREDICT models can be used to identify active small‐molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non‐GPCR targets. Proteins 2004. © 2004 Wiley‐Liss, Inc.