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Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor.
ACS Chemical Neuroscience ( IF 4.1 ) Pub Date : 2018-10-17 , DOI: 10.1021/acschemneuro.8b00489 Haiyi Chen , Weitao Fu , Zhe Wang , Xuwen Wang , Tailong Lei , Feng Zhu , Dan Li , Shan Chang 1 , Lei Xu 1 , Tingjun Hou
ACS Chemical Neuroscience ( IF 4.1 ) Pub Date : 2018-10-17 , DOI: 10.1021/acschemneuro.8b00489 Haiyi Chen , Weitao Fu , Zhe Wang , Xuwen Wang , Tailong Lei , Feng Zhu , Dan Li , Shan Chang 1 , Lei Xu 1 , Tingjun Hou
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The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.
中文翻译：
基于对接模拟结构的GPCR配体基于对接的虚拟筛选的可靠性：血管紧张素II型I受体的案例研究。
已解决的G蛋白偶联受体（GPCR）晶体结构的数量迅速扩大，但大多数GPCR结构仍未解决。因此，诸如同源性建模之类的计算技术已被广泛用于产生用于基于结构的药物设计（SBDD）的各种GPCR的理论结构。由于GPCR的跨膜结构域所共有的序列相似性较低，因此通过同源性建模准确预测GPCR结构非常具有挑战性。在这项研究中，以I型血管紧张素II受体（AT1R）为典型案例，以评估SBDD的A类GPCR同源性模型的可靠性。根据CXCR4趋化因子受体，CCR5趋化因子受体和δ阿片受体的晶体结构，建立了四种处于非活性状态的血管紧张素II型I受体（AT1R）同源模型，并通过分子动力学（MD）模拟和诱导拟合对接进行精炼，以实现主链和侧链的灵活性。然后，根据通常用于SBDD的两个标准评估相对于晶体结构的同源性模型的质量：配体结合姿势的预测准确性和基于对接的虚拟筛选的筛选能力。发现在两次评估中的任何细化之前，晶体结构均优于同源性模型。MD模拟通常可以改善晶体结构和同源性模型的对接结果。此外，通过MD模拟和感应拟合对接优化的优化同源性模型甚至显示出与晶体结构相似的对接评估性能。
更新日期：2018-09-28
中文翻译：
基于对接模拟结构的GPCR配体基于对接的虚拟筛选的可靠性：血管紧张素II型I受体的案例研究。
已解决的G蛋白偶联受体（GPCR）晶体结构的数量迅速扩大，但大多数GPCR结构仍未解决。因此，诸如同源性建模之类的计算技术已被广泛用于产生用于基于结构的药物设计（SBDD）的各种GPCR的理论结构。由于GPCR的跨膜结构域所共有的序列相似性较低，因此通过同源性建模准确预测GPCR结构非常具有挑战性。在这项研究中，以I型血管紧张素II受体（AT1R）为典型案例，以评估SBDD的A类GPCR同源性模型的可靠性。根据CXCR4趋化因子受体，CCR5趋化因子受体和δ阿片受体的晶体结构，建立了四种处于非活性状态的血管紧张素II型I受体（AT1R）同源模型，并通过分子动力学（MD）模拟和诱导拟合对接进行精炼，以实现主链和侧链的灵活性。然后，根据通常用于SBDD的两个标准评估相对于晶体结构的同源性模型的质量：配体结合姿势的预测准确性和基于对接的虚拟筛选的筛选能力。发现在两次评估中的任何细化之前，晶体结构均优于同源性模型。MD模拟通常可以改善晶体结构和同源性模型的对接结果。此外，通过MD模拟和感应拟合对接优化的优化同源性模型甚至显示出与晶体结构相似的对接评估性能。
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