Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation.
Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation.
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DOI：
10.1021/acsomega.6b00086
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发表时间：
2016-08-31
期刊：
影响因子：
4.1
通讯作者：
Beuming T
中科院分区：
文献类型：
--
作者：
Lenselink EB;Louvel J;Forti AF;van Veldhoven JPD;de Vries H;Mulder-Krieger T;McRobb FM;Negri A;Goose J;Abel R;van Vlijmen HWT;Wang L;Harder E;Sherman W;IJzerman AP;Beuming T
G蛋白偶联受体(GPCRs)结构信息的快速增长使得人们对其结构、功能、选择性和配体结合有了更多的了解。虽然已经使用虚拟筛选等方法确定了新的配体，但由于预测相关化合物的结合自由能存在挑战，计算驱动的铅优化仅在孤立的情况下才是可能的。在这里，我们系统地描述了自由能微扰(FEP)计算的性能，该计算使用一致的协议和不可调的参数来预测与GPCR靶结合的同种配体的相对结合自由能。使用FEP+包，首先，我们通过预测四个不同GPCR(腺苷A2AAR、β1肾上腺素能、CXCR4趋化因子和δ阿片受体)对总共45种不同配体的结合亲和力来验证该方案，该方案包括一个全脂双层和显式溶剂。与实验结合亲和力测量结果比较，显示出高度预测性的等级相关性(平均Spearmanρ=0.55)和较低的均方根误差(0.80kcal/mol)。接下来，我们将FEP+应用到一个预期的项目中，在该项目中，我们预测了新型、有效的腺苷A2a受体(A2AR)拮抗剂的亲和力。合成了四个新化合物，并在放射性配基置换分析中进行了测试，得到了纳摩尔范围的亲和值。四个新配体中的两个(加上三个先前报道的化合物)的亲和力被正确预测(在1kcal/mol以内)，其中一个化合物的亲和力比起始化合物增加了大约十倍。对预测背后的模拟的详细分析为这两种情况下亲和力被过度预测的结构基础提供了洞察力。综上所述，这些结果建立了一种系统地将FEP+应用于GPCRs的方案，并为在药物发现和优化项目中识别有效分子提供了指南。
The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.
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