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首页> 外文期刊>Journal of chemical information and modeling >Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A beta-Adrenoceptor Case Study
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Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A beta-Adrenoceptor Case Study

机译:G蛋白偶联受体配体功能的基于结构的预测:β-肾上腺素受体案例研究。

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The spectacular advances in G-protein-coupled receptor (GPCR) structure determination have opened up new possibilities for structure-based GPCR ligand discovery. The structure-based prediction of whether a ligand stimulates (full/partial agonist), blocks (antagonist), or reduces (inverse agonist) GPCR signaling activity is, however, still challenging. A total of 31 beta(1) (beta R-1) and beta(2) (beta R-2) adrenoceptor crystal structures, including antagonist, inverse agonist, and partial/full agonist-bound structures, allowed us to explore the possibilities and limitations of structure-based prediction of GPCR ligand function. We used all unique protein ligand interaction fingerprints (IFPs) derived from all ligand-bound beta-adrenergic crystal structure monomers to post-process the docking poses of known beta R-1/beta R-2 partial/full agonists, antagonists/inverse agonists, and physicochemically similar decoys in each of the beta R-1/beta R-2 structures. The systematic analysis of these 1920 unique IFP structure combinations offered new insights into the relative impact of protein conformation and IFP scoring on selective virtual screening (VS) for ligands with a specific functional effect. Our studies show that ligands with the same function can be efficiently classified on the basis of their protein ligand interaction profile. Small differences between the receptor conformation (used for docking) and reference IFP (used for scoring of the docking poses) determine, however, the enrichment of specific ligand types in VS hit lists. Interestingly, the selective enrichment of partial/full agonists can be achieved by using agonist IFPs to post-process docking poses in agonist-bound as well as antagonist-bound structures. We have identified optimal structure IFP combinations for the identification and discrimination of antagonists/inverse agonist and partial/full agonists, and defined a predicted IFP for the small full agonist norepinephrine that gave the highest retrieval rate of agonists over antagonists for all structures (with an enrichment factor of 46 for agonists and 8 for antagonists on average at a 1% false-positive rate). This beta-adrenoceptor case study provides new insights into the opportunities for selective structure-based discovery of GPCR ligands with a desired function and emphasizes the importance of IFPs in scoring docking poses.
机译:G蛋白偶联受体(GPCR)结构测定的惊人进展为基于结构的GPCR配体发现开辟了新的可能性。然而,基于结构的配体是刺激(完全/部分激动剂),阻断(拮抗剂)还是降低(反向激动剂)GPCR信号传导活性的预测仍然具有挑战性。总共31个beta(1)(beta R-1)和beta(2)(beta R-2)肾上腺素受体晶体结构,包括拮抗剂,反向激动剂和部分/完全激动剂结合的结构,使我们能够探索各种可能性基于结构的GPCR配体功能预测的局限性。我们使用衍生自所有配体结合的β-肾上腺素晶体结构单体的所有独特的蛋白质配体相互作用指纹(IFP)对已知的βR-1 /βR-2部分/完全激动剂,拮抗剂/反向激动剂的对接姿势进行后处理,以及每个R 1 / R 2结构中理化相似的诱饵。对这1920种独特IFP结构组合的系统分析为蛋白质构象和IFP评分对具有特定功能作用的配体的选择性虚拟筛选(VS)的相对影响提供了新的见解。我们的研究表明,具有相同功能的配体可以根据它们的蛋白质配体相互作用图谱进行有效分类。但是,受体构象(用于对接)和参考IFP(用于对接姿势评分)之间的微小差异决定了VS命中列表中特定配体类型的富集。有趣的是,可以通过使用激动剂IFP对激动剂结合的以及拮抗剂结合的结构中的对接姿势进行后处理来实现部分/完全激动剂的选择性富集。我们已经确定了用于识别和区分拮抗剂/反向激动剂和部分/完全激动剂的最佳结构IFP组合,并针对所有结构的激动剂胜过拮抗剂的最小全激动剂去甲肾上腺素定义了预测的IFP。激动剂的富集因子平均为46,拮抗剂的富集因子平均为1%假阳性率)。这项β-肾上腺素受体案例研究为基于选择性结构的具有所需功能的GPCR配体发现提供了新的见解,并强调了IFP在对接姿势评分中的重要性。

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