Virtual screening for the identification of ligands of GPR101, an orphan GPCR involved in X-linked acrogigantism (X-LAG)

支持将虚拟筛选应用于GPCR： 识别GPR101受体的配体 Pi：Stefano Costanzi博士，美国大学化学系，华盛顿特区 合作者：康斯坦丁·斯特拉塔基斯博士，NICHD，NIH 项目摘要/摘要 我们的目标是确定GPR101的新配体，GPR101是一种涉及A类G蛋白偶联受体(GPCR)的孤儿 X连锁肢端巨人症(X-LAG)。我们的理性方法是基于有效同源性的构建 受体模型及其作为虚拟筛选平台的用途，以识别新的配体。我们 还旨在为不同的本科生群体提供生物医学研究和培训 美国大学的学生。拟议的研究具有重要意义，因为：1)它将提供GPR101 配体，可能是反向激动剂(这种孤儿受体的任何配体都将是阐明其 病理生理学作用；特别是反向激动剂有可能成为探索可行性的工具。 阻止GPR101的活动以治疗X滞后)；2)它将提供GPR101的3D模型以及关于其 对虚拟筛选的适用性(这些模型将向科学界提供，并在 没有实验的GPR101结构，可以作为有用的配体发现工具)；3)它将促进我们的 了解同源模型对虚拟筛选的适用性，特别是关于 模型/模板序列同一性和虚拟筛选性能之间的相关性。我们的前提是 所提出的研究是非常可靠的，因为：1)初步结果显示，我们已经确定了8个GPR101 Stratakis实验室通过实验筛选包含3,000种化合物的反向激动剂 与NCATS合作(此数据使我们能够运行建议的受控虚拟筛选)；2)我们拥有 收集的初步结果显示了GPR101在受控虚拟筛查活动中的有效性 与化合物1(初步结果中呈现的最有效的反向激动剂)的复合体模型 -更广泛地说，我们和其他人已经充分证明了同源模型对虚拟 筛选，特别是在与已知配体的络合物中进行优化时；3)Stratakis实验室已经证明 GPR101在X-LAG中的意义。本研究的创新之处在于：1)配体 GPR101受体目前尚不清楚，除了在 初步结果和文献中报道的另外两个假定的配体；2)验证了GPR101模型 3)X-LAG潜在治疗的靶点是 研究不足，缺乏有效的选择来管理这一状况。我们努力实现我们的目标 通过：1)在具有已知反向激动剂的复合体中建立GPR101模型，并对其进行受控虚拟 筛选活动以确定哪些更适合优先处理受体的反向激动剂(特定 目标1)；2)使用最有希望的模型进行预期的虚拟筛查活动，随后 实验测试，以确定新的GPR101配体，可能是反向激动剂(特定目标2)。为了确保 严谨和可重复性，我们将进行彻底和系统的研究，遵循高度可重复性 建模程序，并通过二次测试对实验结果进行彻底验证。

Enabling the Application of Virtual Screening to GPCRs: Identifying Ligands of the GPR101 Receptor PI: Dr. Stefano Costanzi, Department of Chemistry, American University, Washington, DC Collaborator: Dr. Constantine Stratakis, NICHD, NIH Project summary/abstract We aim at identifying new ligands of GPR101, an orphan Class A G protein-coupled receptor (GPCR) involved in X-linked acrogigantism (X-LAG). Our rational approach is based on the construction of validated homology models of the receptor and their use as platforms for virtual screening for the identification of new ligands. We also aim at providing access to biomedical research and training to a diverse cohort of undergraduate students at American University. The proposed research is significant because: 1) It will provide GPR101 ligands, possibly inverse agonists (any ligand of this orphan receptor will be a valuable tool to illuminate its pathophysiological role; in particular, inverse agonists have the potential to serve as tools to probe the feasibility of blocking GPR101 activity to treat X-LAG); 2) It will provide 3D models of GPR101 as well as data on their applicability to virtual screening (these models will be made available to the scientific community and, in the absence of experimental GPR101 structures, can serve as useful ligand-discovery tools); 3) it will advance our understanding of the applicability of homology models to virtual screening, particularly with respect to the correlation between model/template sequence identity and virtual screening performance. The premises of our proposed research are very solid given that: 1) as shown in preliminary results, we have identified 8 GPR101 inverse agonists through the experimental screening of a library of 3,000 compounds by the Stratakis Lab in collaboration with NCATS (this data enables us to run the proposed controlled virtual screenings); 2) we have gathered preliminary results that show the effectiveness in controlled virtual screening campaigns of a GPR101 model in complex with Compound 1 (the most potent of the inverse agonists presented in the preliminary results) – more in general, we and others have amply demonstrated the applicability of homology models to virtual screening, especially when optimized in complex with known ligands; 3) the Stratakis Lab has demonstrated the implication of GPR101 in X-LAG. The innovation of our proposed research rests on the facts that: 1) ligands of the GPR101 receptor are currently not known, with the exception of the 8 inverse agonists presented in the preliminary results and two more putative ligands reported in the literature; 2) validated GPR101 models applicable to virtual screening are currently unavailable; 3) targets for the potential treatment of X-LAG are understudied and there is a lack of effective options to manage this condition. We seek to achieve our objective by: 1) building GPR101 models in complex with known inverse agonists and subjecting them to controlled virtual screening campaigns to identify those that are more suited to prioritize inverse agonists of the receptor (Specific Aim 1); 2) using the most promising models to conduct prospective virtual screening campaigns followed by experimental tests, to identify novel GPR101 ligands, possibly inverse agonists (Specific Aim 2). To ensure rigor and reproducibility, we will conduct a thorough and systematic study, following highly reproducible modeling procedures, and subjecting experimental results to a thorough validation through secondary tests.