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

将虚拟筛选应用于GPCR： GPR 101受体配体的鉴定 主要研究者：Stefano Costanzi博士，美国大学化学系，华盛顿，DC 合作者：Constantine Stratakis博士，NICHD，NIH 项目概要/摘要 我们的目的是鉴定GPR 101的新配体，GPR 101是一种孤儿A类G蛋白偶联受体（GPCR）， X连锁肢端肥大症（X-LAG）。我们的理性方法是建立在验证同源性的基础上的 受体的模型及其作为虚拟筛选新配体的鉴定平台的用途。我们 还旨在为多样化的本科生群体提供生物医学研究和培训的机会 美国大学的学生。该研究具有重要意义，因为：1）它将提供GPR 101 配体，可能的反向激动剂（该孤儿受体的任何配体将是阐明其 病理生理作用;特别是，反向激动剂有可能作为工具，以探索可行性 阻断GPR 101活性以治疗X-LAG）; 2）它将提供GPR 101的3D模型以及关于其 虚拟筛选的适用性（这些模型将提供给科学界， 缺乏实验GPR 101结构，可以作为有用的配体发现工具）; 3）它将推进我们的研究。 理解同源性模型对虚拟筛选的适用性，特别是关于 模型/模板序列同一性与虚拟筛选性能之间的相关性。我们的前提 建议的研究是非常坚实的，因为：1）如初步结果所示，我们已经确定了8个GPR 101 通过Stratakis实验室对3，000种化合物库的实验筛选， 与NCATS合作（这些数据使我们能够运行拟议的受控虚拟筛选）; 2）我们有 收集的初步结果显示了GPR 101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）复合的模型 - 更一般地说，我们和其他人已经充分证明了同源性模型对虚拟生物的适用性。 筛选，特别是在与已知配体的复合物中进行优化时; 3）Stratakis实验室已经证明了 GPR 101在X-LAG中意义。本研究的创新之处在于：1）配体： GPR 101受体目前尚不为人所知，除了在 初步结果和文献中报道的另外两个推定配体; 2）验证的GPR 101模型 适用于虚拟筛选的目标目前不可用; 3）X-LAG的潜在治疗目标是 研究不足，缺乏有效的选择来管理这种情况。我们努力实现我们的目标 通过：1）构建与已知的反向激动剂复合的GPR 101模型，并使它们经受受控的虚拟 筛选活动，以确定那些更适合优先考虑受体的反向激动剂（特异性 目标1）; 2）使用最有前途的模型进行前瞻性虚拟筛查活动， 实验测试，以鉴定新的GPR 101配体，可能是反向激动剂（特异性目的2）。确保 严谨性和可重复性，我们将进行彻底和系统的研究， 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.