A Multi-Angle Approach to Predict Peptide-GPCR Complexes: The N/OFQ-NOP System as a Successful AlphaFold Application Case Study,Journal of Chemical Information and Modeling

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A Multi-Angle Approach to Predict Peptide-GPCR Complexes: The N/OFQ-NOP System as a Successful AlphaFold Application Case Study
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-13 , DOI: 10.1021/acs.jcim.4c00499
Antonella Ciancetta ₁ , Davide Malfacini ₂ , Matteo Gozzi ₁ , Erika Marzola ₁ , Riccardo Camilotto ₂ , Girolamo Calò ₂ , Remo Guerrini ₁

Affiliation

With nearly 700 structures solved and a growing number of customized structure prediction algorithms being developed at a fast pace, G protein-coupled receptors (GPCRs) are an optimal test case for validating new approaches for the prediction of receptor active state and ligand bioactive conformation complexes. In this study, we leveraged the availability of hundreds of peptide GPCRs in the active state and both classical homology and artificial intelligence (AI) based protein modeling combined with docking and AI-based peptide structure prediction approaches to predict the nociceptin/orphanin FQ-NOP receptor active state complex (N/OFQ-NOPa). The In Silico generated hypotheses were validated via the design, synthesis, and pharmacological characterization of novel linear N/OFQ(1–13)-NH₂ analogues, leading to the discovery of a novel antagonist (3B; pK_B = 6.63) bearing a single ring-constrained residue in place of the Gly²–Gly³ motif of the N/OFQ message sequence (FGGF). While the experimental validation was ongoing, the availability of the Cryo-EM structure of the predicted complex enabled us to unambiguously validate the generated hypotheses. To the best of our knowledge, this is the first example of a peptide–GPCR complex predicted with atomistic accuracy (full complex Cα RMSD < 1.0 Å) and of the N/OFQ message moiety being successfully modified with a rigid scaffold.

中文翻译：

预测肽-GPCR 复合物的多角度方法：N/OFQ-NOP 系统作为成功的 AlphaFold 应用案例研究

随着近 700 种结构的解决和越来越多的定制结构预测算法的快速开发，G 蛋白偶联受体（GPCR）是验证预测受体活性状态和配体生物活性构象复合物的新方法的最佳测试案例。在这项研究中，我们利用了数百个处于活性状态的肽 GPCR 的可用性，以及基于经典同源和人工智能（AI）的蛋白质建模，结合对接和基于 AI 的肽结构预测方法来预测伤害感受蛋白/孤儿啡肽 FQ-NOP 受体活性状态复合物（N/OFQ-NOPa）。通过新型线性 N/OFQ（1-13）-NH₂ 类似物的设计、合成和药理学表征验证了计算机模拟生成的假设，从而发现了一种新的拮抗剂（3B;pK_B = 6.63），带有单个环状限制残基代替 Gly^2-Gly ³N/OFQ 消息序列（FGGF）的基序。在实验验证进行期间，预测复合物的冷冻电镜结构的可用性使我们能够明确验证生成的假设。据我们所知，这是以原子精度预测肽-GPCR 复合物（全复合物 Cα RMSD < 1.0 Å）和用刚性支架成功修饰 N/OFQ 信息部分的第一个例子。

更新日期：2024-08-13

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