Probing Mechanisms of Polycystin-1 Regulation Using Peptide Modulators Designed by Sequence- and Structure-Based Learning

使用基于序列和结构的学习设计的肽调制器探索多囊蛋白-1 调节机制

• 批准号: 10917464
• 负责人: Allan Haldane
• 金额: $ 9.89万
• 依托单位: UNIVERSITY OF KANSAS LAWRENCE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2024-09-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10917464
• 关键词: Acceleration; Adhesions; Affect; Agonist; American; Applications Grants; Autosomal Dominant Polycystic Kidney; Base Sequence; Binding; Biochemical; Biological Assay; C-terminal; Cells; Cellular Assay; Characteristics; Computational Technique; Connecting Stalk; Coupled; Cryoelectron Microscopy; Data; Development; Disease; Dissection; Docking; Embryo; Foundations; Free Energy; Future; G-Protein-Coupled Receptors; GTP-Binding Proteins; Genetic Diseases; Goals; In Vitro; Kidney; Knowledge; Lead; Learning; Ligands; Machine Learning; Maps; Mediating; Membrane; Methodology; Methods; Modeling; Molecular; Mutant Strains Mice; Mutation; N-terminal; Organ Culture Techniques; PKD1 gene; Pathway interactions; Pattern; Peptides; Physics; Proteins; Proteolysis; Protocols documentation; Publishing; Reagent; Regulation; Reporter; Sequence Homologs; Signal Pathway; Signal Transduction; Signaling Protein; Site; Structure; Testing; Therapeutic; Transmembrane Domain; United States National Institutes of Health; Work; deep learning; deep learning model; design; experimental analysis; experimental study; extracellular; molecular dynamics; mutant; novel; polycystic kidney disease 1 protein; simulation; synthetic peptide; therapeutically effective; tool; virtual screening

Autosomal dominant polycystic kidney disease (ADPKD) is the most common potentially lethal genetic disease. ADPKD is caused mainly by mutations in the PKD1 gene, which encodes the polycystin-1 (PC1) protein. Therapeutic treatment of ADPKD that targets the proximal signaling functions of PC1 has yet to be discovered. PC1 is an important unusual G-protein-coupled receptor (GPCR) with 11 transmembrane (TM) domains. PC1 shares multiple characteristics with Adhesion GPCRs. These include a GPCR proteolysis site that autocatalytically divides these proteins into extracellular, N-terminal and membrane-embedded, C-terminal (CTF) fragments. A tethered peptide agonist (TA) within the N-terminal stalk of the CTF has been suggested to activate signaling of PC1. Using the cryo-EM structure of PC1, we have recently revealed a novel allosteric TA/stalk-mediated signaling mechanism of PC1 by combining complementary all-atom Gaussian accelerated molecular dynamics (GaMD) simulations and biochemical and cellular assay experiments. Moreover, we have uncovered unique features of activation and allosteric modulation in the A and B classes of GPCRs from sequence coevolutionary “Potts” models and structural contact analysis. We have shown how “Potts” models fit to homologous sequences can be used to generate and detect cryptic functionality of multiresidue sequence motifs involved in allosteric binding and signaling. In addition, we have developed the GaMD, Deep Learning and free energy prOfiling Workflow (GLOW) to predict molecular determinants and map free energy landscapes of functional biomolecules. Building upon these advances, we will design and test novel peptide modulators to probe mechanisms of PC1 signaling regulation by combining state-of-the-art computational techniques (including sequence coevolutionary Potts models, GaMD, GLOW and peptide docking) and complementary cellular signaling experiments. Our specific aims include: (1) Characterize the binding mechanisms of known TA/stalk-derived peptide modulators of PC1 through sequence coevolution analysis, peptide docking, and AI modeling; and (2) Predict and validate new peptide modulators of PC1 through Potts modeling, peptide virtual screening, and cellular signaling assays. Therefore, we will implement a unique computational sequence- and structure-based learning approach coupled with relevant in vitro experimental analyses to develop novel peptide modulators of PC1. Our long-term goals are (1) to develop robust computational and experimental methodologies to characterize protein-peptide interactions, (2) to understand mechanisms of signaling in the wildtype and ADPKD disease mutants of PC1, and (3) to lay the foundation for the future design of effective therapeutics for treatment of ADPKD.

常染色体显性多囊性肾脏疾病（ADPKD）是最常见的致命通用性 疾病。 ADPKD主要是由PKD1基因中的突变引起的，该突变编码了Polycystin-1（PC1） 蛋白质。针对PC1近端信号传导功能的ADPKD的治疗治疗尚未 发现。 PC1是具有11个跨膜（TM）的重要不寻常的G蛋白偶联受体（GPCR） 域。 PC1与粘附GPCR共享多个特征。这些包括GPCR蛋白水解位点 自催化将这些蛋白质分为细胞外，N末端和膜的C末端 （CTF）碎片。已建议在CTF的N末端茎内的链状肽激动剂（TA） 激活PC1的信号传导。使用PC1的冷冻EM结构，我们最近揭示了一种新型的变构 通过结合互补的全原子高斯加速，ta/stalk介导的PC1信号传导机制 分子动力学（GAMD）模拟以及生化和细胞测定实验。而且，我们有 从A和B类中发现了来自A和B类的独特特征 序列协同进化的“ potts”模型和结构接触分析。我们已经展示了“ potts”模型如何拟合 到同源序列可用于生成和检测多膜片序列的加密功能 参与变构结合和信号传导的基序。此外，我们已经开发了大量的深度学习 和自由能谱分析工作流程（GLOW），以预测分子确定器并绘制自由能 功能性生物分子的景观。在这些进步的基础上，我们将设计和测试新型肽 调节器通过结合最先进的计算来探测PC1信号调节的机制 技术（包括序列协调性的Potts模型，GAMD，GLOW和PEPPERED DOCKING）和 互补的细胞信号传导实验。我们的具体目的包括：（1）表征绑定 通过序列协同进化分析，PC1的已知TA/茎衍生肽调节剂的机制， 肽对接和AI建模； （2）通过POTTS预测并验证PC1的新肽调节剂 建模，胡椒虚拟筛选和细胞信号传导测定。因此，我们将实施独特的 基于计算序列和基于结构的学习方法以及相关的体外实验 分析以开发PC1的新型肽调节剂。我们的长期目标是（1）发展强大 表征蛋白质肽相互作用的计算和实验方法，（2）了解 PC1的野生型和ADPKD疾病突变体中信号传导的机制，（3）为 有效治疗ADPKD的未来设计。