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.

常染色体显性遗传性多囊肾病（ADPKD）是最常见的潜在致死性遗传病， 疾病ADPKD主要由PKD 1基因突变引起，PKD 1基因编码多囊蛋白-1（PC 1） 蛋白靶向PC 1近端信号功能的ADPKD的治疗性治疗尚未被证实。 发现了PC 1是一种重要的G蛋白偶联受体，具有11个跨膜区， 域. PC 1与粘附GPCR具有多个共同特征。这些包括GPCR蛋白水解位点 能自动催化地将这些蛋白质分成胞外的N端和膜包埋的C端 (CTF)片段已提出CTF的N-末端茎内的栓系肽激动剂（TA） 激活PC 1的信令。利用PC 1的cryo-EM结构，我们最近揭示了一种新的变构 结合互补全原子高斯加速方法研究TA/stalk介导的PC 1信号转导机制 分子动力学（GaMD）模拟以及生物化学和细胞测定实验。而且我们 揭示了A和B类GPCR中活化和变构调节的独特特征， 层序协同演化“Potts”模型和构造接触分析。我们已经展示了“波茨”模型如何适用于 可以用于产生和检测多残基序列的隐蔽功能性 参与变构结合和信号传导的基序。此外，我们还开发了GaMD，深度学习， 和自由能分析工作流程（GLOW），以预测分子决定簇并绘制自由能图 功能性生物分子的景观。基于这些进展，我们将设计和测试新的肽 通过结合最先进的计算方法， 技术（包括序列共进化Potts模型，GaMD，GLOW和肽对接）和 补充细胞信号实验。我们的具体目标包括：（1）描述约束 通过序列共进化分析已知的PC 1的TA/茎衍生肽调节剂的机制， 肽对接和AI建模;（2）通过Potts预测和验证新的PC 1肽调节剂 建模、肽虚拟筛选和细胞信号传导测定。因此，我们将实施一个独特的 基于计算序列和结构的学习方法，结合相关的体外实验 分析，以开发新的肽调节剂的PC 1。我们的长期目标是（1）发展强大的 计算和实验方法来表征蛋白质-肽相互作用，（2）了解 PC 1野生型和ADPKD疾病突变体的信号传导机制，以及（3）为 未来设计有效的治疗ADPKD的疗法。