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基因 疾病。ADPKD主要由编码多囊蛋白-1(PC1)的PKD1基因突变引起 蛋白。针对PC1近端信号功能的ADPKD的治疗尚未完成 被发现了。PC1是一种重要的G蛋白偶联受体，具有11个跨膜蛋白(TM) 域名。PC1与粘附性GPCRs具有多个特征。其中包括一个gpr蛋白水解点。 将这些蛋白质自动催化分解为胞外N端和膜包埋的C端 (CTF)片段。已建议在CTF的N端茎内使用一种拴系的多肽激动剂(TA)来 激活PC1的信令。利用PC1的低温EM结构，我们最近揭示了一种新的变构 Ta/STRK结合互补全原子加速的PC1信号转导机制 分子动力学(GAMD)模拟以及生化和细胞分析实验。此外，我们有 发现A类和B类GPCRs的激活和变构调节的独特特征 序列协同进化“POTS”模型与结构接触分析。我们已经展示了“Potts”模型如何适用 TO同源序列可用于产生和检测多残基序列的隐藏功能 参与变构结合和信号传递的基序。此外，我们还开发了GAMD，即深度学习 和自由能剖析工作流程(GLOW)，用于预测分子决定因素和绘制自由能图 功能生物分子的景观。在这些进展的基础上，我们将设计和测试新的多肽 调制器通过结合最先进的计算来探测PC1信号调节机制 技术(包括序列共同进化POTS模型、GAMD、发光和多肽对接)和 互补的细胞信号实验。我们的具体目标包括：(1)描述约束性 通过序列共进化分析已知的PC1的TA/茎衍生多肽调节剂的机制， 多肽对接和AI建模；以及(2)预测和验证PC1通过POTS的新的多肽调节剂 建模、多肽虚拟筛选和细胞信号分析。因此，我们将实现唯一的 基于计算顺序和结构的学习方法与相关的体外实验 分析开发PC1的新型多肽调节剂。我们的长期目标是(1)发展壮大 描述蛋白质-多肽相互作用的计算和实验方法，(2)理解 PC1野生型和ADPKD疾病突变体中的信号机制，以及(3)为 ADPKD治疗的有效疗法的未来设计。