Targeting receptor tyrosine kinases with novel methods in computer-aided drug discovery for the treatment of fibrotic renal disease

用计算机辅助药物发现的新方法靶向受体酪氨酸激酶来治疗纤维化肾病

基本信息

批准号：
10197115
负责人：
Benjamin Patrick Brown
金额：
$ 5.1万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10197115
关键词：
Address Age Algorithms Binding Binding Proteins Binding Sites Biochemical Cell model Chemicals Chemistry Chronic Kidney Failure Clinical Collaborations Collagen Collagen Type IV Computer Assisted Computer software Computing Methodologies DDR2 gene Data Deposition Descriptor Development Disease Disease Progression Docking End stage renal failure Evaluation Family Fibrosis Fluorescence Resonance Energy Transfer In Vitro Individual Injury to Kidney Intervention Joints Kidney Kidney Diseases Knowledge Laboratories Lead Learning Module Libraries Ligands Link Machine Learning Mediating Methodology Methods Modeling Molecular Conformation Myofibroblast Patients Pharmacology Phase Phosphotransferases Population Prevalence Proteins Quantitative Structure-Activity Relationship Receptor Protein-Tyrosine Kinases Risk Factors Risk Management Sampling Scientist Severities Site-Directed Mutagenesis Structural Models Structure Symptoms Testing Therapeutic Therapeutic Agents United States Work aged base clinically relevant design discoidin domain receptor 1 discoidin domain receptor 2 discoidin receptor drug candidate drug discovery flexibility global health high throughput analysis high throughput screening hospital readmission improved in silico in vivo Model inhibitor/antagonist innovation kidney fibrosis kinase inhibitor learning algorithm machine learning algorithm mesangial cell molecular dynamics mortality mouse model multi-task learning multitask neural network new technology novel novel lead compound novel therapeutic intervention novel therapeutics protein structure screening small molecule structural biology targeted treatment therapeutic target tool virtual virtual model

项目摘要

PROJECT SUMMARY Chronic Kidney Disease (CKD) is a major disease multiplier in patients aged 65+. CKD is characterized by progressive renal fibrosis mediated through supraphysiologic type IV collagen deposition by renal myofibroblasts. As the US population continues to age, it becomes increasingly critical to identify new therapeutic strategies for CKD. Mouse models of kidney injury suggest reducing the activity of the receptor tyrosine kinase discoidin domain receptor 1 (DDR1) is protective against fibrotic renal disease. Inhibition of DDR1 kinase reduces mesangial cell deposition of type IV collagen. To develop targeted therapeutics for CKD, the laboratory of Jens Meiler (sponsor of this application) partners with the laboratories of Ambra Pozzi (co- sponsor of this application) and Craig Lindsley to create a comprehensive DDR1 kinase inhibitor discovery pipeline. The Meiler laboratory utilizes a combination of ligand-based quantitative structure-activity relationship (QSAR) modeling for virtual high-throughput screening (vHTS) and subsequent protein-ligand docking to identify lead compounds for synthesis/derivatization (Lindsley) and biochemical/functional evaluation (Pozzi). Selective targeting of individual kinases remains a significant challenge, and current methods in vHTS fail to account for protein binding pocket features contributing to binding selectivity. The central objectives of this proposal are to identify novel DDR1-selective inhibitors for the treatment of CKD and to develop new technologies to address current limitations in vHTS. In Specific Aim I, I will generate and use QSAR models to perform vHTS for potential DDR1 inhibitors. I will subsequently define a structural model of DDR1 kinase inhibitor selectivity using molecular dynamics (MD)-generated conformational ensembles of DDR kinases in conjunction with ROSETTA flexible docking. I will also perform in silico and in vitro site-directed mutagenesis to further characterize the determinants of DDR1 kinase inhibitor selectivity. In Specific Aim II, I will develop a multitasking machine algorithm within the Meiler lab BIOLOGY AND CHEMISTRY LIBRARY (BCL) which will leverage protein structural information in addition to conventional ligand-based descriptors to improve vHTS for selective DDR1 kinase inhibitors. The methods developed will address long-standing shortcomings in the field of computer-aided drug discovery (CADD) – namely, that protein structure-based methods are computationally prohibitive for vHTS while ligand-based methods do not include direct information on binding mode. As the methods developed in Aim II become available, they will be integrated in the discovery cycle described in Aim I to ultimately define a structural model of DDR1 kinase selectivity and identify novel therapeutic agents for the treatment of CKD through the use of new and established methods. Furthermore, novel computational methods established in these studies will be broadly applicable to other challenging targets in drug discovery.

项目概要慢性肾脏病 (CKD) 是 65 岁以上患者的主要疾病倍数。肾超生理性 IV 型胶原沉积介导的进行性肾纤维化随着美国人口持续老龄化，识别新的肌成纤维细胞变得越来越重要。 CKD 小鼠肾损伤模型的治疗策略表明降低受体的活性。酪氨酸激酶盘状蛋白结构域受体 1 (DDR1) 可预防纤维化肾病。 DDR1 激酶可减少 IV 型胶原的系膜细胞沉积为了开发 CKD 的靶向疗法， Jens Meiler 实验室（本申请的赞助商）与 Ambra Pozzi 实验室（共同本申请的赞助者）和 Craig Lindsley 共同创建全面的 DDR1 激酶抑制剂发现 Meiler 实验室利用基于配体的定量结构-活性关系的组合。用于虚拟高通量筛选 (vHTS) 的 (QSAR) 建模以及随后的蛋白质配体对接识别用于合成/衍生化 (Lindsley) 和生化/功能评估 (Pozzi) 的先导化合物。选择性靶向单个激酶仍然是一个重大挑战，目前的 vHTS 方法无法解释有助于结合选择性的蛋白质结合口袋特征。建议确定用于治疗 CKD 的新型 DDR1 选择性抑制剂并开发新的解决 vHTS 当前限制的技术。在特定目标 I 中，我将生成并使用 QSAR 模型来对潜在的 DDR1 抑制剂进行 vHTS，随后我将定义 DDR1 激酶的结构模型。使用分子动力学 (MD) 生成的 DDR 激酶构象整体进行抑制剂选择性结合 ROSETTA 灵活对接，我还将进行计算机模拟和体外定点诱变。在特定目标 II 中，我将进一步描述 DDR1 激酶抑制剂选择性的决定因素。 Meiler 实验室生物和化学图书馆 (BCL) 中的多任务机器算法将除了传统的基于配体的描述符之外，还利用蛋白质结构信息来改进 vHTS 选择性 DDR1 激酶抑制剂的开发将解决该领域长期存在的缺陷。计算机辅助药物发现 (CADD) 的发展——即基于蛋白质结构的方法通过计算禁止 vHTS，而基于配体的方法不包括有关结合模式的直接信息。在目标 II 中开发的方法变得可用，它们将被集成到目标 I 中描述的发现周期中最终定义 DDR1 激酶选择性的结构模型并确定新的治疗药物通过使用新的和已建立的方法来治疗 CKD。这些研究中建立的方法将广泛适用于药物发现中的其他具有挑战性的目标。