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.

项目摘要在65岁以上的患者中，慢性肾脏疾病（CKD）是主要疾病倍增子。 CKD的特征是通过肾脏肾上腺生理学上型胶原蛋白典型介导的进行性肾纤维化通过肾脏沉积肌纤维细胞。随着美国人口的持续年龄，识别新的越来越重要 CKD的治疗策略。肾损伤的小鼠模型表明减少了接收器的活性酪氨酸激酶盘状结构域受体1（DDR1）受到纤维化肾脏疾病的保护。抑制 DDR1激酶减少了IV型胶原蛋白的肾小球细胞沉积。为了开发针对CKD的有针对性疗法，詹斯·梅勒（Jens Meiler）的实验室（本申请的赞助商）与Ambra Pozzi实验室合作（共同）该应用程序的赞助商和Craig Lindsley创建了全面的DDR1激酶抑制剂发现管道。 Meiler实验室利用了基于配体的定量结构活性关系的组合（QSAR）虚拟高通量筛选（VHTS）和随后的蛋白质底座的建模确定合成/衍生化（Lindsley）和生化/功能评估（POZZI）的铅化合物。选择性靶向单个激酶仍然是一个重大挑战，而VHT中的当前方法也无法说明蛋白质结合的袋袋特征有助于结合选择性。此的中心对象建议是确定新型的DDR1选择性抑制剂，以治疗CKD并开发新的DDR1选择性抑制剂解决VHT中当前局限性的技术。在特定目的I中，我将生成并使用QSAR模型来对潜在的DDR1抑制剂执行VHT。随后，我将定义DDR1激酶的结构模型使用分子动力学（MD）在DDR激酶中产生的构象合并的抑制剂选择性与Rosetta柔性对接的连接。我还将在计算机和体外定位的诱变中进行进一步表征了DDR1激酶抑制剂选择性的决定剂。在特定的目标II中，我将发展一个 Meiler Lab Biology and Chemistry库（BCL）中的多任务机算法将除了常规配体的描述符外，还要利用蛋白质结构信息来改善VHT 选择性DDR1激酶抑制剂。开发的方法将解决该领域的长期缺点计算机辅助药物发现（CADD） - 即，基于蛋白质结构的方法是计算基于配体的方法对VHT的过度效率不包含有关结合模式的直接信息。作为 AIM II中开发的方法可用，它们将集成到AIM I中描述的发现周期中最终定义了DDR1激酶选择性的结构模型，并确定新颖的治疗剂通过使用新的和已建立的方法来处理CKD。此外，新颖的计算这些研究中建立的方法将广泛适用于药物发现中的其他挑战目标。