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

项目总结 慢性肾脏病(CKD)是65岁患者的主要疾病倍增因素。CKD的特点是 肾上皮IV型胶原沉积介导进行性肾纤维化 肌成纤维细胞。随着美国人口继续老龄化，识别新的 慢性肾脏病的治疗策略。小鼠肾脏损伤模型显示该受体活性降低 酪氨酸激酶盘状结构域受体1(DDR1)对纤维化肾病具有保护作用。抑制 DDR1激酶减少肾小球系膜细胞IV型胶原沉积。为了开发治疗慢性肾脏病的靶向疗法， 延斯·梅勒的实验室(本申请的赞助商)与Ambra Pozzi的实验室(共同 本申请的发起人)和Craig Lindsley创建了一个全面的DDR1激酶抑制剂发现 输油管道。迈勒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)和后续蛋白质-配体对接的(QSAR)建模 确定先导化合物用于合成/衍生化(Lindsley)和生化/功能评估(POZZI)。 选择性靶向单个激酶仍然是一个巨大的挑战，目前在vHTS中的方法未能 解释了有助于结合选择性的蛋白质结合口袋特征。这项工作的中心目标是 建议寻找治疗慢性肾脏病的新型DDR1选择性抑制剂，并开发新的 解决vHTS中当前限制的技术。在具体目标I中，我将生成并使用QSAR模型来 对潜在的DDR1抑制剂进行vHTS。我随后将定义一个DDR1激酶的结构模型 使用分子动力学(MD)生成的DDR激酶构象系综的抑制剂选择性 配合罗塞塔灵活对接。我还将在电子计算机和体外定点突变中进行 进一步表征DDR1激酶抑制剂选择性的决定因素。在具体的目标二中，我将制定一个 Meeller实验室生物和化学库(BCL)中的多任务机器算法 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 选择性的DDR1激酶抑制剂。所开发的方法将解决该领域长期存在的缺陷 计算机辅助药物发现(CADD)--即，基于蛋白质结构的方法是在计算上的 对于vHTS是禁止的，而基于配体的方法不包括关于结合模式的直接信息。作为 在AIM II中开发的方法可用，它们将被集成到AIM I中描述的发现周期中 最终确定DDR1激酶选择性的结构模型并寻找新的治疗药物 通过使用新的和已建立的方法治疗慢性肾脏病。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具有挑战性的目标。

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.