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

项目摘要 慢性肾脏病（CKD）是65岁以上患者的主要疾病乘数。CKD的特点是 通过肾脏超生理IV型胶原沉积介导的进行性肾纤维化 肌成纤维细胞随着美国人口的不断老龄化，识别新的 CKD的治疗策略肾损伤的小鼠模型表明， 酪氨酸激酶盘状结构域受体1（DDR 1）对纤维化肾病具有保护作用。抑制 DDR 1激酶减少肾小球系膜细胞IV型胶原沉积。为了开发针对CKD的靶向疗法， Jens Meiler实验室（本申请的申办方）与Ambra Pozzi实验室（合作方）合作， 本申请发起人）和克雷格林斯利（Lindsley）创建了一个全面的DDR 1激酶抑制剂发现 渠道. Meiler实验室利用基于配体的定量构效关系 用于虚拟高通量筛选（vHTS）和随后的蛋白质-配体对接的QSAR建模， 鉴定用于合成/衍生化（Lindsley）和生物化学/功能评价（Pozzi）先导化合物。 单个激酶的选择性靶向仍然是一个重大挑战，并且目前的vHTS方法未能 解释有助于结合选择性的蛋白结合口袋特征。这一目标的核心 目前的建议是鉴定用于治疗CKD的新型DDR 1选择性抑制剂，并开发新的 技术来解决vHTS中的当前限制。在具体目标I中，我将生成并使用QSAR模型， 对潜在DDR 1抑制剂进行vHTS。随后我将定义DDR 1激酶的结构模型 使用分子动力学（MD）产生的DDR激酶的构象集合的抑制剂选择性， 与ROSETTA柔性对接。我还将进行计算机模拟和体外定点诱变， 进一步表征DDR 1激酶抑制剂选择性的决定因素。在第二个目标中，我将开发一个 Meiler实验室生物学和化学图书馆（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善vHTS， 选择性DDR 1激酶抑制剂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计算 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。为 在目标II中开发的方法可用时，它们将被集成到目标I中描述的发现周期中 最终确定DDR 1激酶选择性的结构模型，并鉴定用于治疗DDR 1激酶的新型治疗剂。 通过使用新的和已建立的方法治疗CKD。此外，新的计算 在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.