CORE C

核心C

• 批准号: 10474993
• 负责人: Rommie E Amaro
• 金额: $ 28.32万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-09 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10474993
• 关键词: 3-Dimensional; BRCA1 gene; Binding Sites; Biochemical; Biological; Biological Assay; Biological Testing; Biology; Biophysics; Breast; CDK4 gene; Chemical Models; Chemicals; Clinical; Collaborations; Complex; Computer Models; Computing Methodologies; Crystallization; Cytosine; Cytosine deaminase; D Cells; Data; Deamination; Defect; Development; Drug Kinetics; Drug resistance; Effectiveness; Enzymes; Estrogen receptor positive; Evolution; Excretory function; Generations; Goals; Graph; Inherited; Kinetics; Length; Ligands; Macromolecular Complexes; Malignant Neoplasms; Mammary Neoplasms; Metabolism; Metastatic breast cancer; Modeling; Molecular; Monoclonal Antibodies; Mutagenesis; Mutation; Neoplasm Metastasis; Nucleic Acids; Outcome; PIK3CA gene; Physiological; Primary Neoplasm; Process; Property; Proteins; Regulation; Reporting; Resolution; Roentgen Rays; Role; Single-Stranded DNA; Source; Structural Models; Structure; System; Techniques; Testing; The Cancer Genome Atlas; Thermodynamics; Time; Uracil; Vertebral column; absorption; base; biophysical chemistry; biophysical model; chemical binding; computational chemistry; data streams; data-driven model; design; experimental study; homologous recombination; improved; in silico; in vivo; inhibitor; innovation; insight; lead optimization; malignant breast neoplasm; model development; molecular dynamics; molecular recognition; multidisciplinary; nanosecond; neoplastic cell; novel; operation; prevent; programs; receptor; resistance mutation; screening; simulation; small molecule; structural biology; tumor

CORE C – COMPUTATIONAL CHEMISTRY & BIOPHYSICS ABSTRACT APOBEC is a recently discovered enzymatic source of mutation in breast cancer. Multiple lines of evidence indicate that APOBEC mutagenesis is an ongoing source of mutation in tumor cells and that the major enzyme responsible is the single-stranded (ss)DNA cytosine deaminase APOBEC3B (A3B). Our Program is therefore united in testing the overarching hypothesis that A3B inhibition will prevent a large proportion of new mutations in estrogen receptor-positive breast cancer, thereby improving the durability of current treatments and resulting in better overall outcomes. Projects 1, 2, and 3 are focused on testing this idea through a carefully organized multidisciplinary team involving biology, chemical biology, and structural biology approaches. Core C – Computational Chemistry & Biophysics provides the computational modeling backbone to support these Projects through 2 well-integrated specific aims. Aim 1 encompasses the development of physically detailed 3D structural models of APOBEC biomolecular systems, including those that prove challenging to resolve experimentally, such as the different macromolecular regulatory complexes being explored in Project 1, or full- length, wild-type A3B in complex with ssDNA in Project 3. In these examples and others, explicitly solvated molecular dynamics (MD) simulations will be used to predict atomic-level interactions, and these dynamic 3- dimensional models will guide wet experiments by the Project teams. The resulting data will drive Core C to develop further refined models for additional testing by the Project teams. Aim 2 consists of in silico small molecule screening and lead optimization. Innovative MD analysis frameworks, such as Markov state modeling, will be used to extract long-timescale dynamics from many short-timescale simulations and elucidate the thermodynamic and kinetic landscapes of APOBEC enzymes that control molecular recognition and functional activity. A key strength of this approach is identification of cryptic pockets that are capable of binding chemical probes but are often absent from x-ray structures. A range of ligand- and receptor-based approaches will be employed in silico to increase the diversity of APOBEC inhibitors. Core C will also perform lead optimization in silico, including computational Absorption Distribution Metabolism Excretion / Pharmacokinetics (ADME/PK) optimization to help avoid potential chemical liabilities and maximize experimental efficiencies. Inhibitors and probes will be developed through continual collaboration with Projects 2 and 1 and Core D. The biochemical and biological testing of candidate molecules identified or predicted in silico will fuel additional rounds of computational refinement, ultimately leading to structural studies by Project 3 and in vivo tumor evolution experiments by Core B.

核心C -基础化学和生物物理 摘要 APOBEC是最近发现的乳腺癌突变的酶源。多种证据 表明APOBEC诱变是肿瘤细胞中突变持续来源， 负责的是单链（ss）DNA胞嘧啶脱氨酶APOBEC 3B（A3 B）。因此，我们的计划是 在检验A3 B抑制将阻止大部分新突变的总体假设时， 在雌激素受体阳性乳腺癌中，从而提高了目前治疗的持久性， 更好的整体结果。项目1、2和3的重点是通过精心组织的 涉及生物学、化学生物学和结构生物学方法的多学科团队。核心C - 计算化学与生物物理学提供了计算建模的骨干，以支持这些 通过两个整合良好的具体目标的项目。目标1包括开发物理详细的 APOBEC生物分子系统的3D结构模型，包括那些证明具有挑战性的解决方案 实验上，如项目1中探索的不同大分子调节复合物，或完整的 长度，野生型A3 B与项目3中的ssDNA复合。在这些实施例和其他实施例中，明确溶剂化的 分子动力学（MD）模拟将用于预测原子级相互作用，这些动态3- 三维模型将指导项目团队的湿实验。由此产生的数据将推动核心C， 开发进一步完善的模型，供项目小组进行额外测试。Aim 2由计算机模拟小 分子筛选和先导物优化。创新的MD分析框架，如马尔可夫状态建模， 将用于从许多短时间尺度模拟中提取长时间尺度动力学，并阐明 热力学和动力学景观APOBEC酶控制分子识别和功能 活动这种方法的一个关键优势是识别能够结合化学物质的隐蔽口袋。 探针，但通常不存在于X射线结构中。一系列基于配体和受体的方法将在 在计算机上使用，以增加APOBEC抑制剂的多样性。Core C还将在 计算机模拟，包括计算吸收分布代谢排泄/药代动力学（ADME/PK） 优化，以帮助避免潜在的化学责任和最大限度地提高实验效率。抑制剂和 将通过与项目2和1以及核心D的持续合作开发探测器。生化 对计算机识别或预测的候选分子进行生物学测试将推动更多轮的 计算精细化，最终导致项目3的结构研究和体内肿瘤演变 核心B的实验。