CORE C

核心C-计算化学与生物物理学 摘要 APOBEC是最近在乳腺癌中发现的一种酶促突变来源。多条证据 表明APOBEC突变是肿瘤细胞中持续突变的来源，而主要的酶 负责的是单链DNA胞嘧啶脱氨酶APOBEC3B(A3B)。因此，我们的节目 联合起来测试压倒一切的假设，即抑制A3B将阻止很大比例的新突变 在雌激素受体阳性的乳腺癌中，从而提高目前治疗的持久性和由此产生的 在更好的整体结果中。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- 计算化学和生物物理学提供了支持这些的计算建模骨干 通过两个整合良好的具体目标的项目。目标1包含物理细节的发展 APOBEC生物分子系统的三维结构模型，包括那些证明具有挑战性的解决方案 在实验上，比如项目1中正在探索的不同的大分子调控络合物，或者完全的- 项目3中与ssDNA形成的复合体中的长度、野生型A3B。在这些例子和其他例子中，显式溶解 分子动力学(MD)模拟将用于预测原子水平的相互作用，这些动态的3- 维度模型将指导项目团队的湿实验。由此产生的数据将驱动Core C 开发进一步细化的模型，以供项目团队进行额外测试。Aim 2由小型硅胶芯片组成 分子筛选和先导优化。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的模拟中提取长时间尺度的动力学，并阐明 APOBEC酶控制分子识别和功能的热力学和动力学图景 活动。这种方法的一个关键优点是识别能够结合化学物质的隐蔽口袋 探头，但在X射线结构中往往不存在。一系列基于配体和受体的方法将是 用于硅胶，以增加APOBEC抑制剂的多样性。核心C还将在以下方面执行领先优化 硅，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 探测器将通过与项目2和1以及核心D的持续合作来开发。 对在硅胶中识别或预测的候选分子进行的生物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进化 核心B的实验。

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.