Myc Transcription Factor Inhibitor Design: Integrating Atomic and Mesoscale with Semi-Supervised Generative Deep Learning Models

C摘要 MYC转录因子抑制物设计：将原子和中尺度与半监督基因相结合 产生式深度学习模型 由于致癌状态的逆转，对Myc等主要调控因子的抑制具有相当大的兴趣 由它们的移除引起的。增加神秘性的是靶向一种具有 大面积的无序区域。尽管被广泛认为是不能下药的，但扰乱Myc功能的点击库 不断增长。一种HIT的化学性质除了高分子量外，还很难推断，而且是一种fi。 斑驳、僵硬和疏水性。了解蛋白质-蛋白质相互作用的更特殊的fic特征 (Ppi)抑制剂是相当不同的fi邪教。为了绕过回答这个问题，机器学习方法 已经被应用于扩展实验确定的命中的库，希望fi和改进的抑制剂 在化学空间附近。最近，生成性深度学习技术在这个问题上的自然应用-- 莱姆已经被报道了。这项提议解释了一个小分子半监督展开的协议。 它们抑制Myc反式激活途径中的各种反应。来自三种公开上市的PPI抑制剂 数据库组成训练集(n=9516)，而已知的Myc抑制剂是测试集(n=100)。为了 超过测试集的有效性，所有已知的Myc抑制剂都从训练集中移除。一批 解决了利用fi算法重建训练集的潜在变量问题。这些变量代表一般结构- PPI抑制剂的结构性质，可能与不同结合部位的活性有关。The EffiEncient 活动的计算是取得良好业绩的关键。因此，一个训练有素的靶子合奏 CONfi方程是在全原子分辨率下预先计算的。此外，为了将人口纳入 多个Myc分子的水平行为进入缓蚀剂设计，不同溶胶中的中尺度粗粒模拟- 执行驱动液-液相分离的通风口。确定与阶段相关的交互 响应时，粗晶相空间中的各个点被转换为全原子分辨率，进一步fiNed，以及 已转换为联系人地图。在评估新的销售线索时，使用基于系综的对接计算，这 计算不同姿势的配体与随机绘制的不同构象结合的平均值 从合唱团来的。将强化学习应用于fi，可以显著减少对接批次所花费的时间 领先的同时保持对结果的信任fi。一旦产生了新的分子，这些新的线索也 采用绝对自由能和相对自由能结合方法进行优化。最终，这项研究将测试 生成性模型，整合多个尺度上的数据，并开发能引起对 本质上无序的蛋白质。

C ABSTRACT Myc Transcription Factor Inhibitor Design: Integrating Atomic and Mesoscale with Semi-Supervised Gen- erative Deep Learning Models Inhibition of master regulators such as Myc have considerable interest due to the reversal of the oncogenic state evoked by their removal. Adding to the mystique is the technical challenge in targeting a protein which possesses large regions of disorder. Though widely considered “undruggable”, the library of hits that disrupt Myc function continuously grows. The chemical features of a hit are difficult to deduce besides high molecular weight, aro- maticity, rigidity, and hydrophobicity. Understanding the more specific features of a protein-protein interaction (PPI) inhibitor is considerably difficult. In order to circumvent answering this question, machine learning methods have been applied to expand the library of experimentally determined hits in hopes of finding an improved inhibitor nearby in chemical space. Recently, the natural application of generative deep learning techniques to this prob- lem have been reported. This proposal explains a protocol for a semi-supervised expansion of small molecules which inhibit various reactions in the Myc transactivation pathway. The PPI inhibitors from three publicly available databases make up the training set (n=9516) while the known Myc inhibitors are the test set (n=100). In order to surpass the effectiveness of the test set, all known Myc inhibitors are removed from the training set. A number of latent variables which suffice to recreate the training set are solved. These variables represent the general struc- tural properties of PPI inhibitors, which may be associated with activities at various binding sites. The efficient calculation of activities is crucial to obtaining good performance. Therefore, a well-tempered ensemble of target configurations is pre-calculated at the all-atom resolution. Additionally, in order to incorporate the population level behavior of multiple Myc molecules into inhibitor design, mesoscale coarse-grain simulations in various sol- vents which drive liquid-liquid phase separation are performed. To identify interactions which correlate with phase response, various points in coarse-grain phase space are converted to all-atom resolution, further refined, and converted into contact maps. When evaluating a new lead, ensemble-based docking calculations are used, which calculate an average of averages of a ligand in different poses binding to different conformations randomly drawn from the ensembles. Reinforcement learning is applied to significantly reduce the time spent docking batches of leads while maintaining confidence in the result. Once new molecules are generated, these new leads are also optimized using absolute and relative free energy of binding methods. Ultimately, this study will test the limits of generative models to integrate data across multiple scales and develop inhibitors which evoke potent inhibition of intrinsically disordered proteins.