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

C摘要 Myc转录因子抑制剂设计：整合原子和介观尺度与半监督基因 交互式深度学习模型 由于致癌状态的逆转，抑制主调节因子如Myc具有相当大的兴趣。 被他们的离去所感动。增加神秘感的是针对一种蛋白质的技术挑战 大范围的混乱。虽然被广泛认为是“不可治愈的”，但破坏Myc功能的命中库 不断增长。除了高分子量外，命中的化学特征很难推断， 立体性、刚性和疏水性。了解蛋白质-蛋白质相互作用的更具体特征 (PPI)抑制剂相当困难。为了避免回答这个问题，机器学习方法 已被应用于扩大实验确定的命中库，希望找到一种改进的抑制剂 在化学空间附近。最近，生成式深度学习技术在这个问题上的自然应用- 已经报告了登月舱。这个提议解释了一个小分子半监督扩增的协议 其抑制Myc反式激活途径中的各种反应。PPI抑制剂来自三个公开可用的 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，从训练集中去除所有已知的Myc抑制剂。一些 解决足以重新创建训练集的潜在变量。这些变量表示一般结构， PPI抑制剂的结构特性，这可能与各种结合位点的活性有关。效率 计算活动对于获得良好的性能是至关重要的。因此，一个训练有素的目标合奏 配置是在全原子分辨率下预先计算的。此外，为了将人口 多个Myc分子的水平行为到抑制剂设计，在各种溶胶中的介观粗颗粒模拟， 进行驱动液-液相分离的排气。识别与相位相关的相互作用 响应，粗粒相空间中的各个点被转换为全原子分辨率，进一步细化， 转换成联系地图。在评估新电极导线时，使用基于集成的对接计算， 计算与随机抽取的不同构象结合的不同位姿的配体的平均值的平均值 从合奏。强化学习被应用于显著减少对接批次所花费的时间， 领导，同时保持对结果的信心。一旦新的分子产生，这些新的线索也 使用结合方法的绝对和相对自由能优化。最终，这项研究将测试 生成模型，以整合多个尺度的数据，并开发抑制剂， 内在无序的蛋白质

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.