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

Myc 转录因子抑制剂设计：将原子和中尺度与半监督生成深度学习模型相结合

• 批准号: 10745272
• 负责人: Gregory John Schwing
• 金额: $ 4.67万
• 依托单位: WAYNE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10745272
• 关键词: Affinity; Automobile Driving; Behavior; Binding; Binding Sites; Biological; Biological Assay; Cancer Biology; Cell Proliferation; Chemicals; Complex; DNA; Data; Databases; Disease; Docking; Excision; Free Energy; Glioma; Goals; Grain; Human; Hydrophobicity; In Vitro; Lead; Learning; Libraries; Ligand Binding; Ligands; Liquid substance; MYC Family Protein; Malignant Neoplasms; Maps; Methods; Modeling; Molecular Conformation; Molecular Weight; Mutate; Oncogenic; Pancreatic Adenocarcinoma; Pathologic; Pathway interactions; Performance; Pharmaceutical Preparations; Phase; Population; Process; Property; Proteins; Proto-Oncogene Proteins c-myc; Protocols documentation; Psychological reinforcement; Reaction; Reporting; Resolution; Role; Signal Transduction; Site; Solvents; Somatic Mutation; Structure; Techniques; Testing; Time; Training; Transactivation; Transducers; Trees; data integration; data mining; deep learning; deep learning model; design; drug discovery; drug efficacy; effectiveness testing; falls; high throughput screening; improved; in vivo; inhibitor; interest; lead optimization; learning strategy; machine learning method; molecular dynamics; nanomolar; peptidomimetics; phase change; protein protein interaction; public database; response; simulation; small molecule; small molecule inhibitor; small molecule libraries; statistics; success; transcription factor; triple-negative invasive breast carcinoma; virtual screening

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.

C摘要 Myc 转录因子抑制剂设计：将原子和介观尺度与半监督生成相结合 迭代深度学习模型 由于致癌状态的逆转，对 Myc 等主调控因子的抑制引起了相当大的兴趣 去除它们引起的神秘感是针对具有这种功能的蛋白质的技术挑战。 尽管广泛认为“不可成药”，但破坏 Myc 功能的命中库。 除了高分子量、芳香族之外，还很难推断出命中的化学特征。 了解蛋白质-蛋白质相互作用的更具体特征。 （PPI）抑制剂是相当困难的，为了回避回答这个问题，机器学习方法。 已应用于扩展实验确定的命中库，希望找到改进的抑制剂 最近，生成深度学习技术自然应用于这个问题。 该提案解释了小分子半监督扩展的方案。 抑制 Myc 反式激活途径中的各种反应。来自三种公开可用的 PPI 抑制剂。 数据库构成训练集 (n=9516)，而已知的 Myc 抑制剂构成测试集 (n=100)。 为了超越测试集的有效性，所有已知的 Myc 抑制剂都从训练集中删除了一些。 解决了足以重新创建训练集的潜在变量，这些变量代表了一般结构。 PPI 抑制剂的天然特性，可能与不同结合位点的活性有关。 因此，活动的计算对于获得良好的性能至关重要。 另外，为了将总体纳入其中，构型也是以全原子分辨率预先计算的。 多个Myc分子的水平行为进入抑制剂设计，各种溶胶中的介观粗粒模拟 进行驱动液-液相分离的通风口以识别与相相关的相互作用。 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，并且 转换为接触图时，使用基于集合的对接计算。 计算不同姿势的配体与随机抽取的不同构象结合的平均值的平均值 应用强化学习来显着减少对接批次所花费的时间。 线索，同时保持对结果的信心一旦产生新分子，这些新线索也将被产生。 最终，本研究将测试结合方法的绝对能量和自由相对能量的极限。 生成模型来整合多个尺度的数据并开发能够引起有效抑制的抑制剂 本质上无序的蛋白质。