A Functional Census of p53 Cancer and Suppressor Mutants

描述（由申请人提供）：转录因子p53是一种控制DNA修复、细胞周期阻滞和细胞凋亡的中心肿瘤抑制蛋白。大约一半的人类癌症有p53突变，在晚期肿瘤中恢复p53功能会导致肿瘤消退。值得注意的是，这些肿瘤中的绝大多数产生全长p53蛋白，由于单个氨基酸的变化而失去了其肿瘤抑制功能。因此，一个有吸引力的新方法，全身癌症治疗是药理学激活p53癌症突变体。p53癌症突变体的再激活是可行的，因为我们和其他人已经表明，引入额外的突变（第二位点抑制突变）可以恢复活性，否则失活的p53癌症突变体。此外，据报道，一些有前途的小分子药物先导化合物具有未知的作用机制，可以重新激活p53癌症突变体。目前的挑战是了解导致p53癌症突变体重新激活的结构变化，并通过小分子诱导这种变化。这是一个复杂的问题，因为在p53癌症突变体中发现的临床相关的单个氨基酸变化的多样性。 我们提出了基于机器学习的计算方法，这些方法得到了高通量生物策略的支持。我们使用新的饱和诱变方法，目录p53拯救突变的50个最相关的p53突变体中发现的人类癌症。在主动学习方案中使用遗传数据来训练基于建模的原子级结构特征的计算分类器，以预测哪些结构变化导致任何给定的p53癌症突变体的再激活。我们进一步建议应用这种改进的计算分类器来发现诱导类似结构变化的小分子，并在p53癌症突变体再激活的生物测定中测试这些预选化合物。从这些体内实验中获得的数据将用于进一步改进小分子的计算预测。 总之，我们使用遗传功能数据来训练基于结构的分类器，以基于建模的结构变化的内部表示来预测p53活性。然后，分类器将用于预测小分子对p53癌症突变体的再激活，目的是识别癌症药物先导物。该研究对生物医学研究和公共卫生具有重要影响。美国每年约有25万人死于全长但突变和失活的p53肿瘤。这项研究的长期目标是开发一种重新激活突变型p53的药物，可以预防或延迟这些死亡。

DESCRIPTION (provided by applicant): The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis. About half of human cancers have p53 mutations, and restoring p53 function in advanced tumors leads to tumor regression. Significantly, the large majority of these tumors produce full-length p53 proteins that have lost their tumor suppressor function due to single amino acid changes. Therefore, an attractive new approach to systemic cancer therapy is pharmacological reactivation of p53 cancer mutants. Reactivation of p53 cancer mutants is feasible because we and others have shown that introducing additional mutations (second-site suppressor mutations) can restore activity to the otherwise inactive p53 cancer mutants. In addition a few promising small molecule drug leads with unknown mechanisms of action have been reported to reactivate p53 cancer mutants. The challenge is to understand structural changes that lead to reactivation of p53 cancer mutants and to induce such changes through small molecules. This is a complex problem due to the diversity of clinically relevant single amino acid changes found in p53 cancer mutants. We propose computational approaches based on machine learning that are supported by high-throughput biological strategies. We use novel saturation mutagenesis approaches to catalogue p53 rescue mutations for the 50 most relevant p53 mutants found in human cancer. The genetic data are used in an active learning scheme to train a computational classifier, that is based on modeled atom-level structural features, to predict which structural changes lead to reactivation of any given p53 cancer mutant. We further propose to apply this improved computational classifier to discover small molecules that induce similar structural changes and test these pre-selected compounds in a biological assay for p53 cancer mutant reactivation. Data obtained from these in vivo experiments will be used to further improve the computational predictions for small molecules. In summary, we use genetic functional data to train a structure-based classifier to predict p53 activity based on an internal representation of modeled structural changes. The classifier will then be used to predict reactivation of p53 cancer mutants by small molecules with the aim to identify cancer drug leads. The proposed research has high impact on biomedical research and public health. About 250,000 US deaths yearly are due to tumors with full length but mutated and inactive p53. The long-term goal of this research, a drug that reactivates mutant p53, could prevent or delay these deaths.