A Functional Census of p53 Cancer and Suppressor Mutants

描述(申请人提供)：广泛的，长期的目标是(1)展示计算和实验方法，以实现对具有重要医学意义的大型突变序列空间的功能普查；(2)促进我们对p53功能救援机制的了解，从而促进寻找对p53具有类似功能救援作用的小分子抗癌药物；以及(3)通过鉴定已知下游p53 DNA结合位点上的p53癌和抑制突变体的谱，阐明癌症的部分系统生物学。肿瘤抑制蛋白P53的突变发生在大约一半的人类癌症中，恢复突变缺陷的P53蛋白的功能是一个长期的医学目标。在第二位P53癌抑制基因突变中发现了挽救P53癌突变的生物学优势。类似的p53药物救援每年将拯救数十万人的生命。了解和预测P53的拯救是朝着这一目标迈出的重要一步。 其具体目的是(1)通过计算预测所有p53癌突变的单个抑癌基因突变，并通过实验验证结果；(2)通过两个或多个协同突变改变，优化已知和可能的p53抑癌基因区域的挽救效果；(3)预测和实验验证p53癌和抑癌突变对已知P53 DNA结合位点的DNA结合特异性。我们的总体战略是协同计算和实验攻击。我们已经有了实验性的P53功能分析和P53活性的计算预测指标，作为我们初步研究的一部分。计算预测值将被用来将实验工作集中在最优先的领域。对预测的实验验证将导致机器学习技术的更大训练集。更大的训练集将导致更准确的预测，因此更有针对性的实验。因此，随着项目的进展，计算和实验之间的相互作用将变得更加有效。这一基本策略的变化适用于我们的每个特定目标，这些目标都依赖于密切协调的实验和计算。

DESCRIPTION (provided by applicant): The broad, long-term objectives are (1) demonstrate computational and experimental methods cooperating to achieve a functional census of a large mutation sequence space of great medical importance; (2) contribute to our knowledge of p53 functional rescue mechanisms, and so facilitate the search for a small molecule cancer drug that effects an analogous functional rescue of p53; and (3) elucidate part of the systems biology of cancer by characterizing the spectrum of p53 cancer and suppressor mutants across known downstream p53 DNA binding sites. Mutations to the tumor suppressor protein p53 occur in approximately half of all human cancers, and restoring function to a mutationally defective p53 protein is a long-held medical goal. Biological precedence for rescuing p53 cancer mutations is found in second-site p53 cancer suppressor mutations. The analogous p53 pharmacological rescue would save hundreds of thousands of lives annually. Understanding and predicting p53 rescue is an important step toward that goal. The specific aims are (1) computationally predict all single suppressor mutations for p53 cancer mutants and validate the results experimentally, (2) optimize the rescue effects of known and putative p53 suppressor regions through two or more coordinated mutation changes, and (3) predict and experimentally validate the DNA binding specificity of p53 cancer and suppressor mutants for known p53 DNA binding sites. Our broad strategy is a coordinated computational and experimental attack. We already have experimental p53 functional assays and computational predictors of p53 activity, developed as part of our Preliminary Studies. Computational predictors will be used to focus experimental work into the highest priority areas. Experimental validation of the predictions will lead to a larger training set for machine learning techniques. The larger training set will lead to even more accurate predictions, thus even more focused experimentation. Thus, the interplay between computation and experiment will become ever more efficient as the project progresses. Variations of this basic strategy apply to each of our Specific Aims, which all rely on closely coordinated experiment and computation.