A Functional Census of p53 Cancer and Suppressor Mutants

p53 癌症和抑制突变体的功能普查

基本信息

批准号：
7426313
负责人：
G. WESLEY HATFIELD
金额：
$ 33.54万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-08-01 至 2010-05-31
项目状态：
已结题

项目摘要

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.

描述（由申请人提供）：广泛的长期目标是（1）展示计算和实验方法的配合，以实现具有重大医学重要性的大型突变序列空间的功能普查； (2) 有助于我们了解 p53 功能拯救机制，从而促进寻找能够实现类似 p53 功能拯救的小分子癌症药物； (3) 通过表征 p53 癌症和跨已知下游 p53 DNA 结合位点的抑制突变体的谱，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。在第二位点 p53 癌症抑制突变中发现了拯救 p53 癌症突变的生物学优先性。类似的 p53 药物救援每年将挽救数十万人的生命。理解和预测 p53 救援是实现这一目标的重要一步。具体目标是（1）通过计算预测 p53 癌症突变体的所有单抑制突变并通过实验验证结果，（2）通过两个或多个协调突变变化优化已知和假定的 p53 抑制区域的救援效果，以及（3）预测并通过实验验证 p53 癌症和抑制突变体对已知 p53 DNA 结合位点的 DNA 结合特异性。我们的总体策略是协调计算和实验攻击。我们已经有了实验性 p53 功能测定和 p53 活性的计算预测因子，这是我们初步研究的一部分。计算预测器将用于将实验工作集中在最优先的领域。预测的实验验证将为机器学习技术带来更大的训练集。更大的训练集将带来更准确的预测，从而使实验更有针对性。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一个具体目标，这些目标都依赖于密切协调的实验和计算。