A Functional Census of p53 Cancer and Suppressor Mutants

p53 癌症和抑制突变体的功能普查

• 批准号: 7613343
• 负责人: RICHARD H LATHROP
• 金额: $ 34.23万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7613343
• 关键词: Affect; Amino Acids; Antineoplastic Agents; Apoptosis; Area; Binding Sites; Bioinformatics; Biological; Biological Assay; Cancerous; Cataloging; Catalogs; Cell Cycle Arrest; Cells; Censuses; Consensus; DNA Binding; Data Sources; Engineering; Gene Targeting; Genomics; Goals; Human; Knowledge; Lead; Letters; Life; Machine Learning; Malignant Neoplasms; Maps; Medical; Methodology; Methods; Missense Mutation; Modeling; Molecular Conformation; Mutation; Positioning Attribute; Proliferating; Protein p53; Research Personnel; Signal Transduction; Site; Source Code; Specificity; Stress; Suppressor Mutations; Surveys; Systems Biology; TP53 gene; Techniques; Training; Tumor Suppressor Proteins; Validation; Variant; Work; Yeasts; base; cancer therapy; computerized tools; design; functional restoration; interest; killings; mutant; programs; repaired; research study; small molecule; transcription factor; tumor; web site

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）证明了合作的计算和实验方法，以实现具有极大医学重要性的大突变序列空间的功能普查； （2）有助于我们对p53功能救援机制的了解，从而促进寻找一种小分子癌药物，该药物会影响p53的类似功能救助； （3）通过表征已知下游p53 DNA结合位点的p53癌症和抑制突变体的光谱来阐明癌症系统生物学的一部分。抑制肿瘤蛋白p53的突变发生在大约一半的人类癌症中，而将功能恢复为突变有缺陷的p53蛋白是长期以来的医疗目标。在第二站点p53癌症突变中发现了营救p53癌症突变的生物学优先级。类似的p53药理学救援将每年挽救数十万生命。理解和预测p53救援是朝着该目标迈出的重要一步。 具体目的是（1）计算上预测p53癌症突变体的所有单个抑制突变，并通过实验验证结果，（2）通过两个或多个协调的突变变化来优化已知和推定的p53抑制区域的救援效应，（3）预测和实验验证了p53癌症和抑制p53 dna的DNA结合特异性P53 dna dna的DNA结合特异性。我们的广泛策略是协调的计算和实验攻击。我们已经拥有p53活性的实验p53功能测定和计算预测指标，这是我们初步研究的一部分。计算预测因素将用于将实验工作集中在最高优先级领域。对预测的实验验证将为机器学习技术提供更大的培训集。较大的训练集将导致更准确的预测，从而更加集中实验。因此，随着项目的进行，计算和实验之间的相互作用将变得更加有效。这种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。