Automated High-Throuput Estimation and Modeling of Protein Network Distributions

蛋白质网络分布的自动高通量估计和建模

• 批准号: 8244428
• 负责人: Gustavo Kunde Rohde
• 金额: $ 29.61万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8244428
• 关键词: Actins; Address; Affect; Affinity; Biology; Cell Count; Cell Line; Cell Shape; Cell Size; Cell physiology; Cells; Computing Methodologies; Confocal Microscopy; Data; Endoplasmic Reticulum; Filament; Fluorescence Microscopy; Future; Growth; Heart; Image; Image Analysis; Intermediate Filaments; Label; Length; Literature; Location; Measures; Medicine; Methodology; Methods; Microtubules; Modeling; Mutation; Neoplasm Metastasis; Pattern; Pharmaceutical Preparations; Property; Protein Databases; Proteins; Proteome; Research; Role; Simulate; Structure; Subcellular structure; Testing; Variant; Work; Wound Healing; base; cell behavior; cell type; design; high throughput screening; interest; model development; novel; physical state; predictive modeling; protein distribution; protein protein interaction; protein structure; prototype; public health relevance; research study; simulation

DESCRIPTION (provided by applicant): This research is aimed at developing and testing methods that will enable proteome-wide high-throughput studies of the subcellular distributions of proteins that form networks (such as microtubules), and for identifying proteins whose distributions are related to these. More specifically we will test and further refine methods that we have developed to estimate the average properties (characterize the statistical variation) of the filament distributions for a large number of cells. Our aim is to use the power afforded by the availability of data arising from many (possibly thousands) of cells to estimate what is really at the heart of the question for high-throughput studies of proteins of this type: what is the overall average effect of some independent variable (drug or other experimental condition) on the network filament distribution of interest. Preliminary work has established the feasibility of this approach, and we propose to test it extensively with real data for microtubules in a variety of cell types under a variety of conditions that perturb distributions in known ways. The results will establish whether a single microtubule growth model (with changes in parameters) is valid for many cell types (i.e., to remove variation due solely to cell size and shape). We will also extend this method to determine the correlation (affinity) of many unknown proteins to filament networks for several proteome wide studies currently generating such data. The methods will be used to analyze images for thousands of proteins from existing and ongoing proteome-scale studies. The identification solely on the basis of image-based modeling of specific proteins as likely to be microtubule-associated will be tested for selected examples by comparison with information in existing protein databases and literature and by additional experimentation. The successful completion of this study would not only provide important new information about the location of many proteins, but will fill a current void in modeling approaches for proteome-wide studies and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments. PUBLIC HEALTH RELEVANCE: This research is aimed at enabling fundamental understanding of subcellular filament-type protein structure and distribution through generative modeling approaches. The successful completion of this study would enable the development of modeling approaches for similar proteins and subcellular structures and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments.

描述（由申请人提供）：本研究旨在开发和测试方法，以实现对形成网络（例如微管）的蛋白质的亚细胞分布进行全蛋白质组高通量研究，并鉴定其分布与这些网络相关的蛋白质。更具体地说，我们将测试并进一步完善我们开发的方法来估计大量细胞的丝分布的平均属性（表征统计变化）。我们的目标是利用来自许多（可能是数千个）细胞的数据的可用性来估计此类蛋白质高通量研究问题的真正核心是什么：某些自变量（药物或其他实验条件）对感兴趣的网络细丝分布的总体平均影响是什么。初步工作已经确定了这种方法的可行性，我们建议在以已知方式扰乱分布的各种条件下，使用各种细胞类型中微管的真实数据对其进行广泛测试。结果将确定单个微管生长模型（参数变化）是否对许多细胞类型有效（即消除仅由于细胞大小和形状引起的变化）。我们还将扩展此方法，以确定许多未知蛋白质与丝网络的相关性（亲和力），以用于目前生成此类数据的多项蛋白质组范围研究。这些方法将用于分析现有和正在进行的蛋白质组规模研究中的数千种蛋白质的图像。仅基于可能与微管相关的特定蛋白质的基于图像的建模的识别将通过与现有蛋白质数据库和文献中的信息比较以及通过额外的实验来测试选定的例子。这项研究的成功完成不仅将提供有关许多蛋白质位置的重要新信息，还将填补目前全蛋白质组研究建模方法的空白，并促进高通量筛选实验中不同药物、siRNA 或突变效应的机械量化。 公共健康相关性：这项研究旨在通过生成模型方法对亚细胞丝状蛋白结构和分布有基本的了解。这项研究的成功完成将有助于开发类似蛋白质和亚细胞结构的建模方法，并有助于在高通量筛选实验中对不同药物、siRNA 或突变的作用进行机械量化。