ROBUST PORTABLE SOFTWARE FOR LOCATION PROTEOMICS

描述(由申请人提供)： 系统地了解蛋白质的亚细胞位置以及这些位置如何随着药物、细胞周期、发育期间和疾病期间的变化而变化，将对当前全面的蛋白质组学工作至关重要。涉及这一主题的新领域，定位蛋白质组学，需要用荧光标记大量蛋白质的方法，快速收集高分辨率荧光显微镜图像，以及关键的是，自动分析结果的分布。墨菲小组此前曾描述过自动化系统，该系统可以识别2D和3D图像中的所有主要亚细胞模式，并能够比肉眼检查更好地区分蛋白质模式。这些系统的一个关键组件是一组数字特征，这些特征描述了每种亚细胞模式，而不会对细胞大小和形状的变化过于敏感。该提案的第一个组成部分是研究，以开发和测试新的分析问题的解决方案。在这个目标中，第一个目标是建立一个最优的蛋白质分级分组，以便将在所有条件下在统计上无法区分的蛋白质放在同一组中。第二种方法是不仅根据图像的静态模式，而且根据这些模式随时间的变化对图像进行描述和分类。三是实现了基于蛋白质定位模式相似性的分布式数据库图像快速检索方法。第二个组成部分是将目前的“研究级”软件转换为“分发级”软件，以便研究人员既可以作为独立应用程序使用，也可以作为图像数据库系统的一部分使用。其中包括图像集的模式分类器和比较器。该项目团队包括多媒体数据库检索、数据挖掘和软件工程原理方面的领先专家。将要开发的软件不仅对大规模蛋白质组学工作有用，而且对传统细胞生物学实验的自动解释也很有用。根据位置对蛋白质进行分级分组，结合它们的序列，将首次使预测位置的程序能够以发现新的靶向基序所需的高分辨率运行。该项目实现了定位蛋白质组学的综合方法，有望确定与疾病相关的蛋白质差异，这些差异可用作治疗的目标或作为早期发现和分类异常的标记。

DESCRIPTION (provided by applicant): Systematic knowledge of the subcellular locations of proteins and how these locations change in response to drugs, during the cell cycle, during development and during disease will be essential to current comprehensive proteomics efforts. The new field dealing with this subject, Location Proteomics, requires methods for fluorescently-tagging large numbers of proteins, rapidly collecting high-resolution fluorescence microscope images, and, critically, automatically analyzing the resulting distributions. The Murphy group has previously described automated systems that can recognize all major subcellular patterns in 2D and 3D images and can distinguish protein patterns better than visual examination. A critical component of these systems is sets of numerical features that describe each subcellular pattern without being overly sensitive to variations in cell size and shape. The first component of this proposal is research to develop and test solutions to new analysis problems. Within this goal, the first aim is the building of an optimal hierarchical grouping of proteins such that proteins whose patterns are statistically indistinguishable under all conditions are placed in the same group. The second is describing and classifying images based not only on their static patterns but on how those patterns change over time. The third is implementing fast methods for retrieving images from distributed databases based on similarity of protein location patterns. The second component is conversion of current "research-grade" software to "distribution-grade" so that it can be made available to researchers both as stand-alone applications and as part of an image database system. These include pattern classifiers and comparators for image sets. The project team includes leading experts in multimedia database retrieval and data mining and software engineering principles. The software to be developed will be useful not only for large-scale proteomics efforts but also for automated interpretation of traditional cell biology experiments. The hierarchical grouping of proteins by location, in combination with their sequences, will for the first time allow programs for predicting location to operate at the high resolution necessary for discovery of new targeting motifs. The comprehensive approach to location proteomics enabled by this project promises to identify protein differences associated with disease, which can be used as targets for therapies or as markers for early detection and classification of abnormalities.