BCSP: ABI Innovation: Collaborative Research: Predicting changes in protein activity from changes in sequence by identifying the underlying Biophysical Conditional Random Field

BCSP：ABI 创新：协作研究：通过识别潜在的生物物理条件随机场，根据序列变化预测蛋白质活性的变化

• 批准号: 1262457
• 负责人: William Ray
• 金额: $ 25.53万
• 依托单位: The Research Institute at Nationwide Children's Hospital
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262457&HistoricalAwards=false
• 关键词: BCSP; ABI; Innovation; Collaborative; Research

Proteins are the molecular machines that are responsible for a vast array of functions that are necessary for life. Understanding how they work is critical to both a better scientific understanding of the fundamental processes of life, and to modifying or improving their function. Despite the fact that proteins are physically 3-dimensional structures of cooperating parts, the current state of the art for representing and studying proteins uses a description that is simply a sequential list of the parts used in their assembly. This sequential-list style of description has biased the development of tools for protein analysis to accentuate the sequential properties of these molecules, and ignores the fact that the parts must work together in unison for the protein to function. This work will broadly impact the study of proteins, improving a range of activities from basic scientific studies of function, to endeavors in protein engineering. The products of this project will be made freely available to the research community as online tools, and the methods will be incorporated into coursework and made available as lesson-plan material appropriate for both primary and secondary education. This project will adapt a recently-developed statistical technique, the Conditional Random Field (CRF), that can quantitatively represent densely-connected networks of features, and a recently-developed visualization tool that enables interactive exploration of these networks, for the task of describing proteins. Structurally, Conditional Random Fields appear to recapitulate the process by which evolution has selected for parts that cooperate in proteins, and protein descriptions based on CRFs will be able to predict whether a change to a protein - a mutation - would have been tolerated by evolution, or selected against as non-functional. This information will aid in predicting the effect of a mutation, or multiple mutations to a protein, using much more of the available information, than is currently utilized by state-of-the-art tools. The "change in protein sequence to change in protein function" problem is a "model organism" for many other types of biological and non-biological systems where rich interactions between parts of the system demand a sophisticated statistical approach. To-date, in most of these fields, models that are similarly limited to those currently used in proteins are the de-facto standard. Developing the tools necessary for applying CRFs to protein data, and methods of establishing testable ground-truth in this system, will enhance the application of CRFs to many other domains where they may provide a significant advantage over current methods. This tool may make interdependencies between features visually explorable and modifications of these dependencies quantifiably predictable, and may promote more thorough consideration of the true complexity of data and systems in many domains. The products of this project will be made freely available to the research community as online tools. As the teachable component matures, products will be made available as lesson-plan material appropriate for both primary and secondary education.

蛋白质是负责生命所必需的大量功能的分子机器。 了解它们的工作方式对于对生活的基本过程的更好科学理解以及修改或改善其功能至关重要。 尽管蛋白质在物理上是合作零件的三维结构，但用于表示和研究蛋白质的当前最新技术还是使用了一种描述，这只是其组装中使用的部分的顺序列表。 这种顺序列表的描述风格偏向于蛋白质分析工具的开发，以强调这些分子的顺序特性，并且忽略了这些部分必须与蛋白质发挥作用的零件必须共同起作用。 这项工作将广泛影响蛋白质的研究，从而改善了一系列活动，从功能的基本科学研究到蛋白质工程的努力。该项目的产品将作为在线工具免费提供给研究社区，并且该方法将被纳入课程中，并作为适合初等教育和中等教育的课程计划材料提供。 该项目将适应最近开发的统计技术，即条件随机字段（CRF），该技术可以定量地表示特征的密集连接网络，以及最近开发的可视化工具，以实现这些网络的交互式探索，以描述这些网络的任务蛋白质。 在结构上，有条件的随机场似乎概括了为在蛋白质中合作的部分选择进化的过程，并且基于CRF的蛋白质描述将能够预测是否会通过进化，进化，是一种突变的变化 - 一种突变 - 或反对非功能。 该信息将有助于预测突变的效果，或使用更多可用信息的蛋白质对蛋白质的效果，而不是当前的最新工具。对于许多其他类型的生物学和非生物系统，蛋白质功能变化的蛋白质序列变化是一种“模型生物”，其中系统之间需要丰富的相互作用需要复杂的统计方法。 迄今为止，在大多数这些领域中，与当前在蛋白质中使用的模型相似，是事实上的标准。 开发将CRF应用于蛋白质数据所需的工具，以及在此系统中建立可测试基地真相的方法，将增强CRF在许多其他域中的应用，在许多其他域中它们可能比当前方法具有显着优势。该工具可能会在特征在视觉探索的特征和这些依赖性的修改之间进行相互依存，并可以量化可预测，并可以更详细地考虑许多域中数据和系统的真实复杂性。该项目的产品将作为在线工具免费提供给研究社区。随着可教学成分的成熟，将作为适合初等教育和中等教育的课程材料提供产品。