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

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更好地科学理解生命的基本过程以及修改或改善它们的功能至关重要。 尽管事实上蛋白质是协作部分的物理三维结构，但用于表示和研究蛋白质的现有技术使用的描述仅仅是在其组装中使用的部分的顺序列表。 这种顺序列表式的描述方式使蛋白质分析工具的发展偏向于强调这些分子的顺序特性，而忽视了蛋白质功能的各个部分必须协调一致地工作这一事实。 该项目将采用最近开发的统计技术，条件随机场（CRF），可以定量表示密集连接的特征网络，以及最近开发的可视化工具，可以交互式探索这些网络，用于描述蛋白质的任务。 从结构上讲，条件随机场似乎概括了进化选择蛋白质中合作部分的过程，基于CRF的蛋白质描述将能够预测蛋白质的变化-突变-是否会被进化所容忍，或者被选择为非功能性的。 这些信息将有助于预测一个突变或多个突变对蛋白质的影响，使用更多的可用信息，而不是目前最先进的工具。这项工作将广泛影响蛋白质的研究，改善从功能的基础科学研究到蛋白质工程的一系列活动。此外，“蛋白质序列变化导致蛋白质功能变化”问题是许多其他类型生物和非生物系统的“模型生物”，其中系统各部分之间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，类似地限于目前在蛋白质中使用的模型是事实上的标准。 开发将CRF应用于蛋白质数据所需的工具，以及在该系统中建立可测试的地面实况的方法，将增强CRF在许多其他领域的应用，在这些领域中，CRF可能比当前方法具有显着的优势。 该项目的产品将作为在线工具免费提供给研究社区，该方法将被纳入课程，首先是在俄亥俄州州立大学的生物物理学研究生课程中，随着可教部分的成熟，可作为适合小学和中学教育的课程计划材料。 通过开发一种工具，使功能之间的相互依赖性在视觉上可探索，这些依赖性的修改可量化预测，我们将促进更彻底地考虑在许多领域的数据和系统的真正复杂性。

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 to ignore the fact that the parts must work together in unison for the protein to function. 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.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. In addition, 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. The products of this project will be made freely available to the research community as online tools, and the methods will be incorporated in coursework, first in the Biophysics Graduate Program at The Ohio State University, and as the teachable component matures, made available as lesson-plan material appropriate for both primary and secondary education. By developing a tool that makes interdependencies between features visually explorable and modifications of these dependencies quantifiably predictable, we will promote more thorough consideration of the true complexity of data and systems in many domains.