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

蛋白质是分子机器，负责生命所必需的大量功能。了解它们是如何工作的，对于更好地科学理解生命的基本过程，以及修改或改进它们的功能都是至关重要的。尽管蛋白质是相互协作的部分的物理三维结构，但目前表示和研究蛋白质的技术状态使用的描述只是它们组装中使用的部分的顺序列表。这种顺序列表的描述方式使蛋白质分析工具的开发产生了偏见，这些工具强调这些分子的顺序属性，而忽视了这样一个事实，即各部分必须协同工作才能使蛋白质发挥作用。该项目将采用最近开发的统计技术--条件随机场(CRF)，它可以定量地表示密集连接的特征网络，以及最近开发的可视化工具，它能够交互探索这些网络，以完成描述蛋白质的任务。在结构上，条件随机场似乎概括了进化为蛋白质中相互作用的部分进行选择的过程，基于CRF的蛋白质描述将能够预测蛋白质的变化--突变--是进化所容忍的，还是被选择为无功能的。这些信息将有助于使用比目前最先进的工具所利用的更多的可用信息来预测蛋白质的一个或多个突变的影响。这项工作将广泛地影响蛋白质的研究，改进从功能的基础科学研究到蛋白质工程的努力的一系列活动。此外，“从蛋白质序列的改变到蛋白质功能的改变”问题是许多其他类型的生物和非生物系统的“模式生物”，在这些系统的各个部分之间的丰富相互作用需要复杂的统计方法。到目前为止，在这些领域中的大多数领域，类似地局限于目前用于蛋白质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基本事实的方法，将加强CRF在许多其他领域的应用，在这些领域，它们可能提供比当前方法显著的优势。这个项目的产品将作为在线工具免费提供给研究社区，这些方法将被纳入课程工作，首先是在俄亥俄州立大学的生物物理学研究生课程中，随着可教学部分的成熟，作为适合小学和中学教育的教案材料提供。通过开发一种工具，使功能之间的相互依赖关系在视觉上可以探索，这些依赖关系的修改可以量化地预测，我们将促进对许多领域中数据和系统的真正复杂性的更彻底的考虑。

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.