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

蛋白质是一种分子机器，它负责生命所必需的大量功能。了解它们是如何工作的，对于更好地科学地理解生命的基本过程，以及修改或改善它们的功能都是至关重要的。尽管蛋白质在物理上是由相互配合的部分组成的三维结构，但目前用于表示和研究蛋白质的技术状态使用的描述仅仅是它们组装中使用的部分的顺序列表。这种顺序列表式的描述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，而忽略了蛋白质的各个部分必须协同工作才能发挥作用的事实。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，可以对这些网络进行交互式探索，用于描述蛋白质的任务。在结构上，条件随机场似乎概括了进化选择在蛋白质中合作的部分的过程，基于crf的蛋白质描述将能够预测蛋白质的变化-突变-是进化所容忍的，还是被选择为无功能的。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，使用比目前最先进的工具所利用的更多的可用信息。这项工作将广泛地影响蛋白质的研究，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，“改变蛋白质序列以改变蛋白质功能”的问题是许多其他类型的生物和非生物系统的“模式生物”，其中系统各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，在大多数这些领域中，同样局限于目前用于蛋白质的模型是事实上的标准。开发将CRFs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础真理的方法，将增强CRFs在许多其他领域的应用，在这些领域，CRFs可能提供比当前方法更大的优势。该项目的成果将作为在线工具免费提供给研究界，其方法将纳入课程作业，首先在俄亥俄州立大学生物物理学研究生项目中，随着可教学部分的成熟，将作为适合小学和中学教育的课程计划材料提供。通过开发一种工具，可以可视化地探索特征之间的相互依赖关系，并且可以定量地预测这些依赖关系的修改，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.